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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Rangu, Gayathri Nair, Shivashankar Robotics Lab. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9798400704956

Conventional approaches to Federated Deep Reinforcement Learning (FDRL) often mandate the participation of all the associated devices and perform indiscriminate aggregation of the models. This can, at times, culminate in a low-performance global model being pumped back to the devices causing a setback in the performances of all the local models. Aggregation should thus, be performed judiciously based on the model performance. Unlike offline Reinforcement Learning (RL) setups, when datasets and ground truths are unavailable and data needs to be gathered in real-time, defining a suitable metric for the model performance, can be challenging. In this paper, we propose a novel Immuno-inspired approach for Selective Aggregation suitable for decentralized FDRL (dFDRL) that can act as a metric for the performance of a model based on which the decision to aggregate could be made. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

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computers, Materials & Continua 2024年第9期80卷 4749-4765页

作者： Dennies Tsietso Abid Yahya Ravi Samikannu Basit Qureshi Muhammad Babar Department of Electrical Computer and Telecommunications EngineeringBotswana International University of Science and TechnologyPalapyePrivate Bag 16Botswana College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11586Saudi Arabia

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems *** computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this ***,accurately segmenting the Region of Interest(ROI)fromthermograms remains *** paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree *** proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground *** features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)*** findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of *** consistency of the method across both sides of the breast suggests its viability as an auto-segmentation ***,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

关键词： Breast cancer CAD machine learning ROI segmentation thermography

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Comparative analysis of navigation algorithms for mobile robot

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3861-3871页

作者： Heng, Han Ghazali, Mohamad Hazwan Mohd Rahiman, Wan School of Electrical and Electronic Engineering Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Daffodil Robotics Lab Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Various applications, including space exploration, transportation, factories, and the military, demand the presence of mobile robots. In those applications, navigation algorithms are essential for enabling mobile robots to operate efficiently and safely in static and dynamic environments. Due to the numerous navigation algorithms available, choosing a suitable and robust one for a specific mobile robot application can be challenging. Therefore, there is a pressing need to conduct a comparative analysis to evaluate the performance and adaptability of different navigation algorithms. This article presents a comparative study of five distinct algorithms in the domain of path planning, which are rapidly random tree (RRT), A*, genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO). The investigation was conducted on three distinct maps, and the strengths and weaknesses of each algorithm are demonstrated. Findings show that increasing the step distance and number of iteration parameters will result in an increase in the planning time. Consequently, the obtained path lengths are also reduced except for the PSO algorithm. Findings show that in the three different environments considered in this paper, the GA exhibits better performance, where a 90% reduction in the planning time is achieved while obtaining the same path length as the A* algorithm, which is the shortest path. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Retrospective of Kazakh-Russian Sign Language Corpus Formation 11

Retrospective of Kazakh-Russian Sign Language Corpus Formati...

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11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources, sign-lang@LREC-COLING 2024

作者： Imashev, Alfarabi Kydyrbekova, Aigerim Mukushev, Medet Sandygulova, Anara Islam, Shynggys Israilov, Khassan Makazhanov, Aibek Yessenbayev, Zhandos Department of Robotics Engineering Nazarbayev University Kazakhstan Computer Science Lab National Laboratory Astana Nazarbayev University Kazakhstan Public Association "Kazakh Deaf Society" Astana Branch Kazakhstan

ISBN: (纸本)9782493814302

Sign language (SL) is a mode of communication that, in most cases, relies on visual perception exclusively and uses the visual-gestural modality. The advent of machine learning techniques has expanded the range of potential applications, not only in industry but also in addressing societal needs. Previous research has already demonstrated encouraging outcomes in developing sign language recognition systems that are both quite accurate and resilient. Nevertheless, the effectiveness and use of algorithms are impacted not only by their accessibility but also, at times to a greater extent, by the presence of substantial quantities of pertinent data. At the start of the local sign language corpus collection in 2015, there was a notable deficit of local Kazakh-Russian Sign Language data available for computer vision and machine-learning tasks. There were already corpora of another lexically close language, Russian Sign Language, but they were aimed at and tailored for lingustic research. We initiated the procedure by collecting data appropriate for machine-learning purposes. The subsets have been incorporated into the principal corpus and will be subject to future enhancements and refinements. This paper provides an overview of the collected components of the Kazakh-Russian Sign Language Corpus and the resulting outcomes derived from them. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

关键词： Machine learning

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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Progress Estimation of an Excavation Pit 39

Progress Estimation of an Excavation Pit

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39th International Symposium on Automation and robotics in Construction, ISARC 2022

作者： Vierling, A. Groll, T. Meckel, D. Heim, K. Walter, D. Scheidhauer, L. Körkemeyer, K. Berns, K. Department of Computer Science Robotics Research Lab University of Kaiserslautern Germany Department of Civil Engineering Construction Management University of Kaiserslautern Germany

ISBN: (纸本)9789526952420

This paper presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility for automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. Experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. © 2022 International Association on Automation and robotics in Construction.

关键词： Excavation

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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