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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition 24

Large Model for Small Data: Foundation Model for Cross-Modal...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400706974

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi's generalizability across various environments. © 2024 Copyright is held by the owner/author(s).

关键词： Zero-shot learning

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Enhancement of PCA-Based Dimensionality Reduction Using BB-BC Optimization Algorithm

Enhancement of PCA-Based Dimensionality Reduction Using BB-B...

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2023 International Interdisciplinary PhD Workshop, IIPhDW 2023

作者： Kaur, Supreet Krishna, C. Rama Kumar, Shakti Singh, Jasmeet National Institute of Technical Teachers Training and Research Department of Computer Science and Engineering Chandigarh India Panipat Institute of Engineering & Technology Department of Computer Science and Engineering Panipat India Hochschule Wismar Department of Electrical Engineering and Computer Science Phillip-Müller-Straße 14 Wismar23966 Germany

ISBN: (纸本)9781665410847

Dimensionality reduction is an important step for various applications where usage of the internet and multimedia systems is involved which requires huge bandwidth and storage space. This paper presents Big Bang-Big Crunch (BB-BC) optimization algorithm based two new approaches to feature selection for dimensionality reduction. In first approach, PCA is used to extract the features (eigenvectors) and defines feature set based on computation of knee point and BB-BC optimization algorithm, in turn selects an optimal subset from the predefined feature set. In the second approach, PCA is used only for feature extraction and BB-BC optimization algorithm is used for optimal feature selection from all extracted features. Olivetti research Laboratory (ORL) face database has been used for performing the experiment and recognition rate is the parameter to be optimized for face recognition as an application area. The experimentation proves BB-BC as a powerful soft computing technique to solve such NP hard problems. © 2023 IEEE.

关键词： Face recognition

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Attacks in Wireless Sensor Networks: Detection and Mitigation using Hybrid Propagation Mitigation Protocol 7

Attacks in Wireless Sensor Networks: Detection and Mitigatio...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Anandh, R. Urmela, S. Rashmi, D. Tamilselvi, C. Manju Bhatia, Deepshikha Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer and Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Computer and Science Engineering Tamil Nadu India CMR Engineering College Department of Information Science and Engineering Karnataka India Dr. MGR Educational and Research Institute Department of Information Technology Chennai India Government Engineering College Department of Electrical Engineering Bihar India Department of Computer Science and Information Technology Jaipur India

ISBN: (纸本)9798350340600

Wireless sensor networks (WSNs) are particularly susceptible to Black Hole attacks (BLA) and other insider attacks. In the BLA, a hostile node drops every packet routed via it, causing the loss of sensitive data and a network outage. The active trust protocol has been created to identify and stop these attacks, but more work has to be done. Mobile ad hoc networks (MANETs) present a potential answer. However, they demand a lot of bandwidth, use a lot of Energy, and are expensive. The Ad Hoc On-demand Multipath Distance Vector (AOMDV) protocol's performance for seeing and stopping assaults within a radius of 0.5 km could be improved. Compared to the current system, our system delivers a performance improvement of around 21 %, yielding a system performance of roughly 96%. This strategy minimizes expenditures while ensuring a secure and dependable WSN. © 2023 IEEE.

关键词： Routing protocols

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HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarchical Attention for Classifying Cervical Cancer with Cytology Images

HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarc...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Mahendran, Rakesh Kumar Ramadass, Parthasarathy Devi, M.Kiruthiga Sanmugasundaram, R. Thirunavukkarasu, Usharani Anandaraj, A.Peter Soosai Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Rajalakshmi Institute of Technology Electronics and Communication Engineering Chennai India Saveetha University Saveetha School of Engineering SIMATS School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr:Sagunthala RandD Institute of Science and Technology Chennai India

ISBN: (纸本)9798331539948

Cervical cancer (CeCA) is a major worldwide health concern, particularly in low- and middle-income countries where screening and treatment facilities are not widely available. Cervical cells, which connect the lower portion of the uterus to the vagina, are where CeCA, a particular type of cancer, originates. Sexually transmitted infections such as certain strains of the Human Papillomavirus (HPV) are the main cause of cervical cancer. But not every HPV infection results in CeCA;other risk factors include smoking, having several sexual partners, and having a weaker immune system.. Additionally, the classification of CeCA using Deep Learning (DL) can pose challenges related to computational resources, model complexity, and robustness compared to state-of-the-art methodsOur solution, HiAt-Net, tackles these challenges by introducing a 3-layer encoder-decoder approach that incorporates a hierarchical attention mechanism. This method is specifically designed to extract features effectively from complex input data, such as the Herlev dataset and SIPaKMeD dataset. By integrating the attention mechanism at multiple levels, our model can capture hierarchical dependencies and selectively focus on relevant features, resulting in enhanced performance in classification tasks. We conducted a thorough comparison and analysis of our proposed method against existing approaches, evaluating performance metrics such as accuracy, precision, recall, and F1-Score. © 2024 IEEE.

关键词： Diseases

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Design and Optimization of Adaptive Network Coding Algorithms for Wireless Networks 15

Design and Optimization of Adaptive Network Coding Algorithm...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nadeem, Syed Mohammed Rao, Deepak Dasaratha Arora, Arpit Dongre, Yashwant V. Giri, Rakesh Kumar Jaison, B. G Pulla Reddy Engineering College Department of Computer Science and Engineering India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India College Sunrise University Department Computer Science & Engineering Alwar301028 India R.M.K.Engineering College Department Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

This paper specializes in the layout and optimization of adaptive community coding algorithms for WiFi networks. The goal of those algorithms is to maximize the performance of network transmissions at the same time as heading off collisions and losses. We analyze the performance of numerous current network coding algorithms, examine their strengths and weaknesses and advocate techniques to optimize their performance. We also increase and compare new algorithms based on the adaptive network coding method. Those algorithms are designed if you want to mechanically adapt to the environment and take full benefit of the available assets to maximize the overall network's performance. The results of our simulations endorse that the proposed adaptive network coding algorithms outperform conventional network coding algorithms in phrases of both throughput and latency. We further propose an optimized model of our adaptive network coding algorithms and evaluate its performance to that of the unique algorithms. Our results demonstrate significant performance profits in terms of both throughput and latency. © 2024 IEEE.

关键词： Algorithms Latency Optimization Throughput Transmissions

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Predictive Modeling and Analysis of Campus Placements Using Machine Learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector Machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of Information technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Federated Learning Framework for Consumer IoMT-Edge Resource Recommendation Under Telemedicine Services

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 252-259页

作者： Ahmed, Syed Thouheed Sivakami, R. Banik, Debajyoty Khan, Surbhi Bhatia Dhanaraj, Rajesh Kumar Vinoth Kumar, V. Mahesh, T.R. Almusharraf, Ahlam REVA University School of Computing and Information Technology Karnataka Bengaluru India Sona College of Technology Department of Computer Science and Engineering Salem636005 India Anurag University Telangana Hyderabad500088 India University of Salford School of Science Engineering and Environment United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Chandigarh University University Centre for Research and Development Punjab Mohali India Symbiosis International Pune India Vellore Institute of Technology University School of Computer Science Engineering and Information Systems Vellore632014 India Princess Nourah Bint Abdulrahman University College of Business Administration Department of Management Riyadh11671 Saudi Arabia

Medical IoT devices and Telemedicine computation is the growing domain and further involving biomedical computation via machine learning ecosystem has generated an insightful results and analysis. The resources sharing and availability in computing and decision support suffer with a higher latency and energy consumption. In this manuscript, a novel TinyML based model for medical consumer devices resources allocation and resource sharing is discussed. The proposed framework is developed using Federated learning (FL) models for extracting the resource utilization patterns at individual user levels. These locally computed models are further facilitated with edge computation layer for locating resource patterns extraction. The technique is deployed on the dynamic server based resource pooling for effective analysis and resource scheduling and expanded to develop a reliable recommendation model for medical resource management. The framework has trained 128 clusters of 6400 rural and 12800 urban IoT devices samples for resource allocation and scheduling using telemedicine protocol (TelMED). The framework has secured an efficiency of 93.21% in urban user recommendation and 94.72% for rural users. © 1975-2011 IEEE.

关键词： Federated learning

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Evaluating Artificial Neural Networks and Emerging Machine Learning Methods for Engine Performance and Emissions Prediction: A Review 4

Evaluating Artificial Neural Networks and Emerging Machine L...

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

作者： Galat, Umesh Rewatkar, Rajendra Kothare, Chandrakant Datta Meghe Institute of Research and Higher Education Aids Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Shri Shankarprasad Agnihotri Collge of Engineering Department of Mechanical Engineering Maharashatra Wardha India

ISBN: (纸本)9798331523923

These due to the shift towards the cleaner energy generation and newer and sophisticated engine technologies the prediction of the performance and emission of engine is of vital importance for the designer for the purpose of ANR as well as the emission control standards. This review also assesses the performance of ANNs and the new machine learning techniques in this area. ANNs especially the single layer and multi-layer networks have shown good levels of predictions of engine performance and emission for ethanol-gasoline, bioethanol and other fuel blends. For example, the models based on Levenberg-Marquardt optimization and the Bayesian regularization have revealed essential correlation coefficients and minimized mean squared errors. Based on comparative analyses, the further improvement of the predictive ability and speed of ANNs is stressed, including applications of ANNs integrated with other approaches like Response Surface Methodology (RSM), and other developing approaches like Extreme Learning Machines (ELM) and Support Vector Regression (SVR). However, challenges are concluded to remains including data quality, model complexities and computational resources. This review reveals that, for tackling these issues and enhancing the progress in developing efficient and sustainable engine solutions, detailed databases and refined mathematical modeling approaches are required. The directions for future research are to obtain better quality data, enhance computational time, and determine more fuel blends and engine settings for the model. © 2025 IEEE.

关键词： Adversarial machine learning

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