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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

computer Modeling in engineering & sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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Using Convolutional Neural Networks for Enhanced Pneumonia Detection via Chest X-Rays

Using Convolutional Neural Networks for Enhanced Pneumonia D...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Babar, Waqar Ali, Raja Hashim Faheem, Alishba Mansoor, Syed Ahmed Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

As a deadly lung disease, pneumonia remains a leading cause of child mortality under five years old. Machine learning, especially deep learning have played a significant role in the improved performance for detection and identification of various diseases in the field of healthcare. Neural networks, especially the recent development of newer architectures, have revolutionized the object identification, and classification applications in clinical diagnosis of various diseases. This study presents the application of Convolutional Neural Networks (CNN) for the timely and accurate detection of pneumonia using chest X-rays, a development with considerable potential in aiding clinical diagnosis. The research deployed dropout regularization in model design to mitigate overfitting and relied on recall and F1 scores for thorough model evaluation. Although comparable studies achieved higher overall accuracy, our models registered a recall rate of 98%, crucial in reducing false negatives and enhancing patient safety. This suggests the potential of our CNN model as a vital tool for healthcare professionals in early pneumonia detection in children and adults, with the capacity to process a high volume of X-ray images rapidly and accurately. The successful model construction was enabled through various parameter tuning techniques, thus enhancing patient care efficiency and the potential to decrease mortality rates. © 2024 IEEE.

关键词： Convolutional neural networks

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Software Reliability Assessment:An Architectural and Component Impact Analysis

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Tsinghua science and Technology 2025年第2期30卷 908-925页

作者： Saleh Alyahyan Mohammed Naif Alatawi Mrim M.Alnfiai Shoayee Dlaim Alotaibi Abdullah Alshammari Zaid Alzaid Hathal Salamah Alwageed Applied College in Dwadmi Shaqra UniverityShaqra 11961Saudi Arabia Department of Information Technology University of TabukTabuk 47713Saudi Arabia Department of Information Technology Taif UniversityTaif 21944Saudi Arabia Department of Artificial Intelligence and Data Science University of HailHail 55431Saudi Arabia College of Computer Science and Engineering University of Hafr AlbatinHafar Albatin 31991Saudi Arabia Department of Computer Science Islamic University of MadinahMadinah 42351Saudi Arabia College of Computer and Information Sciences Jouf UniversitySakaka 42421Saudi Arabia

In the software landscape,understanding component impacts on system reliability is pivotal,especially given the unique complexities of modern software *** paper presents a model tailored for software reliability *** approach introduces the“component influence”to measure a single component’s effect on overall system ***,we adapt a state transition model to cater to the diverse architectures of software *** a discrete-time Markov chain,we predict software *** test our model on an actual software system,finding it notably accurate and superior to existing *** work offers a promising direction for those venturing into software reliability enhancement.

关键词： software reliability architectural analysis state transition model

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MANA Cryptocurrency: Transactional Dynamics & Financial Metrics Study

MANA Cryptocurrency: Transactional Dynamics & Financial Metr...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Joshi, Kireet Raj, Ankush Iqbal, Mohammed Ismail Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Computer Science & Engineering Uttarakhand Dehradun248002 India University of Jammu Department of Computer Science and IT India University of Technology and Applied Sciences Petroleum Engineering Department Nizwa Oman

ISBN: (纸本)9798350394412

This research investigates the emerging domain of the Metaverse, a digital realm that is significantly influencing social interactions and economic landscapes. This study aims to investigate the complex variables that contribute to the economic valuation of virtual properties on the Decentraland platform, which is a famous Metaverse platform. Despite the growing significance of the Metaverse and its status as a prominent economic force, there is a noticeable lack of empirical research examining the distinct dynamics of land valuation within this context. The current corpus of scholarly work mostly focuses on valuations in real-world contexts, resulting in a significant knowledge gap about the specific factors that influence land prices in the Metaverse. This research seeks to address the existing vacuum in knowledge by doing a comprehensive literature review and platform analysis. The primary objective is to provide insights into the various elements that influence the financial prospects of Metaverse lands. The anticipated results of this study are expected to provide significant contributions to the comprehension of Metaverse economics. Additionally, these findings will serve as a foundation for future research endeavors that aim to explore the convergence of virtual and physical realms in relation to financial value. © 2024 IEEE.

关键词： Decentralized finance

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BEESO:Multi-strategy Boosted Snake-Inspired Optimizer for engineering Applications

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Journal of Bionic engineering 2023年第4期20卷 1791-1827页

作者： Gang Hu Rui Yang Muhammad Abbas Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Department of Mathematics University of SargodhaSargodha40100Pakistan University of North Carolina at Pembroke PembrokeNC28372USA

This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering *** a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal *** has a simple structure and offers a delicate balance between exploitation and ***,it also has some shortcomings to be *** proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in *** presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed *** increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population *** Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good *** performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering ***,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum *** findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all *** conclude,the proposed BEESO offers a good alternative to solving optimization issues.

关键词： Snake optimizer Bi-Directional Search Evolutionary Population Dynamics Elite Opposition-Based Learning Strategy Mechanical optimization design

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Towards Realistic Vibration Testing of Large Floor Batteries for Battery Electric Vehicles (BEV)

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Sound & Vibration 2022年第1期56卷 1-19页

作者： Benedikt Plaumann Faculty of Engineering and Computer Science Department Automotive and Aeronautical EngineeringHamburg University of Applied Sciences(HAW Hamburg)Hamburg20099Germany

This contribution shows an analysis of vibration measurement on large floor-mounted traction batteries of Battery Electric Vehicles(BEV).The focus lies on the requirements for a realistic replication of the mechanical environments in a testing *** the analysis on global bending transfer functions and local corner bending coherence indicate that neither a fully stiff fixation of the battery nor a completely independent movement on the four corners yields a realistic and conservative test *** contribution will further show what implication these findings have on future vibration&shock testing equipment for large traction ***,it will cover an outlook on how vibration behavior of highly integrated approaches(cell2car)changes the mechanical loads on the cells.

关键词： Battery electric vehicle BEV shock and vibration vehicle floor bending rechargeable energy storage system RESS

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Towards Underwater Sustainability with AIoT: Underwater Trash Management System Comprised of YOLOv8 with IoT-applied Segmentation 2

Towards Underwater Sustainability with AIoT: Underwater Tras...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Paneru, Biplov Poudel, Buddhi Sagar Shah, Krishna Bikram Paneru, Bishwash Chhetri, Sanjog Poudel, Yam Krishna Nepal Engineering College Pokhara University Department of Electronics and Communication Bhaktapur Nepal Nepal Engineering College Pokhara University Department of Computer Science and Engineering Bhaktapur Nepal Institute of Engineering Tribhuvan University Department of Applied Science & Chemical Engineering Lalitpur Nepal Nepal Research and Collaboration Center Department of Research and Development Kathmandu Nepal

ISBN: (纸本)9798331517984

Effective techniques for managing rubbish underwater are essential due to growing environmental concerns about contamination under the sea. In this research, we offer a novel method for efficient underwater trash detection and management that makes use of the Internet of Things (IoT) and the YOLOV8 object detection model. Using YOLOV8, the suggested system seeks to identify and categorize underwater garbage in real-time. It then uses IoT devices for segmentation and management. We outline the suggested system's design, talk about how YOLOV8 integrates with Internet of Things gadgets, and provide experimental results to validate the system's efficacy. Our findings suggest that the suggested strategy has great potential to advance efforts to manage undersea waste and manage pollution as well as segment using 6-DOF Robotic Arm. The IoT based system allows remote monitoring of trash density and location along with the raspberry-pi server-based video feed allows the users to suitably monitor aquatic conditions remotely. The fusion of IoT and deep learning on YOLO v8 model opens gateway for water sustainability and enhances further opportunities in employing robotic arm-based trash collection system. © 2024 IEEE.

关键词： Robotic arms

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Bank Note Authentication using Deep Learning

Bank Note Authentication using Deep Learning

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Mian, Reiaan Mazhar Khan, Sohaib Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

The swift progression of technology has resulted in a surge in digital transactions, necessitating robust authentication mechanisms to avert fraudulent activities. Because genuine and counterfeit banknotes seem so identical, it is difficult, time-consuming, and error-prone for humans to determine which one is authentic. Counterfeit notes are manufactured at an alarmingly high rate, which means that systems for authenticating bank notes are essential to ensure counterfeit notes are detected and verified quickly and in due time. Such measures guarantee multiple benefits, including safe transactions, uphold the integrity of financial institutions, and guardianship against fake currency notes in the market. In this work, we show that automated solutions may be deployed to improve the accuracy and efficiency of procedures for banknote authentication. Currently, there is a lack of effective and precise models, which are especially trained for verifying or detecting bank note authentication, despite presence of an extensive research in the fields of artificial intelligence and machine learning. Our study seeks to close this gap by creating and implementing a deep learning model that can accurately identify and differentiate between real and fake banknotes. In the study, we noted that the suggested model outperformed conventional techniques with a notable degree of accuracy with the use of an extensive dataset. The model’s potential for practical implementation in banking and financial establishments is shown by its exceptional accuracy and efficiency in detecting fake banknotes. The high accuracy of the model demonstrates that deep learning may be used successfully to improve security protocols, opening the door for more developments in financial technology and otehr allied fields. © 2024 IEEE.

关键词： Deep learning

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A Few-shot custom CNN Model for Retinal Nerve Fibre Layer Thickness Measurement in OCT Images of Epilepsy

A Few-shot custom CNN Model for Retinal Nerve Fibre Layer Th...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Muhammad, Ruqayya Boukar, Moussa Mahamat Adeshina, Steve Dane, Senol Nile University of Nigeria Faculty of Natural and Applied Sciences Department of Computer Science Abuja Nigeria Nile University of Nigeria Faculty of Natural and Applied Sciences Department of Computer Engineering Abuja Nigeria Nile University of Nigeria College of Health Sciences Department of Physiology Abuja Nigeria

ISBN: (纸本)9798350394528

This study aims to assess the effectiveness of employing deep learning models for measuring retinal nerve fiber layer (RNFL) thickness in optical coherence tomography (OCT) scans of epilepsy patients. Conventional OCT scan segmentation methods typically rely on supervised learning, demanding substantial data for training and assuming fixed network weights post-training. To mitigate these challenges, we explore the applicability of few-shot learning (FSL) in CNN architectures, allowing dynamic fine-tuning of network weights with minimal additional data. Experimental results demonstrate enhanced segmentation accuracy, with the proposed Few-shot Custom CNN achieving a notable 91% accuracy, surpassing both the Custom CNN (86%) and the OCT machine data. This suggests the superiority of the few-shot Custom CNN model in segmentation performance compared to OCT scans. © 2024 IEEE.

关键词： Optical tomography

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Determination of Parameters Affecting the Estimation of Iceberg Draft

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China Ocean engineering 2023年第1期37卷 62-72页

作者： Hamed AZIMI Masoud MAHDIANPARI Hodjat SHIRI Civil Engineering Department Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandA1B 3X5St.John’sNLCanada Department of Electrical and Computer Engineering Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandA1B 3X5St.John’sNLCanada C-CORE A1B 3X5St.John’sNLCanada

Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression(LR) analysis, nine LR models(i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical ***, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.

关键词： dimensional analysis iceberg draft subsea assets linear regression(LR) sensitivity analysis

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