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Energy efficient resource utilization and load balancing in virtual machines using prediction algorithms

International Journal of Cognitive Computing in engineering 2023年第1期4卷 127-134页

作者： Udayasankaran, P. Thangaraj, S. John Justin Department of Computer Science and Engineering Saveetha School of Engineering Chennai Tamilnadu India

In recent years due to increase in the number of customers and organizations utilize cloud applications for personal and professionalization become greater. As a result of this increase in utilizing the Cloud services, Cloud data centers found to be huge consumption with boundless energy and harmful materials into the environment. The Primary goal of fully virtual machines is to minimize consumption of boundless energy utilization and providing uttermost resources. Balancing the loads on the servers has become a challenging task for the cloud service provider. Load balancing on servers helps to improve the performance of the virtual machines and to minimize energy and processing time in cloud systems. Here we discuss about various load balance r serves as the single and distributes incoming traffic across multiple targets. Reducing the consumption for the data center industries, power supply capacity has more increased and demand will be continued. With an aim to reduce and bring down active server infrastructure burdens. All about, this includes various challenges are taken care of by the environment. Behavior approaches of systems and consumers are not defined clearly. We present a novel resource optimization framework to discover jobs with minimum utilization. This progress ground work encircle boundless energy utilization. © 2023

关键词： Virtual machine

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Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 10873-10886页

作者： Thumilvannan, S. Balamanigandan, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The survival of patients' deaths owing to Heart Disease (HD) could be improved with the assistance of an enhanced approach for predicting the risk of diabetes and HD. Nevertheless, such schemes are developed rarely. Thus, this paper proposes a new Power Lognormal Distribution-Semi-Supervised Learning-centric Restricted Boltzmann Machine (PLD-SSL-RBM) diabetes and HD risk level prediction model for IoT data. The missing data are removed by partial Derivation of the Hamilton-Cluster Centered-K-means Clustering (DH-CC-KC) to efficiently train the classifier and then, the data are aggregated. Next, to reduce the dataset size, the features are reduced with Shell Sort-Principal Component Analysis (SS-PCA). Then, the fuzzy rule-based decisions are created with the T-test-centric Uniform Distribution-Elephant Herd Optimization Algorithm (T-test-UDEHOA) Correlated Features (CF) to classify the risk levels accurately. Lastly, the risk levels of HD and diabetes are predicted;in addition, by employing the Elliptic Curve Cryptography (ECC)7encryption technique, the data is securely stored on the medical database. The proposed risk prediction model's performance is analyzed on the Framingham dataset. As per the experimental outcomes, when analogized to the prevailing methodologies, the proposed technique attained a higher accuracy of 99.55%. © 2023 - IOS Press. All rights reserved.

关键词： Internet of things

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An Enhanced Security System Using Blockchain Technology for Strong FMC Relationship

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Intelligent Automation & Soft Computing 2023年第1期35卷 111-128页

作者： K.Meenakshi K.Sashi Rekha Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia

Blockchain technology is a shared database of logs of all consumer transactions which are registered on all machines on a *** transactions in the system are carried out by consensus processes and to preserve confidentiality all thefiles contained cannot be *** technology is the fundamental software behind digital currencies like Bitcoin,which is common in the *** computing is a method of using a network of external machines to store,monitor,and process information,rather than using the local computer or a local personal *** software is currently facing multiple problems including lack of data protection,data instability,and *** paper aims to give the highest security for multiple user environments in cloud for storing and accessing the data in *** users who are legitimate are only allowed for storing and accessing the data as like a secured block chain *** like the Blockchain which does not require a centralized system for transactions,the proposed system is also independent on centralized network *** decentralized system is developed in such a way to avoid *** system enables the fabricator to spend less or null resources to perform the validations for its direct operated *** ensures the product fabricator to avoid the circulation of its duplicate *** customer as an end-user is also ensured to have only the genuine products from the *** Fabricator(F),Merchant(M)and consumer(C)forms an interconnected triangular structure without allowing any counterfeiting agents in their secured cloud *** pro-posed approach provides the stability in the security system of the cloud using the chaining mechanism within different blocks at each *** takes roughly 4.7,6.2,and 7.5 ms,respectively,to register each node in the proposed system for 5,10,and 15 *** overall registration time for each scenario is 11.9,26.2,and 53.1 ms,despite the fact th

关键词： Blockchain bitcoin customer merchant data protection digital currencies transaction

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Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

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computers, Materials & Continua 2023年第10期77卷 1209-1226页

作者： P.Kalaiselvi S.Anusuya Department of Computer Science and Engineering Saveetha School of EngineeringChennaiTamil Nadu602117India Information Technology Saveetha School of EngineeringChennaiTamil Nadu602117India

In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so ***,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific *** tumor prediction is a vital chore in analyzing and treating liver *** paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver *** anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver *** proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art ***,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver *** results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor *** can assist radiologists in their diagnosis and treatment *** proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.

关键词： Deep learning convolution neural networks liver tumors CT scans attention mechanism classifier

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Deep Learning and Signal Processing Integration in Automatic Speech Recognition Framework Using Hidden Markov Models 1

Deep Learning and Signal Processing Integration in Automatic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Godvin Mani, S. Rajesh Kumar, T. Saveetha University Saveetha School of Engineering Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518578

Automatic speech recognition is one of the technologies that change the game regarding the transcription of spoken language to text. A new methodology combining both feature extraction methods, including the Mel-Frequency Cepstral Coefficients (MFCC) and deep embedding, with acoustic modeling hybrid approach such as hidden Markov model (HMM) and DNN, shall be presented in the paper. The results exhibit excellent accuracy gains, up to 97.8% by the Transformer-based models in wide-ranging acoustic conditions. The obtained results underscore critical factors that come into play such as advanced preprocessing, feature extraction, and hybrid architectures to push ASR forward to more complicated noisy and noisy speech applications. © 2025 IEEE.

关键词： Hidden Markov models

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 2915-2931页

作者： S.Vijayalakshmi S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： Vegetation mapping deep learning machine learning remote sensing data image processing

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A recover-then-discriminate framework for robust anomaly detection

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science China(Information sciences) 2025年第4期68卷 300-318页

作者： Peng XING Dong ZHANG Jinhui TANG Zechao LI School of Computer Science and Engineering Nanjing University of Science and Technology Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.

关键词： recovery network HOG prompt discriminative network self-correlation loss anomaly detection

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A Task Scheduling Algorithm Based on Clustering Pre-processing in Space-Based Information Network

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Chinese Journal of Electronics 2024年第1期33卷 217-230页

作者： Yufei WANG Jun LIU Shengnan ZHANG Sai XU Jingyi WANG School of Computer Science and Engineering Northeastern University

With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneity in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.

关键词： Scheduling algorithms Heuristic algorithms Clustering methods Clustering algorithms Simulated annealing Dynamic scheduling Search problems

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