D.Y. Patil University

The best solution to a problem is found via optimization algorithms, which are mathematical processes that frequently involve determining a function's lowest or maximum value. These methods are essential for increasing accuracy and efficiency in a variety of domains. Finding the best answers for a particular optimization problem may depend greatly on the optimization algorithm selection. There are many different types of optimization algorithms. The goal is usually to reduce amount of loss that occurs throughout learning process. Data samples and their corresponding results are fed into a model. Upon producing an output, a model compares it to the intended output, calculates difference between the two, and then tries to make generated output more similar to intended result. An optimization algorithm iterates over multiple cycles until it converges, increasing model's accuracy. This chapter highlights the significance of optimization algorithms in fostering creativity and efficiency in problem-solving by giving a high-level overview of their concepts, classifications, and applications.

The rapid evolution of robotics is reshaping the landscape of personal transportation, driving a paradigm shift toward safer, smarter, and more efficient mobility solutions. As autonomous systems become increasingly sophisticated, robots are transitioning from industrial and service environments into everyday transportation platforms. Autonomous vehicles, delivery robots, intelligent drones, and robotic personal mobility devices demonstrate how robotics can enhance convenience while reducing human error, a major cause of road accidents. Integration of advanced sensors, machine learning algorithms, and real-time decision-making capabilities enables robotic transportation systems to navigate complex environments with minimal human intervention. Moreover, robotics supports sustainable transportation by optimizing energy use, reducing congestion through coordinated traffic flow, and enabling compact mobility solutions such as self-balancing scooters and robotic wheelchairs. The rise of connected vehicle ecosystems further amplifies these benefits, allowing robots and vehicles to communicate seamlessly with infrastructure and one another. Despite these advancements, challenges remain—including regulatory frameworks, ethical considerations, cyber security risks, and the need for robust public acceptance of autonomous technologies. Looking ahead, the convergence of robotics with artificial intelligence, 5G connectivity, and smart city initiatives will accelerate the deployment of personalized, autonomous transportation options. These innovations promise not only improved mobility but also greater accessibility for individuals with disabilities, reduced environmental impact, and enhanced quality of urban life. Ultimately, robotics is poised to transform personal transportation into a more intelligent, adaptive, and sustainable system, redefining how people move within both urban and rural environments.

Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for early prediction and preventive strategies to reduce severe cardiac events. This study aims to develop a data-driven predictive model to estimate the likelihood of heart attack occurrence by analyzing major clinical and lifestyle-related risk factors. A publicly available dataset obtained from Kaggle was utilized, consisting of attributes such as age, gender, cholesterol levels, blood pressure, blood sugar levels, heart rate, smoking behavior, obesity, physical inactivity, and dietary habits, all of which contribute significantly to cardiovascular health. The data was processed and analyzed using statistical techniques to evaluate feature relevance, correlation patterns, and risk influence. Multiple machine learning algorithms were then implemented to build a predictive model capable of assessing heart attack risk with improved accuracy. The findings revealed that factors including cholesterol levels, blood pressure, lifestyle behaviors, and physiological health indicators were among the most influential contributors to cardiac risk. The predictive model demonstrated strong potential in risk estimation, supporting the significance of machine learning for medical prognosis. These results indicate that predictive analytics can significantly enhance early diagnosis, assist healthcare professionals in risk stratification, and support timely clinical decision-making. The study concludes that integrating machine learning-based prediction systems into healthcare practice can improve preventive care, enable personalized treatment planning, and contribute to reducing heart disease-related complications and mortality rates.

This paper examines the psychological effects of social media on youth, emphasizing its dual impact on mental health and overall well-being. With social media use nearly universal among adolescents, its influence has become a growing concern for educators, parents, and health professionals. While these platforms encourage creativity, connection, and emotional expression, they also contribute to mental health challenges such as anxiety, depression, sleep disturbances, and cyberbullying. The study explores both the constructive and adverse outcomes of prolonged engagement, drawing from current research findings and global behavioral studies. Key factors linked to negative experiences include excessive screen time, unrealistic social comparisons, and exposure to harmful content. Conversely, responsible use can enhance communication, learning, and peer support, fostering positive psychological development. The paper highlights that promoting digital literacy, establishing time management habits, and encouraging balanced online behavior are essential for protecting mental health. It concludes that collective efforts from parents, educators, policymakers, and technology providers are necessary to create safer, more supportive digital environments for young users, helping them maximize the benefits of social media while minimizing its risks.

The primary technologies used to build this "E-commerce web service using MERN" are Mongodb, Nodejs, Express, and React. All consumers can browse a large selection of products, but to add a product to their shopping basket, they must first register and log in. Customers can read reviews made by other users before making a purchase. The number of items a customer adds to their shopping basket is up to them. The quantity of a product put on the cart can thereafter be modified by the customers. Finally, customers can enter their information during checkout and submit their order by paying the appropriate amount for it. The administrator can then view the customer's data, including order details, the delivery address for the order, and whether the payment has been received. Due to the inability to try on clothing while purchasing clothing online, it might be challenging for a consumer to choose a desirable outfit on the first try. The virtual try-on assignment has the potential to greatly increase the effectiveness of creating fashion catalogues and improve online user experience. This undertaking seeks to address this issue.

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly changing software engineering practices, especially in the education sector. This study examines how artificial intelligence (AI) and machine learning (ML) are transforming traditional software development through intelligent automation, predictive analytics, and learning-driven designs. Unlike traditional educational software, AI-powered platforms constantly analyze learner behavior, assessment performance, engagement, and cognitive progress. This allows the creation of personalized learning experiences in real time. On the engineering side, AI-driven tools, such as GitHub Copilot, Tab Nine, and Code Whisperer, accelerate software development by automatically generating optimized code, identifying vulnerabilities, and improving maintainability. Machine Learning-based testing systems enhance quality assurance by predicting high-risk modules and generating relevant test cases automatically. In addition, AI-enabled DevOps pipelines provide high availability, cybersecurity, and performance improvement through automated cloud management and real-time issue detection. The study also emphasizes how AI affects teaching by enabling student profiling, early dropout prediction, and personalized support for students. However, it highlights challenges such as bias, privacy concerns, and lack of transparency in black-box models. The study concludes that integrating AI and ML is leading educational systems toward more autonomous, data-driven, and constantly evolving learning environments, which is reshaping the future of intelligent academic infrastructure.

Purpose This study examines the dual nature of charismatic and value-driven leadership in contemporary public contexts, where deep loyalty and unity can enhance strategic execution but also heighten conformity and reduce adaptability. It investigates how cultic influence patterns-such as charisma, dependency, identification, boundary control, and spiritual messaging-shape group cohesion and behavioral conformity. Design/methodology/approach A quantitative, cross-sectional design was employed to test a mediation model linking leadership influence patterns to behavioral conformity through group cohesion. Data were collected from 456 participants involved in spiritual or quasi-spiritual leadership contexts. Structural equation modelling (SEM) was used to test direct and indirect effects. Findings Charismatic authority, follower dependency, leader–follower identification, and spiritual messaging intensity all significantly enhanced group cohesion, with spiritual messaging emerging as the strongest predictor. Group cohesion, in turn, fully mediated the effects of leadership traits on behavioral conformity. Social isolation tactics were non-significant, challenging assumptions about boundary control as a unifying strategy. Practical implications The results provide guidance for leaders and policymakers on fostering cohesion without promoting uncritical conformity. Cultivating openness to dissent and ethical reflection can sustain unity while preserving adaptability, innovation, and responsible governance. Originality/value This study reframes cultic influence as a continuum of leadership practices with both strategic value and potential risk. By integrating psychological, relational, and spiritual communication perspectives, it offers a unified framework for understanding how moral authority and cohesion shape collective behavior in complex public leadership environments.

Effective thermal management of electronic devices using passive cooling is a significant engineering challenge. This experimental study looks at how fin material and design affect the natural convection heat transfer of heat sinks attached to a vertical aluminum plate. Four configurations were tested under steady-state conditions: a flat plate, plates with horizontal aluminium fins, vertical aluminium fins, and copper fins arranged in a V-shape. The experiments covered heater input powers from 25 to 150 W, and we measured surface temperatures with calibrated K-type thermocouples. We calculated the heat transfer coefficient, Nusselt number, and the difference between surface and ambient temperatures to assess thermal performance. This experimental study systematically investigates the influence of fin geometry and material on natural convection heat transfer from a vertically heated aluminium plate under identical steady-state conditions. Three fin materials (aluminium, copper, and brass) and three configurations (vertical, horizontal, and V-shaped fins) were examined over a heater input range of 25-150 W. Surface temperatures were measured using calibrated K-type thermocouples (± 0.5°C accuracy). The heat transfer coefficient, Nusselt number, enhancement ratio, and thermal resistance were calculated using energy balance relations. The copper V-fin configuration achieved a maximum heat transfer coefficient of 47.66 W/m2K at 150 W, representing a 30-40% enhancement compared to the plain plate. Surface temperature reduction reached 15-20% depending on heat input. All results are supported by quantitative uncertainty analysis (± 6-8%).These findings provide experimentally validated guidance for the optimization of passive natural convection heat sinks.

As technological development gains more momentum and competition in the global economy intensifies, organizations have resorted to data-driven decision-making (DDDM) and artificial intelligence (AI) as a way of strengthening the strategy management and ensuring the sustainability of a business growth. The strategic management based on the data improves the capacity of the organizations to mince actionable information out of the large heterogeneous and live time data sources so that these organizations can make the better forecast and effective operation and competitive advantage. At the same time, the artificial intelligence technologies, including machine learning, predictive analytics, natural language processing and intelligent automation, are changing the customary frameworks of decision-making and enhancing the abilities of all businesses to think strategically, distributing resources more efficiently, and modifying their business models. This paper will present an argument about the combination of DDDM and AI in strategic management and the possibility of the two issues to cooperate to ensure higher sustainability, value creation in the long-term, minimized risks, and organizational resilience. It deals with a more contemporary literature, and covers the state-of-the-art technique of analysis and relies as well as offers a conceptual side of the ways AI-based decision-systems may assist in strategic planning, performance measurements, stakeholder management and environmental, social and governance (ESG) activities. Besides it, the paper discusses real-life use in the industries as well as outlines the barriers to the adoption and also offers recommendations on the further development of the data-centric and AI-enabled strategic ecosystems. The findings spur the notion that these types of organizations that are open to data intelligence and AI-driven decision architecture are more likely to expand sustainably, at scale, and be future-craft in an extremely dynamic digital landscape.