https://matjournals.net/engineering/index.php/JBDTBA/issue/feedJournal of Big Data Technology and Business Analytics2024-11-18T11:06:56+00:00Open Journal Systems<p><strong>JBDTBA</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 based on Big Data Technology and Business Analytics. It includes topics related to Capturing Data, Data Storage, Data Analysis, Search, Sharing, Transfer, Visualization, Querying, Updating, Information Privacy, and Data Source, Statistical Computing, R Programming Language , Natural Language Processing (NLP), MapReduce, Hadoop Distributed File System (HDFS), Database Management System (DBMS), Cloud Computing, Artificial Intelligence, Algorithm, Data Lake, Hadoop, Dashboards, Data Virtualization, Data Supply Chains, Data Mining, Python, Structured Data, Architectures for Massively Parallel Processing, Distributed File Systems and Databases; and Scalable Storage Systems. The contributions related to Social Media Analytics, Statistics and Econometrics in Business Analytics, Use of Novel Data Science Techniques in Business Analytics, Robotics and Autonomous Vehicles, Marketing Analytics, Methods of Decision Making, Supply Chain Analytics, Transportation Analytics, Ethical and Social Implications of Business Analytics and AI, Applications of AI and Machine Learning Methods in Business Analytics are also welcome.</p>https://matjournals.net/engineering/index.php/JBDTBA/article/view/935Transforming Manufacturing with AI: Advanced Predictive Maintenance Solutions2024-09-13T08:45:19+00:00Mekala Rmekalar@bitsathy.ac.inMohanraj Ecsemohanraj@gmail.comAbirami Aabi.lecturer@gmail.comLakshmanaprakash Slakshmanaprakashs@bitsathy.ac.inRukumanikhandan Crukumanikhandhan.c@gmail.com<p>This paper examines the use of data analytics and Artificial Intelligence (AI) in predictive maintenance in the industrial sector, emphasizing the importance of these technologies in raising operational effectiveness and decreasing downtime. It addresses integrating Artificial Intelligence (AI) techniques with data analytics to anticipate equipment breakdowns and improve maintenance schedules. These approaches include machine learning algorithms and deep learning models like Long Short Term Memory (LSTM). The chapter also includes a case study on the NASA Turbofan Engine Degradation. The simulation uses LSTM models to predict the remaining lifespan of turbine engines using artificial intelligence data from sensors. Evaluation metrics are used to evaluate the models' prediction ability and pinpoint areas that require more improvement. These metrics include the Mean Square Error (MSE), Mean Absolute Error (MAE), and R-squared score.</p>2024-09-13T00:00:00+00:00Copyright (c) 2024 Journal of Big Data Technology and Business Analyticshttps://matjournals.net/engineering/index.php/JBDTBA/article/view/964A Comprehensive Review of Machine Learning Approaches in Livestock Health Monitoring2024-09-25T10:35:24+00:00Shiva Sumanth Reddysumanthdsatm@gmail.comManjunath D Rsumanth-cse@dsatm.edu.inJahnavi Ssumanth-cse@dsatm.edu.inNandini Csumanth-cse@dsatm.edu.in<p>Livestock health monitoring has emerged as a crucial area in agricultural technology, where Machine Learning (ML) approaches offer promising solutions for disease detection, heat stress management, and behavioral anomaly identification. This review explores the latest advancements in applying machine learning models to livestock health monitoring, focusing on methods such as deep learning, IoT integration, and multimodal frameworks. Techniques like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and hybrid models have significantly improved early detection of communicable and non-communicable diseases. Additionally, AI-based systems monitoring heat stress and behavioral changes have enhanced overall livestock management, reducing economic losses and improving animal welfare. This review synthesizes current trends and challenges in deploying ML models in real-world farming environments, particularly integrating IoT devices to collect and analyze health data. Moreover, it highlights future directions in improving the accuracy and scalability of ML models, including optimizing data collection and leveraging real-time analytics. This review aims to guide future developments in AI-driven livestock health monitoring systems by providing a comprehensive overview of current research.</p>2024-09-25T00:00:00+00:00Copyright (c) 2024 Journal of Big Data Technology and Business Analyticshttps://matjournals.net/engineering/index.php/JBDTBA/article/view/1024SpeakSmart: Empowering Public Speakers, Elevating Every Speech2024-10-17T11:45:59+00:00Alfiya Hussainalfiyarahemeen@gmail.comDipesh Nityanand Haldardipeshhaldar787@gmail.comPranjal Yuvraj Lohipranjallohi345@gmail.comAdeebur Mohsinur Rahmanadeeburrahman9021@gmail.comP Harshitaharshitarao121203@gmail.comDipesh Khemchand Siriyadipeshsiriya123@gmail.com<p>Public speaking is one of the most essential skills in many professional fields, but it often comes with challenges due to limited experience or fear. We propose developing advanced public speaking software to tackle these issues and improve users' presentation skills. Using the latest technologies like deep learning and natural language processing, the software will offer real-time recording and analysis of presentations, providing suitable feedback on aspects such as speaking rate, emotional tone, body language, and content delivery. With features for speech analysis, facial emotion detection, and body language evaluation, the software aims to give users detailed insights to enhance their public speaking skills. This project is designed to help users overcome public speaking fear and become more confident and influential speakers.<br>Along with these core features, the software also includes interactive training modules that allow users to practice in a real-time environment to gain confidence over time. The software will guide speakers toward overcoming public speaking anxiety through personalized feedback and adaptive learning paths. The system also supports multilingual analysis, making it accessible to a broader audience. Furthermore, the software can provide customized advice on improving audience connection by using sentiment analysis. This project is designed to help individuals become more confident and compelling speakers and to bridge the gap between theoretical knowledge and real-world practice.</p>2024-10-17T00:00:00+00:00Copyright (c) 2024 Journal of Big Data Technology and Business Analyticshttps://matjournals.net/engineering/index.php/JBDTBA/article/view/1092Multiclass Date Fruit Prediction Using SVM and Logistic Regression with OVO and OVR2024-11-13T10:30:11+00:00G. Ravi Kumargrkondaravi@gmail.comG. Thippannagt.pana2012@gmail.comManinti Venkateswarlugt.pana2012@gmail.com<p>Multiclass classification plays a crucial role in various AI applications requiring simultaneous recognition of multiple classes. This research investigates the application of multiclass classification techniques to predict the species of date fruits using Support Vector Machine (SVM) and Logistic Regression (LR) algorithms with One-Versus-One (OVO) and One-Versus-Rest (OVR) strategies. The dataset comprises 898 samples with 35 elements and 7 distinct labels. The experimental results demonstrate the superiority of SVM over LR, achieving the highest accuracy of 93.68% with the OVR strategy. Moreover, OVR outperformed OVO in both algorithms, showcasing its efficacy for multiclass problems. These findings offer valuable insights for date fruit prediction and further advance the state of multiclass classification techniques in AI applications.</p>2024-11-13T00:00:00+00:00Copyright (c) 2024 Journal of Big Data Technology and Business Analyticshttps://matjournals.net/engineering/index.php/JBDTBA/article/view/1101AI-Driven Cartoonization: Transforming Images into Animated Sequences Using Machine Learning2024-11-18T11:06:56+00:00Raghu Ram Chowdary Velevelavraghuram2021@gmail.com<p>This research is poised to revolutionize the realm of transforming real-life high-quality images into captivating cartoon representations, a process colloquially known as "cartooning". The study employs a sophisticated model that goes beyond conventional methods by decomposing input data into three distinct cartoon depictions—surface, structure, and texture representations. This novel approach guides subsequent transformations, encompassing image enhancement, sketch transformation, and the strategic utilization of Generative Adversarial Networks (GANs). The GAN framework, enriched with Adversarial Loss and Content Loss components, enhances flexibility and produces cartoon images with clear-edge definitions. Moreover, this research extends its groundbreaking capabilities to video creation by seamlessly transitioning from cartooning images to generating animated videos using advanced machine-learning techniques. This system introduces a groundbreaking automated process that yields high-quality results and accommodates diverse cartoon styles by addressing critical challenges associated with traditional cartooning methods, such as maintaining quality, standardization, artistic interpretation, and handling complexity. Including user-friendly interfaces and scalability further positions it as a promising solution for many applications, showcasing its potential as an innovative and scalable approach in automated cartoon representation and video synthesis. This research offers a paradigm shift in cartooning, providing computerized processes that maintain a high standard of quality and offer flexibility in accommodating various cartoon styles, extending its impact from static images to dynamic multimedia content creation.</p>2024-11-18T00:00:00+00:00Copyright (c) 2024 Journal of Big Data Technology and Business Analytics