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New Composite Chaotic Map Applied to an Image Encryption Scheme in Cybersecurity Applications

IEEE Access 2025年 13卷 70676-70690页

作者： Kouadra, Imane Bekkouche, Tewfik Ziet, Lahcene Mehidi, Imane Alshathri, Samah El-Shafai, Walid Ferhat Abbas University LEPCI Laboratory Department of Electronics Setif1900 Algeria University of Bordj Bou Arreridj ETA Laboratory Department of Electromechanics Bordj Bou Arreridj3400 Algeria Ferhat Abbas University Laboratory of Dosing Analysis and Characterization with High-Resolution Department of Physics Setif1900 Algeria Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt

Chaotic systems have long played a crucial role in cryptographic applications due to their inherent unpredictability and sensitivity to initial conditions. Building on this foundation, this paper introduces an Improved Composite Chaotic Map (ICCM), inspired by classical quadratic and cubic chaotic maps. The proposed ICCM is rigorously validated through Lyapunov exponents and bifurcation diagrams, demonstrating its enhanced dynamical complexity and suitability for secure applications. Designed to expand the chaotic range, ICCM incorporates an additional parameter that significantly enlarges the keyspace, thereby improving the robustness and adaptability of encryption methods. To demonstrate its practical utility, ICCM is integrated into a novel opto-digital encryption scheme aimed at enhancing sensitivity and ensuring superior encryption performance. The effectiveness of this scheme is evaluated using a comprehensive suite of tests, including sensitivity analysis, histogram analysis, the NIST statistical test suite ( 1515 passed), and key security metrics. A high mean square error (MSE) value ( > 104} ) indicates a significant difference between the original and encrypted images, while correlation coefficients ( Cr ≈ 0.0001 to 0.0028) and a low peak signal-to-noise ratio (PSNR 674 ) ensures resilience against brute-force attacks. Simulation results reveal that the ICCM-based encryption scheme achieves a Lyapunov exponent of 21.42 (compared to 19 in prior works) and withstands data loss up to 50%. These findings position ICCM as a robust defense against statistical, differential, and chosen-plaintext attacks. In conclusion, the proposed ICCM and its integration into the opto-digital encryption scheme advance the security of encryption systems, setting a new benchmark for chaos-based cryptography. The results underscore the model’s potential to address modern data security challenges and inspire further research in this critical field. © 2013 IEEE.

关键词： Sensitivity analysis

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Potential mechanisms of non-coding RNA regulation in Alzheimer's disease

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Neural Regeneration Research 2026年第1期21卷 265-280页

作者： Yue Sun Xinping Pang Xudong Huang Dinglu Liu Jingyue Huang Pengtao Zheng Yanyu Wei Chaoyang Pang College of Computer Science Sichuan Normal UniversityChengduSichuan ProvinceChina School of Science Xi’an Jiaotong-Liverpool UniversitySuzhouJiangsu ProvinceChina Neurochemistry Laboratory Department of PsychiatryMassachusetts General Hospital and Harvard Medical SchoolCharlestownMAUSA National Key Laboratory of Science and Technology on Vacuum Electronics School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina

Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the *** its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its *** in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's *** non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic ***,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the *** review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding *** comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein *** example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease ***,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease ***,circular RNAs play significant roles in regulating neuroinflammatory *** integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's *** review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially pav

关键词： Alzheimer's disease biomarkers circular RNA long non-coding RNA microRNA ncRNA regulation neurodegeneration non-coding RNA pathogenesis therapeutic targets

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Enhanced Modified Contrast Enhancement and Exposure Fusion: a visionary dehazing approach for clarity in hazy imagery

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Multimedia Tools and Applications 2025年 1-24页

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

In the field of image processing, haze is a type of atmospheric scattering that reduces contrast and clarity of images, frequently masking small details and far-off objects. It is mostly caused by microscopic airborne particles like smoke, dust, and water droplets that scatter incoming light. The scattered light, producing a hazy image, diminishes the direct light that reaches the camera sensor. Many image processing methods have been presened to solve this problem and lessen the impacts of haze. Usually, these methods work by determining how much haze is there in an image, and then using this information to get the image back to its assumed previous quality. In this work, we present the proposed Modified Contrast Enhancement and Exposure Fusion (MCEEF) technique. The MCEEF dehazing technique falls under the umbrella of enhancement-based dehazing techniques. In this technique, hazy frames or images undergo sharpening through a Smoothing-Sharpening Image Filter (SSIF) designed to accentuate the disparity between the haze and objects in images. Subsequently, Gamma Correction (GC) and Color Preserving Adaptive Histogram Equalization (CP-AHE) enhance the sharpened hazy images by augmenting their contrast. Dehazed images are then obtained by fusing the results of the CP-AHE- and GC-enhanced images. The MCEEF dehazing technique is aided with enhanced hazy images or frames as input for the dehazing process. Moreover, essential enhancement tools precede the MCEEF dehazing technique. These tools include homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), which are instrumental in controlling the dynamic range before the dehazing phase. In the proposed approach, a hazy image or frame is initially subjected to homomorphic processing, followed by the application of CLAHE, and finally, the MCEEF dehazing technique is employed. To demonstrate the effectiveness of the proposed approach, we apply it on both visible and Near Infrared (NIR) frames. We

关键词： Demulsification

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Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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2025 IEEE International Students' Conference on Electrical, electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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Weighted Codebook Scheme for RIS-Assisted Point-to-Point MIMO Communications

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IEEE Wireless Communications Letters 2025年第5期14卷 1571-1575页

作者： Yu, Zhiheng An, Jiancheng Gan, Lu Li, Hongbin Chatzinotas, Symeon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Yibin Institute of UESTC Yibin644000 China Nanyang Technological University School of Electrical and Electronics Engineering Nanyang Avenue Singapore639798 Singapore Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Luxembourg City1855 Luxembourg Kyung Hee University Department of Electronic Engineering Gyeonggi Yongin17104 Korea Republic of

Reconfigurable intelligent surfaces (RIS) can reshape the characteristics of wireless channels by intelligently regulating the phase shifts of reflecting elements. Recently, various codebook schemes have been utilized to optimize the reflection coefficients (RCs);however, the selection of the optimal codeword is usually obtained by evaluating a metric of interest. In this letter, we propose a novel weighted design on the discrete Fourier transform (DFT) codebook to obtain the optimal RCs for RIS-assisted point-to-point multiple-input multiple-output (MIMO) systems. Specifically, we first introduce a channel training protocol where we configure the RIS RCs using the DFT codebook to obtain a set of observations through the uplink training process. Secondly, based on these observed samples, the Lagrange multiplier method is utilized to optimize the weights in an iterative manner, which could result in a higher channel capacity for assisting in the downlink data transmission. Thirdly, we investigate the effect of different codeword configuration orders on system performance and design an efficient codeword configuration method based on statistical channel state information (CSI). Finally, numerical simulations are provided to demonstrate the performance of the proposed scheme. © 2012 IEEE.

关键词： Lagrange multipliers

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Semi-Blind Channel Estimation and Achievable Rate Analysis for Uplink RIS-Enhanced Multi-User Networks

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular technology 2025年 6卷 1108-1139页

作者： Farghaly, Samar I. Ismail, Mohamed I. Fouda, Mostafa M. Alwakeel, Ahmed S. Tanta University Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta31111 Egypt Idaho State University Department of Electrical and Computer Engineering College of Science and Engineering PocatelloID83209 United States Modern Academy for Engineering Department of Electronic Engineering and Communication Technology Cairo Governorate 4411602 Egypt Center for Advanced Energy Studies Idaho FallsID83401 United States Suez University Department of Electrical Engineering Faculty of Engineering Suez43221 Egypt

Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35–40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks. © 2020 IEEE.

关键词： Channel state information

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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model 5th

Deep Learning-Based Cellular Nuclei Segmentation Using Trans...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European Conference on Advances in Databases and Information Systems, ADBIS 2024

作者： Erezman, Mateusz Dziubich, Tomasz Computer Vision and Artificial Intelligence Laboratory Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

ISBN: (纸本)9783031704208

Accurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there arises a pressing need for automated image analysis tools to augment medical practitioners’ efforts. In this paper, we present a novel approach utilising Transformer model, originally designed for natural language processing tasks, for automated cellular nuclei segmentation in whole-slide microscopic images. Specifically targeting cell nuclei, this methodology holds significance as the initial phase in diagnosing various illnesses, streamlining the analysis and quantification process. The study introduces a novel model that combines a U-Net architecture with a Transformer-based network functioning as a parallel encoder. This model was compared against three other popular architectures in the literature: U-Net, ResU-Net, and LinkNet-34. The impact of augmentation and colour normalisation techniques was investigated. The average Dice similarity coefficient for the considered images was found to be 0.8041. The obtained results seem to be clinically relevant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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FUCOM-Kendall Model for Evaluating E-Scooter Adoption: A Case Study in Catania, Italy

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Transportation Research Procedia 2025年 86卷 1-8页

作者： Vincenza Torrisi Rosalia Camporeale Sarbast Moslem Tiziana Campisi Department of Electric Electronic and Computer Engineering Catania Italy Department of Technology and Society Division of Transport and Roads Lund Sweden School of Architecture Planning and Environmental Policy University College Dublin Ireland Faculty of Engineering and Architecture University of Enna KORE Enna Italy

Electric scooters (e-scooters) have emerged as a fundamental component of urban micro-mobility, particularly in European cities during the Covid-19 pandemic. This study employs the Full Consistency Method (FUCOM) and Kendall rank correlation method to assess the preferences and priorities of university students in Catania, Italy, regarding e-scooter adoption. By analyzing influencing factors, this research offers a comprehensive understanding of student attitudes towards e-scooters and their adoption, contributing to strategies for enhancing sustainable urban mobility. The findings highlight significant preferences for factors such as parking ease, portability, and intermodality, while also identifying safety concerns and the need for better integration with public transport. These insights underscore the necessity for targeted improvements in e-scooter infrastructure and policies, aiming to optimize urban mobility and meet the specific needs of potential users. Further research will focus on comparative analysis across different urban settings, investigating addition user-centric factors to capture a more comprehensive view of e-scooter adoption.

关键词： e-scooter multi-criteria decision making FUCOM method Kendall rank correlation urban mobility

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