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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2022

作者： Ali Alameri, Mohammed Abdulbasit Ciylan, Bunyamin Mahmood, Basim Gazi University Faculty of Technology Computer Engineering Department Ankara Turkey University of Mosul Computer Science Department Mosul Iraq

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Printed documents continue to be the most commonly used media for information transfer in official context. However, such documents may be subject to illegitimate modification or malicious purposes. Therefore, agencies must be able to verify their authenticity before accepting them. This work aims to identify and classify the current computational methods that are used to identify and detect a forgery in printed documents. We focus on methods that detect forger passively, i.e., no requirement of active information to be included in the document during or shortly after printing. Hence, we classify the methods into four general categories. First, detection based on printer and ink properties. The second is based on character-level features analysis that is based on detecting imperfections produced by scan, edit, and print forgery techniques. It is also possible to detect forgery through analysis of paper properties. Finally, forgery detection can be achieved by analyzing the pixel-level features. © 2022 IEEE.

关键词： Computational methods

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Physical Layer Coding and Cryptanalysis for the Secure Communications 25th

Physical Layer Coding and Cryptanalysis for the Secure Commu...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Yang, Cheng-Ying Lin, Chuan-Bi Yao, Jenq-Foung J. F. Hwang, Ming-Hsiang Department of Computer Science University of Taipei Taipei Taiwan Development of Information and Communication Technology Chaoyang University of Technology Taichung Taiwan Department of Computer Science Georgia College and State University Milledgeville United States Department of Computer Science and Information Engineering Asia University Taichung Taiwan

ISBN: (纸本)9789811995811

The privacy and security are highly requested in the cooperative communication system. The information is sent to the destination with the help of relays. In the system, there might be the eavesdroppers playing the role of the relay. It suffers the system security. Traditionally, the complex and difficult encryption scheme would be applied to a higher network layer. It is not suitable to apply to the equipment with low computing resources. According to Shannon Theory, positive secrecy capacity could support secure communications. In this work, based on the information theorem, the secrecy capacity analysis in the communication is given. Consider the difference between the amount of information at the source and that at the transmitter, the positive secrecy capacity with memory coding scheme under the lossless communication environment might be obtained. Then, the communication security could be implemented with the physical coding scheme. In this work, the physical layer coding schemes are examined. According to the degree of the freedom of the codeword, the secure communication could be achieved. It could be found the secrecy capacity could increase with coding rate decreasing with the multiple code assignments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Secure communication

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A Simple Deep Learning Model for Classifying Oranges in Embedded Manufacturing Settings

SSRN

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SSRN 2023年

作者： Salaiwarakul, Anongporn Mungklachaiya, Sujitranan Program in Computer and Information Technology Faculty of Science and Technology Loei Rajabaht University Loei42000 Thailand Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok65000 Thailand

Recently, deep learning has emerged as a powerful tool for automating various tasks in manufacturing industry. Deep learning has shown promise particularly in classifying food products, e.g., identifying sweet oranges in a factory setting. Traditionally, classification of oranges based on sweetness is a labor-intensive and time-consuming process, requiring manual inspection and tasting by trained personnel. However, by employing deep learning techniques, this process can be automated, giving rise to accurate and consistent results at a much faster rate. In this study, we propose a deep learning model, combined with computer vision, for classifying the sweetness of oranges in a factory setting. By training a deep neural network on a dataset of labeled images, we developed a simple Convolutional Neural Network (sCNN) model with few layers that can accurately predict the sweetness of new oranges based on their images. We evaluated the performance of the model and compare it with four CNN architecture-transfer learning models of orange classification. Upon comparison with four different CNN architecture-transfer learning models, namely, MobileNet-V2, Inception-V3, VGG-16, and ResNet-50, we found that while each of these models yields a satisfactory result, learning rates of 0.001 or 0.0001 yielded superior results in every model. Without the application of dropout and image augmentation, ResNet-50 yielded the best classification accuracy (81.43%) of the five models, and the model performance improved upon employment of these methods. However, the provided images were not suitable for Inception-V3 models. Nevertheless, Simple CNN shows that the augmentation method enables the model to learn, achieving an accuracy of 81.43% with a dropout of 0.3, which is close to the VGG-16's highest accuracy of 83.57% (dropout = 0.0) and MobileNet-V2’s 74.29% (dropout = 0.3). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Sustainable Water Futures: Enhancing Efficiency And Conservation In Madinah AL Munawara

Journal of Applied Science and Engineering

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Journal of Applied science and Engineering 2024年第10期28卷 2027-2042页

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Nawaz, Waqas Namoun, Abdallah Muhammad, Munir Azam Faculty of Computer and Information System Islamic University of Madinah Saudi Arabia Civil Engineering Department Faculty of Engineering Islamic University of Madinah Madinah42351 Saudi Arabia AI Center Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Khwaja Fareed University of Engineering & Information Technology Faculty of Computer and Information System Punjab Rahim Yar Khan Pakistan

Water resource management worldwide faces significant challenges, including high consumption rates, scarcity, and ageing infrastructure. This paper proposes a comprehensive approach to address these issues through five key solutions. Advanced Metering Infrastructure (AMI) first facilitates real-time data collection and consumer awareness campaigns. Secondly, wastewater recycling and reuse are promoted by upgrading wastewater treatment plants and fostering public-private partnerships. Thirdly, reducing network losses is achieved through leak detection systems and regular maintenance. Fourthly, policy enhancement and regulatory frameworks are strengthened by implementing regulations for new developments and incentives for water-saving technologies. Lastly, community engagement and education initiatives are undertaken to promote sustainable water practices. This multifaceted strategy aims to improve water conservation, efficiency, and resilience of the water supply system. This paper proposes solutions that can be applied to the Madinah AL Munawara region of Saudi Arabia, an area of utmost importance, demonstrating the applicability and effectiveness of these solutions in addressing regional water management challenges. © The Author(’s).

关键词： Wastewater treatment

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Developing Novel T-Swish Activation Function in Deep Learning

Developing Novel T-Swish Activation Function in Deep Learnin...

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2022 International Conference on IT and Industrial Technologies, ICIT 2022

作者： Javid, Irfan Ghazali, Rozaida Syed, Irteza Husaini, Noor Aida Zulqarnain, Muhammad University of Poonch Rawalakot Department of Computer Science and Information Technology AJK Pakistan Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Parit Raja Malaysia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Jalan Genting Kelang Kuala Lampur Malaysia The Islamia University of Bahwalpur Faculty of Computing Punjab Pakistan

ISBN: (数字)9781665489454

ISBN: (纸本)9781665489454

Activation functions are an essential component of neural network construction. It is used in the hidden layer to achieve the regulation that lies in the learning capability of the model from the training dataset. Activation functions employed in the output layer play a significant part to determine the type of predictions that the model can produce. It is a significant area that has to be studied further to increase the efficacy of a deep neural network. As a result, our research in this area has grown. Several activation functions were established throughout time to remedy the inadequacies of the earlier functions. Over 40 activation functions, some of which are well-known, have been postulated, such as the Tanh, Mish, sigmoid, swish, and Rectified Linear Unit, however not entirely. Using datasets such as CIFAR-100, CIFAR-10, MNIST, and Fashion-MNIST, we developed a new activation function Tunable Swish (T-Swish) that increases enactment on object classification tasks. Then, to validate its applicability, we compared it to well-known activation functions like ReLU, sigmoid, Tanh, and Swish using Network In-Network, ResNet-56 version 1, LeNet-5, and U-Net architecture. The T-Swish function, in particular, enables quick network learning, which is excellent in the Transfer Learning technique. © 2022 IEEE.

关键词： Deep neural networks

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Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

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computer systems science & Engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari M.Vinoth Kumar Garima Verma S.Balu Dilip Kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an information technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud Computing scheduling ant colony optimization fuzzy logic

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Previs-Real:Interactive virtual previsualization system for news shooting rehearsal and evaluation

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 527-549页

作者： Che QU Shaocong WANG Chao ZHOU Tongchen ZHAO Rui GUO Cheng Wa WONG Chi DENG Bin JI Yuhui WEN Yuanchun SHI Yong-Jin LIU Department of Film and Television Technology Beijing Film AcademyBeijing 100088China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive ComputingTsinghua UniversityBeijing 100084China School of Social Sciences Tsinghua UniversityBeijing 100084China News Production and Broadcasting First Division China Media GroupBeijing 100038China School of Computer and Information Technology Bejing Jiaotong UniversityBejing 100044China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong UniversityBeijing 100044China

Background In the demanding field of live news broadcasting,the intricate studio production procedures and tight schedules pose significant challenges for physical rehearsals by *** paper explores the design and implementation of a lightweight virtual news previsualization system,leveraging virtual production technology and interaction design methods to address the lack of fidelity in presentations and manipulations,and the quantitative feedback of rehearsal effects in previous virtual *** Our system,Previs-Real,is informed by user investigation with professional cameramen and studio technicians,and adheres to principles of high fidelity,accurate replication of actual hardware operations,and real-time feedback on rehearsal *** system's software and hardware development are implemented based on Unreal Engine and accompanying toolsets,incorporating cutting-edge modeling and camera calibration *** We validated Previs-Real through a user study,demonstrating superior performance in previsualization shooting tasks using the virtual system compared to traditional camera *** findings,supported by both objective performance metrics and subjective responses,underline Previs-Real's effectiveness and potential in transforming news broadcasting *** Previs-Real eliminates the requirement for complex equipment interconnections and team coordination inherent in a physical studio by implementing methodologies complying the above principles,objectively resulting in a lightweight design of applicable version of virtual news previsualization *** offers a novel solution to the challenges in news studio previsualization by focusing on key operational features rather than full environment *** design approach is equally effective in the process of designing lightweight systems in other fields.

关键词： News broadcast Previsualization Human computer interaction design User-centered design Interface evaluation

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Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

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computers, Materials & Continua 2023年第2期74卷 2575-2588页

作者： Sakda Noinang Zulqurnain Sabir Muhammad Asif Zahoor Raja Soheil Salahshour Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbul34494Turkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The current investigations are presented to solve the fractional order HBV differential infection system(FO-HBV-DIS)with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation(LMB)neural networks(NNs),i.e.,*** FO-HBV-DIS with the response of antibody immune is categorized into five dynamics,healthy hepatocytes(H),capsids(D),infected hepatocytes(I),free virus(V)and antibodies(W).The investigations for three different FO variants have been tested numerically to solve the nonlinear *** data magnitudes are implemented 75%for training,10%for certification and 15%for testing to solve the FO-HBV-DIS with the response of antibody *** numerical observations are achieved using the stochastic LMBNNs procedures for soling the FO-HBV-DIS with the response of antibody immune and comparison of the results is presented through the database Adams-Bashforth-Moulton *** authenticate the validity,competence,consistency,capability and exactness of the LMBNNs,the numerical presentations using the mean square error(MSE),error histograms(EHs),state transitions(STs),correlation and regression are accomplished.

关键词： Fractional order HBV differential infection system artificial neural networks nonlinear Levenberg-Marquardt backpropagation Adams-Bashforth-Moulton

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Incremental Learning Algorithms for Broad Learning system with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Chen, Guang-Ze Jin, Junwei Sun, Hai-Wei Philip Chen, C.L. Faculty of Science and Technology University of Macau Department of Mathematics 99999 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 510335 China

ISBN: (纸本)9781665410205

The Broad Learning system (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments. © 2024 IEEE.

关键词： Adversarial machine learning

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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