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Secured IIoT against trust deficit - A flexi cryptic approach

Multimedia Tools and Applications 2025年第9期84卷 5625-5652页

作者： Padmapriya, V.M. Thenmozhi, K. Hemalatha, M. Thanikaiselvan, V. Lakshmi, C. Chidambaram, Nithya Rengarajan, Amirtharajan Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613401 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia School of Electronics Engineering Vellore Institute of Technology Vellore632014 India

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 5G mobile communication systems

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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

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computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Review On the Techniques Used for Detection of Fabric Defects Using AI 1

Review On the Techniques Used for Detection of Fabric Defect...

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1st IEEE International Conference on Advances in electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Batra, Isheeta Prasad, S A Hari Arvind, K.S. Faculty of Engineering & Technology Department of Computer Science and Engineering Karnataka India Faculty of Engineering & Technology Department of Electronics and Communication Engineering Karnataka India

ISBN: (纸本)9798350348057

The garment industry is the second-most polluting industry after oil. These mass-produced clothes if rejected are dumped and have an enormous impact on the environment. Therefore, to save the cost post production it is important to identify any sorts of defects pre-production when clothes are in a textile form, so that in case the fabric lacks somewhere in quality it can always be replaced, saving all the time and cost. This detection is possible using techniques like segmentation, or on the basis of texture or by using deep learning algorithms each having its own advantages and disadvantages to efficiently identify the defects in textile fabric. This paper is a comprehensive study of these three techniques and comparison made on basis of brief analysis which say that the deep learning technique is the best of the three for detection of defects keeping in contrast the type of research and developing technologies. © 2023 IEEE.

关键词： Deep learning

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Difussion-Weighted Images Analysis Using Regional Convolutional Neural Network for Stroke Lesion Classification 4

Difussion-Weighted Images Analysis Using Regional Convolutio...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Saad, Norhashimah Mohd Abdullah, Abdul Rahim Kandaya, Shaarmila Mohd Noor, Nor Shahirah Samsudin, Adam Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology and Engineering Melaka Malaysia Universiti Teknikal Malaysia Melaka Faculty of Electrical Technology and Engineering Melaka Malaysia

ISBN: (纸本)9798350395914

Stroke is a major type of brain disorder, and the MRI sequence known as diffusion-weighted imaging (DWI) is often used to evaluate early changes related to strokes. Typically, stroke diagnosis requires manual assessment by neuroradiologists, which is subjective and time-consuming. Therefore, developing an automated stroke diagnosis system is crucial to streamline this process. This study presents a method for automatically detecting and categorizing stroke lesions using deep learning techniques applied to DWI. The dataset comprises 61 samples from Hospital Kuala Lumpur (HKL) and 69 samples from the public ISLES database, including acute hemorrhage, acute ischemic, chronic ischemic, and sub-acute ischemic strokes. The method employs a Regional Convolutional Neural Network (R-CNN) on DWI images, involving four stages: input image, region proposal network (RPN), computation of CNN features, and classification. Performance is evaluated using metrics such as accuracy, sensitivity, and specificity. The technique achieves a classification accuracy of 64.62% with an average training time of 16.3 seconds. Overall, this stroke classification method shows promise for enhancing the diagnosis and categorization of brain stroke lesions using deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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Analysis and Design of the Detection and Control System of Authorized and Unauthorized Users using Internet of Things and Machine Learning 9

Analysis and Design of the Detection and Control System of A...

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9th International Conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Sarshar, Negar Nasiri, Ali Qom University of Technology Faculty of Electrical & Computer Engineering Department of Electronics Engineering Qom Iran Tarbiat Modares University Faculty of Electrical & Computer Engineering Department of Electronics Engineering Tehran Iran

ISBN: (纸本)9798350366013

In this article, a system has been designed using the Internet of Things and machine learning to detect equipment theft, monitor the entry and exit of people, as well as monitor people's activities to prevent equipment theft and provide security to the environment against possible theft. By using the motion detection sensor and the push buttons installed on the entrance doors, the entry and movement of people are monitored in real-time. Then, with machine vision and image processing algorithms, known and unknown people are cleaned and if necessary, a warning message including the video of the person's presence in the environment along with the person's image is sent to the relevant official. Also, the security doors will be locked until the relevant official confirms the safe situation. © 2023 IEEE.

关键词： Internet of things

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Artificial Intelligence-Based Secured Smart Mirror Control Systems with Enhanced Vehicle and Signboard Detection 11th

Artificial Intelligence-Based Secured Smart Mirror Control S...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Sanjay, S. Nithish Soorya, S.S. Vengatesh, R. Prakasam, P. Annamalai, Pushparaghavan School of Electronics Engineering Vellore Institute of Technology Vellore India Faculty of Electrical and Computer Engineering Bahirdar University Bahir Dar Ethiopia

ISBN: (纸本)9783031641503

Vehicle accidents have a significant impact on society, and they have a number of detrimental repercussions on individuals, families, and communities. Vehicles are an essential part of everyone’s daily lives in the modern world;therefore, it is very important to drive them in a cautious manner to prevent accidents. A side mirror is very important equipment in a vehicle as it helps drivers with a view of the area behind and to the sides of the vehicle. The proposed work focuses on the enhancement of artificial intelligence (AI)-based automatic side mirror folding systems in addition with vehicle and signboard detection as driver alert measures. The system proposed inspects authorized persons with the help of face detection and fingerprint verification applications provided via individual phones. The signboard detection helps the driver to notice signboards to make necessary adjustments in very crowded traffic areas, and vehicle detection helps the driver to drive in a cautious manner, specifically when taking turns in highly congested areas with the help of Internet of Things (IOT) applications. The system checks for the presence of signboards, monitors the surroundings, and supervises the security of the vehicle using face detection and android app-based fingerprint verification. The proposed AI-based method achieved the 95.5% testing accuracy for unregistered persons with the help of face detection and fingerprint verification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mirrors

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