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arXiv 2024年

作者： Agheli, Pouya Pappas, Nikolaos Kountouris, Marios Communication Systems Department EURECOM Sophia-Antipolis France Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Computer and Information Science Linköping University Sweden

We design a self-decision goal-oriented multiple access scheme, where sensing agents observe a common event and individually decide to communicate the event’s attributes as updates to the monitoring agents, to satisfy a certain goal. Decisions are based on the usefulness of updates, generated under uniform, change- and semantics-aware acquisition, as well as statistics and updates of other agents. We obtain optimal activation probabilities and threshold criteria for decision-making under all schemes, maximizing a grade of effectiveness metric. Alongside studying the effect of different parameters on effectiveness, our simulation results show that the self-decision scheme may attain at least 92% of optimal performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

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International Conference on Pattern Analysis and Intelligent systems (PAIS)

作者： N. Zerguine Department of Computer Science Networks and Distributed Systems Laboratory Ferhat Abbas Setif 1 University Setif Algeria

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection; this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy.

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Satellite Image Analysis in Health Care—A Systematic Review 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta Department of Computer Science Graphics Era Hill University Dehradun India

ISBN: (数字)9789819995219

ISBN: (纸本)9789819995202

Rapid urbanization, population expansion, and escalating pollution levels have given rise to novel environmental concerns that demand the application of creative, analytical methodologies and diverse data sources. To ensure efficient urban ecological management, it is crucial to consider three levels of phenomena: the environmental system, the physical environment, and the regional surroundings. Utilizing remote sensing data is a viable avenue for enhancing the global environment due to its convenient accessibility and ability to measure crucial physical features regularly. The primary objective of this research study is to ascertain and evaluate suitable methods for analyzing satellite imagery in the healthcare context. The study’s aims encompass examining the diverse array of tools accessible in the market, scrutinizing the characteristics of each device, exploring research papers that have employed these tools, and classifying the tools into open-source and commercial classifications. This study emphasizes the parameters associated with feature extraction and picture enhancement in satellite imagery, as these factors hold significant importance in the analysis of images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Remote sensing

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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A Robust Development of an Efficient Industrial Monitoring and Fault Identification Model using Internet of Things

A Robust Development of an Efficient Industrial Monitoring a...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Al Belushi, Yahya Yaqoob Obaid Jasmin Dennis, P. Deepa, S. Arulkumar, V. Kanchana, D. Ragini, Y.P. Military Technical College Department of Systems Engineering Oman Vi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India RMD Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavarapettai India Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Department of Information Technology Tamil Nadu Chennai India SRM Institute of Science and Technology Ramapuram Department of Computer Applications Tamil Nadu Chennai India SIMATS Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

Internet of Things (IoT) is rapidly developing technologies and the Internet of Things (IoT) is a network of physical items or effects embedded with computer software, detectors, and network connectivity. The Internet of Things also addresses the issue of centralizing physical bias management online. We are leveraging the Internet of Things (IoT) idea to construct a system that will automatically monitor all artificial processes and issue warnings or expert comments when necessary. nowadays, gas leakage is a serious concern in the house and as well as vigilance. If the gas sense is indeed low, it's because we've been too careless, intolerant, or both to look for it. If the gas position is raised, however, calamity may ensue. In this research, the crucial concept is introduced to prevent this catastrophe from occurring. In this study, we propose an alternate approach to this impending calamity and design a system with integrated sensors, controllers, and some IoT-assisted software. The suggested method, known as the Internet of Things based Industrial Safety System (IoTISS), is compared against the more established Industrial Safety System (ISS) to see how well it performs. In this system, we are monitoring the detection of gas leakages with various warning characteristics as well as other devices are used for monitoring various aspects like humidity and temperature using DHT22 sensor, MQ2 gas monitoring sensor, fire sensor and NodeMCU ESP8266 WiFi module. NodeMCU ESP8266, which doubles as a WiFi module, will receive data from all of these types of sensors. © 2024 IEEE.

关键词： Accident prevention

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic Engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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Traffic Prediction- and Explainable Artificial Intelligence-Based Dynamic Routing in Software-Defined Elastic Optical Networks

Traffic Prediction- and Explainable Artificial Intelligence-...

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IFIP Networking Conference

作者： Róża Goścień Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783903176638

ISBN: (纸本)9798350390605

The recent research reveals a high potential and efficiency of machine learning (ML) algorithms applied to aid the optimization of telecommunication networks. Moreover, thanks to the tools of explainable artificial intelligence (XAI), it is possible to better understand and interpret the decisions of ML models, identify the features influencing them the most, and use that knowledge for further targeted optimization improvements. In this paper, we use these approaches to aid the optimization of the well-known and essential problem of dynamic routing in software-defined elastic optical networks (EONS). We use the XAI to verify which transmission and network state parameters influence the performance the most (expressed as a bandwidth blocking probability (BBP)) of a dynamic EON. The analysis reveals that two of the most relevant features are related to characteristics of the incoming traffic requests - the source node and the traffic prediction for the demand's source-destination nodes. Next, we use that data to propose six traffic prediction-and XAI-based demands ordering policies, which are used to improve a benchmark allocation algorithm. Then, we perform simulations to evaluate the policies' efficiency and compare them with reference methods. The results show that our targeted proposal is exceptionally efficient - the best policy allowed to reduce the BBP up to about 6.1%, representing 4203 Gbps of more served traffic compared to the best reference policy.

关键词： Machine learning algorithms Explainable AI Optical design Optical fiber networks Routing Prediction algorithms Resource management

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A Review of Satellite Image Analysis Tools 4th

A Review of Satellite Image Analysis Tools

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Department of Computer Science and Engineering Graphics Era Hill University Dehradun India Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta

ISBN: (纸本)9789819994410

The rapid growth of urban areas, population, and pollution presents a significant environmental challenge. Addressing these challenges requires innovative analytical approaches and data sources. This study focuses on the three-scale phenomena of the environmental system, physical environment, and regional surroundings, considering their implications on a global network scale. Remote sensing data, with its easy accessibility, offers new toolkits and methods to improve the global environment. It provides self-consistent measurements of vital physical attributes and surpasses traditional data sources regarding accuracy, efficiency, and coverage of larger geographical areas. This study aims to identify and analyze appropriate satellite image analysis tools for healthcare. The study objectives include studying available tools, analyzing their features, categorizing tools into open-source and commercial categories, and providing recommendations. This study emphasizes feature extraction and image enhancement parameters, which are crucial for image analysis. Further, this study provides a functional overview of various commercial and open-source satellite image analysis tools and suggests suitable tools for healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geographic information systems

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks

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Alexandria Engineering Journal 2024年 91卷 249-260页

作者： Hosseinzadeh, Mehdi Ali, Saqib Mohammed, Adil Hussein Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Hassan Ahmed, Omed Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si13120 Korea Republic of

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms. © 2024 The Author(s)

关键词： Drones

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