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Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1-28页

作者： El-Shafai, Walid Khallaf, Fatma El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Ahram Canadian University Giza 6th October City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia

Medical image encryption is a mandatory process in various healthcare, Internet of Medical Things (IoMT) and cloud services. This paper provides a robust cryptosystem based on a 3D chaotic map for the medical image encryption in secure IoMT and cloud services. The proposed encryption method depends on a 3D chaotic map. With this map, a nonlinear ciphering process is implemented for pixel value diffusion and position permutation. Five operations are conducted in the suggested cryptosystem on the medical images to be transmitted in order to attain a high security level. These operations are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. To validate the suggested cryptosystem, different medical images with different characteristics are tested. Moreover, the suggested cryptosystem and more recent state-of-the-art security systems are discussed and analyzed in a comparative study. Different assessment parameters are adopted to assess the suggested cryptosystem, which reveals high security performance. The simulation outcomes show that the suggested cryptosystem is trustworthy. Furthermore, it offers high robustness levels and recommended security levels for utilization in healthcare, IoMT and cloud service applications. It is observed that the amount of average entropy obtained by the proposed 3D chaos cryptosystem is 7.95 which is very close to its best value of 8, and the average NPCR value is 99.62% which is also much close to its supreme value of 99.60%. In addition, all other results scored for the test evaluation parameters on the suggested cryptosystem are superior to those of the traditional security systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Cryptography

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Smart Street Lighting for Enhancing Energy Efficiency in Urban Environments 32

Smart Street Lighting for Enhancing Energy Efficiency in Urb...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Krizanovic, Visnja Pejkovic, Ana Pracny, Patrik J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789532901382

In the ever-evolving landscape of urban development, innovative solutions are constantly sought to improve the quality of life of residents. Moving away from conventional static street lighting, one such transformative solution for residential environments is smart street lighting. This intelligent solution brings an increase in energy efficiency. In this paper, the goal is to analyze the implementation of smart street lighting within the concept of a smart city based on the possibilities of saving and efficient use of energy. In selected case studies of urban scenarios, through simulation procedures, the possibility of implementing smart lighting was analyzed from the point of view of energy saving, indicating the importance of this aspect in the context of building smart energy lighting systems in urban environments. The discussions given in the paper show a quantitative assessment of energy savings in urban areas by replacing traditional lighting systems. The results of the analyzes show that the analyzed solutions provide useful guidelines for the application of the concept of smart lighting in urban environments and the selection of suitable solutions with regard to possible reductions in energy costs. © 2024 University of Split, FESB.

关键词： energy efficiency smart cities Smart street lighting

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Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

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computers, Materials & Continua 2023年第3期74卷 5301-5317页

作者： Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain Department of Computer Science The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Bahria University Lahore CampusLahorePakistan Software Engineering Department Faculty of ITUniversity of Central PunjabLahorePakistan Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia

Recommendation services become an essential and hot research topic for researchers *** data such asReviews play an important role in the recommendation of the *** was achieved by deep learning approaches for capturing user and product information from a short ***,such previously used approaches do not fairly and efficiently incorporate users’preferences and product *** proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True *** overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or *** proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed *** results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

关键词： Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence

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Maximizing a κ-Submodular Maximization Function under an Individual Knapsack Constraint 12

Maximizing a κ-Submodular Maximization Function under an In...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Tran, Tan Pham, Canh Ha, Dung Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this work, we consider a novel problem of maximizing monotone k-submodular functions under the individual knapsack constraint () over the ground set V, which has been found numerous applications in machine learning, including data summarization and information propagation. We propose an approximation algorithm that has approximation ratio and takes O(nklog (n)/μ) query complexity, where μ is an input parameter. Alongside theoretical analysis, we conduct extensive experiments on our proposed algorithm via some applications, such as Influence Maximization and Sensor Placement. The experimental results demonstrate that our algorithm gives competitive solution quality with state-of-the-art techniques but significantly reduces the required queries. © 2023 ACM.

关键词： Combinatorial optimization

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2024年 1-23页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Video and image quality improvement using an enhanced optimized dehazing technique

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Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

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Preliminary Analysis of Programming Exercise Logs to Provide Effective Feedback to Students 14

Preliminary Analysis of Programming Exercise Logs to Provide...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Tanaka, Tetsuo Shrestha, Sujan Miyazaki, Tsuyoshi Okazaki, Hidetoshi Nakakawaji, Nozomi Ueda, Mari Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan Kanagawa Institute of Technology Course of Information and Computer Sciences Graduate School of Engineering Atsugi Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information Media Atsugi Japan

ISBN: (纸本)9798350324228

The authors have developed a programming exercise system and are already using it in classes. This system provides students with a web-based programming environment, and monitor the time that students worked on exercises, logging operations such as program editing, building, execution, and testing, and their results. For teachers, it also shows how each student is progressing, the source code creation process, and the occurrence of errors. Teachers' evaluation of these functions is beneficial, but how feedback is given to students is inadequate. In this study, to examine feedback to students, we do a preliminary analysis of the operating log of this system. We used this system in a programming class of about 230 students for 15 weeks, collecting thousands to 100,000 log events such as the number of keystrokes, the number of working hours, the number of exercise programs completed, the number of copies and pastes, etc. per day. We can summarize our results as follows: 'The greater the number of keystrokes, working hours, and program completions in class, the better the results';'The number of keystrokes after the class ends (after the answer example has been published) does not affect the grades', and similar conclusions. In addition, based on these findings, we can identify conditions that increase the probability of becoming a poor performer. © 2023 IEEE.

关键词： Students

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COVID-19 Fake News Detection in Malaysia – A Supervised Approach 23

COVID-19 Fake News Detection in Malaysia – A Supervised App...

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12th International Conference on Software and computer Applications, ICSCA 2023

作者： Ramakrishnan, Kalaimagal Balakrishnan, Vimala Chong, Soo Mun Faculty of Science and Engineering University of Nottingham Malaysia Faculty of Computer Science and Information Technology University Malaya Malaysia

ISBN: (纸本)9781450398589

Social media has been flooded with enormous amounts of COVID-19-related information ever since the COVID- 19 pandemic started back in 2020. Since then, Malaysian citizens have become more reliant than ever on social media for consumption of COVID-19 information. However, the lack of COVID-19 news regulations on social media platforms encouraged people to post unverified, fake and misleading COVID-19 related information. Because of the time-consuming nature of fact-checking, people often take these unverified COVID-19 news for granted. Consequently, people inadvertently spread these fake COVID-19 news to their families, friends and relatives on social messaging platforms like WhatsApp. The spread of COVID- 19 fake news online in Malaysia can have severe sequences, causing widespread panic among fellow Malaysians. In this paper, we proposed a supervised learning approach to detect COVID-19 fake news. The fake news on COVID-19 were scraped from the website called Sebenarnya, and real news were scraped from The Star website. We applied a semantic model with different word representations which include Bag of Words (BOW), Term Frequency - Inverse Document Frequency (TF-IDF), Word2Vec and Global Vectors (GloVe). In the evaluation step, 6 supervised machine learning algorithms were applied such as Multinomial Naive Bayes, Support Vector Machines, Decision Tree, Random Forest, Logistic Regression and Adaboost. Afterward, 10-fold cross validation was used to train and evaluate the 6 supervised algorithms according to performance metrics such as accuracy, precision, recall, AUC-ROC, F1-score. The results showed that Random Forest with the word representation of TF-IDF per- formed the best with over 97% accuracy in contrast to numerous conventional supervised machine learning classifiers. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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A Comparative Study of IDS-Based Deep Learning Models for IoT Network

A Comparative Study of IDS-Based Deep Learning Models for Io...

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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Shaker, Bassam Noori Al-Musawi, Bahaa Qasim Hassan, Mohammed Falih Faculty of Computer Science and Information Technology University of Al-Qadisiyah Iraq Faculty of Engineering University of Kufa Iraq

ISBN: (纸本)9798400708268

The proliferation of connected devices within Internet of Things (IoT) networks has underscored the critical necessity of developing efficient Intrusion Detection Systems (IDS) that can adeptly identify and counteract threats. Deep learning algorithms are better than classical machine learning at automatically learning patterns and representations from raw data, enabling them to detect complex attacks. This research aims to conduct a comparative study of IDSs implemented through three deep learning models: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN). The study employs three datasets (NF-UNSW-NB15, NF-BoT-IoT, and NF-ToN-IoT) to assess model performance in both binary and multiclass classification scenarios. The findings indicate that for binary traffic classification, DNN outperforms the other two models with an accuracy bout (98%) for all datasets. In the context of multiclass traffic classification, the DNN model surpasses the performance of the other model except for NF-ToN-IoT a CNN model score (69.08%). © 2023 Copyright held by the owner/author(s).

关键词： Internet of things

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