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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 72-81页

作者： AlRababah, Ahmad AbdulQadir Aljahdali, Mohammed Khalid Al jahdali, Abdulrahim Abdulhamid AlGhanmi, Mohammed Saleh Al Barazanchi, Israa Ibraheem Department of Computer Science Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Saudi Arabia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq

Arabic script is exhibited in a cursive style, which is a departure from the norm in many common languages, and the shapes of letters are contingent on their positions within words. The form of the first letter is influenced by the subsequent letter, middle letters are shaped by both preceding and succeeding letters, and the shape of the final letter is determined by the preceding letter. Additionally, certain letters are found to have strikingly similar shapes, making Arabic text recognition a formidable challenge in computer vision. The challenge of detecting and recognizing Arabic handwritten text is addressed in this paper by proposing a novel system that integrates two Deep Learning models without the constraints of a predefined dictionary or language model. Object Detection techniques are employed in the first model to accurately identify lines, words, and punctuation marks, providing a robust foundation for text recognition. In the second model, a powerful recognition system is developed, trained on the IFN/ENIT dataset to predict character sequences from handwritten text images. The architecture is comprised of a convolutional neural network (CNN) with residual connections, followed by a Bi-directional Long Short-Term Memory (BLSTM) layer, and culminates in a fully connected layer. State-of-the-art results in unconstrained Arabic text recognition tasks are achieved by this approach. © 2025 The Author(s).

关键词： Long short-term memory

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Controllable False Data Injection Attack 5th

Controllable False Data Injection Attack

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kokkonda, Jayasree Jahnavi, M. Department of Information Technology G. Narayanamma Institute of Technology and Science Hyderabad India Department of Computer Network and Information Security Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819780426

Yet the Internet of Things continues to grow significantly. A cyber-physical system faces a variety of security challenges due to the network connections made by the various kinds of devices and large systems that make up national infrastructures like a smart grid. Here, For the forward flow and the feedback path of a control system, respectively, we suggest two fake data injection attacks. Both are made using a variable parameter that determines the degree of deterioration. On the forward path, a linear forward assault is created together with the least squares approach. The Kalman filter is constructed on the feedback route. According to the numerical analysis, the suggested assaults govern how much the performance of the control system declines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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Ordered Clustering-Based Semantic Music Recommender system Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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Neuroadaptive Containment Control for Nonlinear Multiagent systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Data-driven output regulation control for constrained linear systems

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science China(information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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A Deep Learning Based system to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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ElderEase AR: Enhancing Elderly Daily Living with the Multimodal Large Language Model and Augmented Reality 24

ElderEase AR: Enhancing Elderly Daily Living with the Multim...

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2024 International Conference on Virtual Reality technology, ICVRT 2024

作者： Song, Tianyu Liu, Zhengyi Zhao, Ruibin Fu, Jie Department of Computer Science and Technology School of Computer Science and Technology Intelligent Perception Group Anhui University Anhui Hefei China Chuzhou University Anhui Chuzhou China Department of Computer Science and Technology School of Computer Science and Technology Artificial Intelligence Group Anhui University Anhui Hefei China Department of Computer Science and Technology School of Information Engineering Intelligent Perception Group Chuzhou University Anhui Chuzhou China

ISBN: (纸本)9798400710186

Elderly individuals often face challenges in independent living due to age-related cognitive and physical decline. To address these issues, we propose an innovative Augmented Reality (AR) system, "ElderEase AR", designed to assist elderly users in their daily lives by leveraging a Multimodal Large Language Model (MLLM). This system enables elderly users to capture images of their surroundings and ask related questions, providing context-aware feedback. We evaluated the system’s perceived ease-of-use and feasibility through a pilot study involving 30 elderly users, aiming to enhance their independence and quality of life. Our system integrates advanced AR technology with an intelligent agent trained on multimodal datasets. Through prompt engineering, the agent is tailored to respond in a manner that aligns with the speaking style of elderly users. Experimental results demonstrate high accuracy in object recognition and question answering, with positive feedback from user trials. Specifically, the system accurately identified objects in various environments and provided relevant answers to user queries. This study highlights the powerful potential of AR and AI technologies in creating support tools for the elderly. It suggests directions for future improvements and applications, such as enhancing the system’s adaptability to different user needs and expanding its functionality to cover more aspects of daily living. © 2024 Copyright held by the owner/author(s).

关键词： Augmented reality

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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Advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software systems 5th

Advancing Software Defect Detection and Prevention: Bridging...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Poonia, Ramesh Chandra Upreti, Kamal Alam, Mohammad Shabbir Department of Computer Science CHRIST University Delhi India College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789819795147

Software defect prediction (SDP) is a critical method in modern software development, saving costs while ensuring the delivery of high-quality software systems. This study investigates the vital importance of SDP, focusing on its function in detecting and correcting software faults that might lead to system failures. SDP forecasts defect-proneness and optimizes software-testing processes by using software metrics such as lines of code and change information. The chapter examines the progress of SDP research since the turn of the century, emphasizing the academic emphasis on refining static characteristics and establishing efficient learning methods for building high-performance defect predictors. Recognizing the economic consequences of software flaws, particularly in major engineering projects, this chapter emphasizes the importance of SDP in limiting project failures and economic losses in the twenty-first century. Several defect prediction methods are investigated in the context of software quality, with an emphasis on ongoing attempts to prevent and discover errors early in the software development lifecycle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software quality

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