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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Mohammed Al-Farouni Udita Holkar Aasheesh Shukla M.B Marasulov Sharanpreet Kaur T. Mohanraj Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department of Electronics and Communication Engineering GLA University Mathura India Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Institute of Polymer Chemistry and Physics Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Fifth generation mobile networks are considered as a key technology to effectively disseminate the idea of smart cities and open up diverse applications. Upcoming 5G metropolises will be at the center of vertical commercial use cases with significant social and economic impact. In addition to evaluating recently accepted technologies, this will assess whether 5G has the potential to usher in a new era of the Internet of Things, even though field testing of 5G technology is still underway. This suggests that it is also important. This article describes the 5G field test environment that took place in Italy at the end of 2017. The aim is to evaluate the services likely to be provided and the ability of his 5G network to provide new services in terms of well-structured KPIs. In fact, vertical companies are being deployed and evaluated to tailor their applications to the next 5G cities and have a significant social and economic impact. Specifically, this study provides a detailed overview of the implementation of a real, fully functional 5G network. This shows that 5G is a useful technology that supports a variety of business verticals and use cases. In fact, its versatility allows it to meet the specific performance requirements of the original service. Therefore, future 5G networks will need to support three main use case categories: Interactions that are reliable, have low latency, and can provide reliable, instant feedback to enhance critical services. and improved mobile broadband that can deliver megabytes of bandwidth on demand. 5G networks require features such as self-organization, virtualization, reconfigurability, flexibility, and scalability to meet the needs of all these different use cases.

关键词： Economics 5G mobile communication Smart cities Scalability Network slicing Reliability engineering Telecommunication computing Virtualization Testing Business

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Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Li, Xiaoyu Zhang, Yitian Long, Guodong Hu, Yupeng Lu, Wenpeng Chen, Meng Zhang, Chengqi Gong, Yongshun Shandong University School of Software Jinan250100 China McGill University Electrical and Computer Engineering MontrealQCH3A 0G4 Canada University of Technology Sydney School of Computer Science SydneyNSW2007 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250100 China

Traffic forecasting plays a crucial role in establishing an Intelligent Transportation System (ITS) by providing essential insights. Existing traffic forecasting relies on the assumption that there is a hidden invariant spatial-temporal pattern in the large-scale dataset. However, the traffic patterns are easily influenced by many unpredictable external factors, such as policy interventions and climate changes. Due to the dynamic nature of these exogenous factors, the traffic network's spatial-temporal patterns are also changed, thus impacting the performance of traffic forecasting models. Thus, there is an urgent need to rethink the traffic forecasting model in a fast-adaptive manner. To solve this challenge, this paper proposes an Adaptive Spatio Temporal Context Learning framework named ASTCL, which achieves desired forecasting accuracy using daily basis traffic data collected from dozens of sensors. ASTCL constructs adaptive spatio-temporal contexts for target locations in the traffic network and generates dynamic sequence graphs based on semantic similarities. The adaptive contexts aggregate valuable information from available data, while the graphs reveal dynamic trends in traffic properties. Further, ASTCL introduces a joint convolution and attention mechanism to model intricate spatio-temporal relationships from multiple perspectives. Extensive experiments conducted on four real-world datasets demonstrate that ASTCL achieves remarkable fast adaptability and outperforms other state-of-the-art methods by a significant margin. © 1989-2012 IEEE.

关键词： Global warming

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Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection

arXiv

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arXiv 2022年

作者： Wang, Ge Tan, Li Shang, Ziliang Liu, He School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Chongqing Academy of Educational Sciences Chongqing400015 China

In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge. However, the studies on rumor detection ignore the intense emotions of images in the rumor content. This paper verifies that the image emotion improves the rumor detection efficiency. A Multimodal Dual Emotion feature in rumor detection, which consists of visual and textual emotions, is proposed. To the best of our knowledge, this is the first study which uses visual emotion in rumor detection. The experiments on real datasets verify that the proposed features outperform the state-of-the-art sentiment features, and can be extended in rumor detectors while improving their performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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A BI-LEVEL ASSESSMENT OF TWITTER IN PREDICTING THE RESULTS OF AN ELECTION: DELHI ASSEMBLY ELECTIONS 2020

arXiv

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arXiv 2022年

作者： Singh, Maneet Iyengar, S.R.S. Saxena, Akrati Kaur, Rishemjit Department of Computer Science and Engineering Indian Institute of Technology Ropar India Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands CSIR-Central Scientific Instruments Organisation Chandigarh India Academy of Scientific and Innovative Research Ghaziabad India

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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SpGesture: source-free domain-adaptive sEMG-based gesture recognition with jaccard attentive spiking neural network 24

SpGesture: source-free domain-adaptive sEMG-based gesture re...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Weiyu Guo Ying Sun Yijie Xu Ziyue Qiao Yongkui Yang Hui Xiong Thrust of Artificial Intelligence HKUST (Guangzhou) China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Thrust of Artificial Intelligence HKUST (Guangzhou) China and Department of Computer Science and Engineering HKUST Hong Kong SAR China

ISBN: (纸本)9798331314385

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/.

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Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

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Biomedical engineering Frontiers 2023年第1期4卷 3-20页

作者： Kairu Dong Wen-Che Liu Yuyan Su Yidan Lyu Hao Huang Nenggan Zheng John A.Rogers Kewang Nan College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China National Key Laboratory of Advanced Drug Delivery and Release Systems Zhejiang UniversityHangzhou 310058China College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou310027China Department of Gastroenterology Brigham and Women’s HospitalHarvard Medical SchoolBostonMA 02115USA College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310058China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310058China CCAI by MOE and Zhejiang Provincial Government(ZJU) Hangzhou 310027China Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Biomedical Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA Jinhua Institute of Zhejiang University Jinhua 321299China

Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysiological signals from these *** approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft *** recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue *** review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.

关键词： offering recording millimeter

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Integrating Deep Learning to Decode Meningeal Interleukin-17 T Cell Mechanisms in Salt-Sensitive Hypertension-Induced Cognitive Decline

Integrating Deep Learning to Decode Meningeal Interleukin-17...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Ankit Sachdeva Mohammad Faraz Idris Siddiqui Lalit Khanna Srikantha H. M.P. Karnik N. Thangarasu Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Indian Jamia Millia Islamia India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Civil Engineering JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This study describes a complete strategy for training and refining CNN for picture classification. This approach uses several complicated methods. This approach incorporates fully linked decision-making layers, convolutional and pooling feature extraction layers, as well as learning rate modifications, momentum, regularization, and gradient cutting optimization techniques. The proposed technique uses a softmax output layer to create statistical predictions for each class and improve them via a loss function and backpropagation. Using cross-validation and test data to assess model performance and quitting early to minimize overfitting improves optimization. The recommended technique outperforms cutting-edge methods like deep convolutional neural networks (DCNN), transfer learning with CNN, and hybrid CNN-RNN in several performance tests. The approach has the best precision, recall, and F1 score and 92.5% model accuracy, indicating its effectiveness and balance. The strategy is successful and requires minimal training and logic. The method's high cross-validation scores and confusion matrix demonstrate its classification accuracy and suitability for various datasets. These findings demonstrate that the proposed strategy solves challenging photo sorting tasks well in the classroom and in real life.

关键词： Training Adaptation models Technological innovation Accuracy Computational modeling Transfer learning Signal processing Robustness Convolutional neural networks Sorting

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A Dynamic Approach to Optimizing Cloud Resource Allocation for Enhanced E-commerce Performance

A Dynamic Approach to Optimizing Cloud Resource Allocation f...

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International Conference on Communications and Signal Processing

作者： R. Geetha V. Rajamani V. Parthasarathy Department of Information Technology University College of Engineering Villupuram Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Karpagam Academy of Higher Education (Deemed to be University) Coimbatore Tamilnadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This study tackles the challenge of enhancing Ecommerce performance on the Shopping platform through a dynamic approach to optimizing cloud resource allocation. The problem at hand involves the need for scalable infrastructure to accommodate fluctuating demand, ensuring seamless user experiences during peak periods. Our proposed method employs a sophisticated dynamic allocation algorithm that adapts in real-time to varying workloads. The system’s flow begins with continuous monitoring of platform activity, swiftly identifying resource demands. Leveraging machine learning, the algorithm predicts resource needs, enabling proactive scaling. Results demonstrate a significant improvement in platform responsiveness, reducing latency during traffic spikes and ensuring optimal performance. This dynamic approach not only addresses the resource allocation challenge but also provides a solution for E-commerce platforms, fostering enhanced user satisfaction and scalability.

关键词： Machine learning algorithms Scalability Heuristic algorithms Signal processing algorithms Dynamic scheduling Prediction algorithms Real-time systems

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Clustering and Energy Efficient Power Allocation Scheme for D2D Multicast Network

Clustering and Energy Efficient Power Allocation Scheme for ...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： G Nallasivan A Shajitha Banu V Rajesh M Arumuga Babu Bhuvanesh Ananthan P Mahesh Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of English Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Electrical and Electronics Engineering Christ the King Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

The multimedia content dissemination services with the current cellular networks still met several challenging while performing desired performance. Recently, the improvement of energy efficiency is not considered in most of the existing works and for all receiving users, they discussed about how to deliver reasonable QoS. Based on multicast network, this work proposed a novel framework to solve resource allocation as well as clustering in device-to-device (D2D). This study introduce a novel adaptive energy valley optimization (AEVO) model to enhance the clustered and power allocation with energy efficient. While considering the requirement of QoS in D2D, an efficient power control mechanism and resource allocation is maximized complete D2D multicast clusters energy-efficiency. By doing this, higher computational difficulties with energy-efficiency problems are solved by proposing novel allocation of power model. Based on the existing methods, the proposed algorithm indicated good numerical results with respect to energy consumption and throughput.

关键词： Performance evaluation Computational modeling Power control Quality of service Throughput Energy efficiency Device-to-device communication

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Coronary Artery Disease (CAD) Heart Beats Classification Using Recurrence Plots

Coronary Artery Disease (CAD) Heart Beats Classification Usi...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Usha Desai Roshan J. Martis Ravichander Janapati Kandala N V P S Rajesh Department of Electronics and Communication Engineering SR University Warangal Telangana India Computer Science Artificial Intelligence Global Academy of Technology Bengaluru Karnataka India School of Electronics Engineering VIT-AP University Vijayawada Andhra Pradesh India

Coronary Artery Disease (CAD) is the world’s leading heart disease. The main reason for CAD is fat accumulation in coronary arteries, during which the myocardium lacks the supply of blood and oxygen. Stringent blocks of the arteries may lead to heart attack or stroke. Electrocardiogram (ECG) signal is a standard diagnostic test to identify cardiac abnormalities. The nonlinearities of the ECG can be quantified using Recurrence Quantification Analysis (RQA) structures. This work discriminates Normal Sinus Rhythm (NSR) and CAD using Recurrence Plots (RPs).Further, the RQA parameters are extracted for categorizing NSR and CAD using Decision Tree (DT) and ensemble learning models. The essential (p\lt 0.05) features are fed to these ensemble models: Random and Rotation Forests (RAF and ROF), Bagging (BGG), and Adaboost (ADB) to select an appropriate classifier. Among them, ROF yielded the highest overall accuracy of 99.42%. The proposed system is cost-efficient and can be applied to discriminate the NSR and CAD heartbeats.

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