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All Journal Journal of Applied Data Sciences International Journal for Applied Information Management International Journal Research on Metaverse
S, Priya
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Novel Battery Management with Fuzzy Tuned Low Voltage Chopper and Machine Learning Controlled Drive for Electric Vehicle Battery Management: A Pathway Towards SDG P, Vinoth Kumar; S, Priya; D, Gunapriya; Batumalay, M
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.236

Abstract

Electric vehicles have a significant impact on the SDGs, specifically climate action, affordable and clean energy, and responsible consumption and production patterns. The present work focuses on a battery management system to effectively utilize the power from the battery to drive the brushless DC motor (BLDC) by tuning the low-voltage buck boost converter as a chopper circuit with fuzzy. The photovoltaic system acts as an additional source to charge the battery when the battery is not connected to the load, and at running conditions, fuzzy logic control enhances efficiency and provides smooth, adaptive control under varying load conditions. Also, the machine learning technique is used for drive control and automation operations. The energy in the BLDC is regulated by managing the voltage and current in a photovoltaic-powered low-voltage chopper by tuning the proportional integral derivative (PID) controller for an ideal balance between reliability and a quicker reaction. The K- Nearest Neighbour (KNN) machine learning algorithm, due to its simplicity and effectiveness in classification, ensures the enhanced reliability and efficiency of the BLDC motor system with commutation and speed control. When fuzzy and the KNN machine learning algorithm are used, the development of systems for control and automation is expedited. The work also shows the results of a study that compared the interoperability of proportionate machine learning and fuzzy controlling algorithms developed with MATLAB. In order to do a critical analysis of the data, the results are compared with the graphs. The integration of the Internet of Things (IoT) and cloud technology with the use of KNN for BLDC motor control can enhance system proficiency with monitoring and display of the observed voltage, current values of the motor, sensorless control, fault diagnosis, and predictive maintenance. The work is also connected with the SDG and impacts due to the efficient operation of electric vehicles.
Lower Limb Prosthetics using Optimized Deep Learning Model – a Pathway Towards SDG Good Health and Well Being S, Prakash; M, Jeyasudha; S, Priya; Batumalay, M
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.239

Abstract

This research article aimed at revolutionizing prosthetic leg technologies to enhance accessibility, affordability, and environmental sustainability. With a focus on addressing the diverse needs of amputees globally, the program integrates principles of eco-design, community engagement, technological innovation, and policy advocacy to foster inclusive and resilient societies which leads to the attainment of Sustainable Development Goal (SDG) Good Health and Well Being. Lower Limb Prosthetics of Activity Recognition is an innovative field combining prosthetic technology and activity recognition systems. The challenge of activity recognition in lower limb prosthetics to optimize the performance and responsiveness of mock limbs. In this work, the problem is overcome by using the Optimized deep learning technique, which improves activity recognition in lower limb prosthetics. The proposed methodology consists of (1) Pre-processing (2) Feature extraction (3) Feature classification. The collected images are pre-processed via improved wavelet demonizing and Empirical mode decomposition. From pre-processed data, the features are extracted using an improved sliding window method. The obtained extracted features are moved on to the Feature classification process. The classification process is done by the Optimized Long short- term memory. They are designed to better capture dependencies and patterns in sequential data, which makes them highly effective for tasks involving time series, natural language processing and other sequential data problems. Optimization can be done by proper data preprocessing and tuning the data from data extraction. The weight of the LSTM model is optimized to improve the performance of this model by the improved Black Window Optimization Algorithm. The main contributions of the paper are to obtain the best classification accuracy, an optimized LSTM model is introduced in this paper, and the weight of the LSTM model is enhanced by the improved Black Window Optimization algorithm. It improves the performance of the proposed system.
Convolutional Neural Network for Battery System Monitoring and SOC Estimation for Ev Applications to Achieve Sustainability S, Priya; P, Vinoth Kumar; V, Sridevi; Batumalay, M; D, Gunapriya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.237

Abstract

The necessity to develop an alternative energy source to handle the looming energy crisis has arisen due to the recent rise in energy consumption. This is most likely to happen with grid-synchronized electric vehicles (EVs), since vehicle-to-grid (V2G) technology is one of the smart grid's technological advancements that permits energy exchange between EVs and the grid. The merging of EVs with the grid influences the whole electricity system and is susceptible to imbalances in supply and demand, frequency, and voltage. The proposed work focuses on effective and smart control of the Single Ended Primary Inductance Converter (SEPIC) converter with efficient control techniques employed for battery management systems for electric vehicle charging. PI oversees controlling the converter. The battery's calculated state of charge (SOC) is used to make a paradigm-shifting sequence for the converter with workable optimization strategies to lower imbalance issues when EVs are connected to the grid. This leads to the achievement of sustainable development goals (SDGs). Purpose. When the SEPIC converter is connected to a photovoltaic source, it needs to be analyzed in terms of how it switches operations. The source also needs to be used efficiently when it is connected to a battery. Monitoring and SOC estimation of the battery need to be efficiently performed with a quicker response for EV applications. Methods. Convolutional neural networks (CNN) were used to solve the issue; these networks considerably enhance response times and boost system reliability overall. Results. The system operates on the principle that when the battery level is less than 60%, the battery is charged through buck operation, and it is discharged through the boost mode when the SOC exceeds 60%. When linked to the grid, the PI controller regulates both power and practical value. The proposed system demonstrates how battery management-based CNN and SEPIC can switch at high speeds. The system's research directions were established for the results' later application to experimental samples for energy efficiency and process innovation.
Applying K-Means Clustering to Group Jobs Based on Location and Experience Level: Analysis of the Job Recommendation Kumar, Vinoth; S, Priya
International Journal for Applied Information Management Vol. 4 No. 3 (2024): Regular Issue: September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i3.89

Abstract

Labor market analysis plays a crucial role in helping job seekers identify employment opportunities that align with their qualifications, location, and experience level. This study uses the K-Means clustering algorithm to group jobs based on these critical factors. By analyzing job market data, the research identifies the most sought-after skills across various industries and highlights the geographic and experience-level disparities in job availability. Key findings include the high demand for foundational skills such as customer service, sales, and production planning, as well as more specialized skills like Medical Research in certain sectors. The study provides actionable insights for job seekers and policymakers, suggesting that targeted skill development and training programs are essential for improving job match quality. However, the study also acknowledges its limitations, such as the lack of consideration for broader economic and social factors that influence labor market trends. Future research is recommended to address these gaps, using more comprehensive datasets and advanced analytical techniques.
Harnessing Sentiment Analysis with VADER for Gaming Insights: Analyzing User Reviews of Call of Duty Mobile through Data Mining Batumalay, Malathy; S, Priya; Kumar, Vinoth
International Journal Research on Metaverse Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i2.27

Abstract

This study investigates the application of sentiment analysis to understand user feedback for Call of Duty Mobile, a highly popular mobile game, by analyzing 50,000 reviews sourced from the Google Play Store. The research aimed to extract actionable insights from user sentiments, which could guide future game development and improvement. To achieve this, the sentiment of each review was analyzed using VADER (Valence Aware Dictionary and sEntiment Reasoner), a robust tool for classifying sentiment in textual data. The study categorizes reviews into three sentiment groups—positive, negative, and neutral—to identify and analyze prevailing user emotions. The findings revealed that the majority of reviews were positive, with users primarily praising the gameplay, graphics, and overall mobile experience. These aspects were considered crucial in driving user satisfaction and contributed to a majority of the positive feedback. Conversely, negative reviews were often focused on issues such as network connectivity problems, long loading times, and performance errors, indicating areas where users experienced frustration. These results highlight the importance of technical performance and network stability as key factors influencing player satisfaction. The study also delved deeper into keyword analysis to uncover common themes in the reviews, such as in-app purchases and concerns related to technical performance, which were frequently mentioned by users in both positive and negative feedback. These insights provide developers with a clearer understanding of what players value most in the game and where improvements are necessary. The study concludes that sentiment analysis can serve as a powerful tool for understanding user feedback, offering developers a data-driven approach to enhance game features and address user concerns. Moving forward, future research could benefit from the application of additional machine learning models to refine sentiment classification accuracy, as well as the integration of cross-platform reviews to gain a more comprehensive understanding of player sentiment across different user groups and devices. Such approaches would provide a richer, more nuanced view of user experiences, enabling game developers to create even more engaging and satisfying gaming experiences.
Co-Authors Batumalay, M Batumalay, Malathy D, Gunapriya M, Jeyasudha P, Vinoth Kumar S, Prakash V, Sridevi Vinoth Kumar