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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Albermany, Salah Abdulhadi Razzaq, Iman Saleem Alkhafaji, Mohammed Ayad Fatan, Muna Hakim Zearah, Sajad Ali Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Engineering Technology National University of Science and Technology Dhi-Qar Iraq Scientific Research Center Al-Ayen University Dhi-Qar Iraq

ISBN: (纸本)9789819937158

Telecommunication security is a broad and important field at present. Various models of security have been developed that aim to provide secure communication between the parties. Security sought by the protocol can be demonstrated through appropriate safety models that consider the parties that communicate with each other through the protocol, mutual trust, communication environment, and other aspects. This paper aims to discuss security models and to provide a comprehensive overview in this direction as it is considered an active field. This paper discusses the computational security models that demonstrate the security of protocols and measures their analysis and breakdown by the attacker. Provide a summary of the advantages and disadvantages of each model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Network security

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Exploring the Application of Image Restoration Algorithms in Digital Image Processing 1

Exploring the Application of Image Restoration Algorithms in...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Singh, Akhilendra Pratap Sara, S. Sakhare, Nitin N. Kumar, Rohit Kumar, P. Kishore Saini, Rajat Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350359756

Image recovery algorithms are used to enhance the advent of virtual pix. Those algorithms help enhance the picture by casting off low-frequency noise, polishing edges, and minimizing degradation because of camera noise, motion blur, movement aberration, and aerial distortion. Image recuperation algorithms are utilized in virtual picture processing programs, including scientific imaging, satellite to for pc imaging, object reputation, and competent picture evaluation. This paper explores the software of picture healing algorithms in virtual photograph processing to improve the accuracy and exceptional of the end product. Unique algorithms are used to gain the desired results. Furthermore, this paper discusses the blessings and drawbacks of using those algorithms in digital photo processing and gives possible solutions to demanding situations. Finally, the paper provides an outlook on the future of picture restoration algorithms in phrases of studies and algorithms. © 2023 IEEE.

关键词： Image enhancement

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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The Enhanced Optimization on Deep Learning Technologies for Data Science Practices 3

The Enhanced Optimization on Deep Learning Technologies for ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Reddy, B. Rajarajeswari, S. Ranganathaswamy, M.K. Nivesh Pandey, Megha Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Deep studying technologies (DLTs) were adopted with the aid of many fields, consisting of healthcare, finance, and records technology. This paper ambitions to take a look at how DLTs can be improved for facts technological know-how practices, in addition to to speak about their capacity for driving advances in statistics-pushed studies. First of all, we provide a top level view of the contemporary kingdom of DLTs, and the important thing algorithms that cause them to appealing for data science packages. Then, we delve into a number of the ways wherein DLTs may be optimized for better overall performance. We illustrate the optimization strategies via numerous examples, which include community structure search, computational useful resource management, and active mastering. Moreover, we explore the use of switch studying and reinforcement getting to know to in addition improve the performance of DLTs in information science. In the end, we talk the benefits of DLTs for statistics technology and their ability for advancing the field. This paper highlights the significance of optimizing DLTs for records technological know-how, and the numerous possibilities that leveraging these technology will offer. © 2023 IEEE.

关键词： computational management optimization performance possibilities

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Detection of Thyroid Disease Through Machine Learning Technique 25

Detection of Thyroid Disease Through Machine Learning Techni...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Almomani, Mutaz Al-Qerem, Ahmad Nashwan, Shadi Jebreen, Issam Rajab, Mohammed Alauthman, Mohammad Jeadi, Mohammad Elrashidi, Ali Zarqa University Faculty of Information Technology Computer Science Department Zarqa13110 Jordan Middle East University Faculty of Information Technology Cybersecurity Department Amman11831 Jordan University Headquarter University of Anbar Ramadi Iraq University of Petra Department of Information Security Amman11196 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Thyroid disease, one of the most common endocrine disorders, requires prompt diagnosis to effectively treat it. To improve diagnostic precision and expedite medical procedures, this paper explores the use of Machine Learning (ML) approaches for the early identification and categorization of thyroid diseasees. Sophisticated machine learning methods, including support vector machines, neural networks, and decision trees, are used to leverage a variety of datasets. These models outperform traditional diagnostic techniques in their capacity to identify complex patterns and minute variations in thyroid function parameters. The application of machine learning to the diagnosis of thyroid disease not only increases diagnostic accuracy but also makes preventive healthcare approaches possible. Personalized risk assessments are made possible by ML models, which analyze a wide range of patient-specific characteristics and help to improve the targeting of therapies. Furthermore, decision-making procedures are accelerated by the real-time processing powers of machine learning systems, which may shorten the interval between diagnosis and the start of therapy. this paper emphasizes how machine learning has revolutionized the diagnosis of thyroid disease and how it has the potential to completely change clinical procedures. The integration of sophisticated algorithms and extensive datasets presents opportunities for enhanced precision, effectiveness, and customization of diagnostic methods, which might eventually lead to better patient outcomes and more efficient use of healthcare resources for thyroid condition treatment. In this paper, the results were as follows: Two classifiers performed the best: LMT and LogitBoost. The Trees and Meta-learning strategy performed the best learning strategy, while the CorrelationAttributeEval and InfoGainAttributeEval methods performed the best feature selection. © 2024 IEEE.

关键词： Support vector machines

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Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection

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computer Systems Science & engineering 2023年第11期47卷 2119-2134页

作者： Ali Altalbe Abdul Rehman Javed Department of Computer Science Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Kidney infection is a severe medical issue affecting individuals worldwide and increasing mortality *** Kidney Disease(CKD)is treatable during its initial phases but can become irreversible and cause renal *** the various diseases,the most prevalent kidney conditions affecting kidney function are cyst growth,kidney tumors,and *** significant challenge for the medical community is the immediate diagnosis and treatment of kidney *** failure could result from kidney disorders like tumors,stones,and cysts if not often identified and ***-assisted diagnostics are necessary to support clinicians’and specialists’medical assessments due to the rising prevalence of chronic renal illness,the lack of experts,and the rising rates of assessment and monitoring,mainly in developing *** Intelligence(AI)approaches such as machine,and deep learning has been used in literature for kidney disease detection;however,they still lack *** paper implements a deep learning-based Convolutional Neural Network(CNN)model for the classification and prognosis of kidney *** use a benchmark Computed Tomography(CT)kidney dataset for *** data is pre-processed,and then CNN extracts the features from the *** reveal that the proposed approach accurately classifies kidney disease with a considerable accuracy of 0.992%,0.994%precision,0.982%recall,and 0.987%*** study suggests using the proposed fine-tuned CNN model for kidney disease detection.

关键词： Kidney disease convolutional neural network computed tomography feature extraction deep learning machine learning

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Investigating the Strength and Hardness of AL7075 Composite Reinforced with Natural Fibers 2

Investigating the Strength and Hardness of AL7075 Composite ...

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2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023

作者： Kumar, Dinesh Singla, Vineet Kundu, Ravinder Neha Kumar, Arun Sharma, Sunil Kumar University Institute of Engineering and Technology M.D.U Department of Mechanical Engineering Rohtak India Jamia Hamdard University Department of Computer Science and Engineering New Delhi India Shri Vishwakarma Skill University Skill Faculty of Engineering and Technology Department of Automotive Studies Palwal India Shri Vishwakarma Skill University Department of Green Technology Palwal India

ISBN: (纸本)9798350326666

Stir casting is the easiest way to melt and combine reinforcement in metal matrix composites made from low-melting metals like aluminium. The stir cast process was used to make Aluminium 7075 hybrid composites by reinforcing sugarcane and bamboo leaf ash at 2%, 4%, 6%, and 8% by weight and retaining AL7075-T651 concentration. Tensile testing was performed on standard-sized samples using Universal Testing Machine UNITEK-94100. Results were tabulated and compared to pure AL7075-T651. The tensile test indicates that the reinforcing percentage enhances strength up to a point, then decreases. Rockwell hardness was used to compare hybrid composite hardness to pure AL7075-T651 alloy. The results showed that adding SLA+BLA reinforcement enhances the hardness of the manufactured AL7075-T651 composite compared to the base matrix alloy up to a certain extent, thereafter it decreases. The results were compared to pure AL7075-T651 alloy using graphical methods, showing that reinforcing improved mechanical properties. © 2023 IEEE.

关键词： Hybrid composites

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Beyond Traditional Motion Planning: A Proximal Policy Optimization Reinforcement Learning Approach for Robotics 5th

Beyond Traditional Motion Planning: A Proximal Policy Optimi...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Rjoub, Gaith Drawel, Nagat Dssouli, Rachida Bentahar, Jamal Kassaymeh, Sofian Alweshah, Mohammed Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9783031731501

In today’s technologically-driven era, robotic systems have been integrated into various aspects of our daily lives, handling tasks from household chores to complex industrial applications. With these robotic systems becoming increasingly commonplace in homes, offices, and public spaces, the demand for effective and adaptable motion planning becomes paramount. Traditional motion planning methods, although having made noteworthy progress, still have difficulties with the challenges posed by dynamic environments characterized by unpredictable elements and rapid changes. Recognizing this gap, this paper introduces a novel reinforcement learning-based path planning algorithm specifically designed for dynamic scenarios. Through the integration of Proximal Policy Optimization (PPO) reinforcement algorithm, our approach empowers robots to actively chart their paths, adapting in real-time to unforeseen obstacles and shifts in the environment. To validate our methodology, we undertook extensive experiments comparing our approach to established benchmark methods, notably SARSA, A∗, and Regular Reinforcement Learning (RL) (i.e., non PPO-based). The results not only highlight the effectiveness of our proposed solution but also emphasize its potential to reshape how robotic systems navigate complex, ever-evolving environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Robot programming

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High-Voltage SF6 Circuit Breaker Maintenance 33rd

High-Voltage SF6 Circuit Breaker Maintenance

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Soldo, Luka Knežević, Goran Zovko, Mario Croatian Transmission System Operator Transmission Area Osijek Vukovarska Cesta 217 Osijek31000 Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpmira 2b Osijek31000 Croatia

ISBN: (纸本)9783031805967

Nowadays, the need for uninterrupted power supply is greater than ever. One of the most effective ways to ensure an uninterrupted supply of electricity is the correct selection of the method and interval of maintenance of elements in a high-voltage substation. In this way, not only is a high-quality delivery of electricity ensured, but also the possibility of equipment failure that can be dangerous for people in the substation is reduced. A high-voltage circuit breaker is a device that performs several different functions. It is used for network configuration changes, and also provides protection for equipment and personnel. High voltages and thermal (current) stresses cause aging and wear of switch parts, and through various forms of maintenance and testing, damage parts are determined and potential hazards are safely removed. This paper explains the maintenance procedures of the high-voltage SF6 circuit breaker. Using the example of a high-voltage line-bay circuit breaker, the entire 6Y circuit breaker revision procedure (revision that is carried out in six-year cycles) is presented. In addition to visual inspection, checking, cleaning and replacement of worn parts, the switch on and off times were recorded. The quality of SF6 gas was also tested by measuring the concentration of SF6 gas decomposition products and the concentration of mineral oil aerosols in SF6 gas, by measuring the dew point of the gas and controlling the volume percentage of SF6 gas. The voltage drops at the primary connections of the switches were measured, and the operation of the pressure relays was also checked. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mineral oils

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Beyond Trends: Tiktok's Educational Symphony by Unmasking the Digital Revolution

Beyond Trends: Tiktok's Educational Symphony by Unmasking th...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ghani, Miharaini Md Mustafa, Wan Azani Bin Hashim, Mohd Ekram Alhafis Hanafi, Hafizul Fahri Alzubaidi, Laith H. Universiti Sains Malaysia School of Communication Penang Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau Malaysia Universiti Pendidikan Sultan Idris Faculty of Art Sustainability and Creative Industry Department of Creative Multimedia Perak Tanjong Malim Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Tanjong Malim Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

In the fast-paced digital age, TikTok has emerged as an unlikely protagonist in the realm of education, ushering in a bite-sized learning revolution. This comprehensive study delves into the captivating phenomenon of TikTok's educational content, unveiling its transformative impact on learners across generations and disciplines. Drawing from extensive empirical research and expert insights, the article will explore the intricate interplay between TikTok's snackable video format and its ability to foster knowledge acquisition and skill development. It illuminates how this platform's unique amalgamation of entertainment and education has redefined traditional learning paradigms, empowering users to consume and share knowledge in a highly engaging and democratized manner. Beyond trends unravels the cognitive mechanisms underpinning the effectiveness of bite-sized learning, shedding light on its potential to cater to diverse learning styles and attention spans. It examines the platform's role in democratizing access to education, enabling content creators and subject matter experts to reach unprecedented global audiences. Moreover, It provides a critical analysis of the challenges and opportunities that arise as bite-sized learning gains traction, offering invaluable insights for educators, policymakers, and stakeholders invested in shaping the future of education. With its multidisciplinary approach and forward-thinking perspectives, Beyond Trends serves as a comprehensive guide to navigating the digital learning landscape, empowering readers to harness the transformative potential of TikTok and its bite-sized educational content. © 2023 IEEE.

关键词： E-learning

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