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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Mohsin Murtaza Mohsin Iftikhar Mansoor Syed Raza Zeeshan Hameed Mir Mohammed J. Abdulaal Nidal Nasser STEM College RMIT University Melbourne Australia Faculty of Computer Information Science (CIS) Higher Colleges of Technology (HCT) Fujairah UAE Dept. of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia College of Engineering Alfaisal University Riyadh Saudi Arabia

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

Autonomous Vehicles (AVs) require constant sensing and accurate identification of dynamic environmental conditions for prompt decision-making. Limited onboard processing capabilities can impede these vehicles’ ability to distinguish between road obstacles and harmless objects such as plastic bags, posing potential safety risks. This study examines a reallife fatal crash to highlight the dangers of relying solely on invehicle sensory data. It underscores the importance of employing multiple communication and sensing systems to ensure safe AV operation. The paper advocates for the integration of both onboard sensors and extensive communication networks that connect vehicles with each other and cloud-based resources. This approach facilitates enhanced data processing and the exchange of information critical for improving obstacle detection and managing blind spots. Furthermore, the study proposes an intelligent cloud-connected ecosystem that incorporates advanced network connectivity solutions to match the high speeds of autonomous vehicles, enabling real-time decision-making. This ecosystem aims to ensure seamless information and sensory data flow, which is essential for effective decision-making in autonomous vehicle navigation.

关键词： Clouds Roads Ecosystems Decision making Transportation Standardization Safety Vehicle dynamics Autonomous vehicles Edge computing

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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Analysis of MRI Images for Brain Tumor Detection Using Fuzzy C-Means and LSTM 4th

Analysis of MRI Images for Brain Tumor Detection Using Fuzzy...

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4th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2020

作者： Thapa, Sobit Panday, Sanjeeb Prasad Graduate Student Faculty of Engineering Nepal College of Information Technology Pokhara University Lalitpur Nepal Department of Electronics and Computer Engineering Pulchowk Campus Institute of Engineering Tribhuvan University Lalitpur Nepal

ISBN: (纸本)9789811570612

The collection and identification of medical images are the most important scientific task. MRI processing is also one of the reliable methods underpinning medical diagnosis. In this research, segmentation is performed by fuzzy C-means that detects the infected region from the images of brain MRI. Gray-level co-occurrence matrix (GLCM) is used to extract the features from the MRI images of the brain which gives the better result for classification than existing system. Long short-term memory (LSTM), a recurrent neural network extension method is used to identify brain MRI images. It gives a more precise and effective outcomes for image of MRI image classification. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-computer Interfaces

arXiv

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arXiv 2025年

作者： Jiang, Xiaowei Chen, Yanan Pal, Nikhil Ranjan Chang, Yu-Cheng Yang, Yunkai Do, Thomas Lin, Chin-Teng GrapheneX-UTS Human-centric AI Centre Australian AI Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Psychology and Behavior Henan University China Indian Statistical Institute Kolkata India

Fuzzy logic provides a robust framework for enhancing explainability, particularly in domains requiring the interpretation of complex and ambiguous signals, such as brain-computer interface (BCI) systems. Despite significant advances in deep learning, interpreting human emotions remains a formidable challenge. In this work, we present iFuzzyAffectDuo, a novel computational model that integrates a dual-filter fuzzy neural network architecture for improved detection and interpretation of emotional states from neuroimaging data. The model introduces a new membership function (MF) based on the Laplace distribution, achieving superior accuracy and interpretability compared to traditional approaches. By refining the extraction of neural signals associated with specific emotions, iFuzzyAffectDuo offers a human-understandable framework that unravels the underlying decision-making processes. We validate our approach across three neuroimaging datasets using functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG), demonstrating its potential to advance affective computing. These findings open new pathways for understanding the neural basis of emotions and their application in enhancing human-computer interaction. © 2025, CC BY.

关键词： Near infrared spectroscopy

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Biomedical Image Quality Improvement with Spatial Domains 2

Biomedical Image Quality Improvement with Spatial Domains

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2nd International Conference on Innovative and Creative information technology, ICITech 2021

作者： Fernando, Erick Siagian, Pandapotan School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia

ISBN: (纸本)9781728197463

Digital image processing aims to improve the quality of an original image so that it can display an image that is relatively better than the original image, so as to obtain the detailed information needed for an analysis need. Quality degradation Biomedical images in computerization usually experience changes in brightness, blur, and contrast stretching. Because of the deterioration in the image quality, doctors and patients cannot obtain the information needed for analysis, therefore the need for biomedical image processing techniques with spatial domain methods to improve the quality and details of information in the image so that it helps doctors in diagnosing. Data analysis is biomedical images like X-ray images, CT-Scan (computer Tomographic Scan) images, and USG (Ultrasound Graphic) images. A focused study on improving image quality can be done with digital spatial domain image processing techniques. Digital domain image processing is digital image processing based on the manipulation of pixel values in the image directly, in the form of point processing and mask processing. Process testing for each image for Blur level is CT-scan images with a value of 0.98436, ultrasound images with a value of 0.9875, X-ray images with a value of 0.9836. This value is blur level value get near to 1 which means the image becomes clearer. The analysis results prove that the spatial domain method can clarify the object image in this study. The biomedical image quality processing model is proven to be able to improve the quality of the image that is declining due to the digitization process and assist the user in analyzing the biomedical image. © 2021 IEEE.

关键词： Image quality

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Optimizing Energy Management in Smart Environments: A Review of Strategies, Challenges, and Future Directions

Optimizing Energy Management in Smart Environments: A Review...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2024

作者： Jayakanth, J.J. Saranya, P. Arularasan, A.N. Saranya, D. Dilli Babu, M. Gopalakrishnan, S. Farhaoui, Yousef Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chengalpattu District 603203 India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamilnadu Chennai 600048 India Department of Mathematics Bannari Amman Institute of Technology Tamil Nadu Erode Sathyamangalam India Department of Information Technology Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Kelambakkam 603103 India T-IDMS Department of Computer Science Faculty of Sciences and Techniques Errachidia Moulay Ismail University of Meknes Meknes Morocco

ISBN: (纸本)9783031883033

Technological advances such as IoT have provided a foundation for smart environment that are capable of self-adjustment to the needs of users. As the consumption of resources becomes faster and faster, control of energy in these facilities is a key to optimizing the effectiveness of ecosystems and for the advantage of consumers and utilities. This review examines various management techniques with emphasis on the peak load shaving and demand response, which are critical in managing energy efficiency and stability. Hence, it focuses on important benefits such as lower energy consumption and improved stability of the electricity supply in the grid, as well as threats like high installation costs, the absence of a unified approach to implementing smart homes, and user acceptance. It provides a broad understanding of the current trends, also focuses on the current practices, and also explores future trends and prospects. Thus, it is informative for researchers and practitioners involved in IoT development, integrating the latest findings and acknowledging ongoing difficulties, which create the basis for upcoming studies focused on eliminating obstacles and utilizing IoT effectively for efficient and sustainable energy management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Demand Response Energy Management Internet of Things (IoT) Peak Load Shaving Smart Environments

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COVID-19 Diagnosis From CT-images Using Transfer Learning

COVID-19 Diagnosis From CT-images Using Transfer Learning

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Nouran Waleed Lobna Said Sahar Selim Ahmed Gomaa Radwan Faculty of Information Engineering and Technology (IET) The German University in Cairo(GUC) Cairo Egypt Nanoelectronics Integrated Systems (NISC) Nile University Giza Egypt School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt

In symptomatic patients, a positive COVID-19 test is critical for securing life-saving services such as ICU care and ventilator support; it may cause septic shock, septic pneumonia, respiratory failure, heart difficulties, liver issues, and even death. CAD systems help people in rural places and doctors in the early detection of COVID-19. A diagnostic and severity detection technique utilizing transfer learning and a backpropagation neural network has been developed with the aid of a computer for this purpose. This study aims to compare and analyze multiple deep learning-enhanced strategies for detecting COVID-19 in CT scan medical images. The COVID-19 CT scan binary classification challenge utilized two powerful pretrained CNN models: Inception ResNet V2 and ResNet50. To achieve higher accuracy in the diagnosis of COVID-19 using CT scan images, a new approach called Inception ResNet was employed, and it resulted in 97.3% accuracy and 97.38% specificity. Transfer learning techniques were employed to reduce the training time and get around the shortage of data. The proposed approaches outperformed more than other papers in the literature by 0.2%.

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence Research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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