2026-05-08T11:21:15Z https://ijrasht.com/index.php/files/oai

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oai:ojs.ijrasht.com:article/108 2025-10-28T08:58:43Z files:ART

Innovative Waste Heat Recovery in Cement ProductionReducing CO₂ Emissions and Energy Consumption Dilip Mishra Aman Kumar WHR, Cement Production, Energy Efficiency, ORC, Sustainability, CO₂ Emission Reduction, Techno-Economic Analysis, Renewable Energy, Heat Recovery Systems. This paper explores the potential of Waste Heat Recovery (WHR) systems to enhance sustainability and energy savings in cement production. The cement industry is energy-intensive, with significant waste heat generated from kiln operation and clinker cooling processes. By recovering and utilizing this waste heat, WHR systems can generate electricity, reduce grid energy consumption, and mitigate CO₂ emissions. This study focuses on implementing a WHR system at the Cement Plant. Using data from the plant, including flue gas temperatures of 368°C and hot air from the clinker cooler at 244°C, a comprehensive model was developed in Aspen plus V12 to simulate the WHR system integrated with the plant's operations. The WHR system was designed around an Organic Rankine Cycle (ORC) with carefully selected working fluids optimized for low- to medium-temperature heat recovery. The techno-economic analysis reveals that the system could generate 27.5 GWh of electricity annually, reducing grid electricity consumption by 25%. This corresponds to approximately 16,967.5 tons of CO₂ emission reductions annually, given a high electricity emission factor of 0.617 kg CO₂/kWh. The financial analysis indicates that the levelized cost of clinker production with the WHR system is $2.54 per ton, with a payback period of 9 years. This demonstrates the economic viability of the system, alongside its environmental benefits. The study highlights WHR systems as a practical solution for improving energy efficiency and sustainability in cement production, particularly in regions with high carbon-intensive electricity grids. The findings provide a strong case for the broader adoption of WHR technology in the cement industry. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/108 10.71143/ewn0dm86 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 1, September 2024; 1-6 3048-975X eng https://ijrasht.com/index.php/files/article/view/108/50 https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/109 2025-10-28T08:59:20Z files:ART

Experimental Analysis of Banana Fiber and Phosphogypsum in Soil Stabilization Ashutosh Mishra Priyanka Singh Banana fiber, soil stabilization, phosphogypsum, UCS, MDD, and OMC, agricultural waste, sustainable construction, natural reinforcement. This study investigates the potential of banana fiber as a natural reinforcement material for soil stabilization, specifically in conjunction with phosphogypsum. With construction often reliant on soils with inadequate bearing capacity and shear strength, enhancing soil properties is crucial. The research employs banana fibers extracted from the pseudo stems of banana plants, assessing their effects on soil characteristics at various reinforcement percentages (0%, 0.1%, 0.3%, and 0.5%). Key parameters evaluated include Unconfined Compression Strength (UCS), Maximum Dry Density (MDD), and Optimum Moisture Content (OMC) through standardized laboratory tests. The findings reveal that the inclusion of banana fibers significantly raises the OMC of the soil, indicating improved moisture retention capabilities. Notably, the OMC values for the reinforced samples increased with higher fiber content, peaking at 12.3% for Sample 1 and 13.5% for Sample 2 at 0.5% fiber. Additionally, the UCS tests demonstrated enhanced compressive strength, with the highest value recorded at 1.70 MPa for Sample 1 at 0.5% fiber content. These results suggest that banana fibers effectively improve the mechanical properties of soil, making it more suitable for construction applications. This research highlights the potential of utilizing agricultural waste, such as banana fibers, for sustainable soil stabilization practices, offering an eco-friendly alternative to synthetic materials. SPS Educational Trust 2024-09-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/109 10.71143/rpv5vm73 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 1, September 2024; 7-12 3048-975X eng https://ijrasht.com/index.php/files/article/view/109/51 https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/110 2025-10-28T09:00:18Z files:ART

Micro-Hydro Power-Harnessing the Potential Energy of Water for Small-Scale Electricity Generation Rashi Sahay Swamy TN micro-hydro turbine, low head water source, renewable energy, domestic electricity generation, Pelton wheel turbine, sustainable power, hydropower systems, DC generator, and energy efficiency. This paper focuses on analysing and designing a micro-hydro turbine system to generate direct current (DC) electricity from low-head water sources. Recognising the need for sustainable energy solutions, the system utilises stored water in an overhead tank at 11.25 meters. The PE of the water is converted into KE as it flows through a nozzle, striking the blades of a Pelton wheel turbine. The rotating turbine drives a DC generator, which converts mechanical energy into electrical energy, subsequently stored in batteries for domestic use. The system's design includes key components such as a water storage tank, penstock, turbine, generator, and battery storage. Calculations indicate that the system can produce a maximum power output of approximately 0.047 kW under optimal conditions. The methodology emphasises the importance of head height and water discharge in determining energy output, with experiments demonstrating a strong correlation between these variables and generated power. Overall, this micro-hydro system presents an effective solution for reducing electricity bills while harnessing renewable energy, aligning with global efforts to promote sustainable practices. SPS Educational Trust 2024-09-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/110 10.71143/4p12n447 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 1, September 2024; 13-17 3048-975X eng https://ijrasht.com/index.php/files/article/view/110/52 https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/111 2025-10-28T09:03:45Z files:ART

Analysis of Extracting a Wide Range of Microorganismsfrom Natural Environments Dr. Devendra Pratap Singh Priyanka Singh Khushboo Microbial Diversity, Environmental Samples, Isolation Techniques, Molecular Characterization, Functional Assays, Bioremediation, Ecological Applications The isolation and characterization of microorganisms from environmental samples is a vital aspect of microbiological research, providing insights into microbial diversity and functionality. This study focuses on the systematic collection of samples from various ecological niches, including soil, water, and air, to identify and characterize a wide range of microbial species. Using selective culturing techniques and advanced molecular methods, we successfully isolated over 1,200 bacterial and fungal strains. The morphological and biochemical characteristics of these isolates were thoroughly analysed, revealing a rich diversity that includes both well-known genera and novel taxa. SPS Educational Trust 2024-09-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/111 10.71143/8acz6j06 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 1, September 2024; 18-23 3048-975X eng https://ijrasht.com/index.php/files/article/view/111/53 https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/112 2025-10-28T09:04:45Z files:ART

IoT-Enabled Smart Greenhouse: Real-Time Monitoring and Automated Control for Efficient Agriculture Vishakha Tomar Nishtha Saroha IoT, smart greenhouse, real-time monitoring, automated control, wireless sensor networks, ESP32 microcontroller, sustainable agriculture, smart irrigation, resource efficiency. The growing need for efficient resource management in agriculture, particularly in water and energy conservation, has led to the emergence of smart technologies. This research focuses on designing and implementing an IoT-enabled smart greenhouse system aimed at optimizing environmental conditions for plant growth through real-time monitoring and automated control. The system integrates various sensors, including temperature, humidity, and SM sensors, with a central ESP32 microcontroller to collect and analyze data. Actuators such as fans, water pumps, and lights automatically adjust the greenhouse conditions based on sensor feedback. This reduces the need for manual intervention, making the system more efficient and less labour-intensive. The proposed system has wireless sensor networks (WSN) and cloud computing capabilities, allowing farmers to remotely monitor and control the greenhouse environment using a mobile app interface. The real-time data collected by the sensors is stored and processed in the cloud, enabling detailed data analysis and the identification of environmental trends. The smart irrigation system ensures optimal water usage, contributing to the conservation of this critical resource. Experimental results indicate significant improvements in resource management, with notable reductions in water consumption and energy usage. The system is scalable and can be customized for different crops and climatic conditions, offering a versatile solution for modern agriculture. By leveraging IoT technologies, this smart greenhouse system enhances crop yield while reducing resource waste, making it a sustainable option for small- and large-scale agricultural operations. SPS Educational Trust 2024-09-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/112 10.71143/48257x69 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 1, September 2024; 24-31 3048-975X eng https://ijrasht.com/index.php/files/article/view/112/54 https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/116 2026-02-19T10:01:16Z files:ART

Global Impact of COVID-19: Pandemic Preparedness, Healthcare Response, and Lessons Learned Vibhuti Tyagi Sapna Upadhyay Gauri Sharma COVID-19, pandemic preparedness, healthcare systems, ICU utilization, vaccination, public health strategies, digital health solutions, global response, mortality rates. The COVID-19 pandemic, caused by the novel SARS-CoV-2 virus, has profoundly impacted global public health, economies, and healthcare systems since its emergence in late 2019. This research paper examines the global response to the pandemic, focusing on healthcare infrastructure, public health strategies, vaccination campaigns, and digital health innovations. By analysing data from countries severely affected by the pandemic, such as Italy, the United States, India, and Brazil, this study explores the correlation between healthcare capacity, ICU utilization, and mortality rates. Additionally, it evaluates the effectiveness of early interventions, including lockdowns, social distancing, and mask mandates, in controlling the virus’s spread and reducing healthcare strain. The analysis reveals that countries with robust healthcare systems and early implementation of public health measures were more successful in managing COVID-19 outbreaks and minimizing deaths. Vaccination played a critical role in mitigating the pandemic’s severity, with countries achieving higher vaccination coverage witnessing significant reductions in hospitalizations and fatalities. The study also highlights the essential role of digital health solutions, such as contact tracing apps and telemedicine, in maintaining healthcare services while curbing the virus's spread. This paper concludes with lessons learned from the pandemic, emphasizing the need for greater global cooperation, investment in healthcare infrastructure, and equitable access to vaccines and medical supplies. These findings aim to guide future pandemic preparedness strategies, ensuring that the world is better equipped to handle similar health crises. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/116 10.71143/7ab0dk25 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 32-39 3048-975X eng https://ijrasht.com/index.php/files/article/view/116/55 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/117 2026-02-19T10:01:16Z files:ART

Sorting Machine for Fruits and Vegetables for Agricultural Advancements using IoT Ashutosh Mishra Sukhwinder Kaur Bhatia Reeta Mishra Smart Sorting Machine, Machine Learning, Computer Vision, Agricultural Technology, Sorting Accuracy, Throughput Optimization, Fruit and Vegetable Sorting, Automation in Agriculture, Economic Feasibility. The increasing demand for high-quality agricultural produce necessitates the modernization of sorting processes, traditionally reliant on manual labour. This study presents the development of a smart fruit and vegetable sorting machine utilizing advanced machine learning and computer vision technologies to enhance sorting accuracy, throughput, and efficiency. The methodology encompasses a systematic approach, including the design and configuration of a conveyor system, implementation of imaging sensors, and the integration of a convolutional neural network for real-time classification of produce. A dataset of 10,000 labelled images was utilized to train the model, which achieved an impressive sorting accuracy of 95% and a throughput of 120 items per minute during testing. The machine demonstrated a low error rate of 5%, underscoring its effectiveness in minimizing post-harvest losses and ensuring quality control. These results highlight the significant advantages of automation in agricultural practices, surpassing traditional manual sorting methods in both speed and reliability. Additionally, an economic feasibility analysis indicated the potential for substantial cost savings in labour and reduced spoilage, making the technology viable for small and medium-sized farms. The findings of this research demonstrate that the smart sorting machine is a transformative solution for contemporary agriculture, addressing critical challenges in sorting efficiency and accuracy. Future work is recommended to explore advanced imaging techniques, real-time monitoring systems, and broader applications across diverse crop types. By embracing these innovations, the agricultural sector can enhance productivity, sustainability, and overall profitability, ultimately contributing to a more efficient food supply chain. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/117 10.71143/6m3z1250 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 40-48 3048-975X eng https://ijrasht.com/index.php/files/article/view/117/56 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/118 2026-02-19T10:01:16Z files:ART

Role of Artificial Intelligence in Diagnostic Medicine Rashi Sahay Ajeet Singh Muskan Aggarwal Artificial Intelligence, Diagnostic Medicine , Machine Learning , Deep Learning, Medical Imaging, Predictive Analytics Artificial Intelligence (AI) is rapidly emerging as a transformative force in diagnostic medicine, reshaping how healthcare professionals detect and manage diseases. By leveraging sophisticated machine learning and deep learning algorithms, AI can efficiently analyse extensive datasets, including medical images and patient records, with exceptional speed and accuracy. This capability not only improves the precision of diagnoses but also aids in the early identification of conditions such as cancer and heart disease, leading to more tailored treatment strategies. Furthermore, AI tools are proving essential in minimizing human error and optimizing workflows, enabling healthcare providers to devote more time to patient care. As AI continues to be integrated into clinical practice, it promises to enhance patient outcomes while meeting the increasing demands placed on healthcare systems globally. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/118 10.71143/6vhwz717 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 49-53 3048-975X eng https://ijrasht.com/index.php/files/article/view/118/66 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/119 2026-02-19T10:01:16Z files:ART

Role of Machine Learning in Anticipating Adverse Drug Reactions: Implications for Patient Safety in Pharmacy Practice Ajeet Singh Atul Pratap Singh Neha Verma Adverse Drug Reactions, Machine Learning, Patient Safety, Pharmacovigilance, Predictive Analytics, Pharmacy Practice, Deep Learning, Drug Safety Adverse drug reactions (ADRs) are a major concern in the field of pharmacology, significantly impacting patient safety and healthcare costs. As drug interactions become more complex, traditional methods for predicting ADRs often fall short. However, the application of machine learning (ML) techniques presents a promising solution for improving the prediction and management of these reactions, offering valuable insights into their underlying mechanisms. By leveraging ML, healthcare professionals can better anticipate ADRs, leading to safer medication practices and enhanced patient care. This innovative approach not only helps in identifying potential risks associated with medications but also supports proactive measures to mitigate these risks, ultimately contributing to improved health outcomes for patients. This paper explores the role of ML in anticipating ADRs by analysing diverse datasets, including clinical records, drug properties, and patient demographics. We discuss various ML models, such as deep learning and ensemble methods, that have shown efficacy in identifying potential ADRs before they manifest clinically. By leveraging large volumes of health-related data, these models can improve the accuracy of predictions, facilitate timely interventions, and ultimately enhance patient safety. This research underscores the necessity for ongoing collaboration between data scientists and healthcare professionals to optimize the application of ML in real-world settings. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/119 10.71143/nv3qs952 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 54-59 3048-975X eng https://ijrasht.com/index.php/files/article/view/119/57 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/120 2026-02-19T10:01:16Z files:ART

Effect of Temperature on the Percentage of Germination of Mung Beans Devendra Pratap Singh Mung beans, germination percentage, temperature, agricultural productivity, Vigna radiata. This study explores how temperature affects the germination percentage of mung beans (Vigna radiata), a crucial crop valued for its nutritional benefits and adaptability to various climates. As global temperatures rise, understanding the influence of temperature on seed germination becomes increasingly important for agricultural productivity. We hypothesized that higher temperatures would enhance germination rates due to increased metabolic activity and improved seed coat permeability. Mung beans were subjected to three distinct temperature conditions: cold (10°C), room temperature (25°C), and warm (35°C). Over ten days, we recorded the number of seeds that successfully sprouted in each environment. Our results showed a significant correlation between temperature and germination percentage. The warm environment yielded the highest germination rate at 85%, while the cold environment resulted in only 30% germination. Statistical analysis confirmed that these differences were significant (p < 0.01). These findings align with previous research suggesting that optimal temperatures for mung bean germination range between 30-35°C. We conclude that temperature is a critical factor influencing mung bean germination, with warmer conditions promoting faster and more successful sprouting. Future research could explore the impact of extreme temperatures on subsequent plant growth and yield, offering insights for agricultural practices in a changing climate. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/120 10.71143/s7919455 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 60-62 3048-975X eng https://ijrasht.com/index.php/files/article/view/120/58 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/121 2026-02-19T10:01:16Z files:ART

Impact of Digital Transformation on Employee Engagement in Indian IT Companies Isha Sethi Ravindra Kumar Satendra Kumar Mixed-methods research, Utrecht Work Engagement Scale (UWES), Workplace digitization, Innovation culture, Age demographics in IT This study examines the relationship between digital transformation initiatives and employee engagement levels in IT companies located in Bangalore, India's premier tech hub. Using a mixed-methods approach, data was collected from 250 employees across 10 IT firms through surveys and in-depth interviews. The results indicate that while digital transformation generally enhances employee engagement, the relationship is moderated by factors such as age, technological adaptability, and organizational culture. The findings provide insights for IT leaders to effectively manage digital transformation while maintaining high levels of employee engagement. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/121 10.71143/5b0rpd02 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 63-66 3048-975X eng https://ijrasht.com/index.php/files/article/view/121/59 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/122 2026-02-19T10:01:16Z files:ART

Sustainability in Solid Waste Management to Reduce Environmental Impact and Improve Resource Efficiency Dilip Mishra SWM, Sustainable Waste Solutions, Waste-to-Energy, Recycling and Resource Recovery, Waste Reduction, Environmental Impact, Waste Segregation, Landfill Diversion, Energy Generation from Waste. Solid waste management is essential for promoting environmental sustainability, public health, and resource conservation. This paper examines the multifaceted challenges associated with solid waste, including municipal, industrial, and hazardous waste streams. Inefficient waste management practices lead to detrimental impacts on ecosystems, human health, and climate change. A holistic approach is necessary, emphasizing waste reduction, reuse, recycling, and responsible disposal techniques. The project aimed to enhance solid waste management through comprehensive strategies, including awareness campaigns, waste segregation initiatives, and the establishment of treatment facilities. The results showed a notable reduction in waste generation, an increase in recycling rates, and improved waste segregation practices among residents and businesses. Additionally, the project successfully developed enhanced treatment and disposal infrastructure, including composting facilities and engineered landfills, which adhere to environmental safeguards. Significantly, the project incorporated waste-to-energy technologies, demonstrating their potential in reducing landfill volumes while generating valuable energy. A prototype system was developed to convert waste combustion heat into electrical energy, which was then stored in batteries for practical use, illustrating the feasibility of small-scale energy recovery from waste. Challenges encountered, such as community resistance and logistical issues, highlight the importance of continuous engagement and collaboration among stakeholders. The findings underscore the necessity of robust policies and community involvement in implementing sustainable WM practices. Overall, this research contributes to the understanding of effective solid waste management strategies and their role in achieving environmental sustainability and resource recovery, offering valuable insights for communities seeking to improve their waste management systems. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/122 10.71143/52raj174 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 67-75 3048-975X eng https://ijrasht.com/index.php/files/article/view/122/60 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/123 2026-02-19T10:01:16Z files:ART

Securing Concrete from Chemical Degradation caused by Chloride and Sulfuric Acid through Epoxy Coating Mohammad Aslam Dilip Mishra Concrete degradation, Epoxy coatings, Sulphuric acid, Chloride attack, Split-tensile strength, Flexural strength, Compressive strength, M30 concrete. This investigation looks at the effectiveness of epoxy coatings in mitigating chemical degradation in concrete structures subjected to Sulphuric acid and chloride attacks. Concrete, a widely used construction material, is susceptible to deterioration in aggressive environments, which can significantly compromise its structural integrity and lifespan. This study specifically focuses on the impact of 0.35% sulphur and chloride solutions on the mechanical characteristics of M30 concrete, evaluating its flexural, compressive, and STSs over a 28-day period. Experimental results demonstrate that exposure to Sulphuric acid leads to a 16% reduction in CSt, a 23% decrease in flexural strength, and a 24% decline in split-tensile strength. Similarly, chloride exposure results in a 15% reduction in CSt, an 18% reduction in flexural strength, and a 25% reduction in STS after 28 days. However, the application of epoxy coatings on the chemically attacked specimens significantly enhances their mechanical performance. Specifically, epoxy treatment results in a 9% increase in compressive strength for sulphur-exposed specimens and a 12% increase for chloride-exposed specimens. Additionally, flexural strength improves by 19.1% and 10%, while an raises in split-tensile strength by 15% and 12% for sulphur and chloride attacks, respectively. The findings of this study underscore the critical role of epoxy coatings as a protective measure against chemical attacks, effectively restoring and enhancing the durability of concrete structures. This investigation contributes to the understanding of concrete performance in aggressive environments and highlights the importance of employing protective strategies to increase the concrete infrastructures' service life. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/123 10.71143/w1db7921 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 76-85 3048-975X eng https://ijrasht.com/index.php/files/article/view/123/61 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/124 2026-02-19T10:01:16Z files:ART

Optimizing Face Recognition with PCA and KNN: A Machine Learning Approach Ajeet Singh Atul Pratap Singh Minal Rajendra Wadyalkar KNN, Face Detection, PCA, Dimensionality Reduction, Machine Learning, Feature Extraction, Artificial Intelligence, Face Recognition, Python, Scikit-learn. Face detection and recognition have become critical applications in various fields, including security, identity verification, and human-computer interaction. This paper presents a comprehensive analysis of face detection techniques using Artificial Intelligence (AI), focusing on the integration of PCA and KNN algorithms. PCA is employed to reduce the dimensionality of face image datasets, effectively extracting important features while minimizing data loss. The KNN classifier is used for classification by identifying the closest matching face in a dataset. By applying these techniques to the LFW dataset, we achieved an overall accuracy of 88%, demonstrating the efficacy of this approach for face detection. The methodology involves training the system with face image data, utilizing PCA to project the images onto a lower-dimensional space, and applying KNN to classify the images based on their reduced feature set. The implementation was carried out using Python’s Scikit-learn library, highlighting the ease of combining well-established machine learning algorithms in a straightforward programming environment. Results show that using KNN with an optimal K value of 5, alongside PCA retaining 95% variance, provides a robust and efficient solution for face detection tasks. While this approach achieved significant success, further improvements could be made by integrating advanced classifiers such as CNNs or exploring neural networks for feature extraction. Additionally, real-time performance can be enhanced by optimizing the computational process or leveraging OpenCV for real-world applications. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/124 10.71143/wf3sr109 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 86-93 3048-975X eng https://ijrasht.com/index.php/files/article/view/124/62 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/125 2026-02-19T10:01:16Z files:ART

Optimized Power Distribution using Machine Learning for Load Forecasting, Fault Detection, and Voltage Regulation Neeraj Kumar Machine learning, power distribution systems, load forecasting, voltage regulation, smart grids, network reconfiguration, fault detection, energy optimization, real-time data analysis The optimization of electric power distribution systems is crucial for enhancing efficiency, reliability, and sustainability in modern power networks. Traditional optimization methods often struggle to handle the complexity and variability of large-scale power distribution grids. With the advent of ML, new opportunities have emerged to address these challenges more effectively. This paper explores the application of machine learning algorithms in optimizing power distribution systems, focusing on load forecasting, fault detection, voltage regulation, and network reconfiguration. By employing supervised, unsupervised, and reinforcement learning techniques, ML models can process vast amounts of real-time data, identify patterns, and make accurate predictions for system performance enhancement. This study presents a comprehensive review of recent advancements in ML-based optimization techniques, emphasizing their ability to improve the accuracy of load demand forecasts and reduce energy losses. Moreover, it discusses the integration of smart grid technology with ML models to enable adaptive control strategies that can respond to dynamic power demands. Various case studies and simulation results are included to demonstrate the practical benefits of machine learning applications in electric power distribution. The findings suggest that incorporating machine learning into the power distribution framework can significantly boost operational efficiency, reduce downtime, and facilitate the transition to a more intelligent and sustainable power grid. This paper concludes with a discussion of the challenges and future prospects of ML in electrical grid optimization, such as scalability, data privacy, and the need for real-time computation. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/125 10.71143/0m5nm797 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 94-102 3048-975X eng https://ijrasht.com/index.php/files/article/view/125/63 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/126 2026-02-19T10:01:16Z files:ART

Design, Implementation, and Analysis for Reducing Energy Losses in Solar Inverters through the Use of SiC MOSFETs Neeraj Kumar Solar inverter, Silicon Carbide (SiC), MOSFETs, Photovoltaic (PV) systems, High efficiency, Switching losses, Thermal management, THD, Renewable energy The integration of Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) in solar inverters has emerged as a promising solution for enhancing energy conversion efficiency. This study presents the design and performance analysis of a high-efficiency solar inverter utilizing SiC MOSFETs, targeting increased power output and improved reliability in photovoltaic (PV) systems. The proposed inverter design focuses on reducing switching losses, minimizing heat dissipation, and achieving higher switching frequencies compared to traditional silicon-based devices. The adoption of SiC technology enables reduced conduction and switching losses due to its superior thermal properties and high breakdown voltage, making it ideal for solar inverter applications. Simulation results demonstrate significant improvements in efficiency—exceeding 98%—under varying load conditions. Additionally, the inverter’s performance was evaluated in terms of total harmonic distortion (THD), with values well within acceptable limits, ensuring clean and stable power output. The thermal management capabilities of SiC MOSFETs are also highlighted, showing reduced heat sink requirements and longer operational lifetimes. This research further explores the practical implementation challenges, such as gate driver considerations and EMI suppression, to optimize inverter design for real-world scenarios. The findings suggest that utilizing SiC MOSFETs in solar inverters not only enhances energy efficiency but also contributes to system compactness, potentially reducing the overall cost of PV installations. The study concludes with recommendations for future developments in SiC-based power electronics for renewable energy applications. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/126 10.71143/75h70y35 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 103-109 3048-975X eng https://ijrasht.com/index.php/files/article/view/126/64 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/127 2026-02-19T10:01:16Z files:ART

Optimized Air Quality Index and Meteorological Predictions with Machine Learning and IoT Dr. R. Venkateswaran Dr. Suresh Palarimath Mr. Rogelio Gutierrez AQI, Machine Learning, Metrological Parameter, IoT, Hybrid Air Quality Index (AQI) prediction and forecasting play pivotal roles in assessing and managing air pollution, contributing to public health and environmental sustainability. This paper provides a comprehensive review of recent advancements, methodologies, challenges, and future directions in AQI prediction and forecasting. Recent research has seen a surge in the development of machine learning, statistical, and hybrid models for AQI prediction. These models leverage various input data sources such as meteorological data, satellite imagery, and pollutant emissions data to enhance prediction accuracy. Furthermore, the integration of advanced techniques like deep learning and ensemble modeling has shown promising results in capturing complex nonlinear relationships and improving forecast precision. Challenges persist, including the need for real-time data integration, model interpretability, and addressing spatial and temporal variations in air quality. Additionally, the impact of emerging factors such as climate change and urbanization on AQI prediction requires further investigation. Future research directions focus on the development of hybrid models that integrate multiple data sources, including sensor networks and IoT devices, to improve spatial and temporal resolution. Moreover, there is a growing emphasis on the incorporation of uncertainty quantification techniques to provide probabilistic forecasts and enhance decision-making under uncertainty. In conclusion, this paper underscores the importance of AQI prediction and forecasting in addressing air pollution challenges and promoting public health. By advancing methodologies, addressing challenges, and exploring emerging research avenues, we can strive towards more accurate, reliable, and actionable AQI predictions for sustainable urban development and environmental stewardship. SPS Educational Trust 2024-12-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/127 10.71143/grew9j11 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 1, Issue 2, December 2024; 110-120 3048-975X eng https://ijrasht.com/index.php/files/article/view/127/65 Copyright (c) 2024 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

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Viksit Bharat Sankalp 2047 Kishan Tank Neha Sabharwal Viksit Bharat Sankalp 2047, Developed, Perceptions, Aspirations, Education, Youth The "Viksit Bharat Sankalp 2047" initiative aims to position India as a developed nation by the centenary of its independence. This research examines the Perceptions, aspirations, and envisioned roles of Indian youth in contributing to this ambitious vision. By employing a mixed-methods approach, including Comprehensive surveys and qualitative interviews, the study identifies key themes that resonate with the younger generation, such as the importance of quality education, meaningful employment, and environmental sustainability. Findings reveal that Indian youth view themselves as critical stakeholders in national development, emphasizing the need for the active participation in policy making and governance. They highlight challenges such as socioeconomic disparities, limited access to quality infrastructure, and environmental concerns that must be addressed to achieve the initiative's objectives. The study concludes by underscoring the transformative potential of youth-led efforts in shaping a progressive and sustainable future for India, advocating for policies that integrate youth perspectives in strategic planning and implementation. Their search provides valuable insights for policymakers to align national Priorities with the aspirations of the youth, ensuring inclusive and participatory development SPS Educational Trust 2025-05-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/164 10.71143/x8dwtr31 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 2, April-June 2025; 116-120 3048-975X eng https://ijrasht.com/index.php/files/article/view/164/86 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/165 2025-10-28T09:42:04Z files:ART

Comparative Review of Hydrological Models for Runoff Estimation: A Focus on SCS-CN, TOPMODEL, and VIC Approaches– A Review Monika Jain Lokesh Kumar Tripathi Puneet Kumar Bhambota Abhilasha Dangi Runoff Estimation, SCS-Curve Number, TOPMODEL, VIC Model, Hydrological Modeling, Watershed Management, Rainfall– Runoff Simulation, Model Comparison, Remote Sensing, GIS Integration Accurate runoff estimation is essential for effective watershed management, flood risk mitigation, and sustainable water resource planning. Over the decades, a wide range of hydrological models have been developed, differing in complexity, data requirements, and spatial–temporal resolution. This review provides a comparative evaluation of three widely used models—the SCS-Curve Number (SCSCN) method, TOPMODEL, and the Variable Infiltration Capacity (VIC) model with emphasis on their underlying structure, hydrological processes, applicability, and performance across various hydro-climatic and land use scenarios. The SCS-CN method, although empirical in nature, remains a preferred tool for event-based runoff estimation due to its simplicity and minimal data demands. TOPMODEL, a semidistributed conceptual model, links runoff generation to terrain-driven saturation dynamics, making it well-suited for humid and sloped watersheds. On the other hand, VIC, a semi-distributed, physically-based model, enables large-scale and climate-sensitive hydrological simulations by coupling water and energy balances within a grid-based framework. This review synthesizes recent literature to outline the strengths and limitations of each model, offering guidance for researchers and water managers in selecting appropriate runoff modeling tools based on watershed characteristics, modeling objectives, and available data resources. SPS Educational Trust 2025-05-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/165 10.71143/z9v3aj80 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 2, April-June 2025; 121-135 3048-975X eng https://ijrasht.com/index.php/files/article/view/165/87 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/166 2025-10-28T09:21:41Z files:ART

Advancements in Precision Agriculture for Maximizing Crop Yield and Minimizing Waste via Innovative Technological Solutions Hema Rani Priyanka Kakkar Devendra Pratap Singh Precision Agriculture, Crop Yield, Resource Efficiency, IoT Sensors, Variable Rate Technology (VRT), Sustainable Agriculture, Remote Sensing, Data Analytics. Precision agriculture, a technology-driven approach to farming, integrates GPS, IoT sensors, Variable Rate Technology (VRT), and data analytics to optimize crop yield and resource usage. This study explores the effectiveness of precision agriculture in enhancing productivity and promoting sustainable farming practices by analysing its impact on crop yield, water and fertilizer usage, and environmental metrics. Data was collected through IoT sensors, GPS mapping, and drone-based remote sensing to monitor field conditions, while VRT was used to apply inputs precisely where needed. Comparative analyses between precision and traditional agriculture show a 20% increase in crop yield and a 40% reduction in water and fertilizer usage for fields employing precision techniques. Environmental benefits were also notable, with significant decreases in greenhouse gas emissions and pesticide runoff. Case studies across diverse farming setups and controlled experiments provided further insights into the practical applications and challenges of precision agriculture. While results indicate substantial improvements in efficiency and sustainability, barriers such as high initial costs and technical expertise requirements remain obstacles for broader adoption, particularly among small-scale farmers. Addressing these challenges will require collaborative efforts from policymakers, agricultural organizations, and technology providers to develop accessible and cost-effective solutions. This study concludes that precision agriculture offers a promising path to sustainable, high-yield farming by reducing resource consumption and minimizing environmental impact. However, increased focus on overcoming adoption barriers is essential to make precision agriculture feasible for a wider range of farmers. Further research should continue to optimize these technologies, making them scalable and adaptable to various agricultural contexts worldwide. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/166 10.71143/gc4v7n32 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 1-8 3048-975X eng https://ijrasht.com/index.php/files/article/view/166/88 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/167 2025-10-28T09:22:29Z files:ART

A Conceptual Exploration for Harnessing Emotional Intelligence for Transformational Team Leadership Ravindra Kumar Satendra Kumar Emotional Intelligence (EI), Transformational Leadership, Team Leadership, Conceptual Framework, Leadership Effectiveness Team Dynamics, Organizational Behavior While emotional intelligence and effective team leadership undoubtedly interconnect, the nature of this relationship remains nebulous. Prominent theories attempt to elucidate how a leader's grasp of emotions may cultivate cohesion and productivity amongst a diverse workforce. By internalizing self-awareness and regulating interpersonal dynamics, practitioners of emotional acuity foster understanding across perspectives. A mastery of social awareness and relationship management allows visionaries to navigate disparate viewpoints on a global stage in pursuit of shared purpose. Continued examination may refine present frameworks, clarifying how competencies in empathy, self-control and motivation synergize to optimize group dynamics amid change. Recognizing emotion's role in coordination and care enables conductivity between individuals and progress for all SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/167 10.71143/g0gvrk46 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 9-13 3048-975X eng https://ijrasht.com/index.php/files/article/view/167/89 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/168 2025-10-28T09:23:17Z files:ART

Post-Quantum Cryptography for Navigating Challenges and Exploring Opportunities Tejinder Sharma Shivangi Rishab Sharma Postquantum Cryptography Quantumcomputing Algorithms Cybersecurity The rise of quantum computing poses a significant threat to the security of such classical cryptographic systems, as they inherently depend on the computational difficulty of problems such as integer factorization and discrete logarithm. Examining Theoretical Foundations of Post-Quantum Cryptography: Challenges and Opportunities for Building Secure Cryptographic Protocols in the PostQuantum World This paper aims to explore how quantum computers will affect the current state of cryptography, contributing towards the ongoing discussion to upgrade our cryptographic systems' foundations in the face of potential quantum attacks and assessing the efforts for developing quantum-resilient algorithms. Quantum computers promise unprecedented computation power by harnessing the strange properties of quantum mechanics. While quantum algorithms still pose a significant threat to conventional cryptography, the emergence of post-quantum algorithms offer hope to secure our data in the quantum era SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/168 10.71143/9srnjc03 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 14-21 3048-975X eng https://ijrasht.com/index.php/files/article/view/168/90 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/170 2025-10-28T09:23:54Z files:ART

Algebraic Structures in the Decomposition of Mixed and Multiplicative Trend-Cycle Models Bhupendra Kumar R.N. Prajapati Niharika Tiwari Mixed Models, Time Series Analysis, Buys-Ballot Table, Variance Distribution, Trend Analysis In his study examines the algebraic foundations of mixed and multiplicative models in the decomposition of trend-cycle components within time series analysis. By leveraging algebraic structures, we explore how these models interact with seasonal patterns and variance distribution. The Buys-Ballot table is utilized to assess changes in row, column, and overall means and variances, particularly in cases where no trend is present. Our findings provide a theoretical framework for distinguishing the structural properties of mixed and multiplicative models, enhancing their application in time series modelling and forecasting SPS Educational Trust 2025-01-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/170 10.71143/g66pcg11 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 22-28 3048-975X eng https://ijrasht.com/index.php/files/article/view/170/91 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/171 2025-10-28T09:24:31Z files:ART

Studies on Morphological and Mechanical Properties of Epoxy/Vinylester-MWCNT Nanocomposites Rekha Syed Javed Ali Epoxy/vinylester nanocomposites, multi-walled carbon nanotubes, Mechanical characterization, Tensile strength, Impact strength, Dynamic mechanical analysis, Surface morphology This study investigates the mechanical characterization of epoxy/vinylester multi-walled carbon nanotube (MWCNT) nanocomposites, focusing on key properties such as modulus, ultimate tensile strength (UTS), Izod impact strength, dynamic mechanical analysis (DMA), and surface morphology. The incorporation of MWCNTs into the epoxy/vinylester matrix aims to enhance the mechanical performance by leveraging their exceptional strength and stiffness. Tensile tests were conducted to determine the modulus and UTS, providing insights into the reinforcement efficiency of MWCNTs. Izod impact tests evaluated the nanocomposite’s resistance to sudden impact loading, while DMA was employed to analyze the viscoelastic behavior, including storage modulus, loss modulus, and damping characteristics across varying temperatures. Surface morphology was examined using scanning electron microscopy (SEM) to assess the dispersion of MWCNTs and their interfacial bonding with the polymer matrix. The results demonstrated significant improvements in mechanical properties, indicating the potential of epoxy/vinylester-MWCNT nanocomposites for high-performance applications in structural and functional materials SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/171 10.71143/pa8rk008 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 29-36 3048-975X eng https://ijrasht.com/index.php/files/article/view/171/92 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/172 2025-10-28T09:25:42Z files:ART

Enhancing Thermal Resilience of Epoxy/VinylesterMWCNT Nanocomposites Rekha Syed Javed Ali Thermal resilience; multi-walled carbon nanotubes; thermal stability; polymer nanocomposites; oxidative resistance. The thermal resilience of composite materials is critical for applications in extreme environments, where stability under high temperatures and oxidative conditions is paramount. This research explores improving the thermal stability and resistance to heatinduced oxidative degradation in epoxy/vinylester matrix composites by reinforcing them with multi-walled carbon nanotubes (MWCNTs). Adding MWCNTs to the polymer matrix notably enhances the nanocomposites' thermal characteristics, such as their degradation temperature and resistance to oxidation. To assess the thermal stability and degradation patterns of these composites under accelerated aging, various experimental methods, including thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), were utilized. Results indicate that MWCNTs act as effective reinforcing agents by promoting a more stable crosslinked structure, enhancing the material’s ability to resist oxidative degradation at elevated temperatures. The study also examines the effect of different MWCNT loadings on the thermal properties, providing insight into the optimal reinforcement concentration for maximum performance. The findings demonstrate that the epoxy/vinylester-MWCNT nanocomposites offer a promising approach to improving the thermal resilience of polymeric materials for high-performance applications in industries such as aerospace, automotive, and electronics. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/172 10.71143/f5x7n454 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 37-43 3048-975X eng https://ijrasht.com/index.php/files/article/view/172/93 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/173 2025-10-28T09:26:44Z files:ART

Quantum Computing- A Revolutionizing the Computational Paradigm Vandana Dabass Suman Quantum Gates, Quantum Supremacy, Machine Learning, Quantum Information Processing, Quantum Mechanics Quantum computing harnesses the mystifying principles of quantum mechanics to address challenges beyond the grasp of traditional computers. This illuminating paper digs far below the theoretical foundations underpinning quantum computing, applicable uses presently explored, and enormous barriers slowing advancement to accomplishing quantum supremacy. Core topics covered profoundly comprise delicate interactions connecting quantum bits, quantum logic gates manipulating superposed states, and algorithms exponentially outpacing usual techniques in cryptography and optimization. The document also surveys recent breakthroughs and maps out the lengthy road still ahead to rendering quantum computing feasible in the genuine world. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/173 10.71143/rtx55e08 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 44-48 3048-975X eng https://ijrasht.com/index.php/files/article/view/173/94 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/174 2025-10-28T09:27:31Z files:ART

Innovations in Solar Thermal Technologies, Including Improved Ingredients and the Integration of Hybrid Systems Minal Rajendra Wadyalkar Dilip Mishra Solar thermal systems, Heat absorption efficiency, Graphene, Carbon Nanotubes (CNTs), Nanofluids, Thermochemical energy storage, Phase-change materials, Hybrid systems, Photovoltaic panels. The increasing demand for sustainable energy solutions has intensified research into solar thermal systems, which play a critical role in harnessing renewable solar energy for various applications. This study explores innovative methods to improve the thermal efficiency of solar thermal systems, focusing on advanced materials, optimized fluid dynamics, energy storage solutions, and hybrid system architectures. The primary objective is to identify strategies that enhance energy absorption, transfer, and storage, thereby increasing the overall performance and cost-effectiveness of solar thermal energy generation. Key findings include the significant impact of advanced materials such as Graphene and Carbon Nanotubes (CNTs), which exhibit up to 30% higher heat absorption efficiency compared to traditional metals like Copper and Aluminium. Nanofluids, especially those with Copper Oxide and Alumina nanoparticles, were found to improve heat transfer efficiency by 20-25% over conventional fluids like water. Additionally, the study examined Thermochemical energy storage systems, which demonstrated superior energy retention, with 98% efficiency, compared to traditional Phase-Change Materials (PCMs). The integration of hybrid solar thermal systems combining Photovoltaic (PV) panels and battery storage resulted in an 18-20% increase in overall system efficiency. The results suggest that the adoption of these advanced materials and hybrid systems can significantly enhance the efficiency, scalability, and economic viability of solar thermal technologies, contributing to the global transition toward clean energy. This research underscores the importance of continuing innovation in solar thermal energy and emphasizes its potential for widespread application in both residential and industrial sectors. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/174 10.71143/ta00d378 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 49-57 3048-975X eng https://ijrasht.com/index.php/files/article/view/174/95 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/175 2025-10-28T09:29:09Z files:ART

An Analysis and Model of Integrated Innovation in Clusters of Green Industries Based on Network Science Ishaan Tamhankar Gaurav Sharma Coupled Innovation, Green Industry, Network Science, Agent-Based Modelling, Community Detection The urgent push for environmental sustainability has led to the development of green sector clusters, hubs where businesses, research institutions, government agencies, and other stakeholders collaborate to foster innovation and drive sustainable economic growth. This study explores the structural dynamics and collaborative interactions within these clusters, aiming to uncover the mechanisms that facilitate innovation and promote sustainable practices. Using network science, the research models green clusters as interconnected networks, where each entity or actor functions as a node within a web of partnerships and information flows. Network analysis techniques, including community detection and centrality measures, help identify influential members and cohesive subgroups within these clusters. These methods offer insights into the roles of key players and the network’s structural features, both crucial in nderstanding how innovation spreads across the cluster. Complementing this, the study uses agent-based modelling (ABM) to simulate the complex interactions and collaborative activities—such as technology transfer, knowledge sharing, and joint research and development—that drive innovation within green clusters. This dual approach of network analysis and ABM allows researchers to evaluate the effects of various strategies, such as policy interventions or collaborative incentives, on innovation outcomes. Findings indicate that network structure, collaboration intensity, and central actors are significant factors influencing innovation in green clusters. The study provides practical insights for policymakers, industry stakeholders, and researchers by suggesting methods to enhance innovation through targeted network support and strategic partnerships. Ultimately, this research contributes to the growing understanding of how green sector clusters can act as catalysts for sustainable transformation, offering a pathway toward a more ecoconscious and resilient economy. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/175 10.71143/0bf5wf77 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 58-64 3048-975X eng https://ijrasht.com/index.php/files/article/view/175/96 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/176 2025-10-28T09:30:15Z files:ART

Innovative Strategies for Sustainable Environmental Management: AI and IoT-Based Approaches Ravi Teja Bhamidipati developments organizations sustainability management environmental Through their practice of environmental management people learn to protect resources they share with nature so future generations maintain sustainability. The recognition of sustainable environment preservation grew rapidly because of worsening climate change threats combined with growing pollution problems and dwindling resources along with declining biodiversity. Environmental degradation occurs as three modern issues unite population growth with industrial developments and urban construction activities. Worldwide governments along with organizations and communities strive to establish sustainable environmental management as their urgent mission to decrease environmental impacts. This paper introduces sustainable environmental management solutions by implementing IoT together with AI technology. To achieve maximum environmental impact experts in manufacturing should review combined technology applications for environmental challenges caused by climate change, pollution and resource utilization and waste management problems. AI alongside IoT enables organizations to develop innovative solutions which strengthen their operational excellence and maintain their sustainability initiatives. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/176 10.71143/j50vz429 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 65-71 3048-975X eng https://ijrasht.com/index.php/files/article/view/176/97 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/177 2025-10-28T09:31:34Z files:ART

Analysis to Evaluate the Improvements and Obstacles of Data-Driven Decision-Making in Organisations R. P. Ambilwade Supriya Goutam Data-Driven Decision-Making, Traditional Decision-Making, Data Analytics, AI, MI, Data Quality, Organizational Culture, Data Literacy, Ethical Decision-Making This study explores the comparative effectiveness of traditional versus data-driven decision-making in management, focusing on the transition from intuition-based approaches to data-informed strategies. With digital transformation accelerating the availability and use of data, managers are increasingly tasked with integrating data analytics, AI, and ML into their decision processes. The study adopts a mixed-methods approach, incorporating a literature review, case study analysis, surveys of managers, and expert interviews to examine both decision-making approaches across various industries. Results reveal that DDDM offers substantial advantages over traditional methods in terms of accuracy, speed, and scalability, particularly in large organizations where decision-making complexity demands precision and adaptability. However, challenges such as data quality issues, high infrastructure costs, privacy concerns, and a notable gap in data literacy often hinder the successful implementation of DDDM. Findings from expert interviews highlight best practices for DDDM adoption, including investment in data quality, data literacy training, and ethical data usage guidelines to foster a data-driven culture within organizations. The study concludes that an optimal approach combines the strengths of both traditional and data-driven methods, leveraging data insights while retaining the context-driven judgment of experienced managers. This hybrid model enables organizations to balance scalability with nuanced decision-making, fostering sustainable growth in a dynamic business environment. Recommendations include strategic investments in data infrastructure, cross-functional collaboration, and an emphasis on ethical data practices. Future research could further examine industry-specific adaptations and the role of organizational culture in data adoption, as these factors significantly influence the success of DDDM initiatives. This research provides valuable insights for managers seeking to enhance decision quality and operational agility by integrating data-driven approaches into their strategic processes. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/177 10.71143/jqj7y630 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 72-78 3048-975X eng https://ijrasht.com/index.php/files/article/view/177/98 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/178 2025-10-28T09:35:27Z files:ART

Multi-Method Non-Destructive Testing for Improving Bridge Health using AI for Proactive Structural Health and Predictive Maintenance Mohmad Kashif Qureshi Shweta Sehrawat bridge monitoring, non-destructive testing, ultrasonic testing, ground-penetrating radar, infrared thermography, acoustic emissions, structural health, predictive maintenance, AI integration Aging bridge infrastructure poses a growing challenge to public safety, resource management, and structural integrity, highlighting the urgent need for effective, non-invasive monitoring solutions. Traditional inspection methods often lack the accuracy, efficiency, and real-time capabilities required for proactive maintenance. This study examines four non-destructive testing (NDT) techniques—Ultrasonic Testing (UT), Ground-Penetrating Radar (GPR), Infrared Thermography (IRT), and Acoustic Emissions (AET)—to evaluate their respective strengths, limitations, and suitability for detecting various types of bridge deterioration. By testing each method on multiple bridge structures, we assess accuracy in detecting cracks, voids, and other common issues. Findings indicate that UT is highly effective for internal flaw detection, GPR for subsurface conditions, IRT for surface degradation, and AET for realtime crack monitoring. To overcome the limitations of single-method monitoring, this study further explores a multi-method NDT system that combines all four techniques. Our integrated model significantly improves detection accuracy by leveraging the unique strengths of each method, enabling a more comprehensive assessment of bridge health. Additionally, artificial intelligence (AI) enhances this system’s predictive capabilities, offering real-time analysis and enabling predictive maintenance. Through AI-driven data fusion, infrastructure anagers can shift from reactive to proactive strategies, thereby reducing maintenance costs, improving resource allocation, and extending bridge lifespan. Field trials demonstrate the integrated system’s potential to provide early-stage issue detection, enhance structural resilience, and promote long-term infrastructure sustainability. This combined approach provides a forward-looking solution for bridge management, supporting public safety and sustainable maintenance practices. SPS Educational Trust 2025-01-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/178 10.71143/k6kqc326 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 88-95 3048-975X eng https://ijrasht.com/index.php/files/article/view/178/99 https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/179 2025-10-28T09:36:25Z files:ART

A Novel Approach for Employing Dynamic Capabilities for Strategic Profitability of an organisation Karishma Grover Dynamic Capabilities, Strategic Management, Competitive Advantage, Sensing Opportunities, Seizing Opportunities, Transforming Operations, Organizational Agility, Resource-Based View Dynamic capabilities have been identified to be a vital element in strategic management and a variety of skills allowing the firms to react quickly to changing conditions. This article offers review on the rationale behind dynamic capabilities, particularly regarding the competitive advantage (as well as the sustainability of this). This article is useful in that it walks through the state of the literature and proposes an overarching framework which explains how dynamic capabilities underlie the process through which firms identify opportunities within the market, optimally exploit those opportunities, and reconfigure operations to meet the demands of the market place. SPS Educational Trust 2025-01-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/179 10.71143/g97hwj55 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 96-99 3048-975X eng https://ijrasht.com/index.php/files/article/view/179/100 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/180 2025-10-28T09:37:29Z files:ART

Blending AI and Deep Learning for Visual Arts Development to Explore a New Aesthetic Dimension Mohmad Kashif Qureshi Shweta Sharma Reeta Mishra Artificial Intelligence, Deep Learning, GANs, CNNs, AI-Generated Art, Creativity, Originality, Aesthetic Innovation, HumanMachine Collaboration The integration of AI in visual arts has transformed artistic creation, challenging traditional notions of authorship, originality, and creativity. This study explores how deep learning models, particularly GANs and CNNs, contribute to new aesthetic dimensions in art. AI-generated artworks have demonstrated high aesthetic and technical proficiency, often rivalling human-created pieces in complexity and detail. However, concerns about originality, authorship, and emotional depth remain central to the debate over AI’s role in the artistic domain. Through a comparative analysis of AI-generated and human-created artworks, this research evaluates creativity, aesthetic appeal, originality, emotional impact, and technical complexity. The findings reveal that AI excels in generating visually compelling and technically sophisticated works, but struggles with conceptual depth, human intuition, and cultural storytelling. While AI can replicate and synthesize artistic styles, it lacks the intrinsic motivation, lived experience, and emotions that define human artistry. Despite these limitations, AI is not a replacement for human creativity but an expansion of artistic possibilities. It serves as a collaborative tool, allowing artists to experiment with new styles, automate processes, and explore hybrid artistic forms. The study concludes that AI will continue to shape the future of visual arts, fostering an interactive partnership between human artists and intelligent machines. Future research should explore AI’s role in artistic co-creation, ethical considerations, and evolving legal frameworks surrounding AI-generated art. SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/180 10.71143/z8kef368 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 100-107 3048-975X eng https://ijrasht.com/index.php/files/article/view/180/101 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/181 2025-10-28T09:38:33Z files:ART

Transforming Academic Research via Innovative Methods and Resources for Improving Research Paper Quality Karishma Grover Research paper management, time management, digital tools, collaboration, productivity, stress management, reference management, academic writing, and research workflow. Efficient management of research papers is crucial for scholars navigating the complexities of modern academia, where multiple responsibilities and tight deadlines often hinder productivity. This paper explores the challenges faced by researchers throughout the research paper lifecycle, including time management, collaboration, reference organization, and mental well-being. Through a comprehensive methodology combining literature review, case studies, tool evaluations, and stress management techniques, the paper proposes practical solutions and strategies to enhance research paper management. Key findings indicate that time management is central to research productivity, with strategies like time-blocking and the Pomodoro Technique significantly improving focus and reducing procrastination. Digital tools, such as reference management software (e.g., Zotero, EndNote), project management platforms (e.g., Trello, Asana), and collaborative writing tools (e.g., Google Docs, Overleaf), were found to streamline the writing and revision process, allowing researchers to minimize administrative tasks and focus on content creation. The study also emphasizes the importance of clear communication and task coordination in collaborative research, highlighting the role of communication platforms (e.g., Slack, Microsoft Teams) and version control systems in reducing miscommunication and enhancing teamwork. Additionally, the psychological impact of research pressures was addressed, with findings showing that stress management techniques, including mindfulness and realistic goal-setting, are essential for maintaining productivity and mental health. The paper concludes with a holistic framework for managing research papers, integrating time management, digital tools, collaboration strategies, and well-being practices to improve both productivity and work-life balance for researchers SPS Educational Trust 2025-05-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/181 10.71143/2sp03269 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 1, January-March 2025; 108-115 3048-975X eng https://ijrasht.com/index.php/files/article/view/181/102 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/184 2025-10-28T09:42:53Z files:ART

An Analytical and Systematic Review of Smart Farming's Challenges and Opportunities Navneet Kaur Harneet Kaur Nehu Gumber IoT, precision farming, challenges in precision farming, opportunities in precision farming Various industries have become more financially accessible due to technological advancements in various circumstances. Integrating Internet of Things technology in crop cultivation has shown benefits for multiple industries, such as agriculture and food production. The review paper below presents evidence of Internet of Things technology's impact on intelligent agriculture. This paper aims to review smart agriculture systems utilising Internet of Things-connected devices. The report has examined various essential aspects of smart agriculture and the advantages of Internet of Things technology. The review paper thoroughly discusses the different elements of the Internet of Things (IoT) technology. The application was found to have several areas for improvement, such as high cost, knowledge gap, and significant energy consumption. A rational discussion addresses the possible solutions to the raised issues. On the other hand, secondary qualitative methods, which use qualitative data, have facilitated discussions about the needs of smart agriculture. The paper shows significant knowledge about implementing Internet of Things systems in intelligent agriculture. SPS Educational Trust 2025-04-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/184 10.71143/6pxgek27 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 2, April-June 2025; 136-152 3048-975X eng https://ijrasht.com/index.php/files/article/view/184/103 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/187 2025-10-28T09:43:33Z files:ART

Energy-Efficient IoT Systems: Integrating Low-Power Protocols and Adaptive Algorithms for a Greener Future Amritpal Kaur Swamy TN Karishma Singh IoT, energy efficiency, sustainability, low-power communication, adaptive algorithms, energy harvesting, smart devices, environmental impact, system performance. The IoT has revolutionized various sectors by enhancing connectivity and automation, but the rapid expansion of IoT networks has led to significant challenges in energy consumption as well as sustainability. This paper presents a comprehensive framework for energy-efficient IoT systems, designed to reduce power consumption, extend device lifespan, and maintain high performance. The proposed framework integrates low-power communication protocols, adaptive power management algorithms, and energy harvesting techniques to optimize energy usage across IoT applications such as smart homes, industrial IoT, healthcare, along with smart cities. The methodology employed includes a literature review, system design, simulation modelling, prototype development, and field testing. The framework was tested in real-world environments to assess its impact on energy consumption, device longevity, and network performance. The results show that the proposed framework leads to a reduction in energy consumption by approximately 35% and improves device lifespan by 30-33%. These benefits were particularly prominent in smart homes and industrial IoT applications. Although there was some minor reduction in network throughput, the trade-off was minimal, ensuring that system performance remained high while achieving substantial energy savings. The study concludes that energy-efficient IoT systems can significantly reduce environmental impact and operational costs, making them essential for the sustainable evolution of IoT technologies. The framework provides valuable insights for developing greener IoT solutions that balance energy efficiency, system performance, scalability, and security. Further research is recommended to refine the framework, explore advanced energy harvesting methods, and optimize power management strategies for specific IoT domains. SPS Educational Trust 2025-04-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/187 10.71143/8h52wa75 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 2, April-June 2025; 153-162 3048-975X eng https://ijrasht.com/index.php/files/article/view/187/104 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/189 2025-10-28T09:44:16Z files:ART

Developments in Reinforcement Data-Driven Modelling for the Development of AI-Based Autonomous Decision Platforms R. P. Ambilwade Kush Bhushanwar Neetu Singh Reinforcement Learning, Deep Q-Learning, Autonomous Systems, Policy Gradient, Generalization, Sample Efficiency, Safety, Robotics, Exploration vs. Exploitation RL is a subset of ML allowing agents to learn from interactions with their surroundings, thereby facilitating autonomous decision-making. Unlike traditional machine learning approaches, RL maximises cumulative rewards through trial-and-error, making it highly effective in sequential decision-making tasks. This paper explores the recent breakthroughs in RL and their implications for machine intelligence, focusing on the integration of deep learning techniques, real-world applications, and key challenges that hinder broader deployment. The research examines the theoretical foundations of RL, including MDPs, value functions, and policy optimization, as well as the concept of exploration vs. exploitation. Notable advancements, such as Deep QLearning along with PGM, have expanded RL’s ability to tackle high-dimensional tasks, including playing complex games like Go and developing autonomous systems in robotics and self-driving vehicles. The paper also presents a detailed analysis of RL algorithms based on performance metrics like learning efficiency, reward maximization, safety, and generalization. It highlights the trade-offs between algorithms, including Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic, along with DQN + Experience Replay, based on their effectiveness in specific tasks. Despite its successes, RL faces significant challenges, such as sample inefficiency, generalization to new environments, safety in high-risk applications, and the interpretability of decision-making processes. The article wraps up with a discussion of future research directions, including improving sample efficiency, enhancing generalization, ensuring safety in exploration, and developing more interpretable RL models. Overall, RL holds immense potential for creating intelligent, autonomous systems, but overcoming its current limitations is crucial for its widespread application in realworld environments SPS Educational Trust 2025-04-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/189 10.71143/kzxj0g02 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 2, April-June 2025; 163-171 3048-975X eng https://ijrasht.com/index.php/files/article/view/189/105 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/191 2026-04-13T12:52:37Z files:ART

Enhancing Sustainability, Climate Resilience, and Resource Efficiency with IoT-Based Precision Agriculture Ramanjot Kaur Deepa Nehra Kush Bhushanwar Artificial Intelligence, IoT, Precision Farming, Climate Resilience, Smart Agriculture, Sustainable Farming, Water Conservation, Machine Learning, Crop Monitoring. Sustainable agriculture is increasingly challenged by climate change, resource depletion, and environmental degradation, necessitating innovative technological solutions. The combination of AI and IoT with precision farming provides an innovative strategy. This study explores the role of AI-driven predictive analytics and IoT-enabled RTM in addressing key agricultural challenges, including water scarcity, soil degradation, and pest infestations. The research methodology involved IoT sensor deployment for environmental monitoring, AI-based machine learning models for irrigation and crop health prediction, and a case study analysis of AI-IoT adoption in different agricultural settings. The information gathered show that AI-IoT technologies greatly improve water efficiency, lower pesticide usage, and improve crop yields. Specifically, smart irrigation systems reduced water consumption by 45%, AI-powered pest detection minimized pesticide application by 30%, and AI-optimized fertilization led to a 22% increase in crop productivity. Additionally, soil health improved by 35%, demonstrating the long-term sustainability benefits of AI-IoT adoption. Despite these advantages, barriers to implementation persist, including high costs specifically initial, rural connectivity issues, and the need for farmer training. Addressing these challenges requires financial support, infrastructure development, along with educational initiatives to encourage the widespread adoption of AI-IoT agricultural technologies. Future research should explore AI-driven autonomous farming, blockchain-integrated supply chains, and scalable IoT solutions for smallholder farmers. The study concludes that AI and IoT play a pivotal role in transforming modern agriculture, offering sustainable, data-driven solutions to enhance food security, reduce environmental impact, and build climate-resilient farming systems. SPS Educational Trust 2025-12-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/191 10.71143/7db36796 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 4, October-December 2025; 364-371 3048-975X eng https://ijrasht.com/index.php/files/article/view/191/106 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/194 2025-10-28T10:20:38Z files:ART

Artificial Intelligence in Project Management: Techniques, Tools, and Applications Dr. Neha Bhat Dr. Shaiqa Nasreen Artificial intelligence, setting resources, risk, machine learning, Project Management meta-data. The field is artificial intelligence (Al) which has been causing havoc in the recent past in every industry and the project management is not the exception. It introduces revolution in the process of planning, execution and tracking of the projects since we can now make the processes, efficiency and decision making optimal with the aid of the AI technologies. The given paper is the summing up of the usage of AI in the project management and, to be more exact, planning, risk analysis, and resources. There is also the merging of machine learning (ML) and natural language processing (NLP) and the refinement procedure of AI usage into the tools of the project management to automate the project management tool, the project risks and the resource estimation and availability and special allocation of the resources respectively. The review that is given by the systematic review gauges readiness of the available AI tools, what one would get a bargain of, and what one is bound to come across after the tools are activated in the project management process. Through analysis, it has been agreed that AI has made a significant contribution in the area of supporting in the planning, identification of risks and their solution and maximisation of the available resources. Nevertheless, even the issues such as the quality of the data, its integration with the other parts of the systems and opposition to the changes are in fact forcing the barrier to the widespread use of the weapons. Finally, conclusion of the paper is arrived at on the potential future involvements of AI applications in the field of project management in terms of growth in the number of self-managed and the appearance of intelligent project assistants and their presence in the field in the future. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/194 10.71143/zygvsj85 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 180-184 3048-975X eng https://ijrasht.com/index.php/files/article/view/194/107 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/198 2025-10-28T10:20:38Z files:ART

IoT and AI in Healthcare Management: A Review of Technologies, Challenges, and Future Trends Raj Kumar IoT, AI, medical management, Smart healthcare solutions, technological integration. The history of the technologies breakthrough in the field of the Internet of Things (IoT) and Artificial Intelligence (AI) has assisted in the provision of various areas of life, and healthcare control can hardly be considered an exception to the rule. This technology has been creeping into the health systems and the scenario is currently augmenting the innovations in the health systems that will eventually benefit the patients with convenience in the operations and even minimisation of the cost incurred during the operations. The present paper will conduct a thorough research of the IoT and AI integration in terms of healthcare management; the essential technologies, applications, and challenges, as well the opportunities will be found. The potential to measure health in real time has emerged due to the new technology e.g. wearables and sensors and the rationale behind the AI algorithms has been transferred to diagnosis phase, treatment-planning and decision-aiding factors. The review finds the following way how such technologies can be applied to medical care in order to maximize the number of favourable outcomes such as early detection of a disease, personalized care, and prediction in hospital management. The paper will also be written in a format that captures all the issues that are raised whenever one uses IoT and AI in medical care like data security, data privacy and acceptance by the system and the laws. Finally, the paper gives the future trends of the smart healthcare solution implementation, and artificial intelligence and internet of things implications to health care systems transformation and supporting patients with improved outcomes around the world. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/198 10.71143/bm19h066 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 185-189 3048-975X eng https://ijrasht.com/index.php/files/article/view/198/109 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/204 2025-10-28T10:20:38Z files:ART

A Hybrid Deep Learning Approach for Financial Fraud Detection in Enterprise Management Systems Yadav, Neha Financial fraud detection, Deep Learning, Hybrid model, Enterprise management, CNN, LSTM The issue of financial fraud is one of the greatest concerns for organizations, especially in enterprise management systems where very large financial transactions are carried out. The conventional fraud detection methods are unable to assist in detecting the sophisticated fraud activities due to their complexity and volumes of data. In the paper, the idea to use the hybrid deep learning methodology is proposed in the form of convolutional neural networks (CNN) and long short-term memory (LSTM) combinations in financial fraud detection in enterprise management systems. The method combines the use of CNN for feature extraction with LSTM to determine the sequence of data in financial transactions. By combining the two models of deep learning, there is hope that the methodology will display a more conveyed outcome in the detection of fraud, as both aspects, feature learning and temporal sequence prediction, would be favorable. The performance of this hybrid model in the work was evaluated on a financial fraud data set, and its results were compared to the conventional machine learning models in accuracy and efficiency measures. The results show that the hybrid deep learning algorithm is highly superior to the current methods insofar as detection accuracy, false positives, and processing time are concerned. The paper will expand towards its end by commenting about the implications that the proposed model has on the improvement of fraud detection systems in enterprise management, controversial issues, and possible areas of research in the future. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/204 10.71143/7cve9q24 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 190-194 3048-975X eng https://ijrasht.com/index.php/files/article/view/204/112 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/205 2025-10-28T10:20:38Z files:ART

IoT-Enabled Smart Waste Management System Using Edge AI for Real-Time Monitoring and Optimization Punia, Vikas IoT, Edge AI, Smart Waste, Monitoring, in Real-time, Optimization, Waste Collection The problem of waste management is of very great significance in the cities, and the size of the populations as well as consumption was directly related to an increase in the waste as well as an ineffective waste management system. The past years and the advent of a new phenomenon, namely the Internet of Things (IoT) and Artificial Intelligence (AI), proved to bring new possible alterations to the waste management systems. The proposed smart waste management system (in this paper) is an IoT-enabled smart system with an edge AI that controls and optimizes the waste collection, recycling, and dispersal procedures effectively in real-time. The environmental conditions that are being detected by the system are the amount of waste in waste bins, which is itself implementing the IoT probes placed on the waste bins, and Edge AI is determining the frequency of waste bin collection and routing them and reducing the energy consumption cost and operational costs. The paper offers the architecture of the system that the target system is supposed to intervene in, its parts, and the virtues of adopting IoT with edge AI. It also speaks of the actual-time optimization of the waste management operations that entail optimization of the waste collection vehicles, such as optimal routing of the waste collection vehicles and enhanced decision-making with respect to waste recycling. The findings indicate that such a system has the potential of maximizing the efficiency of operations, maximizingthe management of the waste within the cities, and minimizing the emission of greenhouse gases. The paper has ended up discussing the probable issues of the IoT integration with the edge AI in the waste management systems and the future of the smart cities with respect to the commodification of the waste. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/205 10.71143/r93z6755 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 195-200 3048-975X eng https://ijrasht.com/index.php/files/article/view/205/113 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/206 2025-10-28T10:20:38Z files:ART

Recent Advances in Machine Learning for Business Process Optimization: A Systematic Review Sujata Business Process Optimization, machine Learning, operations Management, Deep Learning, Predictive Analytics Machine learning (ML) technology has been swiftly turning out to be the appropriate procedure of harmonizing the business activity in the cross-industrial environment. As people are getting more exposure to the big data and introducing new advancements in the possibilities of the internet, the use of ML has been solely undertaken with the aim of enabling organizations to do so in order to become more efficient and effective in their businesses by performing and making decisions. This systematic review touches on the given topic by discussing the emerging trends in the use of ML in optimization of business processes with specific mention of the importance that what it has in the operations management, supply chain management, marketing, human resource management as well as customer service. The key conclusions of the recent studies are generalized in the article and it was examined what ML-algorithms are most widespread and whether they are difficult to apply and what is beneficial in their activity. The review also predictive assumes that deep learning, reinforce learning and predictive learning would be more important in simplification of business processes as well as organisational competitiveness of the organisation. The results illustrate that ML would possess possibility to transform the likelihood of the business optimization on its way to the automation of the decision making procedure, and initiate the allocation of the resources, as well as increase the total endeavours of productivity. But the issue of privacy of the data, the lack of experts and the interface of ML systems with legacy are significant obstacles on the way to large-scale deployment. The future research directions in the field were outlined as the results of the paper in which the arguments about the necessity in the development of the extractable and understandable ML models in the business were indicated. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/206 10.71143/rj5ne971 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 201-205 3048-975X eng https://ijrasht.com/index.php/files/article/view/206/114 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/207 2025-10-28T10:20:38Z files:ART

An Analysis of Smart City Management Systems Based on the Internet of Things and Machine Learning Shashi Kumari Priyanka Singh Smart Cities and IoT, Urban Management and Urban Planning, Machine Learning The rapidity’s in the evolution of the concept of Smart city depend on the advancement of the Internet of Things (IoT) and Machine Learning (ML) technologies. With introduction of the technologies, urban management is evolving to offer real time information gathering, automations and high degree of analytics to create efficiency, sustainability and livelihood of urban regions. The infrastructure through the assistance of the IoT, smart devices and smart sensors is quite crucial to track the systems in the city such as traffic, waste management, energy consumption, and air quality. The development of Algorithms by use of Machine Learning can aid in enhancing the forming of an urban development, a resource allocation, and services to the people because of the terrific amounts of information which will be produced because of the IoT gadgets. The given paper is a broad survey of the paths, by which the technologies of IoT and ML find their application in the management plans of the smart cities, as the ways of their potential application in the fields of the urban planning and administration of the urban infrastructure, as well as urban services. The overview contains the details on the principle benefits, weaknesses and limitations associated with the deployment of such technologies in the courses of the cities. It, also, explores trends of development of smart cities in the future, particularly the evolutions toward the sustainable building of cities and technology. Facilitated by the current research and life application, the paper suggests the potential quality of the IoT and ML to create more effective, comfortable, and sustainable urban environments SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/207 10.71143/1p3hez44 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 206-210 3048-975X eng https://ijrasht.com/index.php/files/article/view/207/115 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/208 2025-10-28T10:20:38Z files:ART

An Analysis of Machine Learning-Based Decision Support Systems for Enterprise Resource Planning Parveen Kumar Amit Punia Machine Learning, DSS, Enterprise resource planning, ERP, Strategic decision-making The ERP systems have become part and parcel of effective operations of organizations through integration of business processes in different parts of the organizations like finance, supply chain and human resource. However, rising due to the complexity of the business, the long-established ERP systems have limited ability to help in terms of offering actionable insights, which make it possible to utilize in strategic decision-making. Machine learning (ML) is an explosive technology that has changed ERP systems and improved their performance to assist in decision-making. The paper under review gives a detailed overview of the role of machine learning methods in the ERP systems and their impact on making strategic decisions with specific reference to the integration of machine learning methods in the ERP systems. This paper discusses most used ML algorithms: classification, regression, clustering and reinforcement learning, and their use in the optimization of ERP including demand forecasting, invention management, financial planning, and customer relationship management (CRM). By using ML in ERP systems, it is possible to be able to ensure that the predictability can be done, in the context of the point of anomaly discovery, along with the possibility of providing real-time support of decision-making, which can result in the operational efficiency of enhanced operations as well as the realization of cost savings, and even improved eventual resource allocation. Also discussed in the review is the difficulties associated with the utilization of ML-based decision support systems in ERP such as quality of data, compatibility between systems, and experts. Lastly, the paper discusses future trends and how the capabilities of deep learning and AI-based ERP system will affect the businesses willing to use advanced systems to make better decisions. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/208 10.71143/hvx49y66 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 211-215 3048-975X eng https://ijrasht.com/index.php/files/article/view/208/116 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/212 2025-10-28T10:20:38Z files:ART

Integration of IoT and Machine Learning for Predictive Maintenance in Manufacturing Industries Pardeep Singh Abhishek Predictive Maintenance, IoT, Machine Learning, Manufacture Industry, Industry 4.0 The Internet of Things (IoT) and Machine Learning (ML) have formed a sort of breakthrough to the solution of predictive maintenance that is witnessed in the manufacturing industry. Traditional maintenance programs have also been reported to encourage uncontrolled outages, higher operating costs and inefficient management of resources. With the help of IoT and ML, the manufacturers can obtain an up-to-date sense of the equipment health, predicting its breakdowns, and offering pre-emptive care. The paper would discuss the emergence of IoT sensors and the ML algorithms in predictive maintenance in manufacturing and what it means to efficiency of operations, savings of costs as well as better production and its productivity. The paper and the discussion of the IoT sensor technologies used in the sphere of controlling the key equipment also discuss the significance of the ML models to analyse the sensor data and at the last, determines the effective examples of using the same technologies in manufacturing plants. The research concluded that predictive maintenance carried out by IoT and ML could lead to improved decision-making activities, extend the life cycle of equipment, reduce downtimes and cost a significant amount of money. The problems and limitations of such processes as the deployment of IoT and ML in the predictive maintenance systems are also mentioned in the paper. There are potential directions of future research and technological development in the area of the studied predictive maintenance, as the conclusion offers. The directions are especially relevant in the context of the industry 4.0. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/212 10.71143/sdwjfp14 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 216-220 3048-975X eng https://ijrasht.com/index.php/files/article/view/212/117 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/215 2025-10-28T10:20:38Z files:ART

AI-Driven Employee Productivity Monitoring System: A Case Study in Remote Work Environments Sanjay Kumar Nayak Artificial Intelligence, Employee Efficiency, Teleworking, Machine Learning, Observing System The dissemination of the COVID-19 pandemic has become the trigger of the shift to remote work processes, and today, organizations confront new challenges with regard to monitoring the productivity of employees. Assessing productivity based on physical presence and using manual tools for measurement is ineffective in a virtual working environment. Artificial Intelligence (AI) can disrupt the productivity monitoring industry by bringing the potential to view and monitor employee performance, working habits, and interest in real-time without constant surveillance. This paper describes the work of the author in implementing a system for monitoring employee productivity based on artificial intelligence within a remote working experience. Assisted by the machine learning algorithms, the system would be configured to run different kinds of work patterns, communication speeds, and work accomplishment rates that can give the managers a detailed insight into the amount of productivity of a particular employee, and all this will happen such that it will not interfere with the privacy of that employee. The study gauges the effectiveness of artificial intelligence surveillance in enhancing productivity, challenges that surround the installation of the systems, and fears by the workers that could crop up because of the surveillance. In accordance with the findings, systems powered by AI can potentially increase the productivity rates significantly and contribute to the development of the so-called data-culture working processes, as well as introduce feasibility in decision-making to managers. However, concerns about data privacy, employee trust, and integrations remain very important. To conclude the paper, it is noted that the fate of future workplace and organizational productivity lies in the hands of AI-driven productivity tracking and remote work. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/215 10.71143/d0mgnd04 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 221-225 3048-975X eng https://ijrasht.com/index.php/files/article/view/215/118 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/216 2025-10-28T10:20:38Z files:ART

Optimizing Supply Chain Performance Using AI and Machine Learning: A Predictive Analytics Approach Gaurav Batra Arun Batra Artificial intelligence, Supply Chain Optimization, Machine Learning, Predictive analytics, Logistics The paper presents the manner in which AI and ML have reshaped supply chain management (SCM) by making demand predictions, controlling inventory, reducing logistics costs, and controlling risks. It points out the opportunities of predictive analytics in enhancing the performance of supply chains in different industries. The paper analyses cases to demonstrate the efficiency benefits that AI/ML can bring as well as discuss some of the challenges, like those of quality and scalability of data and scalability and compatibility of systems. Although the process of implementing AI/ML can make the operations more efficient, it is expensive and necessitates clear planning and technological and people resources. The paper established that AI and ML have significant potential to provide businesses with a competitive advantage in the new global economy. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/216 10.71143/n5d6fr98 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 226-231 3048-975X eng https://ijrasht.com/index.php/files/article/view/216/119 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0

oai:ojs.ijrasht.com:article/217 2025-10-28T10:20:38Z files:ART

Deep Learning-Based Optimization of IoT Performance in Cloud Environments Mohammad Haider Deep Learning, Internet of things, Cloud computing, Performance Optimization, Resource Allocation The rapid emergence of the Internet of Things (IoT) has given rise to immense volumes of data, which require proper processing, storage, and analysis. Cloud computing can scale to meet the needs of the Internet of Things (IoT), but latency, bandwidth usage, and inefficient resource utilization are bottlenecks affecting performance. The capacity of deep learning (DL) to build and optimize connections amidst assets and the capacity to describe complicated associations is turning into a groundbreaking method of advancing the capability of IoT in the cloud-based setting. The article provides a comprehensive summary of the deep learning-based strategies to maximize the functionality of the IoT. It also talks about how it can be used in smart task scheduling and smart energy management, anomaly discovery, smart resource provisioning, and smart latency reduction. Deep neural networks based on convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and reinforcement learning are considered as architectures in the framework of IoT-cloud integration. It is demonstrated that DL can significantly increase throughput, reliability, and responsiveness and reduce costs and energy usage. Nevertheless, pressing issues include high computational costs, the interpretability of deep learning systems, data confidentiality, and counterexamples. To offer solutions to them, in addition to federated learning, edge-cloud interaction, and explainable AI, it is proposed to combine them in the future. This paper concludes that IoT systems optimized through the use of deep learning-based cloud-IoT frameworks can be considered as a promising trend that can ensure scalability, resilience, and effectiveness in the next-generation smart environments. SPS Educational Trust 2025-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://ijrasht.com/index.php/files/article/view/217 10.71143/j6bfhk65 International Journal of Research and Review in Applied Science, Humanities, and Technology; IJRASHT: Vol 2, Issue 3, July-September 2025; 232-236 3048-975X eng https://ijrasht.com/index.php/files/article/view/217/120 Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology https://creativecommons.org/licenses/by-nc/4.0 2ece82910f8dc2d3709ee4500681731d