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Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects

Cyborg and Bionic Systems 2022年第1期2022卷 285-302页

作者： Yuki Kuroda Yusuke Yamanoi Shunta Togo Yinlai Jiang Hiroshi Yokoi Joint Doctoral Program for Sustainability Research Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyoJapan Department of Mechan ical and Intelligent System Engineering Graduate School of Informatics and Engineering The University of Electro Communications TokyoJapan Center for Neuroscience and Biomedical Engineering The University of Electro Communications TokyoJapan Bejing Innovation Center for Inelligent Robots and Systems BeijingChina

The usability of a prosthetic hand difers significantly from that of a real ***,the complexity of manipulation increases as the number of degrees of freedom to be controlled increases,making manipulation with biological signals extremely *** overcome this problem,users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from *** previous studies,these have been left to the operating skills of the user,which is extremely difficult to *** this study,we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation *** required grasping technique is achieved by determining the correlation bet ween the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli,such as myoelectricity,sense of touch,and grasping *** agents of the 16 DOF robot hand were trained with the myoelectric signals of six participants,including one child with a congenital forearm ***,each agent could open and close the hand in response to the myoeletric stimuli and could accomplish the object pickup *** the tasks,the agents successfully identified grasping patterns suitable for practical and stable positioning of the *** addition,the agents were able to pick up the object in a similar posture regardless of the partiipant,suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

关键词： robot Objects overcome

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Aquila Optimizer with parallel computing strategy for efficient environment exploration

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4175-4190页

作者： Gul, Faiza Mir, Imran Mir, Suleman Department of Electrical Engineering Air University Aerospace and Aviation Campus Kamra Islamabad Pakistan School of Avionics and Electrical Engineering College of Aeronautical Engineering NUST Risalpur Pakistan Department of Avionics Engineering Air University Aerospace and Aviation Campus Kamra Islamabad Pakistan Electrical Department Fast-National University of Computer and Emerging Sciences Peshawar Pakistan

This paper introduces Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration that can be utilized for a wide range of operations. The proposed strategy incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while minimizing the computation complexity. This ensures acquisition of a collision-free optimal motion in a barrier-filled environment via generating a finite explored map. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). Combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed CME-AO algorithm. A perspective analysis was then performed by comparing the performance of the CME-AO algorithm with latest contemporary algorithms namely conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA) and Frequency Modified Hybrid-whale Optimization Algorithm (FMH-WOA). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. A statistical comparison with both CME and CME-AOA is then carried out by performing multiple simulations under different environmental configurations. The mean and standard deviation of the (%) area explored and total time taken are then calculated. Results indicate that the proposed algorithm presents distinct advantages of enhanced map exploration in a considerably lesser execution time with almost no fail runs. © 2023, The Author(s), under exclusive licence to Springer-V

关键词： Motion planning

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Improving Support Vector Machine Performance with Advanced Kernel Methods 7

Improving Support Vector Machine Performance with Advanced K...

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7th International Conference on Contemporary Computing and informatics, IC3I 2024

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function - which specifies the feature space where the data is linearly separable - has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. Applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study's thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance. © 2024 IEEE.

关键词： Support vector regression

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network 7

Deployment Schemes of In-Band Management Mode Based on Indus...

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7th International Conference on Contemporary Computing and informatics, IC3I 2024

ISBN: (纸本)9798350350067

In order to properly manage and monitor their networks in industrial environments, industrial Software-Defined Networking (SDN) networks need to have an in-band administration mode. This research investigates a wide variety of alternative deployment options for in-band management mode in industrial SDN networks in order to increase the performance of the network, as well as its dependability and level of network security. We study how the performance of the network differs depending on whatever coupling of in-band management and industrial SDN architecture we choose. We analyse a range of potential pairings of in-band management with industrial SDN design. We also discuss the advantages and disadvantages of each deployment technique, providing insights into the creation and management of robust Industrial SDN networks. It provides useful recommendations for increasing the efficacy and security of particular network infrastructures, which might be valuable to both corporate groups and network administrators. The adoption of SDN technology and the installation of tailored management modes are becoming more and more significant as industrial settings continue to expand. This is of the utmost significance in order to guarantee the highest possible standards of both operational excellence and safety. © 2024 IEEE.

关键词： Deployment Schemes In-band Management Industrial SDN Network Network Control Network Monitoring Network Security

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eFuseNet: A deep ensemble fusion network for efficient detection of Arrhythmia and Myocardial Infarction using ECG signals

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Multimedia Tools and Applications 2024年第18期84卷 1-32页

作者： Dwivedi, Amitesh Kumar Srivastava, Gaurav Tripathi, Sakshi Pradhan, Nitesh Johns Hopkins Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States Dell International Services India Pvt. Ltd. Dell Technologies Telangana Hyderabad500081 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur Rajasthan Jaipur302031 India

Myocardial infarction (MI) and cardiac arrhythmias remain leading causes of mortality within cardiovascular diseases. Accurate and timely diagnosis using electrocardiogram (ECG) signals is crucial, yet manual ECG interpretation is prone to error and time-consuming. Addressing these challenges, the authors propose a novel deep neural network architecture for efficient and accurate detection and classification of both MI and arrhythmia from ECG signals. Our model strategically fuses a modified AlexNet for robust feature extraction with Long Short-Term Memory and Gated Recurrent Units networks to exploit the temporal dependencies inherent in ECG data. Fusion techniques combine the insights from these diverse sub-models, aiming to reduce generalization errors and surpass the performance of single-model approaches. Extensive experimentation on the MIT-BIH and PTB datasets demonstrates superior accuracy (98.51% and 99.97%, respectively) and reduced training time compared to other pre-trained networks. Robustness is validated through 10-fold cross-validation, yielding average accuracies of 98.76% and 99.48%. Our findings highlight the potential of this computationally efficient ensemble model for practical clinical implementation, enabling more accurate and timely diagnosis of life-threatening cardiovascular conditions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

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Convolutional Neural Network and Long Short-Term Memory based Ensemble Learning for modulation classification in 5G Network 2

Convolutional Neural Network and Long Short-Term Memory base...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Devi, T. Alkhayyat, Ahmad Armoogum, Vinaye Buvaneswari, B. Manjula, B.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Technology School of Innovative Technologies and Engineering Mauritius Panimalar Engineering College Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350372892

Modulation classification is important for its fundamental role in dynamic spectrum access which supports 5G (fifth generation) wireless communications. When the receiver receives multiple signals, multipath fading exists at the receiver side it is a big challenge to classify the modulation type. When many signals are transmitting to the receiver side, there exists a heterogeneous traffic, where there is a lot of disturbances occur. Due to these reasons, the block error rate for modulation increases simultaneously. To overcome the changing demands like multipath fading, minimize block error rate and to reduce the traffic. In this research a modulation classification algorithm is developed using the combination architectures of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based Ensemble Learning (EL). Dimensionality is reduced through principal component analysis;it also minimizes the training time. The proposed method outperformed same as the ordinary CNN in terms of classification accuracy, but it reduced the training time with the help of Principal Component Analysis (PCA). The results proved that the proposed CNN-LSTM-EL achieved the best classification accuracy of 99.5 % than all other existing methods like CNN, LSTM. © 2024 IEEE.

关键词： Principal component analysis

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Deep Age Estimation Using Structural MRI: Unveiling Neurological Patterns 2

Deep Age Estimation Using Structural MRI: Unveiling Neurolog...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Subbarayudu, Jangam Michael, G. Kumari, V. Sheeja Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365269

There is a lot of hope for understanding the neurological patterns associated with ageing in the relatively new field of deep age estimation using structural magnetic resonance imaging (MRI). The goal of this study is to extract and evaluate complex structural features from MRI data using advanced deep learning techniques, namely convolutional neural networks (CNNs). In this study, we train deep neural networks on large datasets of MRI images in the hopes of building reliable models that can accurately determine the ages of individuals. This method allows us to detect signs of ageing and subtle neurological patterns that could otherwise go undetected by more traditional methods. In addition, we evaluate the potential of using data augmentation and transfer learning techniques to enhance the model's performance and generalise it to different populations. Our ultimate goal in doing this research is to further our understanding of brain ageing and the effects it has on cognitive decline and age-related diseases. Deep age estimation via structural magnetic resonance imaging may, in the end, change the face of clinical diagnosis, individualised treatment, and age-related research. This is because it provides methods for assessing brain health and ageing trajectories that are both safe and non-invasive. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Automated knowledge graphs for complex systems (AutoGraCS): Applications to management of bridge networks

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Resilient Cities and Structures 2024年第4期3卷 95-106页

作者： Minghui Cheng Syed M.H.Shah Antonio Nanni H.Oliver Gao Department of Civil&Architectural Engineering University of MiamiCoral GablesFL 33146USA School of Architecture University of MiamiCoral GablesFL 33126USA Department of Computer Science University of MiamiCoral GablesFL 33146USA Systems Engineering Cornell UniversityIthacaNY 14853USA School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA

With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and power distribution *** these applications,an important family of methods are based on *** DT applications in modeling and managing smart cities,large-scale knowledge graphs(KGs)are necessary to represent the complex interdependencies and model the urban infrastructure as a system of *** this end,this paper develops a conceptual framework:Automated knowledge Graphs for Complex Systems(AutoGraCS).In contrast to existing KGs developed for DTs,AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex *** established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling,Bayesian analysis,and adaptive decision ***,AutoGraCS provides flexibility in support of users’need to implement the ontology and rules when constructing the *** the user-defined ontology and rules,AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple *** bridge network in Miami-Dade County,FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network,traffic monitoring facilities,and flood water watch stations.

关键词： System digital twin Bayesian network Infrastructure systems Knowledge Graph

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Implementation of ML Algorithm for Spyware Detection System Using SVM with KNN Algorithm for Comparison of Accuracy 9

Implementation of ML Algorithm for Spyware Detection System ...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Inba Pranaou, I.V. Christy, S. Poovizhi, T. Saveetha Institution of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505042

The research examines the Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) machine learning algorithms with the goal of using machine learning to detect malware and mitigate cybersecurity threats. Utilizing the malware dataset and the SVM and KNN methods, which consists of 500 rows and 6 attribute columns, the accuracy of spyware detection is determined. This dataset was collected from the well-known kaggle website. Python 3. 12 is the programming dialect used to run the code in IDLE shell. The clinical calculator is used for estimating the sample size. Groups 1 and 2 have a computed sample size of 500 with an alpha 80% power, a 95% confidence level, and a threshold value of 0.05. The Data Analytics lab is where research is done. Using the IBM SPSS statistical software, version 27, the accuracy of the SVM algorithm is compared to that of the KNN method. When comparing the Support vector machines' mean accuracy to the K-Nearest Neighbors algorithm, the results show a substantial improvement. © 2024 IEEE.

关键词： Cybersecurity

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