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2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023

作者： Gupta, Rishabh Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Computer Applications National Institute of Technology Kurukshetra India

ISBN: (纸本)9781839539596

With the wide range of cloud services, including flexibility, high scalability, and the least upfront capital investment, it also offers machine learning services that are utilized in various applications, such as medical diagnosis, risk analysis, and product recommendation. Since it has many advantages, many organizations move their data to the cloud platform for storage, processing, and distribution intentions. The classification service is offered by the cloud service provider so that owners can assess their data. It gathers the data from the owners' side for training the machine learning model and accomplishing the classification task. The cloud servers handled by a third party might not be totally trusted by the data owners. However, data privacy is a significant challenge among the critical barriers to preserving data while distributing it. A novel data protection model is proposed, driven on machine learning and differential approaches that carry out classification tasks while protecting the owners' data through noise addition. The model enables the different owners to store, share, and utilize data on the cloud platform. The experiments are carried out on Cleveland, New-Thyroid, and SA-Heart datasets to determine the effectiveness of the model in terms of accuracy, precision, recall, and F1-score. The results show that it achieves high accuracy, precision, recall, and F1-score up to 88.37%, 83.28%, 98.41%, and 82.91%, respectively. © The Institution of engineering & Technology 2023.

关键词： Risk analysis

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning 4

An Efficient DDoS Attack Detection in SDN using Multi-Featur...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit V. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOVA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance applications

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SN computer science 2025年第2期6卷 1-12页

作者： Mohanapriya, D. Rajalakshmi, K. Helen Josephine, V.L. Panneerselvam, Karthick Department of Computer Science (SF) Sourasthra College Tamil Nadu Madurai India Department of Computer Applications CMR Institute of Technology Karnataka Bangalore India Business Analytics School of Business and Management Christ University Karnataka Bangalore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamilnadu Uthupalayam 641407 India

The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision Loss operator Object tracking Residual network

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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An Efficient Approach for Semantic Segmentation of Salt Domes in Seismic Images Using Improved UNET Architecture

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Journal of The Institution of Engineers (India): Series B 2023年第3期104卷 569-578页

作者： Bodapati, Jyostna Devi Sajja, RamaKrishna Naralasetti, Veeranjaneyulu Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi India IAENG Life Member Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Vignan’s Foundation for Science Technology and Research Vadlamudi India

Many areas of Earth’s surface with large accumulations of gas and oil even have huge deposits of salt under the surface. Exploring such deposits helps many countries to increase the storage capacity of their Petroleum reserves and explore new ones. But finding such deposits is a herculean task. Expert seismic imaging requires human interpretation of salt bodies. But this leads to very biased and highly variable translations. So the idea behind this paper is to build an approach that accurately and automatically identifies if the seismic image contains any region of salt deposit or not. If a surface is found to have salt deposits, then it may contain the accumulations of oil or gas and even the salt domes or caverns can be used as a storage site for already available petroleum or oil. Since semantic segmentation classifies every pixel in the given image to its class label, this can be used to segment the salt deposits from the provided seismic images. In this paper, we introduce a variation of UNet, a popular segmentation model, for seismic image segmentation. We have added a batch normalization layer following every convolution layer as a deeper network helps extract better features which turned out to be true. Some interesting findings of this work are that the augmentation works well as this avoids over-fitting of the model. Sharpening as a post-processing technique has come up with a considerable amount of rise in the performance. Here, we prefer to use the metric as Intersection over Union (IoU) instead of accuracy as it is not as affected by the class imbalances that are inherent in foreground/background segmentation tasks. With the proposed methodology, we achieve an averaged IoU of 85.6 which is far better compared to the IoU achieved with the Segnet approach which stands at 77. © 2023, The Institution of Engineers (India).

关键词： Image enhancement

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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NLP Based Sentiment Analysis Using Particle Swarm Optimization with Logistic Regression for Digital Education 6

NLP Based Sentiment Analysis Using Particle Swarm Optimizati...

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6th International Conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2024

作者： Priyadharshini, G. Clara Barathi Balaji, Prasanalakshmi Chaurasia, Mousmi Ajay Das, Subhranginee Dutta, Sushama Rani Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India King Khalid University Department of Computer Science Abha Saudi Arabia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505790

Identification and categorization of user sentiments from a text passage is known as sentiment analysis into several sentiments. For example, using feelings like happiness, sadness, anger, or positive, negative, or neutral to gauge how the user feels about a certain topic or item. These days, a variety of websites and applications collectively referred to as online educational platforms allow users to obtain a higher degree of education across a range of areas. For this reason, digital education provides a platform for the dissemination of knowledge. Among the most active areas within NLP, web mining, and text mining research is sentiment analysis. It is significant in a variety of subjects, including as the social sciences and management sciences, where evaluating the efficacy of learning technology depends heavily on student input. Due to the proliferation of educational institutions, many students have become enthralled with online learning portals that provide cost-free courses. Every year, a huge number of students enrol in these expansive online courses and then revisit their opinions regarding the course material and level of instruction. Additionally, share ideas on blogs on how to raise the standard of instruction by expressing both good and negative opinions. However, an assortment of ML techniques are available to assess these ratings and ascertain which virtual education platform is the most user-friendly and optimal. This study examined natural language processing (NLP)-based sentiment analysis and social media evaluations that were manually gathered from educational applications. To categorise the reviewer's comment for digital education, Particle Swarm Optimisation with Logistic Regression (PSOLR) outperformed all other existing Naïve Bayes, Decision Tree, and Logistic Regression algorithms in terms of accuracy (93.75%). © 2024 IEEE.

关键词： Students

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Designing an Intelligent Model that Employs Advanced Machine Learning Techniques to Optimize Disease Prediction and Classification in Humans 1

Designing an Intelligent Model that Employs Advanced Machine...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Kumari, Sudesh Lekshmi, V. Arathi Chandran, R.I. Shankar, Jaya Vishnu Priya, P.M. Bps Women University Department of Electronics and Communication Engineering Sonipat India Christ Nagar College Department of Computer Science and Applications Kerala Maranalloor India Sree Narayana Institute of Technology Department of Computer Science and Engineering Kerala Adoor India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350307795

Machine learning (ML) has emerged as a powerful tool with applications across diverse fields, including clinical diagnostics. With the growing prevalence of heart and liver diseases worldwide, early detection is paramount for saving lives. This study presents a hybrid ML model that combines the support vector machine (SVM) approach with a modified particle swarm optimization (PSO) model for the classification of heart and liver disease data. Utilizing datasets from the UCI ML repository, we evaluate the model's performance using various metrics, including categorization accuracy, error rates, correctness, recall, and the F1 score. Comparative analyses with traditional SVM and other hybrid algorithms such as the PSOSVM and Crazy PSO-SVM (CPSOSVM) are conducted. The experiments encompass the prediction of cardiac and liver illness. Results demonstrate that the designed Cauchy Crazy PSOSVM (CCPSOSVM) algorithm outperforms other approaches, achieving the greatest accuracy rate and least error rate. While this study focuses on cardiac and hepatic illness prediction, the CCPSOSVM algorithm holds promise for the extension of disease prediction across various medical domains. © 2024 IEEE.

关键词： Support vector machines

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BiT5: A Bidirectional NLP Approach for Advanced Vulnerability Detection in Codebase 5

BiT5: A Bidirectional NLP Approach for Advanced Vulnerabilit...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Prabith, G.S. Rohit Narayanan, M. Arya, A. Aneesh Nadh, R. Binu, P.K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In this research paper, a detailed investigation presents the utilization of the BiT5 Bidirectional NLP model for detecting vulnerabilities within codebases. The study addresses the pressing need for techniques enhancing software security by effectively identifying vulnerabilities. Methodologically, the paper introduces BiT5, specifically designed for code analysis and vulnerability detection, encompassing dataset collection, preprocessing steps, and model fine-tuning. The key findings underscore BiT5's efficacy in pinpointing vulnerabilities within code snippets, notably reducing both false positives and false negatives. This research contributes by offering a methodology for leveraging BiT5 in vulnerability detection, thus significantly bolstering software security and mitigating risks associated with code vulnerabilities. © 2024 The Authors. Published by Elsevier B.V.

关键词： Natural language processing systems

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Computing for Mobile Ad Hoc Network in IoT Fuelled Smart Environments: computer Integration in Smart Environment 2

Computing for Mobile Ad Hoc Network in IoT Fuelled Smart Env...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Taneja, Dev Gupta, Pooja Taneja, Kavita Manipal University Jaipur Department of Computer and Communication Engineering India Panjab University Chandigarh Department of Computer Science and Applications India

ISBN: (纸本)9798350369960

Present Information Technology scenario is engulfed with exponential rise in mobile computing devices mostly embedded in diverse equipments of our daily life. This has led to evolutionary demand of new solutions to mobile computing for smart environments equipped with Internet of Things (IoT). A plethora of challenges are emerging to ensure effective internet connectivity for such mobile devices. These devices are mostly equipped with sensors and demand continuous computing and data transfers. The globe is evolving to humancentred society, i.e., Society 5.0 loaded with innovations of Big Data, IoT, Artificial Intelligence and much more to make social life comfortable and sustainable. Hence, this trend is the need of the hour but has brought a range of new challenges to MANETs for mobile computing in smart environments. This paper explores the MANET-IoT computing trends and provides a detailed analysis of the challenges for computing in the smart environment having MANET-IoT as backbone for mobile communications. © 2023 IEEE.

关键词： Internet of Things (IoT) Mobile Ad Hoc Network (MANET) Mobile Computing Society 5.0

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