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28th International Congress on Sound and Vibration, ICSV 2022

作者： Peter, Brancevich Yibin, Li Belarusian State University of Informatics and Radioelectronics Faculty of Computer Systems and Networks Minsk Belarus Shandong University School of Information Science and Engineering Jinan China

ISBN: (纸本)9789811850707

The most wear-out is production equipment with rotary motion (turbines, generators, motors, gearboxes, pumps, compressors, fans, etc.). It is possible to reduce the cost of its operation by introducing modern maintenance systems, which are based on the use of technologies for monitoring, assessing the condition, diagnosing, predicting the development of defects, which, in terms of their organization and functioning, are intelligent systems. The state of the observed technical objects is determined by the parameters and characteristics. The aim of the work is to develop a model of the basic element of the decision-making system and consideration of options for its practical application. The proposed model of the basic element of the decision-making system allows you to create various structures designed to assess the state of complex technical and natural objects. An example of the implementation of a decision-making system for assessing the state of a multi-support power unit is presented, as a combination of basic solver modules. © International Institute of Acoustics and Vibration (IIAV), 2022.

关键词： Intelligent systems

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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Renewable Energy Technologies and Modern Communications systems: Future and Challenges, Conference on

作者： A. AL-Shehri Bushra Sahar F. Sabbeh Mohamed M. Dessouky Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

ISBN: (数字)9798331519445

ISBN: (纸本)9798331519452

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography Dataset (Kaggle), the SARS-CoV-2 CTScan Dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography Dataset and 99% accuracy on the SARS-CoV-2 CT-Scan Dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions.

关键词： COVID-19 Deep learning Solid modeling Accuracy Data augmentation Robustness Diagnostic radiography Medical diagnostic imaging X-ray imaging Standards

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An Efficient Scheme for Interference Mitigation in 6G-IoT Wireless Networks

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computers, Materials & Continua 2021年第12期69卷 3889-3902页

作者： Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Mohammed A.Al-Hagery Huda G.Iskander Quang Ngoc Nguyen Babar Shah Ki-Il Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Communications and Computer Engineering Faculty of Science and EngineeringWaseda UniversityShinjuku-kuTokyo169-0051Japan College of Technological Innovation Zayed UniversityAbu DhabiUAE Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

The Internet of Things(IoT)is the fourth technological revolution in the global information industry after computers,the Internet,and mobile communication *** combines radio-frequency identification devices,infrared sensors,global positioning systems,and various other *** sensing equipment is connected via the Internet,thus forming a vast *** these physical devices are connected to the Internet,the user terminal can be extended and expanded to exchange information,communicate with anything,and carry out identification,positioning,tracking,monitoring,and triggering of corresponding events on each device in the *** real life,the IoT has a wide range of applications,covering many fields,such as smart homes,smart logistics,fine agriculture and animal husbandry,national defense,and *** of the most significant factors in wireless channels is interference,which degrades the system *** the existing QR decomposition-based signal detection method is an emerging topic because of its low complexity,it does not solve the problem of poor detection ***,this study proposes a maximumlikelihood-based QR decomposition *** main idea is to estimate the initial level of detection using the maximum likelihood principle,and then the other layer is detected using a reliable *** optimal candidate is selected from the feedback by deploying the candidate points in an unreliable *** results show that the proposed algorithm effectively reduces the interference and propagation error compared with the algorithms reported in the literature.

关键词： 6G networks internet of things resource allocation optimization

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Brain Cancer Tumor Classification from Motion-Corrected MRI Images Using Convolutional Neural Network

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computers, Materials & Continua 2021年第8期68卷 1551-1563页

作者： Hanan Abdullah Mengash Hanan A.Hosni Mahmoud Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Systems Engineering Faculty of EngineeringUniversity of AlexandriaAlexandriaEgypt

Detection of brain tumors in MRI images is the first step in brain cancer *** accuracy of the diagnosis depends highly on the expertise of ***,automated diagnosis of brain cancer from MRI is receiving a large amount of ***,MRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor *** is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive *** neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and *** this paper,a CNN-based technique for brain tumor classification has been *** proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma *** implemented CNN was tested on MRI images that underwent a motion-correction *** CNN was evaluated using two performance measurement *** first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and *** best accuracy for this procedure was 96.26%when k=*** overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation *** results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from MRI images.

关键词： Classification convolutional neural network tumor classification MRI deep learning k-fold cross classification

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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Analysis of Approaches for Identifying Key Skills in Vacancies

Analysis of Approaches for Identifying Key Skills in Vacanci...

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International Conference on Soft Computing and Measurements (SCM)

作者： Pavel V. Korytov Ivan I. Kholod Yan Yu. Gribetskiy Ekaterina A. Andreeva Department of Computer Science and Engineering Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University “LETI” Saint-Petersburg Russia Department of Information systems Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University “LETI” Saint-Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

The task of identifying key skills from job vacancies descriptions is considered in the article. The source data consists of vacancy texts from the “Jobs in Russia” portal, in which skill-related entities were manually annotated. Keyword extraction methods (YAKE, PositionRank and WikiNEuRal, KBIR-InSpec), and a named entity recognition model trained on the source data are compared.

关键词： Analytical models Data preprocessing Data models Trajectory Data mining Task analysis Portals

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Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

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computer systems science & engineering 2022年第10期43卷 397-412页

作者： Sunil Dhankhar Mukesh Kumar Gupta Fida Hussain Memon Surbhi Bhatia Pankaj Dadheech Arwa Mashat Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthan302017India Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Mechatronics Engineering Jeju National UniversityJeju63243Korea Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

In today’s digital era,the text may be in form of *** research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary *** rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords *** primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant *** second goal is to maintain or improve the classification system’s *** experiment included news articles from various genres,such as Bollywood,politics,and *** proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human *** evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.

关键词： Support vector machine(SVM) optimization precision Hindi character recognition optical character recognition(OCR) automatic summarization and compression ratio

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Integrated RFID System for Intralogistics Operations with Industrial Mobile Robots 25

Integrated RFID System for Intralogistics Operations with In...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Pacheco, Fabio Daniel Rebelo, Paulo M. Sousa, Ricardo B. Silva, Manuel F. Mendonca, Helio S. INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal ISEP Polytechnic of Porto Porto4249-015 Portugal Vila Real5000-801 Portugal Faculty of Engineering Porto4200-465 Portugal

ISBN: (纸本)9798331538606

Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository. © 2025 IEEE.

关键词： Mobile robots

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

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14th International Conference on Information and Communication systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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Image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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