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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Dishita Grace, Paul Naik, Nitin Jenkins, Paul Mishra, Durgesh Prajapat, Shaligram Birmingham City University United Kingdom School of Computer Science and Digital Technologies Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom School of Computer Science & Information Technology Symbiosis University of Applied Sciences India International Institute of Professional Studies Devi Ahilya University India

ISBN: (纸本)9798350343274

Machine learning (ML) has been progressively implemented in a distributed manner to harness the data abundance produced on billions of end user devices. Federated learning (FL) is a type of distributed machine learning that provides robust data privacy by training ML models locally on each participating node without directly exchanging raw data with others. The local ML model updates from all nodes are aggregated in order to obtain a global model. There are different ways to aggregate local model updates for obtaining a global model, such as centralized and decentralized/peer-to-peer. Centralized FL (CFL) requires a server to collect and aggregate all model updates for producing a global model, whereas decentralized FL (DFL)/peer-to-peer FL (P2PFL) requires coordination among all the nodes to communicate and aggregate all models updates and produce a global model. In CFL, the server can pose a bottleneck problem as it is a single point of failure, though, this issue can be resolved using DFL/P2PFL. However, the design of the decentralized/peer-to-peer architecture is complex and challenging, and may incur significant communication overhead due to a large number of nodes involved in the learning process. Gossip protocols are one of the most effective ways to communicate in DFL/P2PFL and optimises its performance, therefore, this decentralized learning is also known as gossip protocol-based learning. Considering the importance of gossip protocol in DFL/P2PFL, this paper will analyse gossip protocol, working of gossip protocol, types of gossip protocol, benefits and limitations of gossip protocol, usages of gossip protocol in DFL/P2PFL and its two main types structured P2PFL and unstructured P2PFL. © 2023 IEEE.

关键词： Aggregates

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Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple Contrast-Enhanced Images and Multi-Map Generation

Synergistic Fusion of Multi-Contrast Imaging and Computation...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Mishra, Poonam Khalaf, Hameed Hassan Arabi, Mohammed Yousif Al-Shaikhli, Taha Raad Applied Mathematics Aset Amity University Chhattisgarh Raipur India Al-Manara College For Medical Sciences Maysan Iraq College Of Computer National University Of Science And Technology Dhi Qar Iraq AL-Nisour University College Department Of Medical Laboratories Technology Baghdad Iraq

ISBN: (纸本)9798350366846

Accurate tissue maps and a wide range of imaging modalities are required for medical imaging assessments and therapies. Our exclusive technology employs multi-contrast imaging and cutting-edge computer image synthesis to produce high-quality multi-maps and pictures of body regions and disorders. This approach uses deep learning fusion and computational image synthesis to construct multi-maps of disease markers and tissue features from several imaging modalities. Quality inspection and validation assure the correctness of the images and multi-maps. In terms of memory, accuracy, precision, and other performance measures, our strategy surpassed all other approaches. Reliable data gathering and visualization of physiological structures may enhance patient outcomes and medical imaging technologies. Applying the research results to other clinical contexts necessitates confirming the findings with real clinical data. The technology available might help medical practitioners comprehend and analyse complicated biological systems. There may be personalized treatment regimens and diagnostic imaging. © 2024 IEEE.

关键词： Medical imaging

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A New Methodical Perspective for Classification and Recognition of Music Genre Using Machine Learning Classifiers

A New Methodical Perspective for Classification and Recognit...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Narkhede, Nandkishor Mathur, Sumit Bhaskar, Anandkumar Atalbihari Hiran, Kamal Kant Dadhich, Manish Kalla, Mukesh Shah and Anchor Kutchhi Engineering College Electronics and Computer Science Department Mumbai India Madhav University Facuilty of Engineering and Technology Sirohi India Sir Padampat Singhania University Electronics and Communication Department Udaipur India School of Computer Science & It Symbiosis University of Applied Sciences Indore India School of Management Sir Padampat Singhania University Udaipur India Sir Padampat Singhania University Computer Science and Engineering Department Udaipur India

ISBN: (纸本)9798350314878

To tackle difficulties such as finding similar songs, identifying cultures that would enjoy certain music, conducting surveys and music therapy, Classification and Recognition of Music Genre is necessary. The expansion of music-related content available online has created new opportunities for the development of music referral systems, which support user communities in searching, finding, sharing, and producing digitally. We develop a new methodical perspective to select the most optimum machine-learning algorithm for predicting song genre using Support Vector Machine (SVM), k-nearest neighbor (k-NN), Probabilistic Neural Network (PNN) and Convolutional Neural Network. We got the results for CNN having system's accuracy level is 98.9% and a precision of 98.7%, f1 score of 97.5%, recall of 98.5%. and the accuracy level of the system is 98.3% from SVM, 98.5% from PNN, 99.9% from KNN. © 2023 IEEE.

关键词： Nearest neighbor search

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Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e Performance Evaluation 9th

Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e P...

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9th Future Technologies Conference, FTC 2024

作者： Ali, Ali Mohd Owida, Hamza Abu Al-Qerem, Ahmad Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Medical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13132 Jordan

ISBN: (纸本)9783031731242

Interest in Wireless LAN 802.11 is climbing owing to its cost-effectiveness and simplicity of installation, yet its restricted Quality of Service (QoS) capabilities present obstacles for real-time apps. This endeavour aims to evaluate QoS characteristics and boosts by devising scenarios within the OPNET simulator. Through contrasting Wireless LAN 802.11e with the standard 802.11, our goal is to measure how advancements in allocated channel access can elevate performance levels. Findings show that the improved standard provides a more effective service mechanism, giving priority to traffic that is highly important while keeping latency within acceptable bounds, hence enhancing QoS support for contemporary applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Quality of service

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Online Legal Literacy Platform

Online Legal Literacy Platform

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Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), International Conference on

作者： I Kala Hariharan K Kavisri S Kaviyarasu D Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore

The Motor Vehicle Act is a complex legal framework that regulates various aspects of motor vehicle usage in India. However, many individuals lack knowledge and understanding of the Act, which can lead to unsafe driving practices, non-compliance with regulations, and legal violations. Furthermore, the Act is subject to frequent amendments, making it challenging for individuals to stay up to date on the latest regulations. To address these issues, this project proposes an Online Legal Information Platform for the Motor Vehicle Act. The platform utilizes advanced technologies such as BERT fine-tuning and natural language processing techniques to match user input scenarios with relevant sections of the Act. Users can input their scenarios and the platform suggests the ideal law that can be utilized in the given situation. The system displays matching laws with their title and description and generates a summary for the selected law to facilitate easy comprehension. Additionally, voice-to-text feature is incorporated in order to assist people with visual impairments. The platform aims to bridge the gap between legal language and everyday usage, promoting better compliance with road safety laws. The system will be user-friendly and accessible to individuals with limited legal knowledge, making it easier for them to navigate the legal system. Future work includes expanding the platform to include other regional languages as well to increase its availability to people all around the nation.

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MRDPGA: a multiple restart dynamic population genetic algorithm for scheduling road traffic

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Journal of Electrical Systems and Information technology 2023年第1期10卷 1-18页

作者： Zachariah, Babangida Misra, Sanjay Odion, Philip O. Isah, Saidu R. Department of Computer Science Nigerian Defence Academy Kaduna Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway

A genetic algorithm is a biologically inspired stochastic approach to finding solutions to optimization problems. However, unlike its deterministic counterpart, it cannot guarantee a globally optimal solution since it may be trapped within a local optimum of the search space. Most researchers have focused on proposing new techniques for various parameters of genetic algorithms. That is a mutation, crossover, or selection algorithm. This research proposes a modification to the standard genetic algorithm, which may serve as a framework that can integrate any of these parameters for their contribution to the final solution of the genetic algorithm. The multiple restart dynamic population genetic algorithm (MRDPGA) proposed in this research was used in training the parameters of the adaptive neuro-fuzzy inference system (ANFIS) for scheduling road vehicular traffic flows. The results of training the ANFIS models based on the different clustering methods showed that the MRDPGA-based ANFIS controller performed better with the mean square error (MSE) of 0.299 and root mean square error (RMSE) of 0.547 in the training phase; and MSE of 0.272 and RMSE of 0.521 in the testing phase. Using the controllers for traffic flow scheduling, the results showed that the MRDPGA-trained controllers performed better in terms of average waiting time (AWT) minimization and total arrived vehicles (TAV). The best-performing controller achieved 50.40% AWT minimization and 21.44% TAV improvement. Analyzing the results using a one-tailed t-test for paired two-sample means showed that the MRDPGA algorithm had a significant impact on the controllers. Particularly the FCM controller, where (p = 0.0038) and (p = 0.0003) for AWT and TAV at a 95% confidence level. Thus, MRDPGA algorithms are recommended for further assessment in other optimization problems to ascertain their performance in those problem domains.

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Application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society 3rd

Application of Gradient Boosting Classifier-Based Computatio...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kumar, Ashutosh Sinha, Abhigyan Bakshi, Tamoghno Choudhury, Sibashish Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Jadara Research Center Jadara University Irbid21110 Jordan

ISBN: (纸本)9789819708918

Currently, drug and alcohol addiction has become a major menace to society's youth. As responsible citizens of this country, we must act now to keep these young brains from succumbing to this lethal addiction. In this research, a method based on intelligent analytics is designed for forecasting the threat of drug addiction using machine learning algorithms. Initially, we identify several reasons for drug addiction by speaking with health professionals, individuals addicted to drugs, and reading related studies and articles. Then we gather information from both addicts and abstainers. After preprocessing the data, we use Random Forest, Naive Bayes, Adaptive Boosting, and Gradient Boosting machine and assess the interpretation of each of these classifiers using certain well-known performance criteria. Outcome with Gradient Boosting showed promising with the highest accuracy rate of 95.4% and minimum error rate of 0.0854. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive boosting

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Integrated Control and Modeling of a PV-Wind Hybrid System for DC Electric Micro-Networks 3

Integrated Control and Modeling of a PV-Wind Hybrid System f...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Santhosha, B.M. Negi, Harendra Singh Balaraju, V. Vekariya, Daxa Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Visvesvaraya Technological University Department of Electrical and Electronics Engineering Ballari Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Dr. Ntr College of Agricultural Engineering Department of Basic Engineering and Applied Sciences Andhra Pradesh Bapatla522101 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350361186

The main objective of this work is to model, control and simulate an electrical microgrid. To achieve this objective, models of the subsystems or associated elements (generators, power interfaces, electrical loads, etc.) are built, the control strategies necessary for the integration of each subsystem in the microgrid are implemented, and it is proposed an optimization algorithm that allows the economic dispatch of the system to be carried out under different generation and demand conditions. Finally, after simulating the model, its behavior is validated in a considered operating scenario. The work environment selected to develop this project is Simulink, a tool integrated into MATLAB, due to the ease it offers when modeling and simulating dynamic systems, allowing you to customize the included libraries and export data to MATLAB for subsequent analysis. At the end of this work, it is intended to have a simulated environment to validate the behavior of the proposed microgrid, which allows considering different generation and demand scenarios, and an optimization algorithm that allows carrying out the economic dispatch of the system under the operating conditions. considered, without taking into account the randomness of generation sources and energy demand. © 2024 IEEE.

关键词： Wind power

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An Integration Of Wireless Communications And Artificial Intelligence For Autonomous Vehicles 3

An Integration Of Wireless Communications And Artificial Int...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raghuram, Kadambari Altarawni, Ibrahim Vijayalakshmi, K. Kumar, Ajay Jadeja, Abhijeetsinh Chandra, Pankaj Jntuk Department of Computer Science and Engineering Andhra Pradesh Kakinada India Aqaba University of Technology Applied Computing and Artificial Intelligent Department Aqaba Jordan Ramapuram Campus Srmist India INurture Education Solution Pvt. Ltd It Academics Delhi India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India Guru Ghasi das Viswavidyalaya Dept of Information Technology Chattisgarh Bilaspur India

ISBN: (纸本)9798350399264

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links. © 2023 IEEE.

关键词： Autonomous vehicles

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Change Detection Using Unsupervised Sensitivity Disparity Networks 14

Change Detection Using Unsupervised Sensitivity Disparity Ne...

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Yuan, Xiaochen Li, Jinlong Macao Polytechnic University Faculty of Applied Sciences Macao SAR China School of Computer Science and Engineering Macau University of Science and Technology Macao SAR China

ISBN: (纸本)9798350336313

At present, algebraic operation methods in the field of change detection still holds the dominant position. However, in the face of disturbance features, due to the characteristics of poor expansibility, the performance of algebraic operation methods varies greatly in different scenes, and cannot meet the requirements of practical application. In this paper we propose a change detection model based on Sensitivity Disparity Networks (SDNs) for performing change detection in Bi-temporal Hyper-spectral images captured by AVIRIS sensor and HYPERION sensor over time. The SNDs consist of two deep learning models, Unchanged Sensitivity Networks (USNet) and Changed Sensitivity Networks (CSNet), they have sensitivity disparity in changed and unchanged pixels, and thus to generate effective argument region. Next, we re-evaluate the change probability of argument region, and merge the change result of the argument region with that by one of the SDNs. The detected Binary Change Map (BCM) of the scheme is thus obtained. To train and evaluate the proposed schema we employ two Bi-temporal Hyper-spectral image datasets which contain challenging pseudo-changed features (PCFs) and pseudo-invariant features (PIFs) cause by various external interference factors. The proposed schema outperforms the existing state-of-the-art algorithms on tested datasets. Experimental results show that the proposed schema has good universality and adaptability. © 2022 IEEE.

关键词： Deep neural networks

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