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Development of an AI-Powered Chess Engine Using Minimax Algorithm and Genetic Algorithm for Evaluation Function

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Development of an AI-Powered Chess Engine Using Minimax Algorithm and Genetic Algorithm for Evaluation Function/strong>
Authors:-Rishi Kiran Karnatakam, Kalyani Gullaeni, Sai Tarun Siri Vadlakonda

Abstract- This project demonstrates a high level processing chess engine employing the Minimax algorithm along alpha-beta pruning, one more added feature used is a genetic algorithm which proves useful to make decisions and performance higher. While the Minimax algorithm is a cornerstone of game theory, which helps one to discover best moves and counter-moves in order not to lose in games like chess, with Alpha-beta pruning you can limit the number of nodes that are evaluated and hence restrict computational power needed without loosing optimality. Our evaluation function rates board states, based on which we use a genetic algorithm to fine-tune it. The optimal criteria are formed by the selection and combination of those evaluation functions over generations, while the genetic algorithm evolves a population of candidate solutions. This continuous refinement allows the evaluation function to improve as it gives a better result. While playing, the engine uses the so-called Minimax algorithm with alpha-beta pruning to look ahead and move sequences up to a certain depth for better decision-making. We tackle both tactical and strategic parts of chess in our implementation, showing strong play against humans. The project has had an analysis, which shows that the move selection and game outcomes are superior to conventional Minimax-based engines. This breakthrough in the class of Minimax algorithms achieves higher intelligence levels in computer chess, drastically changing gameplay for both fun and competitive purposes.

DOI: 10.61137/ijsret.vol.10.issue6.393

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Optimizing Information Management, Security, and Analysis with Database Technologies

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Optimizing Information Management, Security, and Analysis with Database Technologies/strong>
Authors:-Greeshma Muraly

Abstract- Database technology has been a central focus for organizations and businesses involved in managing information. As the amount and complexity of data continue to increase, efficient data management has become more critical. This paper examines the wide-ranging uses of database systems across different sectors. It starts with an overview of both relational and non-relational databases, then explores their applications in areas such as enterprise management, retail, education, and government/public services. In enterprise management, databases ensure data is timely, accurate, and reliable, forming the foundation for effective information handling. In retail, they support inventory management, sales analysis, and improve customer interactions. In education, databases help manage student records, support teaching insights, and contribute to online learning platforms. For government and public services, databases enhance information sharing, promote transparency, and are essential for crisis management and emergency response. This paper highlights the diverse and crucial roles of database systems while also addressing current research trends and future advancements in the field.

DOI: 10.61137/ijsret.vol.10.issue6.392

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A Survey on Machine Learning Handling Imbalanced Dataset in Credit Card Fraud

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A Survey on Machine Learning Handling Imbalanced Dataset in Credit Card Fraud/strong>
Authors:-Pawan Panchole, Rajesh Dhakad

Abstract- In the era of digital transaction people prefer to make online payments and purchases due to the convenience of time, transportation, etc. Credit card fraud has also increased significantly due to the growing trend of e-commerce. Fraudsters try to take advantage of card and internet payment information. Credit card and online payment information is often used by fraudsters for fraudulent purpose. Imbalanced dataset and high dimensionality of data are the key issues observed in credit card fraud detection. The use of various machine learning algorithm has been utilized for identifying anomalies in credit card transaction, focusing on the problem of imbalanced dataset and reduction of dimension which were carefully reviewed and studied. The study investigates the impact of imbalanced datasets on PCA-based fraud detection and provided detailed techniques such as Random Oversampling, SMOTE & Random Undersam- pling to handle imbalanced datasets and various classification as well as anomaly detection methods. Additionally, given the labelled nature of the dataset, various methods are reviewed like Logistic Regression, Random Forests, and Decision Trees. This study analyses and compares the performance of these methods before and after applying PCA and addressing data imbalance to assess their effectiveness in detecting credit card fraud.

DOI: 10.61137/ijsret.vol.10.issue6.391

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Optimizing the Influence of Temporal Dynamics, Network Topologies, and Semantics on Unsupervised NLP Algorithms

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Optimizing the Influence of Temporal Dynamics, Network Topologies, and Semantics on Unsupervised NLP Algorithms/strong>
Authors:-Mayank Konduri

Abstract- The purpose of this study was to generate an algorithm able to decipher bots in social media. Prior research shows that variables/parameters affect the detection of AI; however, none attempt to compile an algorithm accurate enough to be deployed into a real-world scenario. Data was collected through mixed methods, in which data was collected online and through questionnaires. Participants included individuals from all demographics, only restricted to demonstrate no bias. Initial results show a strong correlation with variables on the usage of AI. This means that a model which can effectively deduce the usage of AI is plausible. Therefore, the conclusion can be made that it is possible to find bots in social media; however, this is limited to 70% accuracy, given the available resources. Future research should be targeted towards making sure text can be deciphered with more accuracy.

DOI: 10.61137/ijsret.vol.10.issue6.390

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A Study on Factors Affecting to Loan Defaults of Micro Credit (Special Reference to People’s Bank Branches in Anuradhapura Region, Sri Lanka)

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A Study on Factors Affecting to Loan Defaults of Micro Credit (Special Reference to People’s Bank Branches in Anuradhapura Region, Sri Lanka)/strong>
Authors:-Samansiri Sooriyagama

Abstract- This research investigatesthe factors affecting to loan defaults of micro credit (special reference to people’s bank branches in Anuradhapura region, Sri Lanka).The study addresses the critical need to understand the factors contributing to loan defaults, arrears, and loan restructuring, providing valuable insights for microfinance institutions to enhance their risk management strategies. The primary objectives of this study are to identify, analyse, and comprehend the factors influencing loan repayment behaviour among microfinance clients at People’s Bank branches in the Anuradhapura region. The research aims to contribute to the existing body of knowledge in microfinance and provide practical recommendations for enhancing the loan repayment process. A quantitative research approach was employed, utilizing Likert scale questionnaires to gather data on socioeconomic factors, loan characteristics, institutional practices, and borrower financial behaviours. The survey was distributed to a representative sample of microfinance clients in the Anuradhapura region. Data analysis was conducted using SPSS and Microsoft Excel, employing statistical methods to draw meaningful insights. The research revealed significant correlations between certain socioeconomic factors and loan repayment behaviour. Income levels, educational background, and employment status demonstrated notable associations with loan default rates. Additionally, institutional factors, such as the loan approval process and collection procedures, played a crucial role in shaping repayment behaviour. This research contributes valuable insights into the multifaceted aspects of loan repayment behaviour in microfinance. By understanding the key determinants, microfinance institutions can tailor their practices to mitigate risks and foster a more sustainable and inclusive financial environment. While efforts were made to ensure the reliability and validity of the research, certain limitations, such as sample size constraints and potential biases, should be acknowledged. Future research endeavours could delve deeper into the cultural and social dimensions influencing loan repayment behaviour. Longitudinal studies may also provide a dynamic perspective on the evolving nature of microfinance clients’ financial behaviours.

DOI: 10.61137/ijsret.vol.10.issue6.389

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Over the top Platform

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Over the top Platform/strong>
Authors:-Vipashyna Arun Sable, Namrata Yeola, Sanchee Sable, Kanishka Sable

Abstract- Hotstar, (now Disney+ Hotstar), is the most subscribed–to OTT platform in India, owned by Star India.The major cause of the issue might be an unreliable internet connection or connection that is not operating correctly in hotstar. OTT has boosted experimentation to another level. exchange4media Group held the second edition of its one-day event on OTT titled e4m Play Streaming Media Conference & Metal Announcements on May 12, 2021, at 2 pm. The awards honoured excellence in the on-demand video and audio content. OTT platforms deliver content via the Internet, circumventing the need to pay subscriptions to traditional cable broadcast and satellite TV service providers. Therefore, we are building an OTT platform. We are adding subscription model. The web system is developed with PHP, MySQL and Xampp

DOI: 10.61137/ijsret.vol.10.issue6.388

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AI in Healthcare and Medicine

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AI in Healthcare and Medicine/strong>
Authors:-Assistant Professor Santhosh T, Khushi N S, Likhitha K M, Mamatha V

Abstract- AI is the science and engineering of creating intelligent machines, particularly clever computer programs. In fact, AI is already being used in healthcare in a number of ways that are pertinent to nurses in both nursing practice and nursing education. It consists of numerous healthcare technologies that improve patient care and change the duties of nurses. The workload of nurses is lessened as a result of it. AI ethics are crucial since the technology can effect not just the outcome for a single patient but also the way it is used in healthcare during the research, design, testing, integration, and continuous usage phases. Mobile health, clinical decision support, and sensor-based technology like voice assistants and robotics are examples of AI tools for nurses.

DOI: 10.61137/ijsret.vol.10.issue6.387

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Software Evaluation Tools and Testing Methodologies

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Software Evaluation Tools and Testing Methodologies
Authors:-Anil Kumar Behera, Associate Professor Dr S R Raja

Abstract-Testing is a task, which is performed to check the quality of the software and also this process is done for the improvement in software at the same time. Software testing is a critical component of the software development lifecycle, ensuring that applications meet specified requirements and function as intended. Over the years, a wide range of tools and methodologies have been developed to enhance the effectiveness, efficiency, and scalability of testing processes. This paper provides an overview of the most widely used tools and methodologies for software testing, focusing on both manual and automated approaches. It explores popular testing tools for different testing types such as unit testing, functional testing, performance testing, and security testing, with a detailed examination of frameworks like Selenium, JUnit, and TestNG. Additionally, the paper highlights key methodologies, including Agile testing, Behaviour-Driven Development (BDD), and Continuous Integration/Continuous Delivery (CI/CD) integration, emphasizing how these approaches align with modern development practices. The research also addresses the strengths and weaknesses of different tools and methodologies, offering insights into their suitability for various types of projects and testing environments. Challenges related to test maintenance, scalability, and the integration of testing within DevOps pipelines are also discussed. By analysing the current landscape of software testing tools and methodologies, this paper aims to provide valuable guidance for teams looking to improve their testing strategies, optimize workflows, and ensure higher- quality software releases.

DOI: 10.61137/ijsret.vol.10.issue6.434

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Business Transformation Strategies for Sustainable Growth in the Banking Sector

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Authors: Assistant Professor Dr. Sheetal Bhasin Kapoor

Abstract: The banking sector has experienced unprecedented transformation due to rapid technological advancements, evolving customer expectations, regulatory reforms, and increasing sustainability concerns. Digital banking, artificial intelligence, financial technology (FinTech), data analytics, cloud computing, and customer-centric innovations have fundamentally reshaped banking operations and business models. Business transformation has become a strategic imperative for banks seeking sustainable growth, operational excellence, and long-term competitiveness. This study examines the key business transformation strategies adopted by the banking sector and analyses their contribution to sustainable organizational growth. Using a qualitative research approach based on secondary data, the study explores digital transformation, innovation, leadership, customer experience, operational efficiency, and sustainable banking practices. The findings indicate that banks embracing digital transformation and strategic innovation achieve enhanced customer satisfaction, operational resilience, improved financial performance, and long-term sustainability. The study concludes that business transformation should integrate technological innovation, customer-centricity, environmental responsibility, and strategic leadership to achieve sustainable growth in the banking sector.

DOI: https://doi.org/10.5281/zenodo.21454295

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Employing Swarm Intelligence for Optimizing Latency and Energy Consumption for Routing in WSNs

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Employing Swarm Intelligence for Optimizing Latency and Energy Consumption for Routing in WSNs/strong>
Authors:-Khushboo Parmar, Professor Ruchika Pachori

Abstract- Efficient routing is crucial for many practical applications in wireless sensor networks. Nevertheless, they encounter the unavoidable obstacle of restricted energy resources, which underscores the need of developing data transmission mechanisms that optimize the allocated energy to enhance the longevity of the networks and minimize the system’s latency. Implementing efficient clustering and energy management strategies can enhance the longevity of the network while concurrently decreasing the observed delay. The present study introduces a two-tier methodology for reducing unnecessary transmissions in conjunction with particle swarm optimization (PSO). The objective is to minimize the distances inside clusters in order to reduce both latency and energy usage. The evaluation parameters for the proposed method include the delay in the first hop, the latency in the network, and the energy usage. This empirical method has been employed to determine the optimal fitness function so as to optimize latency and energy consumption in WSNs.

DOI: 10.61137/ijsret.vol.10.issue6.386

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