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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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Classification of Brain Strokes in Computed Tomography Images Utilizing Deep Learning 4

Classification of Brain Strokes in Computed Tomography Image...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Tursynova, Azhar Sakhipov, Aivar Omirzak, Islam Ikram, Zhanseri Smakova, Saida Kutubayeva, Madina Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

The growing importance of efficient and accurate medical image classification has led to increased research interest in the application of deep learning techniques. This research paper investigates the utilization of Convolutional Neural Networks (CNNs) for the classification of brain strokes from computed tomography (CT) images. Leveraging a diverse dataset, we systematically examine the performance of the CNN model in distinguishing between 'Normal' and 'Stroke' image classes. The study employs a comprehensive set of evaluation metrics, including accuracy, precision, recall, and F-score, to assess the model's diagnostic efficacy. The training process is rigorously monitored, ensuring convergence of learning rates and model stability. Our results demonstrate promising levels of accuracy and an increasing trend in precision, recall, and F-score, underscoring the potential of deep learning in enhancing stroke diagnosis. Furthermore, the study addresses data scarcity challenges through data augmentation techniques, emphasizing their pivotal role in model training. While acknowledging the need for larger and more diverse datasets and potential model enhancements, this research contributes to the broader goal of harnessing artificial intelligence for improved healthcare, particularly in the timely and accurate detection of critical medical conditions like brain strokes through non-invasive imaging modalities. © 2024 IEEE.

关键词： Convolutional neural networks

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Research and development of sign language recognition system using neural network algorithm 4

Research and development of sign language recognition system...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Matveyas, Yegor Mukasheva, Assel Yedilkhan, Didar Keneskanova, Arailym Kambarov, Dastan Mukhammejanova, Dinargul Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems and Cybersecurity Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

Sign language is an important communication tool for the hearing-impaired community. Due to technological development, it is now possible to develop systems that can recognize, translate and process sign language into text or speech, according to the visual representation of gestures. This article explores the development of a real-time sign language recognition system using a neural network algorithm. The aim of the research is to develop such a sign language recognition and translation system which should be optimized for integration into web applications. The Mediapipe library was used to determine the key points and orientation of the user's hands and fingers. After that, the software module transmits the collected data to a sequential neural network that includes layers of Long Short-Term Memory (LSTM). To build this type of neural network, the open Keras library was used. The key feature of the presented neural network model is the combination and interaction of convolutional and Recurrent neural network (RNN) layers. The considered set of layers provides the ability to track the dependence of data over time, this is achieved by switching between layers of different types and reducing the number of neurons. The LSTM network is trained using a custom editable dataset based on American Sign Language gestures. The dataset was formed on the recording of signs. Each sign representation was preprocessed to extract three-dimensional landmarks. These collected key points were transmitting to the layers of the LSTM neural network, which allowed the model to study the complex relationships between hand movements and the corresponding gestures. Each sign sample is represented by a sequence of 24 frames. The effectiveness of the neural network algorithm is evaluated using various indicators, including an accuracy of the model. The results of the experiment show that the developed software can achieve a high level of accuracy in recognizing the gestures of the sign languag

关键词： Application programs

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Lunatum Prosthetic Replacement: Modeling Based on Volume Rendering of CT Scan Images

Lunatum Prosthetic Replacement: Modeling Based on Volume Ren...

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International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2022

作者： Hamda, Manal Ghali, Btihal El Hilal, Imane Midaoui, Omar El Ngote, Nabil Abdi, Bahia El Megdiche, Kawtar Biomedical Engineering Department Abulcasis International University of Health Sciences Rabat Morocco Department of Data and Knowledge Engineering Information Sciences School Rabat Morocco Rabat Morocco Rabat Morocco Medical Simulation Center Cheikh Zaid Foundation Rabat Morocco

ISBN: (纸本)9789811667749

Additive Manufacturing has immersed the medical field, especially in reconstructive surgery, allowing the creation of a 3D model resembling the anatomical structure of interest. Due to Osteonecrosis also referred to as Kienböck’s disease;carpal bones especially the lunatum are concerned the most with those technologies especially since a prosthetic replacement is an obligation when it comes to advanced stages of this disease. In this article, we propose a method based on direct 3D reconstruction based on volume rendering directly on patients’ medical images (CT scans) to preserve the anatomical shape. For that purpose, we utilized 3D slicer software to create a 3D model based on different cuts of CT scan images. The resulting model was satisfactory, as it was similar to the lunate bone structure preserving all its anatomical characteristics and dimensions. The proposed approach helps in creating a prosthetic replacement with the exact anatomical shape and structure of the bone of interest respecting the dimensions, curves, and facets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Prosthetics

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Improved Adaptive Fairness-Indexed Technique for Congestion Control in Underwater Networks 3

Improved Adaptive Fairness-Indexed Technique for Congestion ...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Balaji Vijayan, V. Manojee, K.S. Mohammed Ibrahim, M. Nivethitha, R. Thamaraimanalan, T. Ranjithkumar, M. Hkbk College of Engineering Department of Information Science and Engineering Karnataka Bengaluru India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.S.R. College of Engineering Department of Computer Science and Engineering Tamilnadu Thiruchengode India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (纸本)9798331528201

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain's Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation. © 2024 IEEE.

关键词： Energy efficiency

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification 5

A Novel Breast Cancer Detection and Classification Using Enh...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Mathiyalagan, P. Kiruthiga, G. Rasmi, A. Vadivel, M. Mahalakshmi, K.V. Poornappriya, T.S. Sri Ramakrishna Engineering College Department of CSE Coimbatore India Karpagam College of Engineering Dept of Artificial Intelligence and Data Science Coimbatore India RR Institute of Technology Information Science and Engineering Bangalore India Excel Engineering College Department of Information Technology TamilNadu Namakkal India B v Raju Institute of Technology CSE Telangana 502313 India Aspire Systems Pvt Ltd Data and Analytics Karnataka Bengaluru560037 India

ISBN: (纸本)9798331518097

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography. © 2024 IEEE.

关键词： Lung cancer

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Enhanced Monarch Butterfly Optimization Algorithm with Rank-Based Selection and Random Jump Strategy for Constrained engineering Problems

Enhanced Monarch Butterfly Optimization Algorithm with Rank-...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Kathiroli, Panimalar Kanmani, S. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India Puducherry Technological University Department of Information Technology Puducherry India

ISBN: (纸本)9798350333244

Nature-inspired algorithms have become increasingly prevalent in engineering and mathematical optimization due to their efficacy and adaptability. Among these algorithms, the Monarch Butterfly Optimization Algorithm (MBOA) is a population-based metaheuristic, characterized by its simple implementation and the utilization of the Migration Operator (MO) and Butterfly Adjusting Rate (BAR) to guide the search path of butterflies. However, the algorithm can suffer from susceptibility to local optima traps, early convergence, and inferior performance due to a lack of random factors. To address these challenges, we propose an Enhanced Monarch Butterfly Optimization Algorithm (E-MBOA) that incorporates Ranking-based Selection (RBS) and a Random Jumping strategy. The RBS prioritizes the search direction based on individuals' ranks in the population, enhancing the search process by encouraging the exploration of feasible areas with lower objective function values. By employing the Random Jumping strategy, the process can effectively investigate the search space and avoid local optima, thus mitigating early convergence issues. The proposed model is assessed on a variety of mechanical and engineering design problems. Results are contrasted to other optimization approaches published in the literature, demonstrating that the proposed model offers faster convergence to global optima and effectively avoids local optima traps. E-MBOA's performance validates its feasibility and effectiveness as a powerful tool for resolving complex engineering optimization problems. © 2023 IEEE.

关键词： Constrained optimization

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