Authors - Sapna B. Warghane, Ajay Pethe Abstract - Modern banking institutions recognize employee engagement and retention as critical to achieving long-term goals. These elements influence productivity, innovation, and customer satisfaction. Public and private sector banks adopt different strategies shaped by political culture, regulatory environments, and competitive dynamics. Public Sector Banks (PSBs) offer job security, structured career growth, and welfare benefits, fostering stability and long-term com-mitment. These factors contribute to employee motivation and consistent performance but may limit agility and innovation. In contrast, Private Sector Banks (PvSBs) emphasize performance-based incentives, career mobility, and a dynamic work environment. While these strategies enhance engagement and drive results, they can also lead to stress and higher employee turnover. Organizational culture, compensation, career development, work-life balance, and employee well-being play significant roles in shaping these outcomes. This review compares engagement and retention strategies across both sec-tors, identifying the strengths and challenges of each. A hybrid approach—combining financial rewards, career development opportunities, and wellness initiatives—is recommended to enhance satisfaction and commitment across the board. By refining these strategies, both PSBs and PvSBs can develop a more engaged and resilient workforce, ultimately supporting long-term organizational success and stability.
Authors - Jannat Khair Chowdhury, S. M. Saikat Hossain, Farzana Sadia, Mahady Hasan, M. Rokonuzzaman, Marufa Akter Abstract - Developing an effective marketing strategy is crucial for the success of Software-as-a-Service (SaaS) businesses, given the dynamic and competitive nature of the market. This systematic literature review looks at major tactics including pricing structures, client acquisition and retention, and digital technologies. It emphasizes the problems of transferring academic findings to industry, with an emphasis on data-driven solutions such as hybrid pricing, freemium models, and tailored content. The report provides recommendations for optimizing SaaS marketing to promote scalable, long-term growth.
Authors - Juan Mejia, Valentino Galindo, Edgar Ramos, Donovan Fuqua Abstract - This study proposes a conceptual model that integrates advanced supply chain planning to enhance supply chain resilience and sustainability. Drawing from previous literature, the study explores how such planning approaches contribute to strengthening supply chains and improving long-term operational stability. The model is developed through a structured review of academic sources and expert perspectives, with an emphasis on theoretical alignment and systemic relevance. Using the Best-Worst Method (BWM) as a decision-making framework, the paper outlines key factors that may influence effective resource allocation and disruption management. While no empirical validation is conducted, the study highlights potential criteria for improving operations, particularly in terms of reducing disruptions and optimizing resource use. This work offers a foundation for future empirical research and supports ongoing discussions on resilient and sustainable supply chain strategies.
Authors - Divya Saleela, Reshma Lucos, Reji R, Teena Joseph, Shyna A, Chinchu M S, Hashna Rasheed, Arjun Rajesh Panicker Abstract - The automatic classification of mental health-related textual data is essential for enabling scalable and proactive psychological support. This study employs a fine-tuned BERT-base transformer model categorise statements into seven clinically relevant mental health conditions. The model was evaluated using a publicly available dataset from Kaggle and assessed with standard performance metrics, including accuracy, precision, recall, specificity, and F1-score. Achieving an overall accuracy of 90.00 per cent, the model demonstrated strong generalisability across a linguistically diverse and imbalanced corpus. The highest classification performance was achieved in the Normal, Depression, and Suicidal categories, with satisfactory results obtained for less frequent classes. These findings highlight the effectiveness of transformer-based architectures in mental health text classification and support their potential application in digital screening and early intervention systems.
Authors - Gokul B, Arumugam Balasubramanian Abstract - Climate change and sustainable ecosystem are gaining momentum in the public domain. Innovative technology is steering towards environmental protection enabled products and processes. Tata motors have captured the public imagination by lining up green vehicles with clean fuel. In order to accelerate the adoption of this innovative product they have embarked on an advertising campaign that not only caters to product marketing but also satisfies their inner desire towards contributing their might in the green initiatives. To this end, content analysis is used as a methodological tool to examine whether archetypal advertising strategies align with Tata Motors’ goal of promoting innovation in a sustainable manner. Analysis has come out quite clearly that archetypal advertising strategy are put to effective use to successfully kindle their desire and satisfy the consumers motivation towards adopting automotive innovation in the context of green commerce.
Authors - Asra Fatema Zakir Baig, Amol Mashankar Abstract - Women in business leadership must be empowered through promotion of diversity, innovation, and sustainable growth. Conceding that there have been noticeable gains, women remain way behind in their quest for leadership positions, due to multifaceted barriers, such as systemic biases, limited access to their networks, and societal expectation; this paper discusses the organizational culture and its gender-based stereotypes and the lack of mentorship, which hold back women's advancement in business leadership. Further, it goes on discussing the various opportunities that now come forth from women's leadership empowerment, further focusing on the advantages of a diverse leadership team: better decision-making and improved performance for companies. Through real examples of best practices, the authors demonstrate which organizations have applied some of the best diversity and inclusion strategies followed by positive results from investing in women's leadership development. The paper speaks to actionable strategies an organization can employ in addressing the challenges and seizing the opportunities available. It focuses first on creating an inclusive workplace culture followed by starting out with mentorship and sponsorship programs, targeted training and development. And finally work arrangements and others that also involve policies that support work-life balance and equal pay and works continually in amplifying the need for systemic change in creating an environment that avails women's advancement. Finally, women's empowerment in business leadership is more about equity and strategic advantage as a group for organizations to leverage benefits. In the end, embracing diversity in leadership can help unlock potential, drive innovation, and develop a resilient workforce for businesses. This paper, therefore, calls for collective efforts from stakeholders across all levels to advance women's leadership and pave the way for unlocking the silos of barriers in the business world.
Authors - Sazzad Hussain Farhaan, Tanvir Hussain Himel, Ahsanul Kaiser, Md. Nayon Ali Abstract - Utilising digital technology is more crucial than ever, especially social media, to address urgent societal issues and encourage creative solutions. Since traditional infrastructures and resources may be few in underdeveloped nations like Bangladesh, social media provides an affordable and easily accessible avenue for impact, creativity, and collaboration. This strategy continues the heritage of Grameen Bank and Dr. Muhammad Yunus, who pioneered creative social entrepreneurship through microfinance. The use of social media to promote social entrepreneurship in Bangladesh is examined in this qualitative study. Based on 20 in-depth interviews with people involved in social entrepreneurship, the study explores how digital platforms may support social transformation, especially in environments with limited resources. Purposive sampling was used to choose participants, guaranteeing that they had at least a year of experience utilising social media for social entrepreneurship. Social media is a vital tool for outreach, community involvement, fundraising, and creating awareness, according to the findings. By overcoming conventional obstacles, it enables business owners to reach a larger audience and rally support for social causes. Future research can be done on digital infrastructure, and community participation affect social media’s ability to promote social entrepreneurship.
Authors - Radhika V. Kulkarni, Spandan Papade, Yash Paryani, Saniya Patil, Rahul Madhawani, Prakash Sharma Abstract - Governments worldwide actively support entrepreneurs tackling pressing social issues, aiming to boost economic opportunities for all, generate employment, and accelerate tech-driven progress. Unfortunately, some recipients exploit these funds for personal gain, sidelining projects that could otherwise drive fair governance, community welfare, and environmentally conscious industries. Manually tracking thousands of funded startups is inefficient, allowing dis-honest actors to slip through the cracks. This study introduces an AI-powered solution to bridge this gap. The system scans public data—such as LinkedIn activity and online footprints—to identify red flags, like founders holding jobs un-related to their funded ventures. Advanced language analysis and pattern recognition techniques assess how closely a founder’s current work aligns with their original proposal, simplifying oversight while fostering synergy between public programs and modern technology platforms. By promoting fairness, transparency, and accountability, the system supports several United Nations Sustainable Development Goals, including SDG 8: Decent Work and Economic Growth, SDG 9: Industry, Innovation, and Infrastructure, and SDG 16: Justice, and Strong Institutions. It enables governments to channel resources more effectively to innovators committed to creating lasting social value.
Authors - Ashiqha Sultana, Kalyani Suresh Abstract - Sustainable fashion apparel purchase on Instagram has utmost importance in recent times, as Indian consumers are currently influenced by many factors that finally lead them to purchase intention. This paper tries to examine and explore the Indian consumer mindset towards sustainable fashion apparel on Instagram and the connection between demographics and consumer identity of the consumers with the sustainable constructs. Responses were taken from 100 respondents using snowball sampling from different parts of India. Results of the study show that there is a significant association between demographics, patterns of interacting with sustainable fashion apparel on Instagram, and Consumer Identity. The study also reveals the strong connection between Indian consumers and purchase intention, consumer identity, sustainable constructs, purchase behaviour, and Instagram motivation.
Authors - Sapna B. Warghane, Ajay Pethe Abstract - Artificial Intelligence (AI) has had a significant effect on decision-making in business by providing data-driven decision-making, improving efficiencies and decision-making accuracy. This literature review will elaborate on the impact of Artificial Intelligence on decision-making in business and its various components, such as predictive analytics, automation, risk management, marketing personalization, strategy, etc. It will also explore issues, ethics, and future prospects of AI in business-related decision-making. AI has already been adopted in various industries, including finance, healthcare, retail, and manufacturing, to increase efficiencies and optimize operations while attempting to optimize the customer experience. On the other side of AI, increasing utilization creates other challenges about bias, security, and regulatory compliance must be considered in the sustainable growth of AI in business.
Authors - Seema Tripathi, Nikita Bhardwaj, Shivani Bansal Abstract - Voice recognition technology has evolved into a cornerstone in contemporary applications, from virtual assistants and intelligent homes to biometric security and healthcare. Its potential to facilitate unobstructed human-computer interaction has hastened the need for more precise and more robust systems. GMM and HMM traditionally paved the way for voice processing. However, deep learning architectures—namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models—have revolutionized the field by achieving unprecedented performance and flexibility. This research paper provides a comparative study of major deep learning models for voice recognition, critically evaluating their design rationales, merits, and shortcomings Some major challenges like scarcity of data, noise tolerance, computational efficiency, and interpretability of models are discussed. The paper also offers the perspectives of the future trends in work like the inclusion of self-supervised learning, light-weight models for edge devices, and multimodal systems that will influence the next voice recognition generation. The goal of this work is to offer an end-to-end perspective of what exists currently and stimulate future work in this very dynamic field.
Authors - Vipul Lanjewar, Sourish Joshi, Sujata Joshi, Menachem Domb Abstract - AI is changing supply chain, transport, and logistics using neural net-works to improve decisions. It helps with real-time analysis, route planning, and demand prediction. A bibliometric analysis with Scopus data was performed, using VOSviewer and Biblioshiny to track trends, citations, and new themes in AI for these fields. The focus was on areas like warehouse automation, self-driving logistics, and smart transportation, while also tackling data security and ethical issues. By finding key research and gaps, this paper shows how AI can shape the future of these industries.
Authors - Anshika Srivastava, Rojalin Patri Abstract - This study explores whether arms and armed forces increase the probability of armed conflict within a country. Though several studies in literature have highlighted the cases where arms and armed forces have influenced armed conflict within a country, none of them have provided empirical evidence to support this phenomenon. This study attempts to bridge the gap by providing a quantitative analysis across 45 countries over 28 years and further substantiates the arguments laid in qualitative studies. Random effect panel logistic estimates and year-wise logistic estimates have been used to capture the overall as well as year-wise significance of arms and armed forces on armed conflict within the country, respectively. Similarly, when the number of armed forces increase by one million, the odds for armed conflict becomes 3.97 times more and when military expenditure increase by 1%, the odds for armed conflict becomes 9.06 times more than before. These insights point towards the fact that arms, armed forces and militarism don’t necessarily help reduce violence.
Authors - Kalyani Yede, Deepak Sharma Abstract - Work-life balance is of utmost importance to career workers managing the challenges of contemporary work environments and family duties. This research employs secondary data to investigate the determinants of work-life balance, its impact on worker satisfaction, productivity, and health, and organizational support as a facilitator of balance. The study finds that flexible work arrangements, supportive management, and organizational policies all contribute strongly to helping employees balance work and personal life. Excessive workload and a lack of support, on the other hand, lead to job dissatisfaction and stress. The study emphasizes a need for cooperative efforts between employers and employees to create a good work-life balance. This study offers useful insights to organizations that want to enhance organizational performance and employee well-being by implementing focused work-life balance programs.
Authors - Namrata Patil, Pravin Desai, Ashvini Patil, Pragati Patil Abstract - In the context of multimedia content of huge scale, effective and efficient video summarization has become more and more important for improving web browsing experiences and information overload. In this paper, we introduce a new video summarization method by modeling it as a sequential decision-making problem. A Deep Summarization Network (DSN) is proposed for predicting the probability of considering each video frame in terms of its possible contribution to the final summary. For training the DSN, we suggest an end-to-end deep reinforcement learning system that works in a fully unsupervised way, without requiring annotated labels or human intervention. Our approach has its foundation in a well-crafted reward function that assesses generated summaries along two primary dimensions: diversity and representativeness. These elements are designed to ensure that the chosen frames not only convey the main content of the video but also encompass a good number of visual and contextual variations. The DSN is trained to maximize these rewards, resulting in brief, informative, and non-redundant summaries. Extensive experiments are performed on two standard datasets to confirm the efficacy of the new method. The experimental results show that our method is capable of rendering competitive performance compared with state-of-the-art methods, especially under unsupervised scenarios, thus revealing excellent potential to be applied in real-world systems for large-scale video analysis and multimedia retrieval.
Authors - Janwale Asaram Pandurang, Minal Dutta, Savita Mohurle Abstract - The machine learning algorithms used for the automated predction of esophageal cancer, a highly destructive disease that frequently exhibits at an advanced stage because it lacks early symptoms, is examined in this work. A huge dataset including clinical, demographic, and diagnostic information is used in the study to relate the classification algorithms of logistic regression and decision trees. With a incredible accuracy of 99.75% compared to 64.87%, the Decision Tree model clearly beaten Logistic Regression. Its capability to recognise complex, non-linear relationships in the data is responsible for Decision Tree's better performance, which makes it a useful tool for early cancer detection. The study identifies the need for validation on a variety of datasets and techniques to reduce overfitting, even as the results illustrate how machine learning may improve diagnostic accuracy. Improved clinical processes and better patient outcomes are made possible by us research's successful development of scalable, interpretable, and effective diagnostic tools.
Authors - Jatin Kumar Sangewar, Chandan Kumar Sangewar, Gaurav Kumawat, Chirag Joshi Abstract - In this research paper, we introduce a novel approach to improving research paper recommendation systems using the Universal Sentence Encoder (USE) to analyze and understand the textual content of academic papers, such as their title, abstract, and publication year. Our ultimate objective in the proposed methodology is to greatly enhance the accuracy and personalization of paper recommendations in favor of scholars, researchers, and students. Our research provides a thorough system architecture review, extensive experimentation with regard to state-of-the-art benchmarks, as well as an assessment of its probable impact on the world of academia. In addition to the technicalities of our method, we explore the larger research paper recommendation system context. This involves a close look at the issues that plague these systems, including data sparsity and the cold start problem, and the potential directions they offer for enhancing academic discovery. We also look at possible futures for the discipline, including the incorporation of user profiling, collaborative filtering methods, and ethical considerations into the building of these systems. The goal of this study is to push the field of research paper recommendations into a new generation based on the use of state-of-the-art Natural Language Processing and Machine Learning methods while ensuring a commitment to enhancing the academic experience. The new use of the USE model in this system offers intriguing possibilities for making more accurate and user-specific recommendations, which will eventually enable a more productive and efficient academic experience for users in general.
Authors - Naina Kokate, Aoudumber D. Londhe, Priyanka Swapnil Raikar, Nikeeta Pande, Sanjana Sanjiv Nikam, Nethra Nadar, Indranil Nayak, Rhia Nevgi, Neil Oswal Abstract - This project demonstrates about working of Peltier module with an new approach, we have tried making a smart way of using it by making a smart water temperature modulation bottle. The targets we wanted to achieve through this project was to make it energy efficient along with cost effective. the dual purpose of heating and cooling to be done as per our wishes. the main components which are used in this project are TEC1-12706 Peltier module, W1209 digital thermostat, two heatsinks and two fans. the project can be used for personal as well as professional fulfilling of needs such as getting water to a certain temperature or maintaining a medicine at a given temperature. the project can work on both battery and electricity.
Authors - Ketaki Paranjape, Rajasi Barapatre, Sandhya Arora Abstract - This study conducts a statistical analysis of the Israeli-Palestinian conflict to uncover patterns and relationships that characterize the dynamics of hostilities. The research examines critical questions about the association between event location, age, citizenship, and types of ammunition, employing statistical methods such as Z-scores, Chi-square tests, and F-tests on a comprehensive dataset. Key findings include significant regional variations in participation in hostilities, with Gaza showing the highest levels of engagement, and the predominance of the 21–30 age group in active involvement. Additionally, asymmetrical impacts were observed, with Palestinian civilians primarily affected by Israeli security forces, and Israeli civilians targeted by Palestinian actors. Analysis of ammunition usage reveals targeted engagement against younger age groups and highlights the varied means of violence in the conflict. These findings provide valuable insights into the conflict's demographic and regional dimensions, offering a foundation for data-driven conflict resolution strategies and humanitarian interventions.
Authors - Kalyani Yede, Deepak Sharma Abstract - This study looks into the effect of work-life balance on IT professionals in Pune, India, through analysis of determinants that affect this balance and measures to enhance it. A thorough review of literature demarcates important areas for study, such as the organizational policy, socio-cultural determinants, and perceptions. The research will fill the gap between research and practice by presenting culturally responsive suggestions to organizations looking to improve employees' job satisfaction and productivity. The research will, with a mixed-methods design, offer insights for policy-making and enhancing the quality of life of IT professionals.
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.
