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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature

arXiv

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arXiv 2024年

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of School of Software Engineering Huazhong University of Science and Technology China

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model’s incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method’s superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method’s effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Blast fragmentation analysis using image processing

Blast fragmentation analysis using image processing

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2020 National Science, engineering and technology Conference, NCSET 2020

作者： Kumar, Ujjwal Shaik, Ayesha School of Computing Science and Engineering Vellore Institute of Technology Chennai600127 India

This paper proposes an image enhancement algorithm for rock particle images during blast fragmentation. Image enhancement of rock piles plays an important role in rock size distribution. To measure the rock size, the original images are not of good quality. So our method is used to enhance the image to help the rock size distribution. The proposed method is implemented in Matlab software. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Rocks

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Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

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Computer Modeling in engineering & Sciences 2025年第5期143卷 2459-2489页

作者： Karim Gasmi Olfa Hrizi Najib Ben Aoun Ibrahim Alrashdi Ali Alqazzaz Omer Hamid Mohamed O.Altaieb Alameen E.M.Abdalrahman Lassaad Ben Ammar Manel Mrabet Omrane Necibi Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia REGIM-Lab National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia College of Computing and Information Technology University of BishaBisha61922Saudi Arabia Cybersecurity Department College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418 Saudi Arabia Department of Computer Science Prince Sattam bin Abdulaziz UniversityAl-Kharj1942Saudi Arabia

The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical *** self-reporting is the foundation of conventional pain assessment methods,which may be *** learning is a promising alternative to resolve this limitation through automated pain *** paper proposes an ensemble deep-learning framework for pain *** framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)*** integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)*** then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s *** improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature *** enables us to reduce the features’dimensionality without sacrificing the classification’s *** improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning *** our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment ***,due to differences in validation protocols,comparisons with previous studies are still *** deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification *** evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.

关键词： Pain assessment ensemble learning deep learning optimal algorithm feature selection

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Ship Remote Sensing Target Recognition Based on YOLOV5 29th

Ship Remote Sensing Target Recognition Based on YOLOV5

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Hao, Ning Li, Yunwei Ma, Yusen Zhang, Xinan Beijing Institute of Spacecraft Environment Engineering Haidian District Space City Beijing100048 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin150001 China School of Physics Mathematics and Computing University of Western Australia PerthWA6009 Australia

ISBN: (纸本)9783031449468

Ship remote sensing target recognition is a critical task in various maritime applications, including surveillance, navigation assistance, and disaster management. However, traditional methods face challenges in detecting and recognizing ships in complex maritime environments, which include various types of ships, sea conditions, and environmental factors. In recent years, deep learning-based object detection algorithms have shown promising results in detecting and recognizing ships in remote sensing images. In this paper, we propose a ship remote sensing target recognition method based on the YOLOV5 algorithm. Our approach uses a deep convolutional neural network to extract high-level features from remote sensing images and detect and classify ships. The proposed method uses anchor-based object detection to identify ship locations and a multi-scale feature fusion strategy to capture different ship sizes and orientations. We also introduce a new ship dataset, which includes various ship types and sea conditions, to evaluate the performance of our proposed method. Experimental results show that our method outperforms other common ship detection algorithms in terms of detection accuracy. Our method can significantly contribute to improving ship detection and recognition in real-world maritime applications, especially in complex scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ships

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Factors Influencing the Acceptance of Blockchain technology: Real Estate Industry 2021

Factors Influencing the Acceptance of Blockchain Technology:...

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4th International Conference on Software engineering and Information Management, ICSIM 2021

作者： Ng, Claudia Siew Hoong Lee, Angela Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia

ISBN: (纸本)9781450388955

Blockchain is an innovative distributed ledger technology enabling the exchange of value online without the need of a central authority. Blockchain provides a decentralized and trustless network, secured by cryptography and consensus mechanisms. The acceptance of blockchain technology greatly impact the real estate owners, financiers, builders and more. Hence, this study attempts to find out how buyers and sellers of real estates are accepting blockchain technology by integrating and analysing a proposed hybrid model of UTAUT (Unified Theory of Acceptance and Use of technology) combined with TRI (technology Readiness Index). This study utilizes a quantitative research method and aims to identify the key influencing factors in the acceptance of blockchain technology. It is crucial to fully comprehend the potential of blockchain technology as it is a new revolutionary technology. This paper proposed a conceptual model that tests the performance expectancy, effort, social influence, facilitating condition, innovativeness, optimism, discomfort and insecurity in influencing the user intention behaviour in accepting blockchain technology. A pilot-test was conducted to test the research model. The results showed that all composite reliability values except for ĝ€Social Influence' were found to be above the recommended value of 0.70, suggesting strong indicator reliability of the constructs. © 2021 ACM.

关键词： Blockchain

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Leader-Follower Flocking Control Over Signed Communication Networks

Leader-Follower Flocking Control Over Signed Communication N...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Lulu Chen Tong Li Yuhua Cheng Jinliang Shao Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Cheng-du China School of Computing Engineering and Mathematics Western Sydney University Sydney NSW Australia

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Existing fully distributed protocols for flocking are built upon the network of mobile agents with only cooperative interactions. Rather than investigating such networks, this paper deals with the problem of leader-follower flocking control over signed communication networks, where there impose less restrictions on the distributions of cooperations and competitions in the network of mobile agents. Firstly, for the second-order dynamics model, a novel state feedback controller that relies only on the relative velocity information of neighboring agents is developed. Secondly, the solvability of flocking control problem is transformed to the asymptotic stability of error system, and the latter is guaranteed by treating the product convergence of infinite super-stochastic matrices. Then, sufficient condition for the solvability of flocking control problem is proposed by establishing the inequality constraints on positive and negative edge weights. Finally, a numerical example is performed to illustrate the correctness of the theoretical result.

关键词： Asymptotic stability Sufficient conditions State feedback Protocols Mobile agents Stability analysis Linear matrix inequalities Communication networks Robots Numerical stability

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Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

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International Conference on ent computing and Control Systems (ICICCS)

作者： K. Priya K. Priyadharshini R. Santhana Krishnan J. Relin Francis Raj Imran Javeed Settu A. Srinivasan Department of Computer Science and Engineering Ramco Institute of Technology India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

关键词： Adaptive systems Smart cities Roads Traffic control Predictive models Air quality Real-time systems Fuels Time factors Traffic congestion

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NLP-Based SMS Spam Detection Using Ensemble Learning

NLP-Based SMS Spam Detection Using Ensemble Learning

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： M. Rajalakshmi R. Rengaraj M. Karthikeyan Aruna S. Dept of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Dept of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Dept of Electrical and Electronics Engineering SRM Insitute of Science & Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

In today's environment, spam text messages are a common issue and a source of frustration for users. This study suggests a novel method for identifying SMS spam that makes use of natural language processing (NLP), multiple model creation, and ensemble learning. The suggested approach entails pre-processing the data using natural language processing (NLP) techniques including lemmatizing, stemming, and stopword removal in order to extract pertinent features from the text data and convert it into numerical representations. The suggested approach's effectiveness was assessed using an actual dataset and contrasted with conventional machine learning algorithms like SVM and Naïve *** altered data is used to train the numerous classifiers, and an ensemble learning technique is then used to combine their predictions and produce a more accurate outcome. More precision was provided by the final model than by conventional models. In order to lessen the quantity of spam messages that users get, the research's conclusions may be helpful in the development of an efficient spam SMS detector. Furthermore, the model was hosted using the streamlit web app framework on the cloud-based platform Railway, making it instantly accessible to users without the need to install any software.

关键词： Training Analytical models Accuracy Stacking Predictive models Feature extraction Message services Data models Natural language processing Ensemble learning

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An Intrusion Detection System for MANET to Detect Gray Hole Attack using Fuzzy Logic System

An Intrusion Detection System for MANET to Detect Gray Hole ...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bala, K. Paramesh, J. Elma, K. Johny Santhanalakshmi, S.T. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600073 India Easwari Engineering College Department of Information Technology Tamil Nadu Chennai600089 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9781665474511

The Mobile Ad hoc network (MANET) is a kind of wireless communication network that is composed of several nodes which are highly movable throughout the various directions in the network. Due to nonexistence of infrastructure in the MANET, the nodes can interconnect with one another in a random fashion. The Intrusion Detection System (IDS) is an observing parameter which is used to identify the suspicious functioning in the network and intimates through alert to the security operation center. Based upon the constraints, the center examines the problem and solves the constraints using necessary steps and solution. The transformation of the gray hole attack. The important consequences in the wireless sensor network involves the grey hole attack. It is a revolution of the black hole attack. This leads to decrease the efficiency of the network. Thus, to overcome the various malicious attacks, the proposed system is implemented with fuzzy logic system to enhance the efficiency in the networking system. © 2023 IEEE.

关键词： Mobile ad hoc networks

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