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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Azar, Joseph Tahir, Mohammad Abdullah Bou Abdo, Jacques Demerjian, Jacques Akkari, Nadine Université Marie et Louis Pasteur CNRS Institut FEMTO-ST BelfortF-90000 France University of Cincinnati School of Information Technology CincinnatiOH45221 United States Lebanese University LaRRIS Faculty of Sciences Fanar Lebanon Faculty of Arts and Sciences Jounieh Lebanon Jeddah International College Department of Computer Science and Information Technology Saudi Arabia

ISBN: (纸本)9798331519957

This paper presents a federated sampling strategy aimed at enhancing data efficiency and privacy in IoT networks. By introducing a novel approach to summarization and synthetic data generation, we enable IoT devices to share compact statistical representations of data rather than raw data, significantly reducing the amount of data transmitted while preserving privacy. Our evaluation, using a protocol classification task, demonstrates that synthetic data generated from these summaries effectively replicates the statistical properties of the original data. This proof-of-concept work lays the foundation for future advancements, including network-wide simulations, intrusion detection mechanisms, and federated learning-inspired aggregation techniques, paving the way for resilient and privacy-preserving IoT systems. © 2025 IEEE.

关键词： Data assimilation

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Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

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Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

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The Role of AI and Machine Learning in the Evolution of 5G and Beyond Networks

The Role of AI and Machine Learning in the Evolution of 5G a...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Latha, Y.L. Malathi Rabbani, Sk. Manike, Chiranjeevi Gami, Sandip J. Kumar, M Kishore Katta, Siva Koteswara Rao Department of Information Technology Hyderabad India Department of Computer Science and Engineering Maisammaguda India MLR Institute of Technology Data Science Hyderabad India Independent Researcher BrambletonVA20148 United States Aditya College of Engineering & Technology Department of ECE AP Surampalem India KITEW Department of Mechanical Kakinada India

ISBN: (纸本)9798350379525

This article will examine the current capabilities of 5G and B5G networks in terms of artificial intelligence and machine learning. The research delves into several applications of AI and ML within the telecom industry. Neural networks are introduced as a class of tools for decision-making and statistical data modelling that are often nonlinear. Data pattern discovery and the representation of complex connections between input and output parameters in systems are common uses for these. This class of neural networks includes recurrent, convolutional, feed-forward, and deep neural networks. When improving a system property or maximising a collective reward, reinforcement learning focuses on the behaviours that intelligent agents must do. One advantage of deep reinforcement learning is its ability to handle unstructured input. This is achieved by combining deep neural networks. We offer hybrid solutions, like expert knowledge aided machine learning and mixed analytical and machine learning models. Lastly, further targeted approaches are showcased, including GANs, unsupervised learning, and clustering. CF M-MIMO technology, which stands for cell-free massive MIMO, is the most promising idea in B5G. The appealing aspects of CF M-MIMO systems, such as power distribution and channel estimation, are with their downsides. There have been numerous successful applications of deep learning (DL) across many scientific domains, including wireless communications. According to the research by Lazaros Alexios Iliadis et al., CF M-MIMO networks use cutting-edge DL techniques. We provide the most popular DL models and review cell-free network fundamentals here. The endnotes emphasise the potential avenues for further study. © 2024 IEEE.

关键词： Deep reinforcement learning

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Sentiments Analysis on Amazon Product Reviews Using Supervised Machine Learning Algorithms

Sentiments Analysis on Amazon Product Reviews Using Supervis...

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2023 International Conference on Integration of Computational Intelligent system, ICICIS 2023

作者： Gore, Ramdas D. Borole, Vipin Y. Gupta, Rohit S. Sonaje, Viabhav Chaudhari, Prafulla Gawali, Bharti W. SOCSE Sandip University Department of Computer Science and Application Nashik422213 India Nashik422213 India Dr. Babasaheb Ambedkar Marathwada University Department of Computer Science and Information Technology Aurangabad431001 India

ISBN: (纸本)9798350318784

To determine the sentiment or emotion indicated in a text, such as a review, tweet, or piece of customer feedback, sentiment analysis, also known as opinion mining, uses computational linguistics, machine learning, and natural language processing techniques. Sentiment analysis seeks to determine whether the automatic sentiment expressed in the text is positive, negative, neutral, or occasionally more nuanced emotions like joy, sadness, rage, etc. The Apple iPhone 14 Pro Max (1 TB, Gold) evaluations from 160 Amazon consumers were used in this study. We used tokenization, stop word removal, stemming, and punctuation mark removal for preprocessing. Positive and negative word analysis is done using word clouds. We received favourable and unfavourable feedback about the Apple iPhone 14 Pro Max. Dynamic, good, improving, lovely, fantastic, great, etc., are positive evaluations. Additionally, unfavourable reviews are a costly, stuck gimmicky, gimmicky hesitant, heck of many complaints, bug bumpy and alarming, break dissatisfied, etc. We used Naive Bayes, Support Vector Machines (SVM), and Logistic Regression, among other machine learning approaches. We got 90%, 91%, and 94% accuracy for Logistic Regression, Support Vector Machines (SVM), and Naive Bayes, respectively. © 2023 IEEE.

关键词： Sentiment analysis

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A Physical Framework for Testing and Evaluating of a Mechatronic Shunting system

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International Journal Bioautomation 2023年第4期27卷 181-192页

作者： Alkharabsheh, Abdelrahman Momani, Lina Qawaqzeh, Yousef Alsakarneh, Amjad Computer Science Department British Applied College Umm Al Quwain United Arab Emirates Mechanical Engineering Department Higher Colleges of Technology Fujairah United Arab Emirates Information Technology Department Fujairah University Fujairah United Arab Emirates Department of Mechanical Engineering Yarmouk University Irbid Jordan

Shunt is a flexible tube called a catheter implanted inside the brain where cerebrospinal fluid (CSF) is produced. Existing treatments rely on passive implantable shunts with differential pressure valves. The authors defined, designed, and programmed an intelligent wireless hydrocephalus shunting system. The shunting system was designed, simulated, and tested to carry out many tasks such as regulating the mechatronic valve, collecting Injection Control Pressure (ICP) readings, analyzing these readings, responding to all emergency cases, and diagnosing the whole shunting system. Nowadays, many challenges face clinical trials for new medical devices. Clinical trials are complex and require following several rules and regulations to ensure compliance with different standards. Due to the difficulty of using medical trials, an urgent need for a physical framework for implementing a virtual model of intracranial pressure and cerebrospinal fluid dynamics in hydrocephalus mechatronic shunt testing. Such a physical framework will play a vital role in assessing the functioning of the whole mechatronic shunting system. A framework of a mechatronic shunting system (implanted and external) is illustrated and integrated with embedded management and diagnosis software. Such a framework will help assess, test, and evaluate the main functions of the proposed shunting system. © 2023 by the authors. Licensee Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of sciences. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/). All Rights Reserved.

关键词： Cerebrospinal fluid

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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Convolutional Neural Network with CBAM Module for Fitness Activity Recognition Using Wearable IMU Sensors 9

Convolutional Neural Network with CBAM Module for Fitness Ac...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Wearable motion sensors are gaining traction for fitness activity tracking due to their utility in assisted living, wellness tracking, and fitness training applications. Such sensors typically incorporate accelerometers and gyroscopes to monitor and log physical activities. This work proposes a deep-learning approach for identifying fitness activities using wearable inertial measurement units (IMUs) signals. Specifically, a convolutional neural network (CNN) integrated with a convolutional block attention module (CBAM) provides the core framework. The CBAM module applies channel and spatial attention mechanisms to enhance CNN's capacity to discriminate salient features. Multichannel IMU time-series data, comprising accelerometer and gyroscope readings as subjects perform various exercises, supply input to the model. By learning via an attention-enhanced architecture, the model can extract robust and informative representations to categorize activities accurately. Experiments on a public IMU dataset with five exercise types across over twenty participants evaluated the approach. Results showed over 99.58% subject-independent activity recognition accuracy when augmenting the CNN with CBAM, constituting significant performance gains. The research findings indicate that an efficient deep-learning solution with attention modeling can recognize fitness activities reliably from on-body sensors. The proposed methodology holds substantial promise for applications in sports analytics and personalized health tracking. © 2024 IEEE.

关键词： Gyroscopes

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A Comprehensive analysis of the Efficiency of Dynamic Spectrum Allocation Methods in Optical Fiber Communications 1

A Comprehensive analysis of the Efficiency of Dynamic Spectr...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Beemkumar, N. Kumar, Aditya Garg, Prateek Femilin Jana, D Little Takale, Dattatray G. Das, Neeraj Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350359756

The efficient utilization of optical fiber communications dramatically affects the community's overall performance. As the ability demand for data communication increased through the years, various dynamic spectrum allocation (DSA) methods evolved to optimize the aid usage of optical fiber networks. This paper discusses a complete evaluation of the performance of various DSA methods, consisting of a detailed comparison of various methodologies and implementation techniques for spectrum allocation. The overall performance metrics considered in the evaluation consist of the variety of failing connections, latency, records fee, quantity of spectrum used, quantity of break up-spectrum assignments, overall latency, and throughput of the simulated network topologies. Experimental consequences reveal that single-track, multi-tune, and dynamic venture DSA techniques have higher efficiency than constant spectrum allocation strategies. Moreover, the effects screen shows that the end-to-give-up overall performance of the dynamic spectrum allocation scheme is akin to that of fixed spectrum allocation. Moreover, power efficiency in phrases of the overall wide variety of failed connections for spectrum admission to optical fiber networks is tested and discussed. The evaluation of the efficiency of numerous DSA methods presented in this paper is helpful for the layout and optimization of verbal exchange structures utilizing optical fiber networks. © 2023 IEEE.

关键词： Optical fiber communication

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Analyzing Adaptive End-to-End Congestion Control Strategies for Underwater High Speed Communication Networks 3

Analyzing Adaptive End-to-End Congestion Control Strategies ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mythili, V. Shinde, Gitanjali R. Swarup, Lakshya Yadav, Dhananjay Sinha, Deepak Kumar Kumar, Rohit Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper investigates modern-day Adaptive stop-to-give up (AEE) congestion manage techniques for underwater high velocity verbal exchange networks. AEE techniques have the capability to provide a greater efficient and strong transmission for underwater acoustic channels. in this paper we examine the one-of-a-kind AEE congestion manage strategies, their overall performance blessings and limitations. We moreover talk the exchange-offs between a couples of criteria, including strength intake, through-placed and put off. A complete analysis of current strategies is made and a comprehensive summary of the relative performance of the techniques are furnished. finally, destiny studies instructions are mentioned, to bridge the gap between theoretical fashions and real-time structures. © 2023 IEEE.

关键词： acoustic fashions performance structures

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A Machine Learning Approach to Predict Autism Spectrum Disorder

A Machine Learning Approach to Predict Autism Spectrum Disor...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Fatema Baqer Suresh Subramanian Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Multimedia science Department College of Information Technology Ahlia University Manama Bahrain

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Autism spectrum disorder, often shortened to ASD, is a developmental disability caused by differences in the brain. Individuals with ASD display deficits in social communication and interaction as well as repetitive behaviors or interests among a list of other symptoms [1]. If left untreated, individuals with autism may observe no progression or a worsening of their symptoms as untreated ASD can increase the risk of the development of depression and anxiety. The best outcomes are expected when the individual is diagnosed as early as possible allowing for early intervention and the provision of therapy and treatments. ASD can be detected in individuals as young as toddlers. Much research has been conducted into the application of machine learning in order to predict ASD in many different age groups, with the category of toddlers having the least dedicated research. This paper aims to explore the application of different machine learning techniques in order to predict ASD in toddlers. Two different toddler datasets were used to train and test the models. Many different traditional machine learning models, ensemble models, and deep learning models were considered in the scope of this research and compared on the basis of performance. K-fold cross validation was applied, and the models were tested. The most positive results in terms of the highest accuracy recorded were seen in ensemble learning which near consistent positive results being seen for all the different experimented techniques. Further favorable results were seen in particular with random forest (100%), decision trees (100%), C4.5 (100%) and logistic regression (100%). In terms of deep learning the most positive results were seen in multilayer perceptron (100%), recurrent neural networks (98.6%), and multilayer kernel machines (100%).

关键词： Deep learning Radio frequency Autism Pediatrics Logistic regression Recurrent neural networks Computational modeling Machine learning Predictive models Ensemble learning

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