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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Mahfuri, Mahmoud Ibrahim, Amer International University for Science and Technology Computer and Information Technology Department Syria United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates

ISBN: (纸本)9798350369106

This paper presents the evolving role of artificial intelligence (AI) in improving internal control and management processes. AI-driven technologies, including Generative Adversarial Networks (GANs) and ontologies, in addition to enabling data-supported decision-making, and empowering local governments to execute administrative tasks more efficiently and effectively. The study explores the wide range of opportunities that AI offers, highlighting the potential for optimizing internal workflows. Besides, it delves into key challenges, models, and methods for fully integrating AI into organizational processes, providing actionable insights for achieving streamlined, data-driven management systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Automated Detection of COVID-19 in Chest Radiographs: Leveraging Machine Learning Approaches

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IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 572-578页

作者： Batool, Raheela Raza, Ghulam Musa Khalid, Usman Kim, Byung-Seo Department of Business Administration Pusan National University Korea Republic of Department of Software & Communication Engineering Hongik University Sejong Korea Republic of Department of Computer Science Comsats University Islamabad Sahiwal Pakistan

The World Health Organization (WHO) has designated the COVID-19 pandemic a global health emergency, prompting responses all over the world. The fatality rate is between 2% and 5%, and millions of people around the world have been infected. While the WHO recommends tests, resource-intensive testing has motivated the development of CNN technology for automated identification. Research employing machine learning models shows great accuracy in classifying X-ray and CT images for COVID-19 detection. These models include denseNet201, resnet50V2, inceptionv3, mobile net, and custom CNNs. The interpretation of chest X-rays has come a long way, yet there are still obstacles to overcome. In this paper, we present a way for using a machine learning model to categorize chest X-ray pictures into normal, COVID-19, viral pneumonia, and lung opacity, demonstrating the model's efficacy in assisting medical diagnosis, especially in time-sensitive situations like COVID-19. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Graph-Based Multitask Transfer Learning for Fault Detection and Diagnosis of Few-Shot Analog Circuits

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IEEE Internet of Things Journal 2025年第12期12卷 21264-21279页

作者： Gao, Zhongyu Yan, Aibin Huang, Zhengfeng Cui, Jie Roh, Byeong-Hee Liu, Guangzhu Girard, Patrick Wen, Xiaoqing Anhui University School of Computer Science and Technology Hefei230601 China Hefei University of Technology School of Microelectronics Hefei230009 China Ajou University Department of Software and Computer Engineering Suwon16499 Korea Republic of University of Montpellier CNRS LIRMM Montpellier34095 France Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka820-8502 Japan

Building an interpretable fault detection and diagnostic model based on few-shot circuit samples and prior information about circuit structures is of significant importance. To fill these gaps, we propose a graph-based multitask transfer learning (TL) method for fault detection and diagnosis of circuits under few-shot conditions. First, in order to model the interconnections of nodes in a circuit, the sample data is organized into a graph structure, and a semi-supervised graph-based structural feature fusion method is proposed. The proposed method can accept graph-structured data and process the data using feature fusion methods. Second, to improve the model performance under few-shot conditions, two TL mechanisms are proposed for the topological structure characteristics of analog circuits as well as circuit signal characteristics. Finally, through a parameter-shared strategy, we propose a task transfer-based fault diagnosis approach. Experimental results on three different circuits show that the proposed method has the best diagnostic accuracy compared to typical detection and diagnosis schemes. © 2014 IEEE.

关键词： Self-supervised learning

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Identifying Athletics Tracks using Keypoint Detection 38

Identifying Athletics Tracks using Keypoint Detection

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Harcombe, Gareth Green, Richard Neshatian, Kourosh University of Canterbury Department of Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes the first known approaches to locating an athletics track from GPS data of a running workout. This is done by mapping the GPS points onto an image and applying computer Vision methods, including: performing keypoint detection using a Keypoint Region-Based Convolutional Neural Network (R-CNN) model with a Residual Neural Network backbone;and applying Convolutional Kernels over the image. The Keypoint R-CNN model achieved a Root Mean Square Error (RMSE) of 1.29 on synthetic data, which is lower than the error of other keypoint detection models of 2.23, but performed poorly on real data. The Convolutional Method achieved an RMSE of 4.20. The methods and results of this paper can be used in future applications for error correction and feature extraction of GPS data for running workout analysis. © 2023 IEEE.

关键词： Neural network models

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Bridging Fourier and Spatial-Spectral Domains for Hyperspectral Image Denoising 24

Bridging Fourier and Spatial-Spectral Domains for Hyperspect...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Xiao, Jiahua Liu, Yang Zhang, Shizhou Wei, Xing School of Software Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Engineering Northwest Polytechnical University Xi'an China

ISBN: (纸本)9798400706868

Remarkable progresses have been made in hyperspectral image (HSI) denoising. However, the majority of existing methods are predominantly confined to the spatial-spectral domain, overlooking the untapped potential inherent in the Fourier domain. This paper presents a novel approach to address HSI denoising by bridging the information from the Fourier and spatial-spectral domains. Our method highlights key insights into the Fourier properties within spatial and spectral domains through the Fourier transform. Specifically, we note that the amplitude inherently embody noise and photon reflection characteristics, while the phase holds structural information. These insights unveil new perspectives on the physical properties of HSIs, motivating us to leverage complementary information exchange between Fourier and spatial-spectral domains. To this end, we introduce the Fourier-prior Integration Denoising Network (FIDNet), a potent yet straightforward approach that utilizes Fourier insights to synergistically interact with spatial-spectral domains for superior HSI denoising. In FIDNet, we independently extract spatial and Fourier features through dual branches and merge these representations to enhance spectral evolution modeling through the inherent structure consistency constraints and continuing reflection variation revealed in Fourier prior. Our proposed method demonstrates robust generalization across synthetic and real-world benchmark datasets, achieves comparable results with state-of-the-art methods in both quantitative quality and visual results. The code is available at https://***/MIV-XJTU/FIDNet. © 2024 ACM.

关键词： Image denoising

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Evaluating Transparency: A Cross-Model Exploration of Explainable AI in Financial Forecasting

Evaluating Transparency: A Cross-Model Exploration of Explai...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Valensky, Dylan Mohaghegh, Mahsa Auckland University of Technology Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Advances in machine learning and deep learning have assisted progress in stock market predictions, which have presented unique opportunities for investors and traders to benefit from predictions. However, understanding how these models work is crucial to trusting their predictions. This research applies three prominent Explainable AI (XAI) techniques - SHAP, LIME, and Permutation Importance - to three distinct forecasting models: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree. This work fills a gap in the literature by comparing XAI methods across a combination of 'black-box' and 'white-box' models. Two key research questions guide this research: the performance of these XAI methods across the chosen models and the consistency between global and local explanations across these models. Findings reveal interesting model-specific behaviors, like CNN's emphasis on slightly older data and LSTM's focus on immediate past data. In the S&P 500 dataset context, features such as 'Close' and 'Adj Close' prices emerged as significant across models, while 'Volume' remained insignificant. This study offers a broader perspective on applying XAI in financial time series forecasting and helps enhance trust and comprehension among stakeholders relying on these model predictions. © 2023 IEEE.

关键词： Forecasting

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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