Mechanical Properties of FSW Joints Magnesium Alloy at Different Rotational Speeds
Magnesium (Mg) has become a focus in the transportation industry due to its potential in reducing fuel consumption and gas emissions while improving recyclability. Mg alloys are also known for their low neutron absorption, good resistant of carbon dioxide as well as thermal conductivity which makes them suitable for use in industrial equipment for nuclear energy. there has been an increasing interest in the research and development of Mg alloys. These are the lightest of all metallic structural materials and are approximately 33% lighter than aluminium (Al) and 75% lighter than ferrous (Fe) alloys and have excellent specific mechanical properties. In this work, FSW of AZ31B Alloy was examined at the various rotational speeds of 900 -1440 rpm, with fixed welding speed of 40mm/min and 2 tool tilt angle using an HSS tool. The mechanical properties were compared for the different rotational speeds. The quality of FSW joints is dependent on input value of heat and material flow rate, which are prejudiced by process parameters., higher rotation speeds may cause abnormal stirring, resulting in a tunnel defect at the weld nugget due to increased strain rate and turbulence. 2024 E3S Web of Conferences
Vijayaraghavan P.; Bisht Y.S.; Almusawi M.; Subramani K.; Sharma D.
https://doi.org/10.1051/e3sconf/202449104015 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188282078&doi=10.1051%2fe3sconf%2f202449104015&partnerID=40&md5=0a1bd5148ad78f3a9889512b3cd4e19b
Vijayaraghavan P., Department of Mechanical Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 600 127, India; Bisht Y.S., Department of Mechanical Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India; Almusawi M., The Islamic University, Najaf, Iraq; Subramani K., School of Business and Management, Christ (Deemed to Be University), Bengaluru, India; Sharma D., Department of Management, Uttaranchal Institute of Management, Uttaranchal University, Dehradun, 248007, India
Surface Roughness Analysis in AWJM for Enhanced Workpiece Quality
AWJM; Process-optimization; Process-parameter
Abrasive Water Jet Machining is a distinctive manufacturing process that effectively removes material from a workpiece by employing a high-pressure stream of water combined with abrasive particles. The final quality of the machined surface is directly influenced by various process parameters, such as the traverse speed, hydraulic pressure, stand-off distance, abrasive flow rate, and the specific type of abrasive used. In recent times, extensive research has been undertaken to enhance the performance of AWJM, with a specific focus on critical performance measures like surface roughness. This paper presents the latest advancements in AWJM research, with particular attention given to enhancing performance measures, implementing process monitoring and control, and optimizing process variables for applications involving high-carbon steel. 2024 E3S Web of Conferences
Manikandan M.; Hemalatha S.; Chandramauli A.; Ambrose S.; Alsalami Z.
https://doi.org/10.1051/e3sconf/202449104019 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188351690&doi=10.1051%2fe3sconf%2f202449104019&partnerID=40&md5=e41cabc48cd1fa6e4811abcbbc173a07
Manikandan M., Pasumpon Muthuramalinga Thevar College, Usilampatti, India; Hemalatha S., Department of Electrical and Electronics Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 600 127, India; Chandramauli A., Department of Civil Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India; Ambrose S., Commerce Dept, Christ (Deemed to Be University), India; Alsalami Z., The Islamic University, Najaf, Iraq
Designing a Precision Seed Sowing Machine for Enhanced Crop Productivity
CATIA and analysis; Design; Seed machine
A seed sowing machine is a valuable agricultural device that facilitates the precise and efficient sowing of seeds in fields. When designing and optimizing such a machine, several crucial factors need consideration including seed size, seed rate, soil type, and field conditions. The primary objective is to achieve uniform seed distribution and optimal seed-to-soil contact, which can be accomplished by incorporating a seed metering mechanism to control the seed rate accurately. Versatility is another important aspect of the machine's design, as it should be able to handle different seed sizes, types, soil conditions, and field variations. To achieve this, utilizing advanced technologies such as sensors, automation, and precision farming techniques can significantly enhance the machine's performance and efficiency while also reducing costs and minimizing environmental impact. The optimization of a seed sowing machine plays a crucial role in ensuring successful crop production. By implementing cutting-edge technologies and precision farming techniques, farmers can increase their yields and decrease the amount of seed and fertilizer needed for a specific area. Ultimately, this leads to improved productivity, increased profitability, and a more sustainable approach to agriculture. 2024 E3S Web of Conferences
Sudha Paulin M.; Pant R.; Naveena S.S.; Mundher Adnan M.; Aggarwal S.; Kandavel R.
https://doi.org/10.1051/e3sconf/202449101016 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188328830&doi=10.1051%2fe3sconf%2f202449101016&partnerID=40&md5=a174c7c0b885ace622489d425bb382ca
Sudha Paulin M., Business and Management-Kengeri Campus, Christ (Deemed to Be University) Bangalore, India; Pant R., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, India; Naveena S.S., Department of Mba, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 600 127, India; Mundher Adnan M., The Islamic University, Najaf, Iraq; Aggarwal S., Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India; Kandavel R., School of Management, Jeppiaar University, Tamilnadu, Chennai, India
Enhancing Dimensional Geometry Casting using Computer Modeling
CATIA; Copper tubes; DOE; Modeling; Sand casting
Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process. 2024 E3S Web of Conferences
Janakiraman V.; Arulpandy P.; Al-Fatlawy R.R.; Ranganayaki S.; Singh R.
Janakiraman V., Department of Mechanical Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Arulpandy P., Department of Mathematics, Christ (Deemed to Be University), Karnataka, Bangalore, 560073, India; Al-Fatlawy R.R., The Islamic University, Najaf, Iraq; Ranganayaki S., Mathematics, Sri Ramakrishna Engineering College, Coimbatore, India; Singh R., Department of Electronics & Communication Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
Managing with Machines: A Comprehensive Assessment on the Use of Artificial Intelligence in Organizational Perspectives
artificial intelligence; Business organizations; CRMs; employer productivity
This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs). 2024 IEEE.
Chib S.; Gandhi M.P.; Bose S.S.; Thirulogasundaram V.P.; Prasanna H.N.; Lakshmi S.R.
2024 International Conference on Cybernation and Computation, CYBERCOM 2024, pp. 483-487.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/CYBERCOM63683.2024.10803230 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216264441&doi=10.1109%2fCYBERCOM63683.2024.10803230&partnerID=40&md5=ffc05cd3bfbbb2d509d2691608b827c7
Chib S., Datta Meghe Institute of Management Studies, Nagpur, India; Gandhi M.P., Yeshwantrao Chavan College of Engineering, Nagpur, India; Bose S.S., Amity Business School, Amity University, Chhattisgarh, Raipur, India; Thirulogasundaram V.P., Donbosco College of Management Studies and Computer Applications, Kumbalagode, Bangalore, 74, India; Prasanna H.N., Sri Venkateswara Coll. of Eng., Bangalore, India; Lakshmi S.R., Christ University, Business and Management, Karnataka, Bangalore, 560073, India
Smart Satellites: Unveiling the Power of Artificial Intelligence in Space Communication-A Study
ML for space data analysis; payload algorithms; Satellite Communication; Satellite operations; security measures in space communication
The incorporation of Artificial Intelligence (AI) into space and satellite communication represents a paradigm shift in the way we explore, navigate, and communicate beyond our planet. This article is about the impact of AI on satellite operations, and the broader field of its communication to the earth. The article explores how AI enhances spacecraft autonomy, mitigates signal degradation, and improves the overall reliability of communication performance Satellite communication benefits from AI-driven advancements, in the areas of signal processing and optimization. Furthermore, examines the integration of AI in space-based challenges and opportunities associated with large-scale satellite networks. AI playing a crucial role in detecting and mitigating cyber security threats in space communication systems. This paper comes up with the perception into the future trends and potential advancements in AI applications for space and satellite communication. 2024 IEEE.
Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024, pp. 1296-1302.
Sathya J., Christ University, Department of Computer Science, Bangalore, India; Ramkumar S., Christ University, Department of Computer Science, Bangalore, India
Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
Action-observation mapping; Human-robot interaction; Language grounding; Natural language understanding; Reinforcement learning
Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. 2024 IEEE.
Adapa S.R.; Gupta H.; Mukherjee S.; Mallupalli P.R.; Avhad P.; Upreti K.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 447-452.
Adapa S.R., OpenLogix LLC, Software Engineer, MI, United States; Gupta H., Software Engineer Meta, NJ, United States; Mukherjee S., VIT Institute, Electrical and Electronics Engg., Tamil Nadu, India; Mallupalli P.R., Arcesium, Hyderabad, India; Avhad P., Veermata Jijabai Technological Inst., Computer Engineering, Mumbai, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India
Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
automated diagnosis; convolutional neural networks; malaria; plasmodium; thin blood smear
There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE.
Mahesh Kumar A.S.; Maindola M.; Salis V.E.; Mitra A.; Janumala T.; Vijayan A.
2024 1st International Conference on Software, Systems and Information Technology, SSITCON 2024
https://doi.org/10.1109/SSITCON62437.2024.10797182 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215667362&doi=10.1109%2fSSITCON62437.2024.10797182&partnerID=40&md5=57809e5b65d0b54e86fcad30779dabe3
Mahesh Kumar A.S., Maharaja Institute of Technology, Department of Electronics and Communication Engineering, Belawadi, India; Maindola M., Graphic Era Deemed to Be University, Department of Computer Science and Engineering, Dehradun, India; Salis V.E., Global Academy of Technology, Department of Information Science and Engineering, Bengaluru, India; Mitra A., Symbiosis Law School, Pune, Symbiosis International (Deemed University), Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR), Pune, India; Janumala T., R v College of Engineering, Department of Electronics and Instrumentation Engineering, Bengaluru, India; Vijayan A., Christ University, Department of Computer Science and Engineering, Bengaluru, India
Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
artificial intelligence in education; convolutional neural networks (CNNs); early childhood education; literacy; numeracy; Optical character recognition (OCR); SDG 4; sequential networks
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU.
Ramasamy G.; Bagula A.; Rajan A.P.; Rengasamy P.
2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World, ITU K 2024
Ramasamy G., Christ University, Bangalore, India; Bagula A., University of the Western Cape, Cape Town, South Africa; Rajan A.P., Christ University, Bangalore, India; Rengasamy P., Christ University, Bangalore, India
Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry
Cryptography; Deep Learning; Image Encryption; Medical Image Security
Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. 2024 IEEE.
Kumar P.; Laroia A.; Kumar M.; Laroia A.; Upreti K.; Parashar J.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 436-441.
Kumar P., Maharshi Dayanand University Computer Science Engineering, Haryana, India; Laroia A., University of Texas at Dallas Ms in Business Analytics, TX, United States; Kumar M., Amity University Computer Science Engineering, Uttar Pradesh, India; Laroia A., Birla Institute of Technology, Civil Engineering, Pilani, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India; Parashar J., Dept. of Computer Science and Engineering, Delhi, India
Application of XAI in Integrating Democratic and Servant Leadership to Enhance the Performance of Manufacturing Industries in Ethiopia
Democratic leadership; LIME; Manufacturing industries; Organizational learning; Performance; Regression; SEM; Servant leadership; SHAP; XAI
This study tests the conceptual model theorizing democratic leadership, servant leadership, learning organization, and performance of manufacturing industries using Structural Equation Modeling (SEM). The impact of democratic and servant leadership on learning organizations and the performance of manufacturing industries in Ethiopia is analyzed, and the role of learning organizations as a mediating variable is examined. Confirmatory Factor Analysis was performed, which includes a well-established Chi-square test, the Chi-square ratio to degrees of freedom, the goodness-of-fit index, the TuckerLewis index, the comparative fit index, the adjusted goodness-of-fit, and the root mean square error of approximation. Further, the performance of manufacturing industries has been assessed using XAI which helps in having a higher clarity on understanding the complexities in production. Based on linear regression, two methods SHAP and LIME have been used for precise predictions and forecast for future production plans in the manufacturing industry. This research contributes to the existing body of knowledge by dissecting the nuanced relationships between the two leadership styles and learning organization and further, their implications for an organizations performance. The findings of the study would provide insights for policymakers and practitioners to improve the performance of manufacturing industries. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Badada B.; Delina G.; Krishnaraj R.; Thiruthuvanathan M.M.
Lecture Notes in Networks and Systems, Vol-1116 LNNS, pp. 123-138.
Springer Science and Business Media Deutschland GmbH
Badada B., Kalasalingam Academy of Research and Education (Deemed to be University), Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Delina G., Kalasalingam Academy of Research and Education (Deemed to be University), Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Krishnaraj R., Dambi Dolo University, Dembi Dollo, 000260, Ethiopia; Thiruthuvanathan M.M., Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India
Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
Binomial logistic regression; KNN; Malware; Random Forest; XGB classifier
Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Ravindra Babu S.; Leisha R.; Medows K.J.; Sreekumar K.; Girish G.P.
Lecture Notes in Networks and Systems, Vol-874 LNNS, pp. 277-286.
Ravindra Babu S., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Leisha R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Medows K.J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Sreekumar K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Girish G.P., Department of Finance, ICFAI Business School, IFHE University (a Deemed to-be-University under Sec 3 of UGC Act 1956), Hyderabad, India
Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting
and security; Economy politics; Fragile State of Index; Machine Learning
In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE.
Maheshwari A.; Malhotra A.; Singh Hada B.; Ranka M.; Amzad Basha M.S.
Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information
https://doi.org/10.1109/NKCon62728.2024.10775198 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215302501&doi=10.1109%2fNKCon62728.2024.10775198&partnerID=40&md5=5e4e15ab92a68efcf33ab4fdf2362ea5
Maheshwari A., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Malhotra A., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Singh Hada B., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Delhi-NCR, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Amzad Basha M.S., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India
Automated Single Responsibility Principle Enforcement: A Step Toward Reusable and Maintainable Code
Code smells; Cyclometric complexity; Halstead volume; Interface analysis; Single responsibility principle
In this study, we delve into the sphere of automated code scrutiny, specifically concentrating on compliance with the single responsibility principle (SRP), a key principle in software architecture. The SRP proposes that a class should have a singular reason for modification, thereby enhancing code cohesion and facilitating its maintenance and reusability. The study presents a pioneering system that utilizes a holistic strategy to ascertain SRP compliance within code. This system rigorously inspects code interfaces, the interaction points among various software components. Through this process, we extract critical insights into the codes maintainability and reusability. An optimally designed interface can significantly improve code management and foster its reuse, leading to superior software design efficiency. Beyond interface inspection, our system also explores complexity metrics such as cyclomatic complexity and hassel volume. Cyclomatic complexity offers a numerical indicator of the count of linearly independent paths traversing a programs source code, serving as a measure of code complexity. Hassel volume is an additional metric that can quantify code complexity. Moreover, our system employs code smell detection methodologies to identify instances of high interdependence between classes, often a sign of SRP breaches. High interdependence, or tight coupling, complicates code modification and maintenance. The system integrates the conclusions from these varied analyses to determine SRP compliance. The outcomes of this investigation highlight a hopeful trajectory toward automated SRP detection. This could provide developers with tools that proactively foster the development of well-organized and maintainable code, thereby enhancing software design quality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1112 LNNS, pp. 343-353.
Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Roy G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India
An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model
Brain Stroke Prediction; Extreme learning machine (ELM); Recursive Feature Elimination (RFE)
Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. 2024 IEEE.
Kavitha K.; Devi V.J.; Alam M.S.; Sindhu V.; Gandhewar N.; Kalra G.
4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings, pp. 1637-1643.
Kavitha K., Gmr Institute of Technology, Department of CSE-AI&ML, Andhra Pradesh, Razam, India; Devi V.J., Aditya Institute of Technology and Management, Department of Information Technology, Andhra Pradesh, Tekkali, India; Alam M.S., College of Engineering and Computer Science, Jazan University, Department of Computer Science, Jizan, Saudi Arabia; Sindhu V., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Gandhewar N., Shri Ramdeobaba College of Engineering and Management, Department of Aiml, Nagpur, India; Kalra G., Maharishi Markandeshwar (Deemed to be University), Department of Mechanical Engineering, Mullana, Ambala, India
Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
Ductility; Non-linear analysis; RC frames; Shear capacity
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Civil Engineering, Vol-607, pp. 979-987.
Umwi D., Department of Civil Engineering, CHRIST (Deemed to be) University, Bengaluru, India; Alex J., Department of Civil Engineering, CHRIST (Deemed to be) University, Bengaluru, India
Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
ARIMA-XGB; Decision Trees; Groundwater level prediction; LGBM; MAE; Random Forest; RMSE; Stacking and ensemble; XGB
In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE.
George J.; Yadav J.; Nair A.M.; Peter M.V.; Alapatt B.P.; Baby R.
2024 3rd International Conference for Advancement in Technology, ICONAT 2024
George J., CHRIST (Deemed to Be University), India; Yadav J., CHRIST (Deemed to Be University), India; Nair A.M., Luxsh Solutions Private Limited, India; Peter M.V., CHRIST (Deemed to Be University), India; Alapatt B.P., CHRIST (Deemed to Be University), India; Baby R., CHRIST (Deemed to Be University), India
Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews
Bag-Of-Words and Term Frequency-Inverse Document Frequency; Bowerbird Optimization Algorithm; Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network; Sentiment Analysis
Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. 2024 IEEE.
Kapruwan A.; Pandey P.; Tripathi A.P.; Dhaundiyal P.; Patil H.; Maranan R.
4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings, pp. 651-657.
https://doi.org/10.1109/ICSES63445.2024.10763067 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214783688&doi=10.1109%2fICSES63445.2024.10763067&partnerID=40&md5=ec20b8acf81337abae00768f2939bc2b
Kapruwan A., Graphic Era Deemed to be University, Department of Computer Science & Engineering, Uttarakhand, Dehradun, 248002, India; Pandey P., Akgec Ghaziabad, Department of Mca, Uttar Pradesh, Ghaziabad, 201001, India; Tripathi A.P., School of Business & Management (SBM), Christ (Deemed to be University), Delhi NCR campus, Uttar Pradesh, Ghaziabad, 201003, India; Dhaundiyal P., School of Business & Management (SBM), Christ (Deemed to be University), Delhi NCR campus, Uttar Pradesh, Ghaziabad, 201003, India; Patil H., School of Computer Science & Engineering, Iilm University, Uttar Pradesh, Greater Noida, 201306, India; Maranan R., Saveetha School of Engineering, Simats, Department of Research and Innovation, Tamil Nadu, Chennai, 602105, India
Sub-Optimization based Random Forest Algorithm for Accurate and Efficient Land use and Land Cover Classification using Landsat Time Series Data
land use/land cover; pan-sharpening; random forest; remote sensing images; sub-forest optimization
The land use and land cover (LULC) play an essential role to investigate the impacts of environmental factors and socio-economic development in the Earth's surface. Extracting the hidden information from the remote sensing images in the observed earth environment is the challenging process. In this research, implemented a model that uses Landsat data to investigate the LULC changes. Utilized the Landsat 5,7 and 8 as inputs for the 1985 to 2019 by Google Earth Engine (GEE) is applied for the robust classification. This paper proposed a Sub-forest optimization based Random forest (SO-RF) classifier with faster diagnosis speed for LULC classification. Moreover, to increase the multispectral Landsat band's resolution from 30 m to 15 m, the pan-sharpening algorithm is utilized. In addition, analyzed the various image configurations grounded numerous spectral indices and other supplementary data such as land surface temperature (LST) and digital elevation model (DEM) on final classification accuracy. The proposed SO-RF produced higher accuracy (0.97 for kappa, 96.78% Overall accuracy (OA), 0.94 for f1-score) than Copernicus Global Land Cover Layers (CGLCL) map and state of art methods like K-Nearest Neighbor (KNN), Decision Tree (DT), and Multi-class Support Vector machine (MSVM). 2024 IEEE.
Parameshachari B.D.; Jasim L.; Shivaraj S.; Suneetha P.; Manjunath C.
Parameshachari B.D., Nitte Meenakshi Institute of Technology, Department of Electronics and Communication Engineering, Bengaluru, India; Jasim L., The Islamic University of Babylon, Babylon, Iraq; Shivaraj S., Raichur (Affiliated to VTU), Hke Society's Sir. M Visvesvaraya College of Engineering, Department of Civil Engineering, Raichur, India; Suneetha P., S.R.K.R. Engineering College, Department of Information Technology, Bhimavaram, India; Manjunath C., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
Comparative Study on GANs and VAEs in Credit Card Fraud Detection
anomaly detection; credit card fraud; fraud detection; generative adversarial networks (GANs); synthetic data generation; variational autoencoders (VAEs)
In today's world, the major issue credit card sectors encounter is fraud. This comparative study deals with how GANs and VAEs detect fraudulent transactions. The dataset comprised 284807 transactions, of which 492 were fraudulent. These two models, GANs and VAEs, are trained on this dataset, during which, in the training process, the models are learned to deal with the imbalance in the dataset. VAEs are trained so that fraud transactions are considered anomalies, and only legitimate transactions are passed onto the model for training. Conversely, GANs generate synthetic data of fraud by addressing the problem of data imbalance and passed on to the ML model for classification. We can observe that Both the models have very good AUC-ROC scores of around 96%, which indicates their distinguishing capability between the classes. In all other aspects, GANs outperformed VAEs, which makes GANs a better option for fraud detection. 2024 IEEE.
Sukruth S.; Haripriya M.; Deepa S.; Jayapriya J.; Vinay M.
Sukruth S., Christ University, Department of Computer Science, Bangalore, India; Haripriya M., Christ University, Department of Computer Science, Bangalore, India; Deepa S., Christ University, Department of Computer Science, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Bangalore, India