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Multi-component attention graph convolutional neural network for Qos-aware cloud job scheduling and resource management enhancing efficiency and performance in cloud computing

Results in Engineering

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Results in engineering 2025年 27卷

作者： Deepak Dharrao sarika Deokate Nagamma Hudgi shrinivas shirkande vinod Mahajan Madhuri Pangavhane Anupkumar bongale Department of Computer Science and Engineering Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Lavale Pune Maharashtra 412115 India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune Maharashtra India Department of Computer Science Engineering (Cyber Security) Akash College of Engineering and Technology Bangalore Karnataka India S B Patil College of Engineering Indapur India AGM(L/A) Bharat Sanchar Nigam LTD. Rajgurunagar Pune Maharashtra India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Lavale Pune Maharashtra 412115 India

Cloud computing is one of the most promising technologies for online business services, scheduling real-time cloud jobs with excellent Quality of service (Qos) remains a challenge with current methodologies. However, in reinforced rewards are typically unavailable until all jobs have been scheduled. In this paper, Multi-Component Attention Graph Convolutional Neural Network for Qos-Aware Cloud Job scheduling and Resource Management Enhancing Efficiency and Performance in Cloud Computing ( MCAGCN-QOs-CJs ) is proposed. The cloud typically follows a long trajectory; to find trajectories with large rewards, resulting in concerns such as inefficient scheduling policies. Here, scheduling user requests improve job success rates and reduces response time. Initially, Qos based Job scheduling and resource management using Multi-Component Attention Graph Convolutional Neural Network ( MCAGCN ) to maximize success Rates in the Cloud. However, MCAGCN does not include optimization strategies for determining optimal parameters for enhanced resource allocation in cloud computing. Hence Multi-Agent Cubature Kalman Optimizer ( MACKO ) is used to enhance weight parameters of MCAGCN . The performance measures including success rate, Average Response Time, Throughput, and Job Completion Time is considered. The MCAGCN-QOs-CJs method attains 23.10 %, 27.54 % and 25.72 %, higher Throughput, 21.48 %, 17.73 %, 25.32 % higher Utilization Rate and 20.98 %, 26.66 % and 16.32 % lower Average Response Time, 10.78 %, 20.47 %, and 12.86 % lower Job Completion Time are compared with existing techniques.

关键词： Cloud computing Job scheduling Multi-component attention graph convolutional neural network Multi-agent cubature Kalman optimizer

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MsRFNet for skin lesion segmentation and deep learning with hybrid optimization for skin cancer detection

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The Journal of Photographic science 2023年第7期71卷

作者： Diwan baskaran Yanda Nagamani suneetha Merugula s P Premnath a Department of Computer Science & Engineering St. Joseph’s College of Engineering Chennai India b Department of Computer Science and Engineering GMR Institute of Technology Rajam Andhrapradesh India c Department of CSE GITAM School of Technology GITAM (Deemed to be University) Visakhapatnam India d Department of ECE Sri Krishna College of Engineering and Technology Coimbatore India

AbsTRACTskin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-scale Residual Fusion Network (MsRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average subtraction student Psychology based Optimization (AssPbO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional AssPbO (FrAssPbO). Moreover, performance of proposed FrAssPbO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%.

关键词： Multi-scale residual fusion network fractional calculus average and subtraction based optimizer student psychology based optimization deep residual network skin cancer Fractional Average subtraction student Psychology based Optimization True Positive Rate

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RETRACTED ARTICLE: Modified adaptive neuro fuzzy inference system based load balancing for virtual machine with security in cloud computing environment

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Journal of Ambient Intelligence and Humanized Computing 2020年第3期12卷 3869-3876页

作者： Durga Devi, T. J. b. subramani, A. Anitha, P. Department of Computer Applications K.S.R. College of Engineering Tiruchengode India Department of Computer Science M.V.Muthiah Government Arts College for Women Dindigul India

In a heterogeneous environment, computation over internet is provided by a popular paradigm called cloud computing. In a cloud heterogeneous environment, service providing has various difficulties. based on service type, difficulties differ. On cloud server, high load is produced by huge amount of request from various users for accessing various applications. security and balancing of load are major concerns. In cloud environment, NP-hard optimization problem corresponds to load balancing. Dynamic load balancing is handled by various methods. They are designed for enhancing workload distribution process between nodes. Overload avoidance, minimization of average response time, data processing time and optimum utilization of resources are the major aim of those methods. An optimal load balancing technique should improve the turnaround time and maximum CPU utilization. because of its opaqueness nature of cloud, security is a biggest challenge. According to Forbes, with introduction of General Data Protection Regulation security in cloud continue to be an issue with cloud computing. In the existing system, a fuzzy based hybrid load balancing algorithm is utilized and the results provided are not satisfactory. There are opportunities for improving CPU utilization and turnaround time and in terms of security. In this proposed research work, dynamic load balancing in a heterogeneous environment is handled by Modified Adaptive Neuro Fuzzy Inference system (MANFIs). Parameters of MANFIs are optimized by introducing Fire-fly Algorithm. security is imposed on user authentication by using the Enhanced Elliptic Curve Cryptography. This is a password-less mechanism to authenticate users. The proposed work attains satisfactory results by proper resource utilization. An experimental result shows that proposed work exhibits better performance by improving the turnaround time and maximizing the CPU utilization and providing secured access to data.

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Accelerated and accurate cervical cancer diagnosis using a novel stacking ensemble method with explainable AI

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Informatics in Medicine Unlocked 2025年 56卷

作者： siddiqui, Md Ismail Hossain Khan, shakil Limon, Zishad Hossain Rahman, Hamdadur Khan, Mahbub Alam Al sakib, Abdullah swapno, s M Masfequier Rahman Haque, Rezaul Reza, Ahmed Wasif Appaji, Abhishek Department of Engineering/Industrial Management Westcliff University Irvine 92614 CA United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los Angeles 90010 CA United States Department of Computer Science Westcliff University Irvine 92614 CA United States Department of Management Information System International American University 3440 Wilshire Blvd STE 1000 Los Angeles 90010 CA United States Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles 90010 CA United States Department of Information Technology Westcliff University Irvine 92614 CA United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka 1216 Bangladesh Department of Computer Science and Engineering East West University Dhaka 1212 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bull Temple Rd Basavanagudi Karnataka Bangalore 560019 India Maastricht University University Eye Clinic Maastricht Minderbroedersberg 4-6 Maastricht Netherlands

Cervical cancer is a preventable yet life-threatening disease that claims hundreds of thousands of lives each year, particularly in low-resource settings where timely screening is scarce. Current Deep Learning (DL) approaches for automated cervical cytology classification encounter challenges such as class imbalance, computational inefficiency, and inadequate generalizability. This study proposes a novel CerviXEnsemble model that integrates multiple pre-trained DL architectures (Inception-ResNetv2, EfficientNet-b6, ResNet152, Inception-ResNetv2, EfficientNet-b6, DenseNet201, and NAsNetMobile) as base learners, along with a dense-layer meta-learner that refines and consolidates predictions for improved robustness. Unlike traditional single-CNN models, our stacking ensemble approach utilizes diverse feature representations to enhance classification stability and generalization across multiple cytology datasets. To validate the model, we experimented with the Herlev and sIPaKMeD benchmark datasets in this study. Techniques like contrast enhancement and data augmentation were employed to optimize feature extraction. The model achieved state-of-the-art performance, attaining an accuracy of 99.38 % and an F1-score of 98.49 % on the Herlev dataset and an accuracy of 98.71 % and an F1-score of 97.53 % on sIPaKMeD. These performances are superior to previous studies in controlling class imbalance and providing stable predictions over different samples. Additionally, Explainable AI (XAI) techniques were incorporated to ensure transparent and interpretable predictions, aiding clinicians in their decision-making processes. An interpratable web application was developed for real-time Pap smear analysis to reduce the diagnostic workload for pathologists by identifying high-risk samples. This solution shows great promise for use in various healthcare settings, maintaining high diagnostic accuracy while requiring minimal computational resources, making it suitable for both urban ho

关键词： Cervical cancer Diagnostic tool Ensemble learning Explainable AI Pap smear image analysis

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sTRUCTURE OF bLOCKs WITH NORMAL DEFECT AND AbELIAN p' INERTIAL QUOTIENT

arXiv

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arXiv 2022年

作者： benson, David Kessar, Radha Linckelmann, Markus Institute of Mathematics Fraser Noble Building University of Aberdeen King's College AberdeenAB24 3UE United Kingdom School of Mathematics Computer Science & Engineering Department of Mathematics City University of London Northampton Square LondonEC1V 0HB United Kingdom

Let k be an algebraically closed field of prime characteristic p. Let kGe be a block of a group algebra of a finite group G, with normal defect group P and abelian p' inertial quotient L. Then we show that kGe is a matrix algebra over a quantised version of the group algebra of a semidirect product of P with a certain subgroup of L. To do this, we first examine the associated graded algebra, using a Jennings-Quillen style theorem. As an example, we calculate the associated graded of the basic algebra of the non-principal block in the case of a semidirect product of an extraspecial p-group P of exponent p and order p3 with a quaternion group of order eight with the centre acting trivially. In the case p = 3 we give explicit generators and relations for the basic algebra as a quantised version of kP. As a second example, we give explicit generators and relations in the case of a group of shape 21+4 : 31+2 in characteristic *** Codes 20C20 © 2022, CC bY-NC-ND.

关键词： Defects

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HOCHsCHILD COHOMOLOGY OF sYMMETRIC GROUPs IN LOW DEGREEs

arXiv

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arXiv 2022年

We compute the dimensions of the Hochschild cohomology of symmetric groups over prime fields in low degrees. This involves us in studying some partition identities and generating functions of the dimensions in any fix... 详细信息

关键词： Topology

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When vision Meets Touch: A Contemporary Review for visuotactile sensors from the signal Processing Perspective

arXiv

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arXiv 2024年

作者： Li, shoujie Wang, Zihan Wu, Changsheng Li, Xiang Luo, shan Fang, bin sun, Fuchun Zhang, Xiao-Ping Ding, Wenbo Tsinghua Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Centre for Robotics Research King’s College London LondonWC2R 2LS United Kingdom Department of Automation Tsinghua University Beijing100084 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： signal processing

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Tomato leaf disease identification by modified inception based sequential convolution neural networks

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The Journal of Photographic science 2023年第5期71卷

作者： R. Dhanalakshmi balakrishnan K bam bahadur sinha R. Gopalakrishnan a Department of Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Sethurapatti Tamil Nadu India b Department of Computer Science and Engineering Indian Institute of Information Technology Ranchi Jharkhand India c Department of Electrical and Electronics Engineering K.S. Rangasamy College of Technology Tiruchengode India

AbsTRACTTomato leaf disease detection has been a challenge for farmers due to the need for quick and accurate diagnosis to prevent plant contamination. Traditional approaches such as visual observation and laboratory testing have limitations such as subjectivity and reliance on human expertise. Deep Learning (DL) approaches, particularly Convolutional Neural Networks (CNN), have emerged as a solution for disease identification and classification in plants. This research proposes a new variant of an inception-based sequential network that combines the advantages of deep residual and dense networks to reduce dimensionality and enhance accuracy, information flow, and gradient. Modified input image characteristics and hyper-parameters were used to classify existing networks on the tomato dataset, with experimental findings showing that the proposed model can achieve an average identification accuracy of up to 96 percent on the tomato test dataset. The model's performance surpasses most existing leaf identification models and requires fewer computation.

关键词： Leaf disease identification convolutional neural networks inception-v3 tomato leaf disease classification diagnosis severity multi-class classification

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synergistic Impact of Magnesium Compound as a Potential Dye Additive for Organic-based sensitizer in Dsscs

SSRN

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ssRN 2022年

作者： Khan, Azlina Ahmad Abdullah, M.H. Rahman, M.A. Faizal Yusof, M. syarifah Adilah Mamat, Mohamad Hafiz bin Radzol, Afaf Md sin, N. Diyana sabani, N. Hayati banu, I.b. shameen Rusop, M. School of Electrical Engineering College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Permatang Pauh13500 Malaysia College of Electrical Engineering Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Permatang Pauh13500 Malaysia Universiti Malaysia Perlis Perlis Kangar01000 Malaysia Department of Physics B.S. Abdur Rahman Crescent Institute of Science & Technology Vandalur Chennai600 048 India

In this study, the dye-sensitized solar cell (DssCs) was successfully fabricated using a dye molecule sensitizer extracted from Mitragyna speciosa (Ms), while MgsO 4 salt compound served as a potent dye additive for dye treatment. The optimal amount of MgsO 4 additive that contributed to the highest power conversion efficiency, PCE was investigated. Field emission sEM, FEsEM, X-ray diffraction, XRD, Fourier transform-IR, FTIR, Uv-vis spectroscopy, current to voltage characteristics, I-v and incidence photon to current efficiency, IPCE were used to investigate the structural, optical, chemical and also the electrical properties of the DssC cells and their components. It was found that the extracted Ms dye provided evidence of the presence of chlorophyll pigment, an effective light-harvesting pigment necessary for the production of charge transfer and electricity. Also, MgsO 4 dye additive-based DssCs outperformed the untreated dye cell. At their optimal dye additive of 0.3 g (6 ml of chlorophyll dye solution), the current density, Jsc and the associated PCE of the DssC was higher at 2.08 mA/cm 2 and 0.39%, respectively. Thus, using Ms as a dye sensitizer with optimized MgsO 4 compound as the additive showed better photovoltaic effects, increased solar light absorption and enhanced photon energy utilisation. © 2022, The Authors. All rights reserved.

关键词： Magnesium compounds

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Highlighting Nerves in Images for Ultrasound Guided Regional Anesthesia 1

Highlighting Nerves in Images for Ultrasound Guided Regional...

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1st IEEE bangalore Humanitarian Technology Conference, b-HTC 2020

作者： Akkasaligar, Prema T. Koolagudi, shashidhar G. biradar, sunanda Hotagi, Poornima badiger, sharan VijayapurKaranataka586103 India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Department of Medicine BLDEDU's Sri B.M.Patil Medical College and Research Centre VijayapurKarnataka India

ISBN: (纸本)9781728187945

surgery is a treatment given to the patient, when patient is having injuries in the body parts. While doing the surgery the patient will undergo pain and discomfort, to reduce the pain of patient anesthesia is given before surgery. There are many types of anesthesia procedure called local, regional and general. Local anesthesia is given to specific part of the body;general anesthesia is where the patient is completely senseless. Regional anesthesia is procedure, where anesthetist does injection near a class of nerves to numb the area of human body that requires surgery. There is difference in structure of every person. This makes doctor hard to detect nerve in internal organ. Ultrasound image provide information about structure of body which can be used for regional anesthesia. Ultrasonography is the safest method compared to the other imaging techniques like Xray, CT, and MRI;because it is not based on ionizing radiation. In this paper, we simplify the issue of detection of nerve in ultrasound image. For input, image such as ultrasound images of brachial plexus are used. Initially in preprocessing, filtering technique is used to remove noise from ultrasound image and then segmentation is carried out using active contour model. Performance of segmentation is evaluated using dice coefficient and Jaccard coefficient. The nerve location in ultrasound image is detected to help the doctors in giving regional anesthesia. © 2020 IEEE.

关键词： Median filters

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