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Synergistic Swarm Optimization Algorithm

computer Modeling in engineering & Sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

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Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecur...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350324136

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information. © 2024 IEEE.

关键词： GANs Images denoising Kyber number theoretic transform

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The Contribution of Advanced Technologies to the Tourism Experience of Disabled People: The Greek Case 4th

The Contribution of Advanced Technologies to the Tourism Exp...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Poli, Maria Malagas, Konstantinos Papakostas, Christos Department of Interior Architecture University of West Attica Aigaleo Greece Department of Shipping Trade and Transport University of the Aegean Chios Greece Department of Informatics and Computer Engineering University of West Attica Aigaleo Greece

ISBN: (纸本)9783031733437

More than 1.3 billion people have a significant disability, and this figure will further increase due to the ageing population and various diseases. Including these people in daily life offers stakeholders significant social and economic benefits. Travel and tourism experiences are essential for disabled people. Providing fully accessible tourism-related services in all tourism chain activities through the adoption of advanced technologies encourages disabled people to travel more and enjoy higher-quality tourism services. This issue was discussed in an online focus group with the participation of disabled people and experts (n = 12). Helpful information was collected in response to the study’s objective. The study’s participants agreed that advanced digital technologies significantly facilitate the travel and tourism experience of disabled people. All parts of the tourism chain should adopt these technologies, and in this direction, the close collaboration and coordination of all stakeholders are required. Governmental organisations should have a key role in promoting the adoption of digital technologies that facilitate the travel and tourism experience of disabled people. Those countries that provide appropriate services to disabled people through digital technologies acquire significant benefits, and Greece should invest more in this direction. Practitioners and policy-makers can gain essential insights from the current study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Leisure

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EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2021年 2卷 1页

作者： Kaselimi, Maria Doulamis, Nikolaos Voulodimos, Athanasios Doulamis, Anastasios Protopapadakis, Eftychios Department of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece Department of Informatics and Computer Engineering University of West Attica Athens12243 Greece

Energy disaggregation, namely the separation of the aggregated household energy consumption signal into its additive sub-components, bears resemblance to the signal (source) separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behaviour that is usually detected in appliances operation and the existence of noise in the aggregated signal. In this paper, we propose EnerGAN++, a model based on Generative Adversarial Networks (GAN) for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator, to enhance robustness to noise. The discriminator performs sequence classification, using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, among with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method's superiority compared to the current state of the art. © 2020 IEEE.

关键词： Generative adversarial networks

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data Science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Rasio Fernandis Daniel Swanjaya Risky Aswi Ramadhani Patmi Kasih Julian Sahertian Dept. Informatics Engineering Faculty of Engineering and Computer Science Universitas Nusantara PGRI Kediri Kediri

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children’s development at school through the results of each semester’s assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children’s anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children’s anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children’s anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children’s anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes.

关键词： Analytical models Data preprocessing Education Coherence Libraries Data models Resource management Information technology

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IoT Home Medical Check-up System Application and Database Design 6

IoT Home Medical Check-up System Application and Database De...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Risitiani, Nadia Saida, Nur Fauzi, Rahmat Noor Darmawan, Helmy Yusuf Sanubari, Agung Oktivasari, Prihatin Wardhani, Rika Novita Riandini Politeknik Negeri Jakarta Department of Electrical Engineering Depok Indonesia Politeknik Negeri Jakarta Department of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350399615

Recording the results of checking blood sugar, cholesterol and uric acid has not made a routine history every time you check, this is what makes us make an application in the Non-Invasive Home Medical Check-up tool, Non-Invasive Home Medical Check-up is a medical device made using non-invasive methods in checking blood sugar, uric acid, cholesterol, and body temperature. This tool uses MAX30105 sensors to check uric acid, blood glucose, and cholesterol levels, and MLX90614 to check body temperature. This application is connected to the device in real-time and there is a history check display to make it easier for users to review previous checks. Making applications using MIT APP INVENTOR and using the PostgreSQL database. To ensure that everything is as expected, testing is carried out to send and store data on the results of checking the device to the database and displayed in the application. Data transmission testing is done by determining the delay time of sending tools to the database using the internet network, and the average delay in the experiment is 0.0206 seconds. Integration testing is done by comparing the data in the database with the application by means of the user must enter the correct and appropriate ID. © 2022 IEEE.

关键词： MAX30105 Sensor Medical Check-up Application MLX90614 Sensor

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Application of Parallel Signal Processing Using Reverse Blockchain Technology in Conditions of Network Instability 4

Application of Parallel Signal Processing Using Reverse Bloc...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Department of Information Technologies Ukraine Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350391183

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing. © 2024 IEEE.

关键词： blockchain dynamic objects and technological processes improving the quality of control systems instability of communication channels loss of information and control signals reverse blockchain technology

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