Authors: R.V.Viswanathan, L.R.Eswari, S.Parameshwari, V.Sushmitha

Abstract: Medication non-adherence and inadequate health monitoring remain significant barriers to effective treatment outcomes. Existing mobile health applications often emphasize fitness tracking or basic reminders, lacking integration of comprehensive medical support features. This work presents MediTracker, a smart health monitoring and medication reminder application designed to enhance patient care, adherence, and remote connectivity with healthcare providers. The system integrates vital signs monitoring (heart rate, blood pressure, glucose, oxygen levels), medication scheduling, and automated multi- channel reminders (pop-ups, SMS, email) within a unified mobile and web-based platform. Built on a layered architecture with a Java backend, MySQL database, and cloud storage, MediTracker enables real-time data synchronization, personalized health record management, and visual trend analysis. The application further supports caregiver and doctor access, ensuring timely interventions and improved treatment compliance. By combining monitoring, reminders, and secure remote access, MediTracker provides a scalable, patient-centric healthcare solution with future potential for AI-driven predictive insights.

DOI: https://doi.org/10.5281/zenodo.17340693

 

Published by: vikaspatanker

Authors: Oluwatosin Bubare Ayoko

Abstract: Hydrogen has played a pivotal role in the United Kingdom’s energy landscape for centuries, with its origins traceable to Robert Boyle’s 1671 experiment at Oxford and Henry Cavendish’s 1766 identification of the gas as “inflammable air.” The landmark discovery of electrolysis by William Nicholson and Sir Anthony Carlisle in 1800 laid the foundation for modern green hydrogen production. As a versatile and low-emission energy carrier, hydrogen offers significant potential for reducing greenhouse gas emissions and transitioning to a low-carbon economy, particularly when derived from renewable sources such as solar and wind. This study examines the evolution and deployment of hydrogen energy technologies in the UK, highlighting their integration into real-world projects that stimulate demand, foster economic growth, and enable decarbonization of hard-to-electrify sectors. It further explores the strategic role of government policies in accelerating hydrogen adoption across power-intensive industries. The findings underscore the symbiotic relationship between the expansion of the hydrogen economy and progress toward national net-zero targets, positioning hydrogen as a cornerstone of the UK’s clean energy transition.

DOI: https://doi.org/10.5281/zenodo.17291753

 

Published by: vikaspatanker

Authors: Pardeep Mehta, Sudhakar Ranjan

Abstract: In today’s digital age, the amount of data being created is growing at an extraordinary pace. Sources like social media, online shopping platforms, IoT devices, mobile apps, and business systems all contribute to this growth. This massive expansion has given rise to big data, which is often described by five key features: volume, velocity, variety, veracity, and value. Handling such huge and complex datasets requires storage systems that are flexible, scalable, and efficient in storing, managing, and retrieving information. Traditional storage models, such as centralized databases and file systems, often fall short when it comes to big data. They face challenges like limited scalability, poor fault tolerance, redundant data issues, and slow performance. To address these problems, new storage designs have shifted toward distributed and cloud-based systems, which provide better scalability and high availability. As data continues to grow across industries, the need for advanced storage solutions has become more urgent. Old systems struggle to keep up with fast data intake, quick access requirements, and the ever-changing demands of large-scale analytics. This research explores ways to optimize modern storage systems to improve the performance of big data processing. It looks at distributed file systems, object storage, and cloud-native methods, focusing on aspects such as data distribution, replication, metadata management, and efficient resource use. The study also considers how to balance scalability, fault tolerance, and consistency while integrating with platforms like Hadoop and Spark. By testing and evaluating performance, the research aims to develop solutions that increase speed, reliability, and cost-effectiveness. Ultimately, the findings are expected to guide the creation of next-generation storage systems that can support the rapid expansion of big data.

DOI: https://doi.org/10.5281/zenodo.17285290

 

Published by: vikaspatanker

Authors: Amit Kumar, Ritesh Chaudhary, Mohammad Razique, Nabin Prasad Chaudhary, Prasant Sah

Abstract: This paper presents the design, architecture, and implementation of an AI- and ML-based Online Voting Sys- tem (OVS) that integrates face recognition, anomaly detection, and chatbot support to enhance authentication, security, and accessibility. We describe system requirements, architecture, and workflow; provide improved UML and flowcharts; and include screenshots of an implemented prototype. Security analysis in- dicates reductions in impersonation and fraud risk compared to traditional methods.

Published by: vikaspatanker

Authors: John Mathew Iacouzzi, Justin Paul Iacouzzi

Abstract: This study examines the similarity-attraction paradigm and its vital role in enhancing leader-member relationships and organizational outcomes. It investigates how perceived similarity between leaders and employees—encompassing shared attitudes, values, and cognitive styles—increases interpersonal attraction, psychological safety, and trust, thereby improving Leader-Member Exchange (LMX) quality. Meta-analytic and experimental evidence show similarity-based matches significantly increase employee engagement, reduce turnover, and improve performance (e.g., γ = .41, p < .001; r = 0.45, p < .001). Importantly, these dynamics extend beyond leader-employee dyads to relationships where leaders mentor and train other leaders as well as employees, supporting leadership development pipelines and continuity in organizational culture. The study addresses ethical concerns related to algorithmic matching, including bias and privacy, and underscores the need for organizations to incorporate similarity awareness into diversity and inclusion training to mitigate affinity bias. Qualitative data were collected through semi-structured interviews and thematic analysis to uncover nuanced relational mechanisms. Limitations include reliance on self-reports and a focus on perceived rather than objective similarity. Practical implications recommend comprehensive similarity assessments and continuous feedback loops in leadership programs to foster trust, empathy, and open communication. Future research should further investigate moderating cultural, structural, and neuroscientific factors impacting similarity in hybrid and global workforces. Null hypotheses tested posit no significant relationships between similarity and LMX or organizational citizenship behaviors (OCBs), which were rejected. This research validates similarity’s foundational role in building sustainable, inclusive, and effective leadership relationships.

DOI: https://doi.org/10.5281/zenodo.17278833

 

Published by: vikaspatanker

Authors: S.Jagadesh, M.Ganesh Reddy, A.Durga Prasad, B.Prema Sai, Rahul Kumar(Assistant professor)

Abstract: The project entitled Eco Friendly Route Finder (Transit Buddy) focuses on developing a simple, interactive, and efficient navigation application that helps users find routes between two different locations with ease. The system is designed to allow users to register and log in with their credentials, manage their profile, and then enter the source and destination addresses. Once the inputs are provided, the application generates a polyline route that visually indicates the path to be followed and finally redirects the user to Google Maps for actual navigation support. The main objective of this project is to minimize the confusion faced by users in switching between input interfaces and map services by providing a clean, direct, and user-friendly flow. In the current digital environment, many applications are overloaded with features that often complicate the basic task of route finding. This project, therefore, attempts to simplify the process by focusing only on essential requirements such as location entry, route visualization, and map redirection. It ensures that even casual users, such as students or city travelers, can quickly access the information they need without unnecessary complexity. The system has been developed with the aim of enhancing usability, accessibility, and interactivity. Another important aspect of the project is its potential to expand in the future. Though the present version does not claim to replace advanced navigation systems, it creates a strong foundation for enhancements such as voice-based commands, route saving, integration with calendar events, and traffic-aware suggestions. It also provides opportunities for incorporating eco-friendly routing features by integrating real-time traffic and environmental data.

DOI: https://doi.org/10.5281/zenodo.17278537

 

Published by: vikaspatanker

Authors: Seema Solanki

Abstract: The integration of Artificial Intelligence (AI) into service cloud platforms has transformed the way organizations approach customer support, issue resolution, and long-term service management. Intelligent case management, driven by AI technologies such as natural language processing, predictive analytics, and machine learning, ushers service operations into an era of proactive, personalized, and efficient solutions. Unlike traditional service management strategies that often rely on manual interventions and reactive measures, AI-powered service clouds provide an end-to-end automated system that places intelligence at the center of customer experiences. By leveraging insights from large datasets, AI enhances decision-making processes, recommends appropriate solutions, and empowers customer service teams to improve both speed and accuracy in their responses. Additionally, advancements in sentiment analysis allow AI systems to not only classify issues but also assess customer emotions, which further enriches the quality of engagement. This convergence of smart technology and cloud capabilities ensures that businesses can scale their operations, promote consistency, and deliver hyper-personalized experiences to diverse customers across industries. Intelligent case management thus becomes more than a process of resolving tickets—it evolves into an ecosystem of predictive support and customer-centric adaptability. As organizations progressively invest in AI-driven service technologies, the service cloud becomes an essential hub of innovation where KPIs such as resolution time, customer satisfaction scores, and retention are consistently optimized. This article delves deeply into the mechanisms, benefits, challenges, and prospects of AI integration within service clouds, examining how intelligent case management reshapes customer service and positions enterprises to thrive in an increasingly digital and experience-driven economy.

DOI: http://doi.org/10.5281/zenodo.17278076

Published by: vikaspatanker

Authors: Vandana Tomar

Abstract: The Salesforce ecosystem stands today as one of the most influential platforms in the global business technology landscape, transforming the way organizations build customer relationships, automate processes, and enhance engagement. At its core, Salesforce extends far beyond the traditional concept of customer relationship management (CRM) by offering an integrated suite of cloud-based solutions that empower enterprises across industries to foster innovation, drive productivity, and scale operations seamlessly. Among its many offerings, Service Cloud and Experience Cloud emerge as two of the most impactful tools designed to elevate customer service operations and provide highly personalized digital experiences. Service Cloud optimizes support workflows, case resolution, and omni-channel communication, while Experience Cloud enables businesses to build branded portals, partner portals, and customer communities that enhance connectivity and collaboration. Together, these two solutions form an integral part of Salesforce's larger value proposition centered around delivering customer-centric excellence. This article intends to provide an in-depth exploration of the Salesforce ecosystem by examining the broad functionalities and strategic value of its interconnected tools. Beginning with a comprehensive overview of the Salesforce platform, the discussion will then move into the specific strengths and applications of Service Cloud and Experience Cloud, while also analyzing other critical innovations within the ecosystem including Sales Cloud, Marketing Cloud, Commerce Cloud, and advanced capabilities such as AI-driven insights and analytics. Furthermore, this discourse evaluates how Salesforce has become an indispensable strategic asset for digital transformation, influencing industries from healthcare to retail to financial services. Special focus is placed on the ways in which organizations integrate Salesforce into their operations to achieve higher levels of personalization, efficiency, and customer loyalty. The article is structured under eight distinct sections, beginning with this abstract, followed by a thorough introduction and six detailed insights into different aspects of Salesforce, concluding with reflections on the ecosystem’s overall significance. Keywords chosen for this study highlight the central themes of this ecosystem and its offerings, presenting a valuable resource for both business leaders and technical professionals who seek to maximize the potential of Salesforce solutions. Ultimately, this work captures not just the technological framework of Salesforce, but also the cultural and strategic paradigms it represents in the era of digital-first, customer-driven business models

DOI: http://doi.org/10.5281/zenodo.17278072

Published by: vikaspatanker

Authors: Manoj Kataria

Abstract: Artificial Intelligence (AI) has become an integral part of modern digital transformation, influencing decision-making, automating workflows, and redefining customer experiences across industries. As AI technologies continue to evolve within platforms like Salesforce, ethical considerations take center stage, ensuring that responsible and trustworthy AI becomes a reality rather than an aspiration. The Salesforce platform, with its inclusive and customer-centric design, provides organizations with tools that can both empower and challenge ethical standards depending on how AI is implemented. This guide presents a comprehensive discussion on the ethical dimensions of AI development specific to Salesforce, including issues of fairness, transparency, accountability, privacy, inclusivity, and security. It also explores the regulatory frameworks and industry best practices that organizations must follow when embedding AI features into Salesforce ecosystems. The exploration highlights the intersection of machine learning, cloud computing, and ethics, shedding light on potential pitfalls such as biased models, lack of explainability, misuse of data, and short-sighted deployment practices. In doing so, the paper emphasizes a proactive framework where ethical AI is not treated as an afterthought but as a fundamental design principle. The discussion delves into the importance of developing trust with users and stakeholders through transparent algorithms, respectful data stewardship, informed consent, and bias mitigation methods. It also considers the alignment between Salesforce’s AI-powered tools like Einstein AI and global policy directions, making a case for harmonizing technological innovation with moral accountability. Ultimately, the framework presented here equips businesses, developers, and decision-makers with the knowledge for responsible AI integration, ensuring sustainability, trust, and future readiness in their digital strategies. By exploring real-world examples, compliance strategies, and human-centered design models, this guide aims to foster confidence for companies adopting Salesforce AI without compromising on ethical standards. The goal is to build AI systems that are not only technologically advanced but socially responsible, trustworthy, and aligned with Salesforce's vision of equality and ethical digital engagement.

DOI: http://doi.org/10.5281/zenodo.17278004

Published by: vikaspatanker

Authors: Baljit Singh

Abstract: The digital age has transformed how businesses approach customer relationship management (CRM), with artificial intelligence (AI) now playing an integral role in shaping strategies, streamlining workflows, and enhancing decision-making. Salesforce, as one of the leading CRM platforms, has consistently evolved to meet the growing complexities of modern business ecosystems. The introduction of Salesforce Einstein laid the foundation for intelligent automation, predictive analytics, and seamless customer engagement. With the integration of Einstein Copilot and large language models (LLMs), Salesforce is entering a new era of enhanced functionality, where AI not only supports but actively empowers users to make better decisions, reduce workloads, and personalize customer experiences. Einstein Copilot functions as an intelligent assistant within Salesforce, enabling users to interact with data using conversational commands and receive contextual, real-time insights. Meanwhile, LLMs bring advanced natural language understanding and generative capabilities that revolutionize how employees and customers engage with data, processes, and applications across industries. These advancements signify a paradigm shift in CRM operations, moving from reactive strategies toward proactive, predictive, and automated solutions. Organizations are now equipped to harness conversational AI for sales optimization, customer service, marketing campaigns, and business forecasting. Furthermore, these tools are not limited to a single department but integrate across the enterprise, ensuring productivity gains at scale. The Einstein Copilot and LLM framework thus stands as more than an incremental innovation; it symbolizes the democratization of AI in business, making complex processes accessible to everyone. With these breakthroughs, organizations can anticipate—not simply respond to—customer needs, creating a seamless bond between corporate strategy and consumer experience. This article explores the integration of Einstein Copilot and LLMs in Salesforce, examining their impacts and potential applications in sales, marketing, customer service, and beyond. By highlighting the synergy between AI-driven assistants and data-powered language models, this analysis demonstrates how businesses can unlock new possibilities, build adaptive organizations, and drive sustainable, customer-centric growth in an increasingly AI-first economy.

DOI: http://doi.org/10.5281/zenodo.17277993

Published by: vikaspatanker