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Effective Adversarial Examples Detection Improves Screw Detection in Dust Pollution Within Industry System

IEEE Transactions on Consumer Electronics 2024年第1期70卷 1963-1971页

作者： Tseng, Fan-Hsun Zeng, Jiang-Yi Chen, Chi-Yuan Cho, Hsin-Hung National Cheng Kung University Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan

Screws play a critical role as essential components across various industries. To meet market demands and standards, screw manufacturers are embracing digital transformation and leveraging artificial intelligence (AI) techniques. AI models have also been proposed for identifying defective products. However, most studies are limited to controlled environments, and the presence of dust particles during screw manufacturing poses challenges for visual-based AI applications. To address this, we propose a solution involving the use of adversarial examples (AE). These examples are employed to simulate dust particles on camera lenses. We introduce a robust AE detection method designed to enhance the accuracy of screw recognition. This approach aims to improve the overall efficiency of the screw manufacturing industry. Experimental results show that our proposed mechanism can effectively improve screw identification accuracy in dust-polluted environments. © 1975-2011 IEEE.

关键词： Screws

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A hyperledger fabric-based EMR sharing mechanisms with proxy re-encryption and IPFS

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Cluster Computing 2024年第5期27卷 6953-6983页

作者： Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan Chen, Chin-Ling Zeng, Kuang-Wei Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan School of Information Engineering Changchun Sci-Tech University Jilin Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we can see the application of big data and cloud in various fields and levels, making induction, arrangement, transmission, and exchange of data easier and faster. The electronic and systematisation of patient medical records in the medical environment is an example. The use of electronic medical records can help improve the quality of medical care, reduce duplication of treatment, and reduce the waste of examination resources. It has many advantages, but also many risks. Medical institutions may abuse electronic medical data without the consent of patients, leading to the leakage of patient privacy data and causing serious social impact. Therefore, this study proposes a medical record exchange and sharing mechanism based on the Hyperledger blockchain architecture combined with proxy reencryption. When pharmacists need to obtain medical records for related research, patients can decide whether to share medical records, and hospitals can also revoke pharmacist access rights at any time to achieve more private, secure, and convenient sharing of medical records. The proposed scheme meets various blockchain security requirements and the BAN (Burrows–Abadi–Needham) logic proof model (Burrows–Abadi–Needham) is applied to assess the correctness of the proposed scheme. The proposed scheme performs well in terms of computational and communication costs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Towards a lightweight security framework using blockchain and machine learning

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Blockchain(Research and Applications) 2024年第1期5卷 107-118页

作者： Shereen Ismail Muhammad Nouman Diana W.Dawoud Hassan Reza School of Electrical Engineering and Computer Science University of North DakotaND 58202USA Department of Computer Science COMSATS University IslamabadIslamabad 44000Pakistan College of Engineering and Information Technology University of DubaiDubaiUnited Arab Emirates

Cyber-attacks pose a significant challenge to the security of Internet of Things(IoT)sensor networks,necessitating the development of robust countermeasures tailored to their unique characteristics and *** prevention and detection techniques have been proposed to mitigate these *** this paper,we propose an integrated security framework using blockchain and Machine Learning(ML)to protect IoT sensor *** framework consists of two modules:a blockchain prevention module and an ML detection *** blockchain prevention module has two lightweight mechanisms:identity management and trust *** management employs a lightweight Smart Contract(SC)to manage node registration and authentication,ensuring that unauthorized entities are prohibited from engaging in any tasks,while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network’s lifetime and tracking historical node *** and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance(VBFT)mechanism to ensure network reliability and *** ML detection module utilizes the Light Gradient Boosting Machine(LightGBM)algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their *** investigate the performance of several off-the-shelf ML algorithms,including Logistic Regression,Complement Naive Bayes,Nearest Centroid,and Stacking,using the WSN-DS *** is selected following a detailed comparative analysis conducted using accuracy,precision,recall,F1-score,processing time,training time,prediction time,computational complexity,and Matthews Correlation Coefficient(MCC)evaluation metrics.

关键词： Blockchain Machine learning IoT Security Integration Smart contracts

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Detecting ugly and derogatory words in Serbian language using a web browser extension 32

Detecting ugly and derogatory words in Serbian language usin...

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32nd Telecommunications Forum, TELFOR 2024

作者： Milakovic, Adrian Jocovic, Vladimir Cincovic, Jelica Micovic, Marko Radenkovic, Uros Draskovic, Drazen University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Serbia

ISBN: (纸本)9798350391053

In natural language processing, data acquisition and preprocessing techniques are significant for experiments involving training models on cleaned data. This paper describes the formation of a dataset of ugly and derogatory words in the Serbian language and the development of a web extension to analyze texts in which such words appear and then their censoring. Developing such an add-on is essential in creating a safer and healthier digital environment for young users who are just developing their Internet habits and adults who want to avoid unwanted content. It is crucial to point out that there are few software tools for natural languages with limited resources, such as the Serbian language. This software could analyze the frequency of certain foul words on different sites, which would contribute to a better understanding of the impact of such language on users. © 2024 IEEE.

关键词： Web browsers

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 1-28页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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Aerodynamics-Based Collision-Free Control of Connected Drones in Complex Urban Low-Altitude Airspace Using Distributional Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2024年第7期73卷 9763-9775页

作者： Liao, Bing-Hao Lee, Chao-Yang Wang, Li-Chun National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu City30010 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin County64002 Taiwan

Recent growth in the number of drones has made traffic management unworkable, particularly in urban areas. The safe operation and optimized navigation of drone swarms are now growing concerns. In this article, we use distributional reinforcement learning to prevent drone collisions via aerodynamic acceleration and deceleration. Although the instantaneous acceleration or deceleration of drones during an encounter influences drone kinematics, we note that drone kinematics itself can prevent collisions via instantaneous acceleration or deceleration. This article's central question, then, is how to take drone kinematics into account to improve air traffic intersection flow performance. First, the intercommunication between drone swarms is established via an Internet of drones to send and receive the current state of each drone in the swarm and detect potential collisions. In the presence of obstacles, we first use distributional reinforcement learning to find the inevitable change in kinematic height and guide descents and ascents through air traffic intersections. Here, the drone's energy consumption during instantaneous acceleration or deceleration cannot be ignored. A properly designed speed control is crucial to the power management of the drone. Distributional reinforcement learning is used to control drone acceleration and deceleration to avoid collision risk while minimizing energy consumption according to the reference energy recorded by each drone. We flew drones to gather energy consumption data and, to validate our approach, we subsequently conducted simulations in which travel time and energy consumption were measured. We demonstrate that the proposed kinematics-based drone collision avoidance method exhibits excellent performance in terms of travel time and power management. © 1967-2012 IEEE.

关键词： Kinematics

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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