Authors - Shamama Kamal, Shailesh Saxena Abstract - This paper examines the transformative impact of Internet of Things (IoT) technologies on agriculture, highlighting their role in enhancing efficiency, productivity, and sustainability. By integrating sensor devices, data analytics, and real-time connectivity, IoT enables precision farming practices and interconnected farming ecosystems. Key components include soil monitoring, crop health analysis, and smart irrigation systems, optimizing resource management and enhancing crop yields. Despite challenges like data security and interoperability, the potential benefits of IoT in agriculture, such as resource optimization and sustainable practices, underscore its significance. In conclusion, IoT emerges as a vital enabler for sustainable and efficient farming practices, promising a more resilient agricultural sector and contributing to global food security.
Authors - Priteshkumar Prajapati, Jugal Mehta, Prince Tadhani, Parth Shah, Amit Thakkar, Dipak Dabhi Abstract - In the current era of mobile technology, Android’s widespread use and customisation by developers have also made it simpler for hackers to identify and exploit security flaws. Android malware continues to evolve, and one of the biggest concerns is how it quietly exploits app permissions and runs in the background to carry out harmful tasks without users even being aware of it. To address this emerging issue, the research proposes a static analysis-based solution for Android malware detection, utilising features derived from application permissions and API call behaviour. The study investigated a range of machine learning algorithms, including traditional methods such as Logistic Regression, SVM, and KNN, as well as more advanced approaches like Neural Networks and ensemble techniques like Random Forest, XGBoost, and CatBoost. We trained and tested the models using a well-organised set of Android app datasets and then measured their performance by analysing the results with a confusion matrix, focusing on key metrics such as accuracy, precision, recall, and F1-score. The confusion matrix clearly shows where our approach performs well, especially in real-world situations where there’s an uneven number of malicious and safe apps. Our results highlight the effectiveness of ensemble learning methods in enhancing mobile security while also providing a clear comparison of how different algorithms approach the task of detecting Android malware.
Authors - Nayan Suresh Patil, Shabana Pirjade, Yash Dattatray Londhe, Yogeshwar Anil Patil, Tej Tushar Jadhav Abstract - This study presents an AI-powered mobile application designed to enhance athletic performance and promote active lifestyles. By evaluating existing sports applications, it explores their roles in boosting user engagement, motivation, and achievement of fitness goals. Challenges such as user consistency and data privacy are also considered. The findings suggest that well-designed sports apps, supported by responsive feedback systems, can play a significant role in personal training. To address the difficulties faced by aspiring athletes, especially in urban settings, this project introduces a feature-rich platform that connects users to nearby sports academies and offers structured guidance across various disciplines like cycling, athletics, and running. A key innovation is the integration of IoT-based monitoring devices, which provide real-time feedback through sensor-based data analysis. This facilitates accurate performance tracking and injury prevention, empowering users with personalized insights for continuous improvement.
Authors - Radhika V. Kulkarni, Gaurav Sulsule, Sanika Kolawale, Urmila Kakarwal, Prathmesh Deshmukh, Prakash Sharma Abstract - The burgeoning startup ecosystem necessitates robust and efficient methods for evaluating the credibility and historical performance of founders and their ventures. Traditional due diligence processes are often manual, time-consuming, and susceptible to information overload and inherent biases. This paper presents "Founder Verifier," an Artificial Intelligence (AI) driven web application designed to automate and enhance the initial stages of founder reputation analysis. The system integrates web scraping capabilities with Natural Language Processing (NLP) techniques to provide a multifaceted assessment. Key functionalities include sentiment analysis of web-derived text using the NLTK VADER lexicon, Named Entity Recognition (NER) for geographical context ex-traction via spaCy, keyword-based potential controversy detection, and assimilation of failure/industry insights. These analyses culminate in a consolidated web report featuring a heuristically derived reputation score, categorized sentiment snippets, links to relevant external profiles, and downloadable PDF summaries. An operational dashboard further provides analytics on system usage and observed trends. This paper details the system's architecture, the methodologies employed for data acquisition and multi-modal analysis, the formulation of the reputation score, and discusses the structure and utility of its outputs. The Founder Verifier aims to provide a rapid, data-driven preliminary assessment tool for stakeholders in the startup ecosystem.
Authors - Ch Sahyaja, Gorripotu Eekshitha Bhagavathi, Karthick.M, Ch Shankar Abstract - This study presents a comprehensive, systematic, and bibliometric review of Green Human Resource Management (GHRM) literature from 2000 to 2025, highlighting key trends, influential authors, research clusters, and emerging themes. Using Scopus as the primary data source, a total of 137 articles were analyzed with tools including VOSviewer, R (Biblioshiny), and Excel. The findings reveal a significant growth in GHRM publications, especially after 2020, with South and Southeast Asian countries leading in research output and collaboration. Co-authorship and co-citation analyses revealed some well-known contributors, including Ramayah T., Yusliza M.Y., and Jabbour C.J.C., and important publication outlets such as the Journal of Cleaner Production and IJHRM. From the keyword co-occurrence mapping, we observe dominant research themes surrounding sustainability, environmental performance, green behavior, and innovation. Besides, this study provides a roadmap for future studies by outlining areas of theoretical void, emergent domains of study and a call to an interdisciplinary and empirical approach.
Authors - Aishwarya V. Kadu, KTV Reddy Abstract - This research investigates the effectiveness of machine learning and deep learning models in multi-class crop classification using an agricultural dataset comprising 3,450 records from the Wardha district of Maharashtra, India. The dataset includes essential soil and climatic parameters such as pH, nitrogen (N), phosphorus (P), potassium (K), Mn, Fe, Cu, Zn, S i.e Nutrients and micronutrients temperature, humidity, wind speed, Cloud and rainfall, covering 11 crop types. Data preprocessing involved label encoding for su-pervised learning tasks. The study developed and evaluated three predictive models: Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed using classification metrics like accuracy and confusion matrix, as well as regression metrics such as MAE, MSE, RMSE, and R-squared. Feature importance analysis was performed to deter-mine the influence of input variables on model predictions. XGBoost showed the highest classification accuracy, while MLP demonstrated strong learning capability. The findings emphasize the value of combining predictive performance and interpretability, highlighting the applicability of AI-driven methods in enhancing crop recommendation systems and advancing precision agriculture across varied agro climatic zones.
Authors - Purva Agarwal, Somya R. Goyal Abstract - Diabetic Retinopathy (DR) is a widespread stage of diabetes that influences the retina and, if untreated, can lead to blindness. DR is one of the primary reasons for blindness, especially among working-age populations in developing countries. Treatment can solely assist in preserving vision due to the irreversible nature of the disease. Timely identification and intervention substantially diminish the likelihood of blindness. Manual diabetic retinopathy diagnosis with retinal images is costly, time-consuming, and labor-intensive. The automatic detection of diabetic retinopathy disease can be possible using deep leaning methods specially CNN. In this paper, several diabetic retinopathy detection methods based on deep learning are evaluated. The accuracy of the suggested method is 94.04%.
Authors - Damini Wankhede, Shubham Kishor Kadam, Utkarsha Wanjari Abstract - Quantum computing is a ground-breaking new technology, and its disruptive potential can affect workforce management and HR analytics. This paper talks about its prominent role in addressing new challenges, including workforce honing, talent management, employee retention, cobalt matrix personalization, and diversity management. Organizations can leverage quantum algorithms to optimize their allocation of resources, recruitment and finding patterns in multidimensional datasets for better decision-making. These innovations are not only improving operational efficiencies — they are also facilitating more personalized and equitable approaches to the workforce. However, the addition of quantum computing to HR systems poses its own set of problems. Technical roadblocks, like high infrastructure costs and a shortage of skilled workers, prevent wider adoption. HR schedules powered by automation can lead to biased results when data privacy and fairness of algorithms-related ethical concerns are addressed. Implementing such technology is beset by further complications like organizational readiness and change resistance needing to be effectively planned for and managed. The paper recommends actionable recommendations for HR leaders, including phased integration, investment in quantum literacy, and the establishment of pilot projects. Further studies should employ realistic architectures that prioritize scalability, cost-efficiency, and the reduction of algorithmic biases that make quantum solutions more widely accessible and equitable. Because of that, quantum computing can change the way HR analytics are performed, and new opportunities for diverse industries emerge.
Authors - Rohini Chavhan, Michael Savariapitchai, Priyanka S. Dhore Abstract - Hospital administrators contribute substantially to healthcare quality advancement through their dual performance as operational managers and strategic leaders who drive changes. This study examines the multifaceted responsibilities of hospital administrators who enhance patient care quality by implementing leadership roles and executing policies and managing resources and developing their workforce. This study surveyed healthcare workers across the Sawangi region through 104 structured questionnaires which measured training delivery and resource distribution along with communication channels and leadership activities. The analysis of administrative behaviors against quality improvement outcomes utilized Spearman's Rank Correlation with Independent Samples t-tests. The research demonstrates that standardized staff workshops together with adequate financial resources and well-defined communication goals lead to improved staff involvement as well as superior patient security alongside superior clinical outcomes. Administrators who underwent formal training displayed superior abilities to lead continuous quality improvement efforts. The study demonstrates how hospital administrators drive patient-centered and safe healthcare delivery systems through strategic leadership execution which connects organization objectives to evidence-backed procedures. Strong leadership development along with policy support stands essential for maintaining quality care at high levels within transforming healthcare systems.
Authors - Pranesh P, Adwaitha K Raj, Durga Lashmi C.V Abstract - Many individuals are interested in the Unified Payments Interface (UPI) technology since it is secure, user-friendly, and widely used in India. UPI (Unified Payments Interface) is incredibly popular in India, with millions of users and billions of transactions processed daily. This study explains about the UPI usage and the awareness about the migrant workers in Tamil Nadu. This study consists of 100 responses from the migrant workers, and their opinion towards the UPI usage. Most of the people think that the UPI usage is convenient for them to use in their smartphone. This study also examines the UPI preference of the respondents and their purposes. It also has the dominant results that all the respondents are aware of the UPI systems and pursuing it. This study has also consisted an insight of the digital literacy of the migrant workers. It also brings us the challenges that are faced by the people at the time of using UPI. UPI, the Unified Payments Interface, has been experiencing intermittent technical issues leading to partial transaction declines, according to the National Payments Corporation of India (NPCI). These issues can cause problems with fund transfers, payments, and app functionality.
