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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nwafor, Ebelechukwu Baskota, Ujjwal Parwez, Md Salik Blackstone, Jeremy Olufowobi, Habeeb Villanova University Department of Computing Sciences PA United States University of Texas at Arlington Department of Computer Science & Engineering TX United States Howard University Department of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350351255

The Internet of Things (IoT) landscape has grown exponentially in recent years, making robust and efficient intrusion detection systems (IDS) even more critical. While Large Language Models (LLMs) have gained significant traction, their effectiveness in network intrusion detection remains largely unexplored. This paper proposes an LLM-based framework for enhanced threat detection and analysis in IoT networks. We explore using advanced LLMs like OpenAI's Generative Pre-trained Transformer (GPT) model, focusing on techniques such as fine-tuning and embedding similarity. Using real-world intrusion datasets, we evaluate the proposed LLM's performance in detecting common network attacks and compare it with ensemble-based IDS solutions. We assess the efficiency of the LLM in binary class and multiclass classification task using standard metrics, such as accuracy, recall, precision, and F1 scores. While the fine-tuning approach does not produce comparable results to the current baseline ensemble-based IDS models, the embedding approach, however, yields comparable results. This analysis represents a starting point for exploring the utilization of advanced large language models for intrusion detection within an IoT ecosystem. © 2024 IEEE.

关键词： Network intrusion

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Novel Symbol over Multiplexing Technique for Transmitted Power, Bandwidth and Computational Load Reduction

Novel Symbol over Multiplexing Technique for Transmitted Pow...

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2023 International Conference on engineering, Applied and Nano Sciences, ICEANS 2023

作者： Jabbar, Ahmed N. Kadhim, Abdulkareem A. Al-Muraab, Samir J. University of Babylon College of Engineering Department of Electrical Engineering Babylon Iraq Al-Nahrain University College of Information Engineering Computer Networks Engineering Department Baghdad Iraq University of Babylon College of Engineering Department of Electrical Babylon Iraq

ISBN: (纸本)9798350341973

This paper introduces a new technique that merges two binary streams into a single stream and demerges them successfully using the LSB bit index. The technique is called Symbol Over Multiplexing using Bit Index Embedding (SOMBIE). The SOMBIE offers a single solution to reduce the consumed power, required bandwidth, computational load, and storage space simultaneously. The merging process allows for halving the data computation load for digital processors and the transmitted power and bandwidth for multicarrier systems. The SOMBIE also allows more symbols to be stored without increasing the storage area. The SOMBIE provides higher data security due to the scrambling nature of its operation. The circuits used to implement the SOMBIE are simple and operate in parallel mode, providing a high processing speed with minimum delay. The circuits are simulated in Simulink to merge two streams, resulting in a storage space reduction of 3.125% with a negligible error root mean squared of about 11times 10-6, which can be reduced further. The reduction in processing load, storage space, transmitted power, and bandwidth can be increased using multiple stages or by simple adjustments, giving the SOMBIE high flexibility to satisfy any system's specifications. © 2023 IEEE.

关键词： Bandwidth

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C-AFA: A Conditionally Approximate Full Adder for Efficient DNN Inference in CIM Arrays 21

C-AFA: A Conditionally Approximate Full Adder for Efficient ...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Park, Juhong Ko, Jong Hwan Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

ISBN: (纸本)9798350377088

Digital compute-in-memory (DCIM) architectures are becoming crucial for real-time and accurate deep neural network (DNN) inference due to their capacity for precise computations. However, traditional DCIM systems often struggle to balance precise data processing with computational efficiency. In scenarios where exact computations are not always necessary, especially in DNNs featuring sparse data, existing multi-bit operations frequently fail to achieve an ideal balance between accuracy and efficiency. In this paper, we introduce a novel approach to DCIM architecture that selectively skips computations by using probabilistically determined values, effectively reducing the computational load. Our simulation results demonstrate that this method significantly reduces computing cycles, achieving a speedup of up to 1.5× compared to traditional methods, while maintaining accuracy with only a minimal decrease of 1.5%. © 2024 IEEE.

关键词： Adders

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Navigating IoT Security:Insights into Architecture,Key Security Features,Attacks,Current Challenges and AI-Driven Solutions Shaping the Future of Connectivity

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computers, Materials & Continua 2024年第12期81卷 3499-3559页

作者： Ali Hassan N.Nizam-Uddin Asim Quddus Syed Rizwan Hassan Ateeq Ur Rehman Salil Bharany Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Biomedical Engineering HITEC UniversityTaxila47080Pakistan Department of Electronics Engineering University of ChakwalChakwal48800Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabad38000Pakistan School of Computing Gachon UniversitySeongnam-si13120Republic of Korea Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Enhancing the interconnection of devices and systems,the Internet of Things(IoT)is a paradigm-shifting *** security concerns are still a substantial concern despite its extraordinary *** paper offers an extensive review of IoT security,emphasizing the technology’s architecture,important security elements,and common *** highlights how important artificial intelligence(AI)is to bolstering IoT security,especially when it comes to addressing risks at different IoT architecture *** systematically examined current mitigation strategies and their effectiveness,highlighting contemporary challenges with practical solutions and case studies from a range of industries,such as healthcare,smart homes,and industrial *** results highlight the importance of AI methods that are lightweight and improve security without compromising the limited resources of devices and computational *** networks can ensure operational efficiency and resilience by proactively identifying and countering security risks by utilizing machine learning *** study provides a comprehensive guide for practitioners and researchers aiming to understand the intricate connection between IoT,security challenges,and AI-driven solutions.

关键词： Internet of Things(IoT) artificial intelligence(AI) IoT architecture security attacks in IoT

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Au/SiC Microfluidic Devices Fabricated by Rapid Laser Cladding for Photocatalytic Degradation of Water Pollutants

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ACS Sustainable Chemistry and engineering 2024年第11期12卷 4486-4496页

作者： Chang, Chun-Hao Lin, Hsiu-Pen Chen, Chien-Wei Lu, Yu-Cheng Hsiao, Fu-He Hsieh, Chi-Hsiang Lin, Bo-Cheng Yu, Bang-Ying Ma, Dai-Liang Kuo, Hao-Chung Tu, Chang-Ching Li, Bor-Ran Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Centre for Emergent Functional Matter Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrophysics College of Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Taisic Materials Corp. Taoyuan320 Taiwan Department of Electrical Engineering National Central University Taoyuan32001 Taiwan Medical Device Innovation and Translation R&D Center National Yang Ming Chiao Tung University Taipei112304 Taiwan

The increasing proliferation of third-generation semiconductor silicon carbide (SiC) products has led to the emergence of SiC powder as an industrial byproduct. To reutilize the SiC efficiently, we propose a facile method for fabrication of photocatalytic plates from reclaimed SiC. Our approach involves affixing the SiC onto a glass surface with laser cladding, followed by sputter deposition of a gold film and laser annealing. The resulting nanogold-coated SiC plate exhibited enhanced photocatalytic activity due to the injection of hot electrons into the SiC via surface plasmon resonance. Unlike chemical reduction, which requires hours for the synthesis, purification, and isolation of Au/SiC, our approach accomplished this in just 30 min. When illuminated, the Au/SiC plate generated electron-hole pairs that formed radicals in the presence of water and oxygen, leading to the decomposition of methylene blue. Furthermore, the Au/SiC plate can be integrated into a microfluidic device for an enhanced degradation rate and efficiency. This research provides a simple and environmentally friendly approach to the production of photocatalytic plates from reclaimed SiC and contributes to sustainable development. © 2024 The Authors. Published by American Chemical Society.

关键词： Silicon carbide

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Machine Learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication 1

Machine Learning for Wireless Network Optimization: Algorith...

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1st International Conference on Advances in computer Science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Srinivas, Madhira Surya, S. Thakar, Prachi Deepak Jain, Amit Kumar Department of Computer Science & Engineering Hyderabad501301 India Chennai600116 India KJ college of Engineering & Management Research Department of Computer Engineering Pune India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798331518578

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function. © 2025 IEEE.

关键词： 5G Optimization Adaptive Communication Bandwidth Data Pre-processing EDA Energy Efficiency Latency Machine Learning Network Elasticity Predictive Models Resource Allocation Scalability Signal Strength Wireless Networks

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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AI-Inspired Algorithm for the Automated Recognition of the World’s Oldest Script “Brahmi”

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SN computer Science 2025年第1期6卷 1-12页

作者： Jain, Trang Jain, Arpit Dwivedi, Rakesh Kumar Saxena, Rakhi College of Computing Sciences and Information Technology Teerthanker Mahaveer University UP Moradabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada India Department of Computer Science Deshbandhu College University of Delhi New Delhi India

One of the internationally known oldest script is Brahmi whose digitisation may be helpful for the archaeologists as well as it may help in the digitisation of other languages. Optical character recognition techniques with genetic algorithms will be quite useful for this purpose. In this paper, a review of the work, done on the images of Brahmi letters using CNN till now, is presented. It will be useful to check the possibility of improvement in the performance of a model used for character recognition. A model is designed where tuning of hyper parameters is done using genetic algorithms which improves the accuracy of recognition to 97%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ashok shilalekh Brahmi script CNN Kharavela shilalekh OCR

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A Hybrid Precision Network with Efficient Processing Elements for 3D Hand Pose Estimation 21

A Hybrid Precision Network with Efficient Processing Element...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： So, Jaehyeon Ko, Jong Hwan Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

ISBN: (纸本)9798350377088

This paper presents a hybrid precision network that combines binary and multi-bit layers for efficient 3D hand pose estimation on resource-constrained devices. By transforming the state-of-the-art HandFoldingNet (HFN) architecture, we significantly reduce memory usage and computational complexity while maintaining accuracy. Our design integrates specialized binary XNOR and multi-bit processing elements optimized for low-power and high efficiency. Implemented on FPGA, the hybrid processing element units demonstrate up to 46% higher efficiency compared to traditional methods. This hybrid approach enables the deployment of advanced neural networks like HFN on mobile and edge devices for real-time applications. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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computer Modeling in engineering & Sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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