Journal of Intelligent Decision Technologies and Applications <p class="contentStyle"><strong>JoIDTA</strong> is a peer reviewed journal of Computer Science domain published by MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of research and review papers that provides information related to Intelligent Technologies and Systems that support Decision Making. The contributions that are related to areas such as Artificial Intelligence, Fuzzy Techniques, Genetic Algorithms, Intelligent Agents, Multi-Agent Systems, Cognitive Science and Mathematical Modelling are invited. It also includes the topics on Neural Systems, Neural Networks, Computer-Supported Cooperative Work, Geographic Information Systems, User Interface Management Systems, Informatics, Knowledge Representation, and applications of Intelligent Systems.</p> <h6 class="mt-2"> </h6> <div class="card"> <div class="card-header text-center bg-info text-white"> </div> </div> en-US Journal of Intelligent Decision Technologies and Applications Survey on E-Commerce Product Price Monitoring System <p>In our fast-paced digital era, online shopping has become integral to daily life, prompting consumers to seek the best deals and lowest prices. The E-commerce product price monitoring system addresses this need by offering a sophisticated solution, allowing users to actively track and monitor product prices across diverse E-commerce platforms. The perpetual challenge faced by online shoppers– identifying the optimal time to purchase price fluctuations – is efficiently managed by the system. This innovative tool provides real-time notifications, alerting users when the prices of desired products drop. By eliminating the need for continuous platform monitoring, it empowers users to capitalize on the most favorable deals effortlessly. In the dynamic landscape of digital commerce, the E-commerce product price monitoring system serves as a reliable companion, reshaping the online shopping experience. Its integration into the consumer journey introduces unprecedented convenience, ensuring that users are well-informed and equipped to make purchase decisions at precisely the right moment. Ultimately, the system maximizes savings, enhances overall satisfaction, and establishes itself as an indispensable asset in navigating the intricate world of E-commerce.</p> Yogesh Patil Rahil Desai Anand Gudnavar Copyright (c) 2024 Journal of Intelligent Decision Technologies and Applications 2024-01-12 2024-01-12 1 1 1 5 Review of Variants of Bloom Filters for Detection of Malicious URL <p>The proliferation of malicious URLs poses a significant cybersecurity threat, necessitating the development of effective detection methods. Bloom filters, a probabilistic data structure, have been widely used for URL detection due to their efficiency in set membership testing. However, traditional Bloom filters suffer from limitations such as false positives, false negatives, and the inability to dynamically update entries. To address these challenges, various variants of Bloom filters have been proposed for detecting malicious URLs. This review explores the merits and demerits of different Bloom filter variants, including Counting Bloom Filter (CBF), Count-Min Sketch (CMS), Stable Bloom Filter (SBF), Scalable Bloom Filter (SBF), Cuckoo Filter, Bloomier Filter, and Bloom Filter Tree (BFT). Evaluation of these variants based on empirical studies and real-world applications reveals their performance, scalability, and accuracy in URL detection tasks. Challenges and open research questions in the field of URL detection using Bloom filter variants are identified, including handling dynamic threats, reducing false positives and false negatives, optimizing performance in real-time systems, and addressing privacy and security concerns. Potential future directions for improving Bloom filter-based URL detection methods are discussed, such as hybrid approaches, dynamic Bloom filters, probabilistic data structures, privacy-preserving techniques, standardization, benchmarking, and real-world deployment and evaluation.</p> Mangadevi Atti Manas Kumar Yogi Copyright (c) 2024 Journal of Intelligent Decision Technologies and Applications 2024-04-01 2024-04-01 1 1 6 12 Harnessing Social Media Sentiment: Advanced Techniques for Analyzing Public Opinion and Emotions <p>Social media platforms have become powerful channels for expressing public opinions and emotions, shaping discussions on a wide range of topics from politics to entertainment. This study explores advanced techniques for analyzing sentiment on social media, with a focus on the complex relationship between public sentiment, topic dynamics, and emotional responses. Using sentiment analysis, aspect-based sentiment analysis, and emotion detection methodologies, we analyze a dataset of social media content from popular platforms like Twitter, Facebook, and Reddit. Our findings provide valuable insights into the intricate landscape of social discourse, including patterns of sentiment distribution, variations in sentiment towards specific topics, and prevailing emotional tones in discussions. These research findings go beyond academic exploration, offering actionable insights for marketers, policymakers, and researchers navigating the complex field of social media sentiment analysis. However, challenges such as data biases and algorithmic complexities highlight the importance of continuously refining and adapting analytical methods. Through the power of social media sentiment analysis, our goal is to uncover a deeper understanding of the collective consciousness of our digitally connected society, enabling informed decision-making and societal progress.</p> Ikrama Dayyabu Hayatu Adamu Nuhu Bari Khadija Abdullahi Nashe Shivam Tiwari Copyright (c) 2024 Journal of Intelligent Decision Technologies and Applications 2024-04-19 2024-04-19 1 1 13 18 Synergistic Effects of Cloud Computing on Big Data Innovation, Scalability and Security <p>In the era of digital transformation, the synergy between cloud computing and big data has revolutionized the landscape of data management, analytics, and decision-making. This study explores the synergistic effects of cloud computing on big data innovation, scalability, and security, aiming to provide comprehensive insights into the evolving dynamics of cloud-based big data solutions. Through an extensive literature review, data synthesis, and qualitative analysis, this study investigates the adoption of emerging technologies, scalability optimization techniques, security best practices, integration strategies, and ethical considerations in cloud-based big data environments. The study reveals that cloud computing catalyzes big data innovation by enabling the adoption of emerging technologies such as serverless computing and edge computing. Organizations leverage cloud-native services and platforms to streamline data integration processes and accelerate time-to-insight, driving transformative change and fostering a culture of innovation. The findings of this study contribute to the advancement of knowledge in the field of cloud computing and big data and also enable organizations to meet evolving business needs and performance requirements when utilised.</p> Kabiru Uba Kiru Saeed Usman Aliyu Ali Aminu Ahmad Shivam Tiwari Copyright (c) 2024 Journal of Intelligent Decision Technologies and Applications 2024-04-23 2024-04-23 1 1 19 27 A Comprehensive Discourse on Shallow Learning and its Applications <p>Shallow learning, a fundamental approach in machine learning, encompasses a variety of algorithms and techniques aimed at learning patterns and making predictions from labelled data. Unlike deep learning, which involves complex architectures with multiple layers of abstraction, shallow learning focuses on simpler models with limited complexity. This abstract explores the essence of shallow learning, its algorithms, applications, and challenges. Shallow learning algorithms include classic methods such as decision trees, support vector machines, k-nearest neighbours, and logistic regression, among others. These algorithms are typically trained using supervised learning techniques, where the model learns from input-output pairs to make predictions on new, unseen data. Shallow learning models excel in tasks such as classification and regression, where the goal is to assign labels or predict continuous values to input data. Applications of shallow learning span across various domains, including healthcare, finance, marketing, and cyber security. In healthcare, shallow learning models are used for disease diagnosis and prognosis prediction based on patient data. In finance, these models aid in fraud detection, credit scoring, and stock market prediction. Marketing applications involve customer segmentation and churn prediction, while in cyber security; shallow learning is utilized for malware detection and network intrusion detection.</p> Bonam Geetha Chitti Jyothi Manas Kumar Yogi Copyright (c) 2024 Journal of Intelligent Decision Technologies and Applications 2024-04-24 2024-04-24 1 1 28 37