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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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Breast Cancer Classification Using Graph Convolutional Networks and DenseNet121 with Pruning

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Journal of Shanghai Jiaotong University (science) 2025年 1-18页

作者： Kavitha, S.J. Sridevi, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai600117 India

Breast cancer remains a significant global health burden, particularly among women, necessitating accurate and accessible diagnostic systems for early detection and intervention. Despite advancements in medical technology, challenges persist such as high dimensionality, variability, and complexity. Hence, this study introduces a novel approach that amalgamates DenseNe5t121 architecture within graph convolutional networks (GCNs) for breast cancer classification. The proposed method combines the strengths of a pre-trained DenseNet121 model, pruning techniques, and GCN with a cross-space filter (CSF). DenseNet121 is employed for feature extraction, while pruning enhances efficiency and prevents overfitting. GCNs capture structural and semantic information from the data and the CSF aids in regularization. Furthermore, the incorporation of the CSF enhances model performance by effectively integrating information from both graph topology and node attributes. The proposed approach was trained and evaluated using the BreakHis dataset, a widely recognized resource for breast cancer classification through histopathological images. By leveraging several performance measures consisting of precision, recall, F1-score, AUC, MCC, and accuracy, the proposed approach was compared against existing models to assess its effectiveness. The results indicate that the proposed method, particularly when combined with the CSF, outperforms existing approaches, demonstrating its potential as an advanced tool for accurate and interpretable breast cancer diagnosis. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Diseases

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A machine learning scheme to assess the radio propagation conditions on wireless communication networks

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Journal of Ambient Intelligence and Humanized Computing 2025年 1-14页

作者： Galvan-Tejada, Giselle M. Martinez-Enriquez, Ana Maria Department of Electrical Engineering Center for Research and Advanced Studies of IPN Av. IPN 2508 Mexico City San Pedro Zacatenco07360 Mexico Department of Computer Science Center for Research and Advanced Studies of IPN Av. IPN 2508 Mexico City San Pedro Zacatenco07360 Mexico

Wireless communication has become the primary means of electronic communication around the world. Particularly, we focus on the radio propagation conditions of a wireless sensor network (WSN), our case study, where common methods exist for measurements of WSN’s performance. However, these methods are expensive and not fully automated, and therefore they have severe drawbacks and limitations. Thus, this paper describes an automatic, simple, and fast-answer system for solving practical radio-communication problems by resorting to two branches of artificial intelligence: machine learning and knowledge-based systems (KBS). The non-incremental unsupervised learning module classifies a dataset into clusters that are then computed in order to induce concepts, which are specific features shared by every dataset within a cluster. These concepts serve to construct implication rules being the knowledge used by the KBS in charge of answering queries about WSN in radio propagation conditions. Furthermore, given that WSN is a dynamic environment, our system also incorporates an incremental supervised learning module in order to process a new received reduced dataset, and potentially to induce new concepts and update the knowledge base of implication rules. Such process is performed unchanging the inference engine module of our KBS. Finally, we provide illustrating examples of our approach’s abilities for query-answering, highlighting that its response time fulfills restricted real-world conditions thanks to the conception of polynomial-complexity algorithms on which our system runs, and which are among our contributions. © The Author(s) 2025.

关键词： Unsupervised learning

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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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Rotten and Fresh Fruits Classification using Deep Learning 5

Rotten and Fresh Fruits Classification using Deep Learning

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mishra, Prashant Kumar Singh, Jagrati Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350383522

Accurate and timely classification of fresh and decaying fruits is crucial for upholding quality standards in the food industry. This study introduces a deep learning approach that utilizes images to distinguish between fresh and rotten fruits. The proposed solution effectively addresses this classification challenge by control the capabilities of Convolutional Neural Networks. In this research a diverse dataset is used, comprising labeled images of various fruits in varying states of freshness, encompassing both rotten and fresh examples. Key preprocessing steps, including resizing and normalizing the dataset, is implemented to ensure that the model receives a uniform input. The core of the methodology involves initializing a pre-trained CNN architecture. This approach enables the adaptation of the pretrained model to the specific fruit classification task, ultimately providing an efficient and effective solution for quality control in the food sector. Through the utilization of this approach, our model can inherit valuable feature representations from its pretrained counterpart. To evaluate the performance of the trained model, a distinct test dataset is employed and the results encompass key measurements of performance. With an impressive accuracy of 97.5%, the proposed model outperforms the base paper, which achieves 96.05%. This advancement highlights the model's proficiency in distinguishing between fresh and rotten fruits, underscoring its practical effectiveness. © 2024 IEEE.

关键词： Convolutional Neural Networks KNN Rotten and Fresh Fruits Classification VGG16

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Incentive mechanism design via smart contract in blockchainbased edge-assisted crowdsensing

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Frontiers of computer science 2025年第3期19卷 65-85页

作者： Chenhao YING Haiming JIN Jie LI Xueming SI Yuan LUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Jiao Tong University(Wuxi)Blockchain Advanced Research Center Wuxi 214101China

Edge-assisted mobile crowdsensing(EMCS)has gained significant attention as a data collection ***,existing incentive mechanisms in EMCS systems rely on centralized platforms,making them impractical for the decentralized nature of EMCS *** address this limitation,we propose CHASER,an incentive mechanism designed for blockchain-based EMCS(BEMCS)*** fact,CHASER can attract more participants by satisfying the incentive requirements of budget balance,double-side truthfulness,double-side individual rationality and also high social ***,the proposed BEMCS system with CHASER in smart contracts guarantees the data confidentiality by utilizing an asymmetric encryption scheme,and the anonymity of participants by applying the zero-knowledge succinct non-interactive argument of knowledge(zk-SNARK).This also restrains the malicious behaviors of ***,most simulations show that the social welfare of CHASER is increased by approximately when compared with the state-of-the-art ***,CHASER achieves a competitive ratio of approximately 0.8 and high task completion rate of over 0.8 in large-scale *** findings highlight the robustness and desirable performance of CHASER as an incentive mechanism within the BEMCS system.

关键词： mobile crowdsensing edge computing blockchain smart contract incentive mechanism

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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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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Composer Classification Using Maximum Probability Partitioning Based on Compression Principles

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Transactions of the Japanese Society for Artificial Intelligence 2024年第2期39卷 1-10页

作者： Takamoto, Ayaka Hironaka, Shiori Umemura, Kyoji Department of Computer Science and Engineering Toyohashi University of Technology Japan Academic Center for Computing and Media Studies Kyoto University Japan

Music classification is a fundamental task in the field of Music Information Retrieval. This paper focuses on composer classification, a specific task within music classification. Compressive techniques are commonly employed in such music classification tasks. In this study, we propose a method to apply the Computing Information Quantity using Maximum Probability partitioning to music classification. To evaluate the effectiveness of our proposed method, we perform composer classification, specifically distinguishing between Haydn and Mozart, who are well-known for their stylistic similarities. The experimental results demonstrate that our proposed approach outperforms traditional compression-based classification methods. Furthermore, we compare our method with non-compressive techniques, discussing the significance of feature extraction methods. Our proposed method is a parameter-free classification approach that does not require domain-specific musical expertise or feature extraction based on such expertise. © 2024, Japanese Society for Artificial Intelligence. All rights reserved.

关键词： Classification (of information)

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