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Improving agility in projects using machine learning algorithm

Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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Security on mobile cloud computing using cipher text policy and attribute based encryption scheme

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Materials Today: Proceedings 2023年 80卷 3059-3063页

作者： Swetha, Madireddy Latha, M. Department Of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu India Vels Institute of Science Technology & Advance Studies Tamil Nadu India

Mobile Cloud Computing (MCC) is a new paradigm that has been emerged by the advances in the Cloud Computing for Mobile devices to access Cloud services. The data security challenges against the data thefting, deleting, corrupting, or exploiting are existed as the data storage and access to/from the cloud has been most popular. In order to handle these data security issues in the cloud and to provide protection for the data, some innovative encryption methods have been developed. One of such encryption methods is Attribute-Based Encryption (ABE) that has a more control on the data in cloud as which is accessed by whom. Since from the recent years, many of academic researchers have paying a widespread of attention on using advanced cryptographic techniques to securely store, process, also share data over the untrusted cloud environment. One of the promising cryptographic techniques which can solve the open challenge of regulating the fine-grained access control of important data over the distributed cloud is the Cipher text Policy – Attribute Based Encryption (CP-ABE). Therefore, a survey of work is presented in this paper on different CP-ABE methods for the secure mobile cloud computing architecture. This paper presents some works that are focused on secure data storing, accessing and sharing using CP-ABE methods over the untrusted cloud environments. All the works that are studied are analyzed and compared with each other in the results to find the best out of it. © 2021

关键词： Mobile cloud computing

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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

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SN computer science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 46-66页

作者： Jayakrishnan, R. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

The word “cancer” denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and understandable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analyse various methods for multiclass categorising cancer subtypes using deep structured reinforcement learning (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combining the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Cancer subtypes Deep neural networks Deep-learning Gene expression Machine-learning Reinforcement learning

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Interactive Sign Language Learning System-A Comprehensive Application for Sign Language Alphabet and Word Recognition 4

Interactive Sign Language Learning System-A Comprehensive Ap...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chandrasekaran, R. Vijayaraj, S. Balakumaran, M. Arul Stephen, C. Abhijjit, D. Soundarya, M.K. Vels Institute of Science Technology & Advanced Studies Department of Biomedical Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Electrical and Electronics Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Civil Engineering Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

The Interactive Sign Language Learning System is a ground breaking application designed to provide a comprehensive and interactive learning experience for sign language alphabet and word recognition. Built on advanced technologies, this system aims to bridge the gap between individuals with hearing impairments and non-sign language users, enabling effective communication and inclusion. The application offers a user-friendly interface, where learners can easily navigate through various sign language alphabets and individual words, receiving real-time visual feedback and guidance. With a vast library of sign language gestures and clear video demonstrations, learners can practice their signing skills with confidence and accuracy. The system utilizes computer vision and machine learning algorithms to recognize and interpret users' sign language gestures, providing immediate feedback to ensure proper hand movements and positioning. Additionally, the system offers interactive exercises and quizzes, allowing learners to test their knowledge and track their progress over time. The application also incorporates gaming elements, making the learning process engaging and enjoyable. Whether used by individuals wanting to learn sign language or organizations looking to enhance accessibility, this comprehensive learning system is an invaluable tool that empowers users to communicate effectively and inclusively with the deaf community. © 2025 IEEE.

关键词： Adversarial machine learning

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication 4

Advancements in Speech Recognition: Exploring Feature Extrac...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bhavani, R. Kathirvelu, Kalaivani Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy. © 2024 IEEE.

关键词： feature extraction Signal processing Signals Sound Speech recognition

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An Enhanced Detection System of Autism Spectrum Disorder Using Thermal Imaging and Deep Learning

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SN computer science 2025年第2期6卷 1-11页

作者： Jegan Amarnath, J. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Tamil Nadu Chennai 600117 India

Autism Spectrum Disorder (ASD) is a brain-based condition characterized by social difficulties and repetitive behaviors. Traditional diagnostic methods can be subjective and time-consuming. Current diagnostic techniques for ASD often rely on behavioral assessments and clinical observations, which can be inconsistent and prone to subjectivity. Additionally, there is a need for more reliable and objective methods to differentiate between autistic and non-autistic individuals. Existing imaging techniques require further refinement to improve accuracy and efficiency in detecting ASD. The primary objective of this research is to develop and evaluate an enhanced detection system for ASD using thermal imaging and Improved Mask Faster Recurrent Convolutional Neural Network (IMFRCNN). This research focuses on enhancing the detection of ASD using Deep Learning (DL) methods and thermal imaging. Traditional diagnostic techniques for ASD often rely on subjective behavioral assessments, highlighting the need for more reliable methods. By examining cerebral imaging data from the thermal face image datasets collect facial thermal images using thermal cameras. Ensure the dataset is diverse, including varying expressions and environmental conditions. The study compares the effectiveness of IMFRCNN and the ResNet 50 system in classifying thermal images of 50 autistic and 50 non-autistic individuals. Emotions were evoked using audio-visual stimuli, and temperature variations in facial areas such as the nose, forehead, cheek, and eyes were measured. Anger showed the greatest temperature differences, with a 12.6% variation between autistic and non-autistic individuals. The IMFRCNN achieved a reliability of 96%, while the ResNet 50 achieved 90%. These findings indicate that computer-aided assessment techniques utilizing thermal imaging and DL can provide a consistent and objective method for identifying individuals with ASD. © The Author(s), under exclusive licence to Springer Nature Sing

关键词： Deep learning algorithms Diagnostic tool Improved mask faster recurrent convolutional neural network Performance measures

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System 3

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Preetha, P. Packialatha, A. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350374643

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics. © 2024 IEEE.

关键词： Prediction models

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model 5

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Basha, Shaik Abdul Yunus Priya, K.Ulaga Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350379945

As artificial speech generates technological evolution;fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches. © 2024 IEEE.

关键词： Audio recordings

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A Comprehensive Exploration of Storage Attacks in Information-Centric Networking Environments 10

A Comprehensive Exploration of Storage Attacks in Informatio...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sethu, S. Manikandan, A. Vels Institute of Science Technology & Advanced Studie Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

To help users overcome the limitations of current IP networks, Data Networking (NDN) and Content-Centric Network (CCN) architectures have been created to move communication from host-centric to content-centric. NDN is essentially a network for the dissemination of content. In NDN, a user's request for material will be the only reason for contact. The descriptive capabilities of NDN technology leverage the globally unique names that encryption and content-based security provide. As part of its design, it upholds content validity and reliability. We examine several storage risks that NDN caches face, including content poisoning attacks, cache pollution attacks, and man-in-the-middle attacks. © 2024 IEEE.

关键词： Internet protocols

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