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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Dubey, Rahul Hickinbotham, Simon Buchanan, Edgar Colligan, Andrew Briel, Imelda Price, Mark Tyrrell, Andy M. Missouri State University Computer Science United States School of Physics Engineering and Technology University of York United Kingdom School of Mechanical & Aerospace Engineering Queen's University Belfast United Kingdom

ISBN: (纸本)9798350308365

In recent years, the evolutionary developmental (Evo- Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo- Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviors to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best-evolved solution exhibits generalizability and adaptability across different simulation parameters. © 2024 IEEE.

关键词： Genes

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AcquainLay: An Efficient Blockchain Block Relay Method Based on Acquaintance Immunization

AcquainLay: An Efficient Blockchain Block Relay Method Based...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Wang, Hui Yang, Zhenyu Li, Ming Hu, Yanlan Hu, Donghui School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Physics Hefei University of Technology Hefei China

ISBN: (纸本)9798350316308

The propagation of information in blockchain plays a key role in its scalability improvement, and it is difficult to maintain the original decentralized characteristics and security in the existing methods of accelerating block propagation. In this paper, we combine blockchain and complex network epidemic immunization strategies and then propose an approach (Acquain-Lay) based on acquaintance immunization to find suitable nodes to disseminate information without the need for global topolog-ical information. The evaluation results show that AcquainLay improves transaction throughput by 43.53 % at best and reduces the orphan block rate significantly. In addition, AcquainLay is easy to deploy because it does not change the key data structures and network topology of the blockchain. © 2023 IEEE.

关键词： Complex networks

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Comparative Analysis of Machine Learning Techniques for Bearing Fault Classification in Rotating Machinery 17

Comparative Analysis of Machine Learning Techniques for Bear...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Kumar, Anischal Groza, Voicu Raj, Krish K Assaf, Mansour H Kumar, Shahil Kumar, Rahul R The University of the South Pacific School of Information Technology Engineering Mathematics and Physics Suva Fiji University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (纸本)9798350321104

This paper provides a comprehensive analysis of techniques used for bearing fault classification, which is essential for detecting anomalous conditions in rotating machinery. The focus is on identifying and categorizing various types of bearing faults to monitor equipment performance and prevent repairable motor breakdowns. The authors use experimental data to identify bearing faults and extract significant features from the dataset, and then apply Principal Component Analysis (PCA) and Curvilinear Component Analysis (CCA) techniques for exploratory analysis. The study compares the classification accuracy of various machine learning models, including support vector machines, k-nearest neighbors, ensemble models, and neural network models such as Multilayer feedforward neural network (ANN) and Convolutional neural network (CNN). The results of this study provides valuable insights for future research in bearing fault classification since it is the most important component in rotating machines.. © 2023 IEEE.

关键词： Principal component analysis

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ECG Multi Class Classification Using Machine Learning Techniques

ECG Multi Class Classification Using Machine Learning Techni...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kumar, Vijayeskar Kumar, Shahil Raj, Krish Kumar Assaf, Mansour H. Groza, Voicu Kumar, Rahul R The University of the South Pacific School of Information Technology Engineering Mathematics and Physics Suva Fiji University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (纸本)9781665493840

This paper presents a project focused on utilizing Artificial Intelligence (AI) tools to improve the process of diagnosing heart diseases. The research indicates that 10 to 15 percent of Pacific Islanders are diagnosed with at least one form of heart disease, leading to around 20,000 deaths annually. The proposed project uses the Physio net database and ECG signals of 162 patients to design a multi-class classification method that accurately recognizes different patterns under 3 classes, namely, Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The study utilizes two feature extraction methods, Continuous Wavelet Transform, and Wavelet Scattering, to extract the principal characteristics from the ECG data. MATLAB Software is used to train three models, an AlexNet Model, an SVM Model, and an LSTM Model, to diagnose cardiovascular diseases and their severity. The results of the different classification methods showed that the SVM Model had the best performance with a classification accuracy of 98%. This project offers a dependable and effective diagnostic tool for the diagnosis of heart diseases with a minimized risk of human error. Additionally, it has the potential to serve as a valuable resource for future studies in the medical field aimed at enhancing cardiovascular disease diagnosis and treatment. © 2023 IEEE.

关键词： MATLAB

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Intruder trajectory tracking in a three-dimensional vibration-driven granular system:Unveiling the mechanism of the Brazil nut effect

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Chinese physics B 2023年第10期32卷 106-111页

作者：李拓程可彭政杨晖厚美瑛 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Soft Matter Laboratory Institute of PhysicsChinese Academy of SciencesBeijing 100190China School of Physics Central South UniversityChangsha 410012China College of Medical Instrumentation Shanghai University of Medicine&Health SciencesShanghai 201318China

We employ a Hall-effect magnetic sensor array to accurately track the trajectory of a single magnetic sphere,referred to as the“intruder,”within a three-dimensional vibro-fluidized granular bed to unravel the underlying physical mechanism governing the motion of the *** the acceleration range of 3.5 g≥Γ≥1.5 g,we find that,regardless of the intruder's initial position,it consistently reaches the same equilibrium depth when the vibration acceleration(Γ)and frequency(ω)are ***Γ≤2.5 g,the equilibrium position lies on the surface of the granular bed,while forΓ>2.5 g,it shifts below the ***,intruders with different densities exhibit varying equilibrium depths,with higher density resulting in a deeper equilibrium *** understand the mechanism behind the intruder's upward or downward motion,we measure its rising or sinking velocities under different vibration *** findings demonstrate that the rising velocity of the intruder,under varying vibration accelerations(Γ)and frequencies(ω),can be collapsed using the ratioΓ/ω,while the sinking velocity remains unaffected by the vibration *** confirms that the upward motion of the larger sphere,associated with the Brazil nut effect,primarily arises from the void-filling mechanism of the bed ***,our experiments reveal that the presence of convection within the bed particles has minimal impact on the motion of the intruder.

关键词： granular matter segregation Brazil nut effect vibro-fluidization

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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Synchronous Machine Modelling Using Linear Regression Approaches 17

Synchronous Machine Modelling Using Linear Regression Approa...

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17th International Conference on engineering of Modern Electric Systems, EMES 2023

作者： Kumar, Shahil Malai, Michael Kororua, Ganitoga Groza, Voicu Assaf, Mansour H. Kumar, Rahul R School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9798350310634

Synchronous motors (SMs) play an important role in various industrial applications due to their unique characteristics and advantages. One of the key advantages of a synchronous motor (SM) is its ability to operate at a constant speed, making them suitable for applications where precise speed control is required. SMs also have a high-power factor, which means they consume less reactive power. The excitation current of a SM is crucial for optimal motor performance and efficiency, as insufficient current can reduce speed and power output while excessive current can overheat the motor and reduce efficiency. Thus, it is essential to estimate the excitation current of a SM. Accurate estimation and control of the excitation current can be achieved through synchronous machine data modeling. The study showcases the estimation of the excitation current of a SM using regression-based approaches. In particular, the usage of Principal Component Analysis has generally improved the performance of the regression models. The best models belong to the family of Linear Regressors as highlighted in the results. © 2023 IEEE.

关键词： Principal component analysis

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Diffusion Scale Factor Least Logarithmic Absolute Difference Algorithm Based on P-norm 23

Diffusion Scale Factor Least Logarithmic Absolute Difference...

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7th International Conference on Digital Signal Processing, ICDSP 2023

作者： Zhang, Yin Huo, Yuanlian Qi, Yongfeng Ding, Ruibo School of Physics and Electronic Engineering Northwest Normal University Lanzhou730000 China School of Computer Science and Engineering Northwest Normal University Lanzhou730000 China

ISBN: (纸本)9781450398626

In order to improve the performance of diffusion adaptive filtering algorithm in non-Gaussian noise environment, a new diffusion scale least logarithmic absolute difference adaptive filtering algorithm based on p-norm is proposed. The algorithm based on least logarithm absolute deviation criterion by introducing the norm p and the scale factor q to design a new cost function. The least logarithm absolute deviation criteria to ensure the algorithm in the impulsive noise environment good robustness on the one hand;on the other hand, through the norm p and scale factor q can make the algorithm according to the laws of the error change to a more reasonable adjustment cost function. Thus, the algorithm performance is further improved. The simulation results show that the steady-state error performance of the proposed algorithm is improved compared with other robust algorithms in the environment of α stable distributed noise. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adaptive filtering

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The Information Complexity of Navigating with Momentum

The Information Complexity of Navigating with Momentum

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2022 Conference on Artificial Life, ALIFE 2022

作者： Riegler, Bente Polani, Daniel Steuber, Volker School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom

Many models of organism navigation concern themselves in essence just with the sequence of locations visited and how to manage it. However, larger and bulkier organisms have also to deal with managing momentum. We expect that this affects the cognitive management of movement. Here we propose a simple model for the information processing complexity of navigation when velocity and acceleration are considered, moving away from a kinematic perspective to a partially dynamic model, to separate the effects of location and momentum management. The work is discussed in the context of recent neurobiological research suggesting that biological agents plan around acceleration and deceleration phases, showing high neural activity during their body’s velocity changes. © Artificial Life Conference Proceedings 2022.

关键词： Information management

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