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Novel Image Encryption and Compression Scheme for IoT Environment

computers, Materials & Continua 2022年第4期71卷 1443-1457页

作者： Mesfer Al Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Manar Ahmed Hamza Anwer Mustafa Hilal Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images *** IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT *** proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA)abbreviated as ST-NMWOA. The optimal key generation of signcryptiontechnique takes place with the help of NMWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed *** obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

关键词： Data compression image security encryption signcryption optimal key generation

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. 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 Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt 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

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

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ANN-Based Modeling and Control of a Pick and Place Manipulator 1

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8th International Congress on information and Communication Technology, ICICT 2023

作者： Mostafa, Mohamed Essam Mostafa, Aya Essam Ammar, Hossam Hassan Shalaby, Raafat School of Engineering and Applied Science NU Giza Egypt School of Information Technology and Computer Science NU Giza Egypt SESC Center School of EAS NU Giza Egypt Faculty of Electronic Engineering Menofia University Menouf Egypt

ISBN: (数字)9789819930913

ISBN: (纸本)9789819930906

Pick-and-place robots are used in the manufacturing industry. This paper will focus on one of the most used functionalities in the industry. In addition, it will focus on this functionality and how to achieve more precision with the least cost. The robot is used for packaging, picking, placing factory jobs, and inspection. This paper is focusing on the design of the linear pick-and-place robot. The target of this paper is to tackle the design, construction, control, and analysis of the linear pick-and-place robot. The main target of this paper is to find the best design and the most cost-effective way to develop it. It will be done by comparing several delta robots designs to find which one will be the most economical. It will be done via a survey. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost effectiveness

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An Implementation of El-Gamal Digital Signature Scheme to Authenticate Datasets

An Implementation of El-Gamal Digital Signature Scheme to Au...

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International Multidisciplinary information Technology and Engineering Conference (IMITEC)

作者： Cheslin Van Wyk Phumezo Ntlatywa Lucky Makhoere Nthabiseng Modiba Mnoneleli Nogwina Colin Chibaya Faculty of Natural & Applied Sciences Sol Plaatje University Computer Science & Information Tech Kimberley South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

This paper presents an implementation of the El-Gamal digital signature scheme to authenticate datasets. In a context where secure data distribution is crucial, data authentication schemes play a vital role in verifying the integrity of data transmission. The study conducted 20 replications, observing fluctuations in CPU time as the prime number length increased, indicating that finding larger prime numbers required more time. However, the use of very large prime numbers did not significantly compromise CPU time or RAM demands. The study highlights the flexibility to select prime number ranges, facilitated by the BigInteger class in Java. Future work includes the development of a complete white-box implementation of the El-Gamal scheme, detailed experiments to explain performance variations, and testing the model on real-life datasets. This paper contributes to enhancing the visibility of the El-Gamal scheme in data security and demonstrates the feasibility of using very large prime numbers for enhanced security.

关键词： Java Fluctuations Random access memory Environmental factors Data communication information technology Digital signatures Message authentication Glass box Testing

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The Rise of Ransomware: A Review of Attacks, Detection Techniques, and Future Challenges

The Rise of Ransomware: A Review of Attacks, Detection Techn...

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2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Kamil, Samar Siti Norul, Huda Sheikh Abdullah Firdaus, Ahmad Usman, Opeyemi Lateef Al-Mansour University College Department of Computer Science and Information Systems Baghdad Iraq Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia University Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pahang Darul Pekan Malaysia Tai Solarin University of Education Department of Computer Science Ogun State Nigeria

ISBN: (纸本)9781665409209

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim's files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges. © 2022 IEEE.

关键词： Cybersecurity

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Edge-Cloud Computing systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay 2

Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Ca...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Cyberbullying Detection Using Deep Learning: A Comparative Study 2

Cyberbullying Detection Using Deep Learning: A Comparative S...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alkasassbeh, Mouhammd Almomani, Ammar Aldweesh, Amjad Al-Qerem, Ahmad Alauthman, Mohammad Nahar, Khalid Mago, Beenu Princess Sumaya University For Technology Computer Science Department Amman Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan Yarmouk University Faculty Of I.T Computer Science Department Irbid Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350394962

Cyberbullying has become a serious issue with the rise in social media usage, especially among adolescents. Victims of cyberbullying often suffer from psychological distress, anxiety, depression and even suicidal tendencies. With the rapid growth of user-generated content online, manual detection of cyberbullying is extremely difficult. Hence, automated cyber-bullying detection systems are needed. In recent years, deep learning models have emerged as a promising technique for cyberbullying detection. This paper provides a comprehensive review of the use of deep learning for cyberbullying detection. The key contributions are: (1) a taxonomy of cyberbullying detection techniques, (2) a review of datasets used for model training and evaluation, (3) a comparative analysis of various deep learning models including CNN, RNN, LSTM, GRU and Transformer- based models, (4) a discussion of key challenges and limitations, and (5) suggestions for future research directions. The paper provides a critical perspective on model performance across multiple datasets. Key findings indicate RNNs and specifically LSTM and GRU models demonstrate state-of-the-art performance for sequential cyberbullying detection. The study concludes that deep ensemble models combining LSTMs and pretrained contextual embeddings show significant promise to advance the field. © 2024 IEEE.

关键词： Social networking (online)

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