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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic Systems

IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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DURATION-BASED INVESTIGATION OF USER CONTENT CHOICES IN THE EXIT OF FILTER BUBBLES 6

DURATION-BASED INVESTIGATION OF USER CONTENT CHOICES IN THE ...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Bunza, Rabiatu DEPT. OF COMPUTER SCIENCE FACULTY OF SCIENCE AND ENGINEERING SWANSEA UNIVERSITY United Kingdom

ISBN: (纸本)9798350394634

Content personalization on social platforms has been linked to the creation of filter bubbles. The algorithms provide content recommendations based on the user’s browsing history and interests, which limits content diversity. Studies have been conducted on platforms such as Facebook and YouTube to investigate user awareness and strategies to exit filter bubbles. However, no study has employed a multi-platform, user-centered perspective in investigating filter bubble exit through change in search content, as well as the duration of the content transition across multiple platforms. This study examines the duration of user exit of filter bubbles, across three social platforms;TikTok, YouTube Shots, and Instagram Reels, as well as the impact of word similarity on the duration of the transition between contents, across the platforms. The methodology applied involved triggering the filter bubble effect through user interactions with the search content across the different platforms. Findings revealed that the mean time of a transition between search terms on TikTok is significantly higher than the mean time on Instagram reels, and YouTube shorts. Similarly, the study identified differences in the duration of transition across the different platforms, due to the similarity of the search terms. Apart from improving user awareness of a filter bubble exit strategy, this study contributes to Human-computer Interaction research by highlighting some design implications for future social media application design. © 2024 IEEE.

关键词： Social networking (online)

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Developing a Low-Cost Prosthetic Hand with Real-Time Machine Learning Capabilities 23

Developing a Low-Cost Prosthetic Hand with Real-Time Machine...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Adani, M. Syakhisk Naufal Widasari, Edita Rosana Setiawan, Eko Dept. Computer Engineering Faculty of Computer Science Brawijaya University Indonesia

ISBN: (纸本)9798400708503

The global prevalence of arm amputations, particularly transradial or below-elbow amputations, presents a pressing challenge due to the high costs of commercially available prosthetic hands. This issue is particularly acute in developing countries, where financial constraints hinder access to functional prosthetic solutions. Addressing this gap, this research introduces an innovative approach that merges electromyography (EMG) signal recognition and machine learning to develop an affordable and versatile bionic below-elbow prosthetic hand. The integration of EMG signals for control, coupled with machine learning algorithms, enables accurate classification of hand movements, resulting in enhanced movement precision and a broader range of gestures. This solution overcomes limitations in existing prosthetic hands, offering advanced EMG-based control and comprehensive movement capabilities, empowering users to perform intricate tasks and regain functional autonomy. The study undertakes a comprehensive analysis of the developed system's performance, encompassing hardware, software, and machine learning components. Results show promising accuracy levels for movement classification using the K-Nearest Neighbors (KNN) machine learning method. Execution time variations are observed among subjects and gestures, influenced by factors such as subject sensitivity and gesture complexity. Energy consumption analysis reveals disparities in power usage among different movements, emphasizing the impact of movement complexity on power requirements. A cost analysis demonstrates the affordability of the prototype, positioning it as a cost-effective alternative to existing prosthetic hand options. Comparing this solution to other research and commercial prosthetic hands highlights its uniqueness, particularly its emphasis on affordability. © 2023 Owner/Author.

关键词： Nearest neighbor search

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

A Novel Audio-Visual Multimodal Semi-Supervised Model Based ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yaqin Sun, Chenjian Dong, Yihong Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (纸本)9798350368741

There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients' audio and facial visuals, depression assessments can be conducted. However, existing work is predominantly based on single modalities. Additionally, acquiring a sufficient amount of labeled data in clinical settings is challenging and costly. To leverage multimodal audio-visual data while addressing the issue of lacking trainable labeled data, we propose an audiovisual multimodal semi-supervised depression detection model based on Graph Neural Networks (AVS-GNN). This model first extracts dual-modality temporal information from audio features and facial visual features of patients and obtains modality-specific high-level embedding representations through graph representation learning. Subsequently, it utilizes graph-based contrastive unsupervised learning to capture consistency information between pairs of unlabeled samples across different modalities and to facilitate cross-modal interactions. We specifically designed a hybrid weighted pseudo-labeling strategy to assign high-confidence pseudo-labels to unlabeled data and further retraining the model. Experiments on two depression datasets show that this model outperforms baseline methods across all evaluation metrics. © 2025 IEEE.

关键词： audio-visual multimodal depression detection graph neural networks semi-supervised

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EUR/USD Forex Market Prices Prediction Using MLP 16

EUR/USD Forex Market Prices Prediction Using MLP

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Diab, Raed Nohra, Alain Atwi, Ahmad Haidar, Gaby Abou Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Department of Computer and Communications Engineering United States

ISBN: (纸本)9798350333510

Neural networks is one of the most developed concepts in artificial intelligence owing to their ability to solve complex computational tasks, and its efficiency in finding solutions. There is a wide range of applications that adopt this technique, one of which is in the financial investment domain. This paper presents an approach to predict foreign exchange market prices using a neural network. It considers MLP techniques using sequential models with dense layer methods. Tests were conducted on one of the forex major pair data sets, EURUSD. The work is divided into 5 parts: prediction for the next minute, hour, day, week, and month resulting in 5 different models. Accordingly, a best-case accuracy of 99% for MLP algorithms is demonstrated. © 2023 IEEE.

关键词： Investments

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A hybrid CNN-SVM model optimized with PSO for accurate and non-invasive brain tumor classification

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Neural Computing and Applications 2025年 1-30页

作者： Semwal, Tanay Jain, Sania Mohanta, Agradeep Jain, Ankur School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Kothri Kalan India Faculty of Science The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

Brain and central nervous system (CNS) cancers are the leading cause of cancer-related mortality, presenting significant diagnostic challenges due to their aggressive nature and diverse manifestations. While biopsies are the gold standard for diagnosis, their invasiveness and associated risks necessitate the development of noninvasive, automated tools for accurate and rapid tumor classification. This research presents a hybrid approach that combines a convolutional neural network (CNN) for feature extraction with a support vector machine (SVM) classifier, optimized by particle swarm optimization (PSO). The proposed model achieves multiclass classification across four tumor categories: glioma, meningioma, pituitary, and no tumor. The model incorporates data augmentation, dropout layers, pooling, batch-normalization, and hyperparameter tuning via PSO, to enhance generalization and minimize overfitting. The experimental analysis on the brain tumor MRI dataset highlights the model's strong performance, with an overall accuracy of 84.77%, a macro-average F1-score of 0.80, and a macro-average precision of 0.91. The performance of the PSO-optimized CNN-SVM model was benchmarked against traditional machine learning (ML) and deep learning (DL) methods, which demonstrated mean accuracies between 48 and 72%. The proposed hybrid model significantly surpasses these conventional approaches, establishing its superiority in classification accuracy and reliability. The results highlight the proposed model's reliability as a noninvasive diagnostic solution, offering considerable upside for enhancing early brain tumor detection and aiding in treatment planning. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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computer Modeling in engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid 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 of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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