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Hybrid Deep Convolutional Generative Adversarial Networks (DCGANS) and Style Generative Adversarial Network (STYLEGANS) Algorithms to Improve Image Quality 3

Hybrid Deep Convolutional Generative Adversarial Networks (D...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Hariharan, B. Karthic, S. Indra Priyadharshini, S. Nalina, E. Wilfred Blessing, N.R. Senthil Prakash, P.N. Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Tamil NaduCampus Chennai India R.M.D. Engineering College Tamil Nadu India It University of Technology and Applied Sciences Ibri Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Generative Adversarial Networks or GANS, are another way of achieving generative modeling using various deep learning methods like convoluted neural networks. They have a wide range of applications like :- image to image translation, improving the resolution of the images, creating multiple images using a single image, checking if the image is real or fake and the list goes on. One of the implementations of GANS, i.e, DCGANS would be discussed in this paper. DCGANS stands for Deep Convolutional Generative Adversarial Networks, it uses convolutional and convolutional-transpose layers in the generator and discriminator, respectively. This DCGANS give prompt result while processing many real time images. In their study Unsupervised representation Learning with Deep Convolutional Generative Adversarial Networks, Radford et al. proposed it. This algorithm generates fake images after being trained on a dataset of human faces. However to create these fake images, the quality of these images should be compromised and this can also make them easily identifiable as fake image. So, here StyleGANS will be used as another implementation of GANS. StyleGANS is not only used for improving the quality of these images but also it helps us to increase fakeness of the images. This project uses the CelebA dataset from Kaggle to train the models. © 2022 IEEE.

关键词： Generative adversarial networks

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Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis 2

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anitha, M. Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Department of Artificial Intelligence & Data Science CARE College of Engineering Tamil Nadu Trichy India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India Department of Computer Science and O. Business Systems R.M.D Engineering College Tamil Nadu Thiruvallur India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India

ISBN: (纸本)9798350383867

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting. © 2024 IEEE.

关键词： Adversarial machine learning

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Large-scale group decision-making method based on knowledge differentiation and the grouping of alternatives into modifiable scenarios 9th

Large-scale group decision-making method based on knowledge ...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision making

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Research on Comprehensive Blockchain Regulation and Anti-fraud System 5th

Research on Comprehensive Blockchain Regulation and Anti-f...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Zhuo, Shaoxuan Wu, Weigang Zhou, Yongzhang Cao, Rong Bian, Jing School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Computational Science Sun Yat-sen University Guangzhou510006 China Guangzhou510006 China School of Earth Science and Engineering Sun Yat-sen University Zhuhai519082 China Guangdong Provincial Key Laboratory of Geological Processes and Mineral Resources Exploration Zhuhai519082 China

ISBN: (纸本)9789819981007

The blockchain technology has attracted attention due to its characteristics of anonymity, openness, decentralization, traceability, and tamper-resistance. However, with the development of the blockchain industry, various financial fraud activities have also emerged on the blockchain. Effective regulation is a necessary condition for the healthy development of the blockchain ecosystem. A comprehensive blockchain anti-fraud prototype system is a system that combines multiple technologies to achieve fraud detection and anti-fraud techniques, aimed at addressing the existing fraud issues in the blockchain field. In this paper, we design and implement an anti-fraud system prototype based on a graph database platform and neural network model. The prototype system has the characteristics of fast, accurate, and reliable, and can effectively combat blockchain fraud activities by identifying blockchain transaction patterns and detecting illegal transaction behavior using new heuristic learning methods and graph neural network methods to detect frequent frauds. The system monitors and detects fraudulent activities such as phishing, ICO, price manipulation, money laundering, and illegal fundraising. The research results of this paper can provide an effective solution for anti-fraud work in the blockchain field and provide technical support for future wider applications. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Improved Classification of Alzheimer's Disease with Convolutional Neural Networks

Improved Classification of Alzheimer's Disease with Convolut...

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2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Muhammed, S. Upadhya, J. Poudel, S. Hasan, M. Donthula, K. Vargas, J. Ranganathan, J. Poudel, K. Middle Tennessee State University Computational and Data Science MurfreesboroTN United States Middle Tennessee State University Computer Science MurfreesboroTN United States Optum Inc MurfreesboroTN United States Middle Tennessee State University Engineering Technology MurfreesboroTN United States

ISBN: (纸本)9798350341256

This work focuses on classifying MRI images using machine learning models to identify Alzheimer's disease (AD), the most common form of dementia, at an early stage [1]. It is now possible to identify and forecast the onset of AD by analyzing brain scans collected through Magnetic Resonance Imaging (MRI) and using artificial intelligence (AI) technologies, classifying patients as either at risk or not. The main goal is to make precise predictions. Improved prediction and detection tools for radiologists, physicians, and caregivers will be made available as a result of the study's determination of the likelihood that individuals would develop AD and proper categorization of them. Using machine learning methods such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and fastai - a user-friendly deep learning library and framework - we developed models using a dataset of 6400 MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 4 class for the early detection and classification of AD [2]. The models achieve an impressive accuracy rate of 84.4%, with the addition of a pre-trained Visual Geometry Group (VGG) layer to the sequential model, outperforming other algorithms. This study demonstrates a viable method for classifying and diagnosing Alzheimer's disease early on, utilizing MRI images and machine learning models. The acquired accuracy rates show the models' potential to assist doctors and other caregivers in AD diagnosis and management, thereby maximizing overall effectiveness. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Advanced Image Ciphering: 1-D Group Cellular Automata and S-Box Strategies

Advanced Image Ciphering: 1-D Group Cellular Automata and S-...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Dept. of Computational Sciences Brainware University Kolkata India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digital images. The proposed approach amalgamates Cellular Automata and S-Box mechanisms within a framework of symmetric-key cryptography to augment the strength of encryption. Through the generation of pseudorandom sequences using 1-D GCA and the utilization of S-Box for substitution, the method accomplishes efficient pixel diffusion and confusion. The assessment criteria demonstrate noteworthy enhancements: Number of Pixels Change Rate (NPCR) at 99.9864%, Unified Average Changing Intensity (UACI) at 33.6130%, Peak Signal-to-Noise Ratio (PSNR) at 9.2336 dB, and Mean Squared Error (MSE) at 0.1193. These findings underscore the robustness of the algorithm against statistical and differential attacks. The study justifies the promise of integrating GCA and S-Box strategies to reinforce cryptographic systems, proposing future avenues for enhancing computational efficiency and broadening applicability across diverse digital contexts.

关键词： PSNR Digital images Instruments Automata Information processing Robustness Encryption S Box Resilience Random sequences

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Challenges in Fuzzy Decision Making for Future Research 21

Challenges in Fuzzy Decision Making for Future Research

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9781643683164

Fuzzy Decision Making is a line of research that has had an important presence in the literature since 1965 until nowadays. From that year to the present, the line of research has been consolidated. The research field goals have evolved along with all the research that makes use of these systems and the new technologies requirements. Therefore, new challenges, different from the ones that were faced at the beginning, need to be solved. The aim of this paper is to describe the recent challenges faced by those working in this line of research. This paper will help researchers to know how their work should be oriented in the future. © 2022 The authors and IOS Press. All rights reserved.

关键词： Decision making

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Decoding Speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Mahmud, M. Hasan, M. Yeasin, M. Bidelman, G. Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Department Of Electrical And Computer Engineering TN United States Indiana University Language And Hearing Sciences Department Of Speech BloomingtonIN United States

ISBN: (纸本)9798350388572

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing. © 2024 IEEE.

关键词： Hidden Markov models

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Personalized Stress Detection from Chest-Worn Sensors by Leveraging Machine Learning

Personalized Stress Detection from Chest-Worn Sensors by Lev...

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Electrics and computer (INTCEC), Interdisciplinary Conference on

作者： Md Nahid Hasan Mithun Saha Md Sultan Mahmud Samir Poudel Satish Wagle Khem Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Computer Science University of Memphis Memphis TN USA Electrical and Computer Engineering University of Memphis Internal Medicine University of Tennessee Health Science Center Memphis TN USA Computer Science Middle Tennessee State University Murfreesboro TN USA Computational and Data Science Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

Stress detection is crucial for individual well-being. We investigated stress detection using machine learning (ML) from chest-worn sensor data. We used the WESAD dataset that recorded physiological data via a chest-worn sensor when 15 subjects were in stress and non-stress. We empirically evaluated different combinations of window sizes and ML models for stress detection. Our analysis shows XGBoost achieved the highest accuracy of 99.71%, F1-score 99.57%, and area under the curve (AUC) 99.61% in detecting population-level stress with an optimal window size of 500 ms. We also developed personalized stress detection models using the same window size. Subject-level results show stress can be more accurately detected (accuracy 99.95%, F1-score 99.90%, and AUC 99.87%) with personalized models. Our findings also demonstrate a 2% improvement in stress detection compared to prior studies. This work could prove beneficial for quick stress detection and understanding the cause of stress.

关键词： Training Accuracy computational modeling Human factors Machine learning Feature extraction data processing

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Generative Multi-Form Bayesian Optimization

arXiv

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arXiv 2025年

作者： Guo, Zhendong Liu, Haitao Ong, Yew-Soon Qu, Xinghua Zhang, Yuzhe Zheng, Jianmin The Data Science and Artificial Intelligence Research Center School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Energy and Power Engineering Dalian University of Technology Dalian116024 China Singapore The Computational Intelligence Lab. School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Many real-world problems, such as airfoil design, involve optimizing a black-box expensive objective function over complex structured input space (e.g., discrete space or non-Euclidean space). By mapping the complex structured input space into a latent space of dozens of variables, a two-stage procedure labeled as generative model based optimization (GMO) in this paper, shows promise in solving such problems. However, the latent dimension of GMO is hard to determine, which may trigger the conflicting issue between desirable solution accuracy and convergence rate. To address the above issue, we propose a multi-form GMO approach, namely generative multi-form optimization (GMFoO), which conducts optimization over multiple latent spaces simultaneously to complement each other. More specifically, we devise a generative model which promotes positive correlation between latent spaces to facilitate effective knowledge transfer in GMFoO. And further, by using Bayesian optimization (BO) as the optimizer, we propose two strategies to exchange information between these latent spaces continuously. Experimental results are presented on airfoil and corbel design problems and an area maximization problem as well to demonstrate that our proposed GMFoO converges to better designs on a limited computational budget. © 2025, CC BY-NC-ND.

关键词： Budget control

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