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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Askari, Bahman Cavone, Graziana Carli, Raffaele Grall, Antoine Dotoli, Mariagrazia The Department of Electrical and Information Engineering The Polytechnic of Bari Italy The Department of Civil Computer Science and Aeronautical Technologies Engineering The University Roma Tre Italy The LIST3N-LM2S University of Technology of Troyes France

ISBN: (纸本)9798350320695

The integration of artificial intelligence in mechanical fault detection and diagnosis (FDD) helps to increase reliability, reduce costs, and improve the overall performance of mechanical systems in Industry 4.0 applications. Most interesting industrial applications nowadays come from dynamic environments where data are generated continuously over time and where the labeled data are scarce and expensive. Therefore, semi-supervised learning (SSL) can be particularly useful in FDD because faults may be rare or difficult to identify, and may not be fully represented in the labeled data. By using a combination of labeled and unlabeled data, SSL can help to identify these rare or difficult-to-detect faults, leading to more effective FDD. In this paper, graph-based SSL relying on label propagation is combined with conventional classification algorithms to detect potential failures in complex mechanical systems. Experimental results on realistic pneumatic and hydraulic systems from the related literature show that the proposed method can effectively enlarge the labeled datasets and interestingly identify different types of non-nominal conditions with higher accuracy compared to baseline methodologies. © 2023 IEEE.

关键词： Graphic methods

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Comparing Ensemble Learning Techniques on Data Transmission Reduction for IoT Systems 1st

Comparing Ensemble Learning Techniques on Data Transmission...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Salah, Ahmad Bekhit, Mahmoud Alkalbani, Asma M. Mohamed, Mohamed A. Lestari, Nur I. Fathalla, Ahmed Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig Sharkia Egypt Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Ad Dhahirah516 Oman Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Kent Institute Australia SydneyNSW Australia 2P Perfect Presentation Riyadh Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031337420

The Internet of Things (IoT) systems include a massive number of connected devices. The communication between these devices requires a huge amount of communication. Thus, it is very crucial for an IoT system to reduce communication volumes by minimizing the amount of data transmission. There are several approaches to achieving the goal of data transmission reduction, such as data compression and dual prediction. Dual prediction schemes received more attention in comparison to data compression. The mainstream techniques for proposing dual prediction schemes in the literature can be classified into two main groups, namely, filter-based and deep learning-based methods. The filter-based methods, such as the 1-D Kalman filter, are lightweight in terms of running time and the model’s memory requirements. On the other hand, deep learning-based methods require more memory space and training time, but deep learning models are more accurate as predictive models in comparison to filter-based methods in dual prediction schemes. There are very limited efforts to utilize machine learning methods in dual prediction schemes as a compromise between the aforementioned mainstream techniques. In this work, we extended one of these limited efforts, which utilizes boosting ensemble learning as a machine learning predictive method in a proposed dual prediction scheme. The current work proposes exploring the performance gap between the three main approaches to ensemble learning, namely, boosting, stacking, and bagging. The three proposed ensemble learning models are evaluated on a real dataset and compared against state-of-the-art methods. The obtained results show that among the ensemble learning models, boosting and bagging models are better than the stacking model, but the three proposed ensemble learning models outperformed the state-of-the-art methods of comparison. For instance, the average numbers of mispredictions for all of the experiments were 580, 551, and 1,106 for boosting, ba

关键词： Forecasting

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Bangla Social Media Cyberbullying Detection Using Deep Learning 1st

Bangla Social Media Cyberbullying Detection Using Deep Learn...

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1st International Conference on Intelligent Systems and Data science, ISDS 2023

作者： Rodela, Anika Tasnim Nguyen, Huu-Hoa Farid, Dewan Md. Huda, Mohammad Nurul Department of Information and Communication Technology Bangladesh University of Professionals Mirpur Cantonment Dhaka Bangladesh College of Information and Communication Technology Can Tho University 3/2 Street Ninh Kieu District Can Tho City Viet Nam Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh

ISBN: (纸本)9789819976485

The growth of social media over the past decade has been nothing short of phenomenal. An exponential increase has been seen on platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube, in their user base, accumulating billions of active users worldwide. technology advancements, the ubiquitous use of smartphones, and the innate human desire for connection don’t always contribute constructively;they might additionally end up in spreading violence in the form of bullying of others which is known as cyberbullying. As a result of cyberbullying, the victims will frequently experience anxiety, depression, and other mental diseases which can even result in suicide. Therefore the extensive need of detecting and controlling cyberbullying motivated us to automate this process. In this paper, we have introduced an approach to detect cyberbullying from social media data by using deep learning models. We have used Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network in the proposed hybrid approach along with word embedding technique called fastText. We achieved an accuracy of 91.63% with the proposed model by using a publicly available dataset containing 16,073 samples which outperformed all the state of the art models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication 35

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Dutta, Joy Eldeeb, Hossien B. Ho, Tu Dac Khalifa University C2PS Research Center Department of Computer Science Abu Dhabi United Arab Emirates University of Cambridge Electrical Engineering Division CambridgeCB3 0FA United Kingdom Faculty of Information Technology and Electrical Engineering Trondheim Norway

ISBN: (纸本)9798350362244

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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A Preliminary Comparative Study on Image Stabilization Techniques Projected on the Floor Through Chest-Mounted Projector 24th

A Preliminary Comparative Study on Image Stabilization Tech...

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24th International Conference on Human-computer Interaction, HCI International, HCII 2022

作者： Chiba, Yuji Tsuji, Airi Fujinami, Kaori Department of Computer and Information Sciences School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Tokyo Koganei184-8588 Japan Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Tokyo Koganei184-8588 Japan

ISBN: (纸本)9783031063930

Mobile projectors have come under the spotlight as a personal device to support a wide range of user tasks;however, projected image should be stabilized for applications used in motion because a projector shakes with a body sway during moving and, accordingly projected images swing. We present a preliminary comparative study on the image stabilization techniques, aiming at projecting images on the floor at a fixed position from the user: 1) inference-based, 2) selective suspension-based, and 3) prediction-based. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stabilization

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Bangladeshi Native Vehicle Classification Employing YOLOv8 1st

Bangladeshi Native Vehicle Classification Employing YOLOv8

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1st International Conference on Intelligent Systems and Data science, ISDS 2023

作者： Salekin, Siraj Us Ullah, Md. Hasib Khan, Abdullah Al Ahad Jalal, Md. Shah Nguyen, Huu-Hoa Farid, Dewan Md. Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh College of Information and Communication Technology Can Tho University 3/2 Street Ninh Kieu District Can Tho City Viet Nam

ISBN: (纸本)9789819976485

Traffic congestion poses a significant challenge in Bangladesh due to the growing number of vehicles. To tackle the obstacle needs an effective intelligent system that can reduce the traffic congestion. With a vision of building such an intelligent system, this paper presents a study on vehicle classification using the YOLO (You Only Look Once) v8 transfer learning model, customized for Bangladeshi native vehicles. Besides, we propose a transfer learning model-based system that helps to analyse the video footage of vehicle movements from the elevated viewpoints of foot over-bridges. Initially, the Bangladeshi Native Vehicle Image dataset is gathered, processed, and used to train the model. Once the model is trained and evaluated, the model is integrated into the vehicle detection system. The system detects and tracks the vehicles, providing practical traffic volume and movement insights. After the result analysis, We have found a high mean average precision (mAP) of 91.3 % using intersection over union (IoU). The model’s performance enables proactive measures to reduce congestion and optimise traffic flow. To build an efficient transportation network, this system can assist the Bangladesh Road Transport Authority (BRTA) and Bangladesh Police Traffic Division to address the challenges of increasing traffic and enhance traffic management. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

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computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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On-Demand Collaborative Sensing with Digital Twin-Driven Resource Allocation 100

On-Demand Collaborative Sensing with Digital Twin-Driven Res...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Li, Mushu Gao, Jie Zhou, Conghao Zhao, Lian Shen, Xuemin Lehigh University Department of Computer Science and Engineering BethlehemPA18015 United States Carleton University School of Information Technology OttawaONK1S 5B6 Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Toronto Metropolitan University Department of Electrical Computer & Biomedical Engineering TorontoM5B 2K3 Canada

ISBN: (纸本)9798331517786

This paper introduces a real-time collaborative sensing scheme for wireless sensor networks in time-varying environments. The objective is to maximize the sensors' performance by effectively allocating communication resources for data sharing. Specifically, we utilize digital twins (DTs) to characterize dynamic collaborative sensing demands for each sensor through data-driven methods. Building on the DT design, we propose a resource allocation scheme to optimize the communication resources allocated at each stage of collaborative sensing and determine the most effective collaborative sensing policy. By profiling sensors using DTs, the network controller can effectively coordinate the sensors without exhaustively exploring all collaborative sensing policies. Numerical results demonstrate the effectiveness of our proposed scheme in optimizing the sensing performance for all sensors. © 2024 IEEE.

关键词： Genetic algorithms

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An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1756-1767页

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada Computer School Hubei University of Arts and Science Xiangyang441000 China Future Technology Research Center National Yunlin University of Science and Technology Douliu City64002 Taiwan Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan Riken Center for Advanced Intelligence Project Riken Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization. © 2005-2012 IEEE.

关键词： Bandwidth

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