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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ramesh, P.S. Mathu Sudhanan, S.R. Dineshkumar, R. Manigandan, K. Parthipan, V. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Mohan Babu University School of Liberal Arts & Sciences Department of English Andhra Pradesh Tirupati India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364699

This paper focuses on the advancements of the edge computing paradigms regarding the creation of new emerging architectures and algorithms for improving real-time computing. Looking at the results obtained, the proposed architecture provided 40% reduced latency rates, and 50% higher throughput than the baseline models while using 20% less energy. Key algorithms, including data compression, stream processing, and adaptive algorithms, were evaluated, with the hybrid approach showing the most significant improvements: The improvements were made in the latency where there was a 50% decrease in the latency and in the throughput where there was a 40% increase, and in the accuracy which is 30% enhanced. The above outcomes demonstrate the efficiency of offered solutions in the context of the discussed problems of real-time data processing;the recognition of the edge computing prospects regarding the further evolution of different applications. © 2024 IEEE.

关键词： Adaptive Algorithms Advanced Algorithms Data Compression Edge Computing Energy Efficiency Latency Reduction Novel Architectures Real-Time Processing Stream Processing Throughput Improvement

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Enhancing Wireless Mesh Network Performance Through AI-Based Routing Algorithms 2

Enhancing Wireless Mesh Network Performance Through AI-Based...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Senthilvel, P. Gururama Thirupurasundari, D.R. Rajesh Kumar, K. Palani, Hari Kumar Sundravadivelu, K. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering TamilNadu Chennai India Madha Engineering College Kundrathur Department of Electronics and Communication Engineering TamilNadu Chennai India School of Engineering Ajeenkya D y Patil University India School of Information Technology Madurai Kamaraj University Department of Computer Science Tamil Nadu Madurai India

ISBN: (纸本)9798350369960

Wireless Mesh Networks (WMNs) are critical in ensuring ubiquitous internet coverage, particularly in environments and terrains with unique challenges. The increasing number of wireless devices and services has created a critical need for routing techniques that are more efficient and adaptable. Although conventional routing algorithms exhibit resilience, they frequently remain fixed and fail to adapt to swift fluctuations in network circumstances. By incorporating Artificial Intelligence (AI)-based routing algorithms, this article proposes a novel method for improving the efficacy of WMNs. We present an innovative AI-driven routing algorithm that dynamically adapts to fluctuating network conditions by leveraging reinforcement learning and deep learning techniques. Our methodology considers various elements, including interference, node dependability, bandwidth consumption, and node mobility. In contrast to conventional approaches, the algorithm powered by artificial intelligence perpetually acquires knowledge from its surroundings, forecasting possible breakdowns or congestion, and subsequently redirects data packets along a more efficient trajectory. Experiments performed on simulated and real-world WMN scenarios revealed significant enhancements in packet delivery ratio, latency, and throughput compared to traditional routing methods. In addition, our AI model's adaptability guarantees the network's resistance to external disturbances and its capacity to expand to accommodate an increasing number of nodes. The study's results highlight the capacity of artificial intelligence to fundamentally transform the domain of wireless networking. Incorporating intelligent algorithms into wide-area networks (WMNs) infrastructure can guarantee end-users a more streamlined, effective, and resilient internet experience. Further investigations are warranted to determine whether the proposed method can be implemented in alternative wireless architectures and to enhance the algorit

关键词： AI-based routing deep learning network performance reinforcement learning Wireless Mesh Network

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SWIPT-enabled Cell-free Millimeter-wave MIMO with Multiple IRSs

SWIPT-enabled Cell-free Millimeter-wave MIMO with Multiple I...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Do-Duy, Tan Van, Nguyen Thi Thanh Luong, Nguyen Cong Niyato, Dusit Ho Chi Minh City University of Technology and Education Faculty of Electrical and Electronics Engineering Viet Nam Phenikaa University Faculty of Electrical and Electronic Engineering Hanoi12116 Viet Nam Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

ISBN: (纸本)9798350370218

In this paper, we investigate a simultaneous wireless information and power transfer (SWIPT)-enabled cell-free millimeter-wave multiple-input multiple-output (MIMO) system. Therein, multiple base stations (BSs) serve multiple users by using the SWIPT based on power-splitting (PS) protocol. To further improve the system throughput and harvested energy, multiple intelligent reflecting surfaces (IRSs) are deployed. We formulate an optimization problem that aims to optimize the beamformers of the BSs, phase shifts of the IRSs, and PS factors at users to maximize the minimum energy efficiency of the users. The max-min energy efficiency optimization problem is computationally intractable, and we propose an alternating descent algorithm to solve it with low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： cell-free mmWave MIMO intelligent reflecting surface SWIPT

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Enhancing Alzheimer's Disease Detection Using Optimized Attribute Selection and Random Forest Classifier for Improved Accuracy

Enhancing Alzheimer's Disease Detection Using Optimized Attr...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sathiya, A. Basha, Ch Hussaian Vasanth, S. Sharmila, Jasphin Jeni Prabakaran, S. Indhumathi, R. Department of Electronics and Communication Engineering Karur639113 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai600124 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Cms College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore641032 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331521394

This research aims at improving the identification of Alzheimer's dementia by using the optimized attribute selection method and preliminary diagnosis data-driven learning algorithm, namely Random Forest Classifier. Alzheimer's is a progressive neurological disorder exhibiting barriers in preliminary diagnosis primarily due to the high density and intricate nature of medical information. In this study, attribute selection techniques are used to minimize the data dimensionality, by selecting only key features for classification with low computational complexity and improved model accuracy. The Random Forest Classifier is used because it is accurate, can process big data and because of its diverse uses. Through selecting the most relevant features of the model, it provides a prominent level of precise and acute identification of the early stage of Alzheimer's and could significantly enhance the medical treatment effects. The outcomes from this study indicate that feature optimization can be used interchangeably with preliminary diagnosis data-driven learning algorithms for accurate disease prediction. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Bit-Interleaved Polar Coded Modulation with Iterative Successive Cancellation List Decoding

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China Communications 2022年第5期19卷 54-68页

作者： Hang Mu Zheng Ma George K.Karagiannidis Panagiotis D.Diamantoulakis The 10th Research Institute of China Electronics Technology Group Corporation Chengdu 610036SichuanChina the School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610036SichuanChina the Departmentof Electrical and Computer Engineering Aristotle University of ThessalonikiThessaloniki 54124Greece

This paper introduces bit-interleaved polar coded modulation with iterative detection/decoding(BIPCM-ID).In order to enable the soft output of successive cancellation list(SCL)decoding,two types of re-encoders are proposed,namely the max-re-encoder and min-re-encoder,*** the iterative decoding,we analytically verify that the average mutual information(AMI)between the transmitted and decoded symbols can approach the Shannon bound with the proposed ***,bit error rate(BER)and block error rate(BLER)in single-user and multi-user scenarios are ***,simulation results show that the performance of BIPCMID outperforms other bit-interleaved coded modulation(BICM)systems with LDPC and Turbo codes,while also reducing the computational complexity.

关键词： BIPCM-ID Polar code SCMA Reencoder SCL AMI

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A Fault Classification Prediction Model Based on Loose-Type Wavelet Neural Network for Server Health Management 2

A Fault Classification Prediction Model Based on Loose-Type ...

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2nd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2023

作者： Wei, Yi Nong, Yingxiong Li, Zhe Pan, Jian Yang, Zhenyu Lu, Xiaoqi Chen, Ningjiang China Tobacco Guangxi Industrial Co. Ltd Nanning China Guangxi University School of Computer Electronics and Information Nanning China Guangxi University School of Computer Electronics and Information China Guangxi Intelligent Digital Services Research Center of Engineering Technology China Key Laboratory of Parallel Nanning China

ISBN: (纸本)9798350331448

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers. © 2023 IEEE.

关键词： Neural networks

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DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1738-1751页

作者： Zhang, Bolun Huynh, Nguyen Van Hoang, Dinh Thai Nguyen, Diep N. Pham, Quoc-Viet University of Warwick School of Engineering CoventryCV4 7AL United Kingdom University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia The University of Dublin School of Computer Science and Statistics Trinity College Dublin DublinD02 PN40 Ireland

The End-to-end (E2E) learning-based approach has great potential to reshape the existing communication systems by replacing the transceivers with deep neural networks. To this end, the E2E learning approach needs to assume the availability of prior channel information to mathematically formulate a differentiable channel layer for the back-propagation (BP) of the error gradients, thereby jointly optimizing the transmitter and the receiver. However, accurate and instantaneous channel state information is hardly obtained in practical wireless communication scenarios. Moreover, the existing E2E learning-based solutions exhibit limited performance in data transmissions with large block lengths. In this article, these practical issues are addressed by our proposed deep deterministic policy gradient-based E2E communication system. In particular, the proposed solution utilizes a reward feedback mechanism to train both the transmitter and the receiver, which alleviates the information loss of error gradients during BP. In addition, a convolutional neural network-based architecture is developed to mitigate the curse of dimensionality problem when transmitting messages with large block lengths. Extensive simulations then demonstrate that our proposed solution can not only jointly train the transmitter and the receiver simultaneously without requiring prior channel knowledge but also can obtain significant performance improvement compared to state-of-the-art solutions. © 2015 IEEE.

关键词： Convolutional neural networks

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Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics

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International Journal of Information technology (Singapore) 2024年 1-11页

作者： Kumar, Ankit Singh, Sushil Kumar Bhardwaj, Indu Singh, Prakash Kumar Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Department of Electronics and Communication Engineering Galgotias University U.P. Greater Noida India Rajkiya Engineering College U.P Mainpuri India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology (GGSIPU) New Delhi India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA United States

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial Networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, im

关键词： Audio spectrogram Convolutional neural network (CNN) Emotion recognition Generative adversarial networks (GAN) Harmonic to noise rate (HNR) Mel-frequency cepstral coefficient (MFCC) Zero crossing rate (ZCR)

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An Review of the Effective Energy Consumption Within the Green IT and Green Energy Strategies 14

An Review of the Effective Energy Consumption Within the Gre...

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14th International Scientific and Practical Conference on Environment. technology. Resources, ETR 2023

作者： Mamchur, Dmytro Peksa, Janis Department of Information Technology Turiba University Engineering and Electronics Department Kremenchuk Mykhailo Ostrohradskyi National University RigaLatvia Kremenchuk Ukraine Department of Information Technology Institute of Information Technology Riga Technical University Faculty of Computer Science and Information Technology Turiba University Riga Latvia

ISBN: (纸本)9781713874881

The paper provides analysis on the current state of the art in the field of green technologies, including green information technologies, and methods aimed to achieve effective energy consumption goals. Recent advances in the field summarized in prospective publications, were analysed. Modern Green IT and Green Energy strategies, aimed to reduce power consumption, environmental influence and improve sustainability, which assume the use of Cloud Technologies, renewable energy sources, proper recycling and reuse of devices and resources, were singled out. Finally, recommendations for IT and industrial companies aimed helping to achieve green strategies' goals, were formulated. © 2023 Rezekne Higher Education Institution. All rights reserved.

关键词： Green computing

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A Review of Enhancement in Security of an IoT System Using BlockChain

A Review of Enhancement in Security of an IoT System Using B...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Nilima Karankar Anita Seth Department of computer engg. Institute of Engineering & Technology DAVV INDORE INDIA Department of Electronics & Telecommunication Institute of Engineering & TechnologyDAVV INDORE INDIA

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

The Internet of Things is thriving rapidly in terms of quality and quantity, helping us to provide better services every day. With the wide-spread growth of the Internet of Things, it introduces new technical challenges, such as managing a vast number of devices. In almost every field, like smart homes, smart transactions, automobiles, and healthcare, IoT is being used very effectively. Even though so many architectures and frameworks are proposed to overcome these challenges, security and scalability are major challenges for an IoT system. Additionally, IoT technology has been merged with other advanced technologies, such as cloud computing, big data, and blockchain, to form a strong IoT system. Blockchain is an emerging technology that is based on distributed protocols. It has the potential to ensure the security, scalability, and reliability of an IoT system and can provide secure digital transactions. This paper attempts to identify the current contributions to IoT security using blockchain and highlights various research gaps in this area. The paper concludes with a discussion on open challenges and research issues.

关键词： Sentiment analysis Protocols Reviews Scalability Smart homes Medical services Blockchains

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