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

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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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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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer systems science & Engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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The Reliability of information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

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2nd International Conference on Next-Generation Computing and information systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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Guest Editorial: Special Issue of Selected Extended Papers From the IEEE International Conference on Flexible, Printable Sensors and systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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Can metacognition predict your success in solving problems? An exploratory case study in programming

arXiv

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arXiv 2024年

作者： Bubnic, Bostjan Kovačević, Željko Kosar, Tomaž Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Computer Science and Information Technology Zagreb University of Applied Sciences Zagreb Croatia

Metacognition has been recognized as an essential skill for academic success and for performance in solving problems. During learning or problem-solving, metacognitive skills facilitate a range of cognitive and affective processes, leading collectively to improved performance. This study explores the predictive potential of metacognition in the second introductory programming course. A two-dimensional model has been proposed, consisting of metacognitive awareness and metacognitive behavior. To evaluate the predictive capacity of metacognition empirically, an exploratory case study with 194 participants from two institutions was conducted in the second introductory programming course. A latent approach was employed to examine the associations between metacognition and performance in object-oriented programming. Our findings indicate that both metacognitive dimensions have a positive effect on programming. Likewise, the results of the structural equation modeling show that 27% of variance in programming performance is explained by metacognitive behavior. Following the results, metacognition has the potential to be considered as one of the important predictors of performance in introductory programming. © 2024, CC BY-NC-ND.

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Biometrically-independent fuzzy signatures: signing with biometrics without embedding them

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EURASIP Journal on Image and Video Processing 2025年第1期2025卷 1-29页

作者： Otsuki, Saki Higo, Haruna Isshiki, Toshiyuki Mori, Kengo Obana, Satoshi Tamiya, Hiroto Yasunaga, Kenji Secure System Platform Research Laboratories NEC Corporation Kawasaki Japan Faculty of Information Systems University of Nagasaki Sasebo Japan Faculty of Information and Computer Science Hosei University Koganei Japan Department of Mathematical and Computing Science School of Computing Institute of Science Tokyo Meguro-ku Japan

In cryptocurrency transactions, effective wallet key management by users is crucial for asset protection. Fuzzy Signature (FS) schemes enhance the binding between signers and their keys by using biometric information instead of traditional signing keys, offering secure and convenient key management. However, FS faces two significant privacy concerns. First, using biometric information as the signing key restricts users’ ability to manage multiple distinct keys for different applications. Second, the design that absorbs biometric fuzziness during verification results in publicly available verification keys that contain data dependent on biometric information. This paper introduces Biometrically-Independent Fuzzy Signature (BIFS), a variant addressing existing FS scheme problems. We propose a generic construction for BIFS and present a BIFS scheme compatible with BLS signatures supported by Ethereum 2.0. Experimental results confirm that the success rate of signature generation maintains the same authentication accuracy as the employed facial recognition system. Moreover, our results show that the proposed scheme is practical since the signature generation process requires approximately 2 ms.

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Tai Chi Exercise Posture Detection and Assessment for the Elderly Using BPNN and 2 Kinect Cameras

Tai Chi Exercise Posture Detection and Assessment for the El...

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2023 International Technical Conference on Circuits/systems, computers, and Communications, ITC-CSCC 2023

作者： Kanchanapaetnukul, Sarawin Aunkaew, Rungarun Charernmool, Piyanuch Daoudi, Mohamed Saraubon, Kobkiat Visutsak, Porawat King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Bangkok Thailand Chaopraya University Faculty of Science and Technology Nakhon Sawan Thailand Imt Lille Douai Univ. Lille Cnrs Umr 9189 CRIStAL LilleF-59000 France

ISBN: (纸本)9798350326413

Exercise and recreation are beneficial to all genders and ages, exercise reduces stress and makes people healthy. Physical limitation among the elderly is the major concern and needed to be taking care for the elderly exercise. Low-impact exercises such as walking, slow jogging in the park, and Tai Chi are recommended for the elderly. Tai Chi is a slow and gentle exercise, which can help the circulatory system and dementia in the elderly;it also helps the elderly to get socialized and make new friends. Unfortunately, in the COVID-19 pandemic, people must stay in the house and avoid social activities including outdoor exercises and recreation. This paper aims to develop Tai Chi exercise posture detection and assessment system for helping the elderly to practice Tai Chi at home by themselves. The system provides Tai Chi video clips for demonstration and the graphics user interface (GUI) for capturing the movement of the elderly while they are exercising Tai Chi. The system will detect and assess the elderly's movement whether it is correct or not by using 2 Kinect cameras. The Kinect is used for joints detection and the series of joints movement will be used to compare with the correct Tai Chi postures stored in the system. The questionnaire, which was developed based on the usability criteria defined by the ISO 9241-11 and the users' experience, was used to evaluate the system. The precision, recall, F1-score, and accuracy of our system are 0.94, 0.98, 0.96, and 0.93 respectively. © 2023 IEEE.

关键词： Parks

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Predicting Drug-Drug Interactions with Machine-Learning and ATC-SMILES Combined Representation

Predicting Drug-Drug Interactions with Machine-Learning and ...

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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Yasmin Atef Radwan Karam Abdelghany Gouda Ibrahim Zaghloul Abdelbaky Mona Mohamed Arafa Information Systems Department Higher Institute of Computer Science & Information Technology EI-Shorouk Academy Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Egypt

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Polypharmacy is a potential strategy for managing such intricate disorders, encompassing conditions like cancer, diabetes, and age-related issues in older individuals. Nonetheless, when a medication is combined with one or more drugs that either enhance, diminish, or counteract its intended effects, it can lead to undesired adverse reactions. In severe cases, these interactions can cause serious morbidity and increased mortality rates globally. In this study, we collected a Drug-Drug interaction dataset from the DrugBank database. Various chemical features were then extracted from the Simplified Molecular-Input Line-Entry System of interacting drug pairs. Our emphasis was on representing the Molecular Access System fingerprints of these drug pairs. Molecular Access System fingerprints signify the presence or absence of specific substructures in the molecule and were generated using the RDKit Open-Source Cheminformatics Software. Furthermore, we incorporated Anatomical Therapeutic Chemical classifications into our analysis. Finally, we employed various machine learning algorithms, using K-Nearest Neighbor, Random Forest, Logistic Regression, Support Vector Machine, and XGBoost for learning the extracted features and predict large-scale Drug-Drug interactions among various drug pairs. Among these models, the XGBoost model exhibited superior performance across most measurement metrics.

关键词： Drugs Support vector machines Machine learning algorithms Fingerprint recognition Feature extraction Prediction algorithms Vectors Encoding Chemicals Random forests

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