Authors - Nita Dakhare, Shailesh Gahane Abstract - Kidney disease poses a significant global health challenge, necessitating innovative approaches for early detection and intervention. This study delves into the realm of predictive analytics through the utilization of machine learning algorithms to enhance kidney disease risk assessment. The research employs a comprehensive dataset comprising clinical and demographic variables, fostering a robust analysis of potential risk factors. The initial phase involves a systematic exploration of the dataset, employing statistical methods to identify correlations and patterns within the data. Subsequently, a comparative analysis of various machine learning algorithms, including but not limited to support vector machines, decision trees, and ensemble methods, is undertaken. Development of hybrid algorithm for kidney disease prediction using machine learning involves combining different techniques to improve accuracy, robustness or efficiency in predicting this condition. This evaluation aims to pinpoint the most effective model in terms of accuracy, sensitivity, and specificity in predicting kidney disease onset. The model development phase focuses on the implementation of the chosen machine learning model, incorporating features that contribute significantly to predictive accuracy. The model undergoes rigorous validation using distinct datasets to ensure its generalizability and reliability. Additionally, interpretability and transparency are prioritized to enhance the model's clinical applicability and acceptance. The study's findings provide valuable insights into the identification and understanding of key predictors of kidney disease, offering a potential tool for early diagnosis and intervention. The integration of machine learning in kidney disease prediction not only aids healthcare professionals in risk stratification but also contributes to the broader landscape of predictive analytics in preventive healthcare. The implications of this research extend to improving patient outcomes, reducing healthcare costs, and fostering a proactive approach to managing kidney disease on a global scale.
Authors - Vedant Vaidya, Shailesh Gahane, Prachi Mandade, Deepak S. Sharma, Pankajkumar Anawade Abstract - Pharmaceutical storage management is an important aspect of the health care system It makes sure medicines are on hand and stops fake drugs from spreading, while boosting overall operations. Old ways of tracking stock, like counting by hand or using barcodes, face many issues. These methods tend to be slow, prone to mistakes, and need lots of manual work. This leads to high running costs and inefficiencies. This study looks at how NFC card tech might solve these problems in drug inventory control. We focus on key areas such as accelerating inventory checks, reducing expenses, preventing counterfeit medications, protecting patients, and streamlining the supply chain. NFC cards help stop fake drugs by giving each item a secure tamper-proof ID. NFC cards aid in the fight against fake medicine. This guarantees that genuine medications pass through the supply chain. Additionally, patients are safer when utilizing NFC cards. It reduces drug mix-ups, provides reliable data on drug usage, and enables accurate prescription tracking. We also demonstrate how NFC technology improves supply chain efficiency. It streamlines the entire process of sending medications where they need to go by enabling real-time updates and reducing stock management delays. Besides, NFC calling card boost patient safety. They allow exact prescription monitoring thin down on medicinal drug mistakes, and propose trustworthy datum on drug usage. We too highlight how NFC tech further supply chemical chain productiveness. It activate live updates and cutting off delays in stock management making the whole drug distribution appendage smoother. Our research wraps up by showing that NFC placard tech offers a growth-friendly, budget-friendly fix for the crowing topic in drug inventory control. It impart major gains in precision, f number, costs, and safety. This spend a penny it a hopeful answer to bring drug supply Chain up to date.
Authors - Vanshika Landge, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - The relocation to a new city poses significant challenges to the students, especially with the search for safe and relatively affordable accommodation, food service, and transportation. Stress associated with academic demands tends to be amplified in light of these difficulties, indicating the need for a fully integrated solution that would correspond to the needs of a student. This paper explores a web application aimed to help students during their relocation period to new urban environments. Key services include housing listings, food delivery options, community engagement tools, and transportation services while incorporating budgeting features that enable financial responsibility. The application is user-centric and makes relocation easier for students and fosters a sense of community among them. The research indicates that there are critical gaps in the literature. It shows that current digital solutions miss the specific needs of students, especially with regard to affordability, safety, and ease of access to essential services. The methodology includes requirement analysis, exhaustive literature reviews, development in iterations, and rigid testing to ensure that this application will meet the expectations of the users. Utilizing contemporary web technologies and real-time data integration, this project addresses both the logistical problems and emotional support to facilitate students in informed decision making. Ultimately, this research shall contribute to a better understanding of the student experience while alleviating the stress involved in moving to unknown environments and enables the students to focus on their academic pursuit while becoming an integral part of the new community. It is, therefore, an important step toward a comprehensive solution to the multifaceted problems students face from relocation.
Authors - Reena Bhagat, Smita Urkunde, Payal Khode, Shailesh Gahane Abstract - The dynamic interplay between Human Resource (HR) practices and business analytics has emerged as a pivotal factor in driving organizational performance. This research investigates the integration of HR practices with business analytics to enhance operational efficiency and strategic decision-making at Varron Autokast LTD., Nagpur. It also explores the impact of HR Analytics and Performance Management Systems on organizational outcomes at Wipro Limited, Pune. Employing a mixed-methods approach, the study delves into how HR analytics tools and data-driven strategies optimize talent management, improve workforce productivity, and align HR objectives with organizational goals. The research emphasizes the role of advanced analytics in identifying key performance indicators, fostering employee engagement, and enabling predictive insights for proactive HR interventions. Key findings aim to provide actionable frameworks for leveraging HR analytics in diverse corporate contexts, ensuring scalable, adaptive, and measurable improvements in HR processes. This study contributes to the broader understanding of HR analytics as a transformative tool for achieving sustainable competitive advantage in a rapidly evolving business landscape.
Authors - Shrinivas Patwardhan, Shailesh Gahane, Pankajkumar Anawade, Vanshika Landge, Prachi Mandade Abstract - The provision of essential medicines in rural health facilities is a complex issue, primarily influenced by frequent stock repletion, drug wastage, and poor record-keeping. Most of these problems are as a result of limited resources, old organizational systems, and poor infrastructure that characterizes most rural settings. This study evaluates the possible applicability of advanced technologies, like Radio Frequency Identification (RFID), the Internet of Things (IoT), and cloud computing, in meeting the above-mentioned requirements and to better inventory management of rural health facilities. It shall be considered with a mixed-methods approach based on survey and interview methodologies and case studies as well as cost-benefit analysis for testing feasibility, benefits, and drawback regarding the introduction of these technologies into low resource environments. The findings of this study indicate that the implementation of RFID, IoT, and cloud computing technologies possesses the capacity to significantly reduce drug wastage, enhance operational efficiency, and increase inventory accuracy. The primary obstacles to the adoption of these technologies include insufficient internet connectivity, constrained financial resources, and the necessity for specialized training. This study supports stepwise implementation, with key attention to pilot testing, financial assessment, and scalable approaches to these technological innovations. Finally, the investigation determines that, despite the considerable promise these technologies hold in transforming rural healthcare systems, there exists an urgent requirement to address technical, logistical, and financial obstacles to render them feasible and appropriate for application in resource-constrained environments.
Authors - Ritika Tiwari, Shailesh Gahanae Abstract - This research work suggested brain tumor detection and the use of a combination of deep learning and reinforcement studying techniques applied to magnetic resonance imaging (MRI) records. The mixing of deep mastering models, specifically convolutional neural networks (CNN) and reinforcement gaining knowledge of algorithms, aims to enhance the accuracy and performance of brain tumor detection structures. A comprehensive assessment of machine overall performance is carried out using standards such as sensitivity, specificity, accuracy, and computational performance. Early treatment for mind tumors is critical. The only way to identify a tumor is by biopsy, which requires mind surgical treatment. Medical doctors can locate and classify brain tumors with the help of equipment primarily based on Computational algorithms. To help medical doctors perceive early Tumor with high ac-curacy, we are able to suggest deep gaining knowledge of and diverse system studying strategies using magnetic resonance imaging mind and enable the prognosis of numerous varieties of tumors as well as healthy tumors. Massive image files need to be processed and this may be a completely time-eating undertaking. due to the fact brain tumors and normal tissues have similar findings, it is able to be tough to differentiate nearby tumors. Consequently, there's a want for a rather sensitive automatic tumor detection technique. Experimental effects demonstrate the effectiveness of our technique, with vast improvements in accuracy, sensitivity, and specificity in comparison to conventional strategies. Moreover, we discuss the consequences of our findings for scientific practice, highlighting the capacity of deep getting to know-based strategies to beautify the performance and reliability of brain tumor detection. Standard, this research contributes to advancing the sector of clinical photo evaluation and underscores the importance of leveraging deep mastering and MRI within the combat in opposition to mind tumors.
Authors - Shrinivas Patwardhan, Shailesh Gahane, Pankajkumar Anawade, Prachi Mandade, Vedant Vaidya Abstract - Pharmaceutical inventory management in health care settings is important to ensure accessibility, access and ability to essential medicines. However, the challenges in rural areas include limited infrastructure, insufficient storage systems, disabled tracking methods, poor visibility in the supply chain and lack of monitoring of real-time portfolio. These factors cause frequent warehouses, drugs and disruption in the patient's care, affecting health results in signed areas. This paper examines the current status of pharmaceutical inventory management in rural health systems, including both manual and automatic systems to determine the efficiency, efficiency and scalability of these approaches. It then examines the effect of poor inventory management on medicines, patient safety and general lack of health care. In addition, the study in existing research and training, especially in the environment with low resources, where cost effective, technology -driven solutions are necessary, intervals within.
Authors - Reena Bhagat, Smita Urkunde, Payal Khode, Shailesh Gahane Abstract - Data driven strategy is already on the rise for better performance of Human Resource in its decision making, thus helping to attract business in the current global market. The escalating growth, hands in glove with human resources, is the transformation of HR analytics within performance management systems, motivating organizations to consolidate the objectives and performance of individuals. The current research is about the integration and impacts of HR Analytics made in Wipro Limited, Pune and aims to identify the role of HR Analytics toward improvement in the performance of the workforce, aligning their goals, and mean to enhance the overall productivity of the organization. This research would cover both the methods: quantitative and qualitative analyses to establish the use and effectiveness of HR Analytics when it introduces quantitative data analysis along with the instrument with qualitative data. Some commonly faced challenges where HR analytics could be used are: silos in data, lack of technological infrastructure, employee resistance, and so on. This research will also embody certain benefits of the HR analytics among some of which: it helps in decision-making, talent management, and allocation of resources in a better manner. It further gives strategic recommendations to organizations for optimum integration of HR analytics and brings out actionable insights to better guarantee performance and subsequent organizational growth. The new findings contribute to HR Analytics and HRM Literature Growth, which can serve as praxis toward the solution for HR professionals and organizational leaders or policymakers.
Authors - Lal Mohan kumar, Shailesh Gahane, Chandan Kumar, Deepak S. Sharma, Pankajkumar Anawade Abstract - This paper does go into the roles cloud computing has in changing the face of online education, but this time, it focuses on its advantages and the flip-side of it all. Advantages reaped from using cloud computing in the education sector include resource access to scalable, flexible, and accessible learning, where students are able to learn through various personalized learning experiences with collaborative learning environments from which the students and their educators interact and share insights in real time. Most importantly, this paper discovers that cloud-based platforms offer many benefits, such as improving access to educational resources and data analytics to achieve personalized learning support for diversity in learning styles. However, despite the widespread benefits, this study also considers inevitable critical challenges that may limit equal access to education, such as creating considerable difficulties related to data privacy issues, digital literacy, and the digital divide. Therefore, research needs to be con-ducted to apply cloud computing solutions in education to improve understanding of its benefits and limitations. Such recognition would lead to better incorporation of cloud computing solutions to facilitate learner engagement, improve educational outcomes, and support inclusive educational ecosystems in those institutions. Thus, this paper suggests more empirical research be conducted to understand the long-term impact of cloud computing on student performance, engagement, and retention in different educational contexts.
Authors - Vanshika Landge, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - Public transportation systems face rising pressure to provide services that are secure, efficient, and accessible to users, while still having a major segment dependent on outdated infrastructure which cannot fulfill the demands of modern-day commuters. Some key challenges include inefficient fare-collection mechanisms, rigid travel routes and poor provision of real-time information. This paper covers the adoption of Radio Frequency Identification (RFID) and Near Field Communication (NFC) technologies within the public transportation system as one of the comprehensive approaches. The proposed solution integrates safe and contactless fare collection along with dynamic travel flexibility through real-time GPS updates with help of smart cards as well as mobile applications. Its multi-phase research approach toward requirement analysis, prototype building, pilot testing, and scaling up ensures the robustness as well as practicality in the system. Modular architectures for scalability, safe use of advanced encryption, as well as intuitive interfaces towards users are integrated into this proposed solution. Pilot implementations show considerable improvements in operational efficiency, transaction accuracy, passenger satisfaction, and system reliability. The results show that RFID and NFC technologies are promising innovations to trans-form public transportation to address essential weaknesses in security, adaptability, and user convenience. This work lays a foundation for introducing innovative, integrated solutions to urban mobility in a manner that promotes sustainable, adaptable, and commuter-centered transit systems.
