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CBAR-UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention-based deep residual neural network

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Multimedia Tools and Applications 2024年第37期83卷 85047-85063页

作者： Kumar, Rakesh Gupta, Meenu Agarwal, Aman Nayyar, Anand Department of Computer Science and Engineering Chandigarh University Punjab140413 India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

computer-aided Medical Image Segmentation (MIS) plays a leading role in diagnosing diseases automatically. MIS is used extensively in diagnosing medical ailments to obtain clinically relevant information of the shapes and volumes of the target organs and tissues. In order to facilitate accurate segmentation, a Block Attention Based Deep Residual Neural Network, i.e., CBAR-UNet model, is proposed to perform cardiac image segmentation on short axis Magnetic Resonance Images (MRI) stacks from top to bottom slice. The Automated Cardiac Diagnosis Challenge (ACDC) dataset is used in the proposed work, which comprise of a 3D MRI of 100 patients (i.e., 200 MRI stacks). Further, 3D MRIs are converted into 2D by slicing each image to train the model, and Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the 2D dataset. Residual connections are introduced that aid in training deeper models. Convolutional Block Attention Module (CBAM) is also added in the proposed network, which consists of Spatial and Channel attention that enhances the spatial and channel-wise features in the image by providing information like ‘what’ and ‘where’ to pay attention respectively. To test and validate the proposed methodology, extensive experimentations were conducted and it was observed that proposed image segmentation model delivers better results by 2.47% when compared to both 2D and 3D State-Of-The-Art (SOTA) methods with a Dice Score of 0.9428. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Digital Twin Enhanced Critical Infrastructure Life Cycle Security 9

Digital Twin Enhanced Critical Infrastructure Life Cycle Sec...

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9th IEEE Smart World Congress, SWC 2023

作者： Itapelto, Taru Mathematics and Computer Science University of Twente Faculty of Electrical Engineering Enschede Netherlands

ISBN: (纸本)9798350319804

The cyber security of critical infrastructures (CIs) is crucial for society's functioning. Digitalization has connected previously isolated systems, creating complex networks with many interdependencies. Despite increased interest in security standards and legislation, testing and ensuring cyber security for these systems is still challenging due to their long life cycle (LC) and continuous availability requirements. Traditional security testing methods like pen-and-paper and penetration testing are insufficient, and the latter may compromise system availability. This paper presents a PhD project aiming to enhance CIs' life cycle security (LCS) using a digital twin (DT)-enhanced solution. We discuss the project's current status and future plans, as well as propose utilising DTs to improve testing and quality assurance, specifically for the defence-in-depth (DiD) strategy's implementation during the system's LC. © 2023 IEEE.

关键词： critical infrastructure cyber security Defence-in-Depth Digital Twin life cycle security

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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The Basics of Neutrosophic Simulation for Converting Random Numbers Associated with a Uniform Probability Distribution into Random Variables Follow an Exponential Distribution

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Neutrosophic Sets and Systems 2023年 53卷 358-366页

作者： Jdid, Maissam Alhabib, Rafif Salama, A.A. Faculty of Informatics Engineering Al-Sham Private University Damascus Syria Department of Mathematical Statistics Faculty of Science Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

When performing the simulation process, we encounter many systems that do not follow by their nature the uniform distribution adopted in the process of generating the random numbers necessary for the simulation process. Therefore, it was necessary to find a mechanism to convert the random numbers that follow the regular distribution over the period [0, 1] to random variables that follow the probability distribution that works on the system to be simulated. In classical logic, we use many techniques in the transformation process that results in random variables that follow irregular probability distributions. In this research, we used the inverse transformation technique, which is one of the most widely used techniques, especially for the probability distributions for which the inverse function of the cumulative distribution function can found. We applied this technique to generate neutrosophic random variables that follow an exponential distribution or a neutrosophic exponential distribution. This based on classical or neutrosophic random numbers that follow a regular distribution. We distinguished three cases according to the logic that each of the random numbers or the exponential distribution follows. We arrived at neutrosophic random variables that, when we use them in systems that operate according to an exponential distribution, such as queues and others, will provide us with a high degree of accuracy of results, and the reason for this is due to the indeterminacy provided by neutrosophic logic © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Random variables

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural Computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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Multi-dimensional Attention Spiking Transformer for Event-based Image Classification 5

Multi-dimensional Attention Spiking Transformer for Event-ba...

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5th International Conference on Communications, Information System and computer engineering, CISCE 2023

作者： Li, Lin Liu, Yang Faculty of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China

ISBN: (纸本)9798350326796

Image classification is a vital research area in deep learning. However, the use of Artificial Neural Networks (ANNs) in conventional approaches requires vast computational power and memory. As a potential energy-efficient alternative, Spiking Neural Networks (SNNs) leverage temporal information and low-power sensors. Nonetheless, extracting spatio-temporal features from event-based image sequences for improved classification accuracies in SNNs poses a significant challenge. To address this, we propose a Multi-Dimensional Attention Spiking Transformer (MAST) model that integrates attention mechanisms and SNNs to capture spatio-temporal features in event-based image sequences. Consequently, the MAST model achieves state-of-the-art performance in various classification tasks, as shown by the evaluations on the CIFAR, DVS128 Gesture, and CIFAR10-DVS datasets. Overall, MAST exhibits promise in event-based image classification tasks, providing a new perspective on the integration of attention mechanisms and SNNs for improved image classification. © 2023 IEEE.

关键词： Image classification

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Assessing the Performance of PINN and CNN Approaches in Solving the 1D Burgers' Equation with Deep Learning Architectures 4

Assessing the Performance of PINN and CNN Approaches in Solv...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Li, Cheng School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau999078 China

ISBN: (纸本)9798400709449

Deep learning has emerged as a promising approach for solving complex partial differential equations (PDEs) using data-driven methods, particularly in scenarios where traditional numerical techniques face limitations. The 1D Burgers' Equation, which combines convection and diffusion terms, is a fundamental PDE used to model various physical phenomena, including fluid dynamics and shock waves. While analytical solutions for this equation are challenging due to its nonlinearity, specific cases do have exact solutions. This research systematically evaluates the effectiveness of two popular deep learning methodologies, Physics-Informed Neural Networks (PINNs) and Convolutional Neural Networks (CNNs), in solving the 1D Burgers' Equation. A particular focus of the assessment is to explore potential bias in the training dataset. The neural networks are trained using biased samples, and their results are rigorously compared with those obtained from unbiased samples. This investigation provides insights into the performance of these deep learning architectures and their sensitivity to the quality of training data. The research reveals significant sample bias effects on neural network (NN) models. Primitive bias notably impacts Convolutional Neural Networks (CNNs) more than Physics-Informed Neural Networks (PINNs). This is attributed to differences in loss function calculation, making bias selection more straightforward for CNNs. © 2023 Owner/Author.

关键词： Deep learning

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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String Figure Simulation with Multiresolution Wire Model 23

String Figure Simulation with Multiresolution Wire Model

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12th International Conference on Software and computer Applications, ICSCA 2023

作者： Nishita, Seikoh Department of Computer Science Faculty of Engineering Takushoku University Hachioji Tokyo Japan

ISBN: (纸本)9781450398589

String figure is a traditional game with a loop of a string played by hooking and/or unhooking strands of the loop from fingers to produce patterns representing certain objects. The patterns of the string figure change dynamically by the string manipulations by fingers. A computer-based method based on knot theory has been proposed to make string figure patterns. This method represents a string in the string figure as an extended knot diagram and generates string figure patterns by monotonically decreasing the number of crossing points. However, there are string figures where this method does not make the patterns correctly. In contrast, this paper proposes a method using physical simulation from the viewpoint that the string figure patterns are determined according to the tension and frictional force applied to the string. To evaluate the proposed method, we conducted two types of experiments. In the first experiments, we showed that physical simulations using an adaptive multi-resolution wire model have sufficient capability to compute the string figure patterns. In the second, we conducted experiments to make patterns for instances of the string figure. The experimental results indicate that the proposed method can correctly make the string figure patterns in most cases. We also found that the proposed method can make some of the string figure patterns that cannot be generated by conventional methods. © 2023 Copyright held by the owner/author(s).

关键词： Wire

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