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25th International Arab Conference on information technology, ACIT 2024

作者： Khattab, Nada Al-Qerem, Ahmad Nashwan, Shadi Jebreen, Issam Rajab, Mohammed Alauthman, Mohammad Jeadi, Mohammad Elrashidi, Ali Zarqa University Faculty of Information Technology Computer Science Department Zarqa13110 Jordan Middle East University Faculty of Information Technology Cybersecurity Department Amman11831 Jordan University Headquarter University of Anbar Ramadi Iraq University of Petra Department of Information Security Amman11196 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

the proliferation of fake news has become a widespread problem in the contemporary era of information, posing possible harm to public discussions, political stability, and community confidence. To tackle this difficulty, it is necessary to develop strong and effective detection methods that can accurately recognize misinformation in several languages, including Arabic. The aim of this study is to create and assess different models specifically designed for the identification of fake news in Arabic *** of deep Learning models like Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Bidirectional long short term memory (Bi-LSTM) and convolutional neural network (CNN) are used in this study. On the other hand, some of the traditional machine learning models like k-nearest neighbors (KNN), Random Forests (RF), Extreme Gradient Boosting (XGBoost), decision tree (DT) and multi-layer perceptron (MLP) are also used. Also, transformers, like Generative pre-trained Transformer 3 (Gpt-3) and Bidirectional Encoder Representations from Transformers (BERT), are used as a feature extraction to convert text into numerical representation and assign different number to each token. All of these models were used in evaluation which of them is the best model in the process of fake news detection for the Arabic Language *** findings demonstrate strong performance across deep learning models, with CNN-LSTM, Bi-LSTM and CNN-Bi-LSTM exhibiting the best accuracy rates of 93.2% ,92.6%, 93.6% respectively. The LSTM model achieves a moderate accuracy of 87.4%, whilst the RNN model demonstrates a more modest accuracy of 56.8%. The results emphasize the capability of deep learning methods in reducing the dissemination of fake information in Arabic-language datasets. © 2024 IEEE.

关键词： Long short-term memory

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Brain magnetic resonance image (MRI) segmentation using multimodal optimization

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Multimedia Tools and Applications 2025年第16期84卷 16971-17020页

作者： Akan, Taymaz Oskouei, Amin Golzari Alp, Sait Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center ShreveportLA71103 United States Software Engineering Department Istanbul Topkapi University Istanbul34020 Turkey Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering İstinye University İstanbul Turkey Department of Artificial Intelligence Engineering Trabzon University Trabzon61335 Turkey

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters. © The Auth

关键词： Image segmentation

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare 3

Reinforcement Learning for Predictive Modeling and Managemen...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Chaudhary, Pavan Inzimam Hannah Jessie Rani, R. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Electrical and Electronics Engineering Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350343274

The difficulties caused by rare genetic abnormalities in pediatric healthcare highlight the need for advances in predictive modeling and treatment choices. An innovative method is discussed in the article 'Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare.' Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critical are the three RL algorithms used in the suggested approach. To determine the most effective treatment for children with uncommon genetic illnesses, we used these algorithms to create a prediction model. To improve healthcare quality, we carefully evaluate the suggested approach alongside six alternative methods with overlapping but distinct names. We also take accuracy and precision into account while making our assessments. The suggested strategy, which we refer to as the 'Novel Genetic Predictor (NGP),' excels in every metric we tested. The results show a considerable improvement in the detection of rare genetic disorders in children. NGP also demonstrates an enhanced ability to make precise predictions, which opens the path for treatment alternatives that are both patient-specific and more effective. This research not only shows the promise of RL in pediatric healthcare, but also emphasizes the need for tailored care for children with rare genetic disorders. The results show that NGP is better than other methods, and they provide doctors a tool they may use to better care for their patients. © 2023 IEEE.

关键词： Reinforcement learning

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Image Enhancement Using Adaptive Fractional Order Filter

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computer Systems Science & engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat Shakila Basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors 8

Human Activity Recognition for People with Knee Abnormality ...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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Self-adaptive Bat Algorithm With Genetic Operations

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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Comparison of Cost-effectivnes of Fixed and Two-axis Tracking PV System in Market Conditions 5

Comparison of Cost-effectivnes of Fixed and Two-axis Trackin...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Kljajić, Ružica Kraus, Zorislav Tudan, Marko Marí, Predrag Computer Science and Information Technology Osijek University of Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

PV systems are increasingly encouraged, and different technologies are differentiated between them. This paper provides a comparison of a fixed PV system with the two-axis tracking PV system. Two-axis tracking, although more expensive, provides numerous environmental benefits, primarily in the utilization of the area required for system installation as well as higher production per utilized area. The paper gives a comparison on the example of a small system with a power of 6.4 kW, comparing a fixed and two-axis system in terms of energy produced, income, and payback time. © 2022 IEEE.

关键词： Tracking (position)

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Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

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IEEE Internet of Things Journal 2023年第19期10卷 17254-17264页

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi Macquarie University School of Computing SydneyNSW2109 Australia CSIRO Data61 SydneyNSW1710 Australia Deakin University School of Information Technology Waurn PondsVIC3125 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Qilu University of Technology Shangdong Computer Science Center Jinan250316 China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems. © 2014 IEEE.

关键词： Speech recognition

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Interpretable SHAP-Driven Machine Learning for Accurate Fault Detection in Software engineering 5th

Interpretable SHAP-Driven Machine Learning for Accurate Fau...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Kassaymeh, Sofian Rjoub, Gaith Dssouli, Rachida Bentahar, Jamal Almobydeen, Shahed Bassam Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031731501

In order to design and develop secure and reliable software, accurate prediction of software errors is crucial. The complex, nonlinear relationship between software features and bugs that occur during development despite precautions and measures taken to prevent them makes it difficult for traditional empirical models to predict these bugs with any degree of accuracy. An integration between the Decision Tree (DT) model and the SHAP (Shapley Additive exPlanations) technique was developed in this work with the aim of predicting software faults and providing informative explanations of the expected results. The synergistic advantages of integrating DT and SHAP allow the creation of an accurate, efficient, and fully interpretable technique. In order to make the process visible and reliable, SHAP provides a global explanation of how features of the developed software affect quality and a local explanation of how features contribute to each prediction. The tendency of software features to influence software quality was revealed by feature dependence analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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