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An enhanced QoS approach for multi-objective optimization using social spider optimization 5G enable IoMT network

International Journal of Information Technology (Singapore) 2025年第3期17卷 1851-1864页

作者： Jena, Rasmita Barik, Ram Chandra Yadav, Devendra Kumar Pradhan, Saroj Computer Science and Engineering C.V. Raman Global University Odisha Bhubaneswar India School of Computer Science and Engineering XIM University Odisha Bhubaneswar India Electrical Engineering Parala Maharaja Engineering College Odisha Berhampur India

Social Spider Optimization (SSO) is a swarm algorithm, based on the mating and cooperating behaviour of social spiders. This approach is basically used for global optimum search during allocation of resources and data compression in Internet of Things (IoTs) networks. Some of the Swaram Intelligence (SI) algorithm such as Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO), Social Spider Optimization (SSO), Parallel Social Spider Optimization (PSSO) are numerically simulating to decrease computation time for resource allocation, to improve the energy efficiency and data compression rate in IoT networks. However, still some deficiencies exist in the system. To overcome these deficiencies, an Adaptive Social Spider Optimization (ASSO) algorithm is suggested for global search optimum. This proposed model will update the both the female and male individuals’ positions, and each individual position can be calculated during the search process. This will help to strengthen the ability of the search performance. ASSO has been compared with the existing algorithm to obtain experimental results that shows the suggested ASSO conquered better result with an allocation rate of 96.90% and consumed energy 0.6mj that is comparatively better than PSSO. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Internet of Things (IoT) Resource Allocation Sensor Social Spider Optimization (SSO)

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Towards a Low-Cost Distributed AWOS: Machine Learning for Optical Ceilometry

Towards a Low-Cost Distributed AWOS: Machine Learning for Op...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Dhakal, Rabin Mourning, Chad School of Electrical Engineering and Computer Science Ohio University AthensOH45701 United States

ISBN: (纸本)9781624107238

Automated Surface/Weather Observing Systems (ASOS/AWOS) are the surface weather observation stations located throughout the United States. This weather station relies upon sensing instruments that disseminate the weather data and broadcast it over the radio or the ground link in METAR Format. ASOS station accumulates complex weather data and is useful for weather forecasting or climatology applications. On the other hand, AWOS focuses on aviation-specific weather data, catering primarily to the needs of pilots and airport operations. Weather data is comprised of visibility, wind speed, cloud coverage information, temperature and humidity, etc. AWOS depends upon the ceilometer for estimating the cloud coverage and its location from the ground. Accurately estimating cloud-base height typically requires a costly ceilometer system, which uses advanced instrumentation to measure cloud height precisely. This research addresses this challenge by proposing a cost-effective alternative that uses optical cameras, offering a more affordable solution without compromising functionality. Implementing computer vision and synthetic data in a virtual environment, our proposed method achieves an RMSE of 193.91 meters on 442 previously unseen synthetic test samples, demonstrating its potential as a reliable and efficient alternative. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Wind

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Towards Explainable Network Intrusion Detection using Large Language Models 11

Towards Explainable Network Intrusion Detection using Large ...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Houssel, Paul R. B. Singh, Priyanka Layeghy, Siamak Portmann, Marius The University of Queensland School of Computer Science and Electrical Engineering BrisbaneQLD 4072 Australia

ISBN: (纸本)9798350367300

Large Language Models (LLMs) have revolutionised natural language processing tasks, particularly as chat agents. However, their applicability to threat detection problems remains unclear. This paper examines the feasibility of employing LLMs as a Network Intrusion Detection System (NIDS), despite their high computational requirements, primarily for the sake of explain-ability. Furthermore, considerable resources have been invested in developing LLMs, and they may offer utility for NIDS. Current state-of-the-art NIDS rely on artificial benchmarking datasets, resulting in skewed performance when applied to real-world networking environments. Therefore, we compare the GPT-4 and LLama3 models against traditional architectures and transformer-based models to assess their ability to detect malicious NetFlows without depending on artificially skewed datasets, but solely on their vast pre-trained acquired knowledge. Our results reveal that, although LLMs struggle with precise attack detection, they hold significant potential for a path towards explainable NIDS. Our preliminary exploration shows that LLMs are unfit for the detection of Malicious NetFlows. Most promisingly, however, these exhibit significant potential as complementary agents in NIDS, particularly in providing explanations and aiding in threat response when integrated with Retrieval Augmented Generation (RAG) and function calling capabilities. © 2024 IEEE.

关键词： Network intrusion

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Resonant Beam Communications:A New Design Paradigm and Challenges

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Journal of Communications and Information Networks 2024年第1期9卷 80-87页

作者： Yuanming Tian Dongxu Li Chuan Huang Qingwen Liu Shengli Zhou Future Network of Intelligence Institute and School of Science and Engineering the Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and the Future Network of Intelligence Institute the Chinese University of Hong KongShenzhen 518172China College of Electronics and Information Engineering Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering University of ConnecticutStorrsCT 06250USA

Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.

关键词： resonant beam communications(RBCom) wireless optical communications(WOC) echo interference elimination mobility multiple access

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Revisiting LARS for Large Batch Training Generalization of Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1321-1333页

作者： Do, Khoi Nguyen, Minh-Duong Hoa, Nguyen Tien Tran-Thanh, Long Tran, Nguyen H. Pham, Quoc-Viet Trinity College Dublin School of Computer Science and Statistics Dublin 2 D02PN40 Ireland Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam University of Warwick School of Computer Science CoventryCV4 7AL United Kingdom The University of Sydney School of Computer Science DarlingtonNSW2006 Australia Kyung Hee University Department of Computer Science and Engineering Yongin446-701 Korea Republic of

This article investigates large batch training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings. In particular, we first show that a state-of-the-art technique, called LARS with the warm-up, tends to be trapped in sharp minimizers early on due to redundant ratio scaling. Additionally, a fixed steep decline in the latter phase restricts deep neural networks from effectively navigating early-phase sharp minimizers. To address these issues, we propose time varying LARS (TVLARS), a novel algorithm that replaces warm-up with a configurable sigmoid-like function for robust training in the initial phase. TVLARS promotes gradient exploration early on, surpassing sharp optimizers and gradually transitioning to LARS for robustness in later stages. Extensive experiments demonstrate that TVLARS consistently outperforms LARS and LAMB in most cases, with up to 2% improvement in classification scenarios. In all self-supervised learning cases, TVLARS achieves up to 10% performance improvement. © 2020 IEEE.

关键词： Self-supervised learning

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A Heart Rate Extraction Method Using Millimeter Wave Radar 2

A Heart Rate Extraction Method Using Millimeter Wave Radar

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Zhou, Tao Ding, Li Wang, Jingyu University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China University of Shanghai for Science and Technology School of Health Science and Engineering Shanghai China

ISBN: (纸本)9798331515669

With the development and commercialization of radar technology, real-time monitoring of human heart rate in a resting state using Frequency Modulated Continuous Wave (FMCW) radar is of great significance for assessing human health. This paper addresses the issue of interference in heartbeat signals caused by environmental noise, respiration signals, and their harmonics in radar signals. A heart rate extraction method based on the combined processing of Singular Spectrum Analysis (SSA) and Empirical Wavelet Transform (EWT) is proposed. The method involves using SSA on the preprocessed signal to obtain the main components and eliminate noise peaks near the heartbeat, followed by using EWT to decompose the signal into several components. The heartbeat signal is reconstructed by selecting components within a specific frequency range and correlating them with vital sign characteristics. Experimental findings reveal that the method achieves an average relative error of 2.76% for heart rate estimation over a 5-second window. © 2024 IEEE.

关键词： EWT FMCW Radar Heat Rate SSA

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Image Transmission with Semantic Communication using Alamouti Scheme

Image Transmission with Semantic Communication using Alamout...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Doha, Shadman Rahman Abdelhadi, Ahmed School of Electrical Engineering & Computer Science University of North Dakota ND United States

ISBN: (纸本)9798350367560

In this study, we explore a new approach for reliable image transmission in wireless communication by integrating Semantic Communication and the Alamouti Scheme. In poor channel conditions, traditional communication struggles to maintain the reliability of the image because it focuses on transmitting raw data rather than the meaning of the image. A paradigm shift is offered by Semantic Communication which focuses on transmitting the meaning of an image rather than the raw data, thereby ensuring reliability of image transmission. Reliability can be further improved by improving the diversity gain. Alamouti scheme offers a low complexity implementation of space-time coding (STC) that can significantly improve the diversity gain of the wireless communication system and enhance transmission robustness by exploiting multipath propagation. Our work investigates the synergy between these two techniques, highlighting the potential of integrating these two approaches for improving the reliability of image transmission under challenging environments with noise, fading, shadowing, and interference. © 2024 IEEE.

关键词： Alam-outi Scheme MIMO Reliable Image Transmission Semantic Communication

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Cooperative Gradient Coding for Semi-Decentralized Federated Learning

Cooperative Gradient Coding for Semi-Decentralized Federated...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Weng, Shudi Li, Chengxi Xiao, Ming Skoglund, Mikael Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350351255

Stragglers' effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local model updates to the central parameter server (PS) through fading channels. To tackle communication stragglers without dataset sharing or prior information about the network at PS, we propose cooperative gradient coding (CoGC) for semi-decentralized FL to enable the exact global model recovery at PS. Furthermore, we conduct a thorough theoretical analysis of the proposed approach. Namely, an outage analysis of the proposed approach is provided, followed by a convergence analysis based on the failure probability of the global model recovery at PS. Nevertheless, simulation results reveal the superiority of the proposed approach in the presence of stragglers under imbalanced data distribution. © 2024 IEEE.

关键词： Fading channels

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Development of Dementia Care Training System Using AR and Large Language Model

Development of Dementia Care Training System Using AR and La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miyawaki, Tomoya Nishiura, Yuuki Fukuda, Ryouta Nakashima, Kazuto Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Kyushu University Faculty of Engineering Department of Electrical Engineering and Computer Science Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan

ISBN: (纸本)9781665410205

We have developed HEARTS, a dementia care training system using augmented reality based on Humanitude. Humanitude is a multimodal comprehensive care technique for dementia, and has attracted attention as a method to reduce the burden on both caregivers and patients. However, the HEARTS developed so far could not evaluate 'speaking' skills based on the content of conversations among 'seeing,' 'touching,' and 'speaking,' all of which are fundamental skills in Humanitude. Therefore, we attempted a new quantitative evaluation of trainees' 'speaking' skills by estimating the emotional value of conversational content using GPT-4 named HEARTS 5. A survey of caregivers was conducted using the developed system and was well received by the participants. We also developed a HEARTS 5 conversational version based on HEARTS 5, with the addition of GPT-4 conversation generation and Azure Text-to-Speech. © 2024 IEEE.

关键词： Augmented reality

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Multimodal survival prediction using TabTransformer and BioClinicalBERT on MIMIC-III

Multimodal survival prediction using TabTransformer and BioC...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Insalata, Beatrice Schmidt, Fabian Vlassov, Vladimir Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350362480

This paper explores the development and evaluation of a multimodal system for survival prediction in clinical settings, leveraging both structured electronic health records and unstructured clinical notes. The core objective is to enhance the accuracy and reliability of survival predictions in Intensive Care Units by integrating diverse data types through advanced machine learning models. The system combines the novel architecture of Tabular Transformers, adapted to process structured data such as patient demographics, medical history, and diagnoses, with Multi-Layer Perceptrons for text embeddings obtained from BioClinicalBERT, a specialized model for clinical narratives. These models' integration aims to capture the complex and multifaceted nature of patient profiles, thereby improving prediction performance. The finalized system, a Logistic Regression instance that aggregates the obtained predictions, demonstrates superior performance on evaluation metrics, highlighting the system's ability to identify high-risk patients. Comprehensive benchmarking against decision trees, standalone MLPs, and various configurations underscored the robustness of the proposed system. This research highlights the transformative potential of multimodal data integration in medical predictive modeling. Thus, medical professionals can guide their efforts and prioritization of patient care, enabling more efficient and targeted allocation of resources during triage. © 2024 IEEE.

关键词： Electronic health record

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