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The Development of Assistive Robotics: A Comprehensive Analysis Integrating Machine Learning, Robotic Vision, and Collaborative Human Assistive Robots 2nd

The Development of Assistive Robotics: A Comprehensive Analy...

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2nd International Conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2024

作者： Crnokić, Boris Peko, Ivan Gotlih, Janez Faculty of Mechanical Engineering Computing and Electrical Engineering University of Mostar Matice Hrvatske b.b. Mostar8800 Bosnia and Herzegovina Faculty of Science University of Split Ruđera Boškovića 33 Split21000 Croatia Faculty of Mechanical Engineering University of Maribor Smetanova ul. 17 Maribor2000 Slovenia

ISBN: (纸本)9783031620577

With integration of collaborative robotics, robotic vision, and machine learning, the high-end frontier reached in assistive robotics is likely to help develop promising solutions to the challenges associated with the needs of aging populations and persons with disabilities. This paper examines recent developments, challenges, and opportunities that relate to the application of these technologies for human assistance. The paper will begin with an explanation of the basic concepts and the rationales driving assistive robotics, and then move on to the technological ground with basic collaborative robots, robotic vision, and machine learning, stressing how they can make human assistance better. Case studies and real-world applications will help to present the potential of integrated assistive robotics not only in healthcare and rehabilitation, but also in domains such as elder care and living on one’s own. This paper also highlights some of the critical challenges inclusive of robustness, reliability, human-robot interaction, safety, and ethical considerations along with some of the emerging trends and future research directions related to it. The paper sets up a visionary outlook, where integrated assistive robotics—realized fully through human-centered design, interdisciplinary collaboration, and innovation in technology development—could revolutionize care and support toward fostering the inclusivity, dignity, and independence of those with diverse needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Novel Deep Learning Pipeline for Automatic Weapon Detection

Novel Deep Learning Pipeline for Automatic Weapon Detection

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2023 IEEE Asia-Pacific Conference on Computer Science and Data engineering, CSDE 2023

作者： Sivakumar, Haribharathi Vijay Arvind, R. Pawan Ragavendhar, V. Balamurugan, G. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu 603 203 India

ISBN: (纸本)9798350341072

Weapon and gun violence have recently become a pressing issue today. The degree of these crimes and activities has risen to the point of being termed as an epidemic. This prevalent misuse of weapons calls for an automatic system that detects weapons in real-time. Real-time surveillance video is captured and recorded in almost all public forums and places. These videos contain abundant raw data which can be extracted and processed into meaningful information. This paper proposes a novel pipeline consisting of an ensemble of convolutional neural networks with distinct architectures. Each neural network is trained with a unique mini-batch with little to no overlap in the training samples. This paper will present several promising results using multiple datasets associated with comparing the proposed architecture and state-of-the-art (SoA) models. The proposed pipeline produced an average increase of 5% in accuracy, specificity, and recall compared to the SoA systems. © 2023 IEEE.

关键词： Pipelines

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Optimal Robustification of Linear Quadratic Regulator

Optimal Robustification of Linear Quadratic Regulator

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Anel Tahirbegovic Adnan Tahirovic Faculty of Electrical Engineering and Computing University of Zagreb Faculty of Electrical Engineering University of Sarajevo

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper presents a robust control design for linear systems with matched external disturbances based on linear-quadratic regulator (LQR) and sliding mode control (SMC). The design includes a quadratic integral sliding manifold for which LQR is equivalent control, so it does not force the system to change its optimal dynamics during the transient response like in other commonly used state-of-the-art SMC design procedures. The simulation study suggests that the proposed control design robustifies optimal LQR with minimal deterioration of optimality.

关键词： Manifolds Linear systems Robust control Transient response Regulators System dynamics Heuristic algorithms Partial differential equations Nonlinear systems Sliding mode control

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Improving the Cost of Updates in Virtual Knowledge Graphs

Improving the Cost of Updates in Virtual Knowledge Graphs

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Wandji, Romuald Esdras Calvanese, Diego Department of Computing Science Umeå Universitet Umeå Sweden Research Centre for Knowledge and Data Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy

Virtual Knowledge Graph (VKG) is known as a data integration paradigm used to efficiently manage the heterogeneity of richly structured data that is common inside several organizations, in inter-organizational settings, and more openly on the Web. Although such a paradigm continues to gain importance in both foundational and applied research, updates in VKG systems remain an open challenge that has received less attention. Yet, a solution to such a problem would be of great importance, as it would allow VKG systems to be full-fledged, thus allowing end-users to fully manage source data through the lens of the ontology they are exposed to. This research aims to propose a comprehensive framework for instance-level updates in VKGs, where updates posed over the ontology have to be translated into source-level updates and, more importantly, how the side effects related to the propagation of ontology-based updates to the underlying data source can be minimized. © 2024 Copyright for this paper by its authors.

关键词： Knowledge graph

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Human Detection in crowd using One-stage and Two-stage object detection models 14

Human Detection in crowd using One-stage and Two-stage objec...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Rajeshirke, Vedant Nitin Subalalitha, C.N. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350335095

Object detection has various uses for recognition, detection, and tracking. Detecting humans in crowded low-quality images is an emerging topic and is a difficult task. In this paper, different object detection are compared for our purpose. Here we use the two models - YOLO and Faster R-CNN and test their working for human detection on low-quality images. We take a low-quality image collection and utilize pre-processing techniques to improve its quality. Using the same experimental setting, we build YOLO and Faster R-CNN and evaluate their results based on accuracy and speed. After comparing the results it shows that in terms of detection speed, YOLO outperforms Faster R-CNN. And YOLO lags than Faster R-CNN when it comes to accuracy. Further we also discuss the advantages and disadvantages of using these models and which is best suited for the scenario we intend to work on. Our findings give useful insights for the development of low-quality image object detection systems for accurate human detection in crowded images, with practical implications for a variety of applications. © 2023 IEEE.

关键词： Object detection

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Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs

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Discover Artificial Intelligence 2023年第1期3卷 31页

作者： Buczek, Patryk Zidan, Usama Gaber, Mohamed Medhat Abdelsamea, Mohammed M. School of Computing and Digital Technology Birmingham City University Birmingham100190 United Kingdom Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Department of Computer Science Faculty of Computers and Information Assiut University Assiut71515 Egypt

Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination of class decomposition and transfer learning has proven to be effective in classifying imbalanced medical imaging datasets. Nevertheless, in order to further augment the performance gains achieved through the utilisation of class decomposition within deep learning frameworks, we propose a novel model coined imbalance-Aware Decomposition for Class-Decomposed Classification (iDeComp) model. By incorporating a conditional Generative Adversarial Network (GAN) model, iDeComp is capable of generating additional samples specifically tailored to underrepresented decomposed subclasses. This paper investigates the application of iDeComp using two different medical imaging datasets. iDeComp selects underrepresented samples from the training set of the sublevel classes within each dataset, which are then employed to train separate conditional Deep Convolutional GAN (DCGAN) models and verification models. The conditional DCGAN model is responsible for generating additional samples, while the verification model critically evaluates the appropriateness of the synthesised images. Subsequently, the resulting augmented samples are utilized to train the classification model. To assess the effectiveness of iDeComp, we employ various evaluation metrics including accuracy, precision, recall, and F1 score. The results obtained from our experiments clearly indicate that iDeComp outperforms existing approaches in terms of classifying both imbalanced datasets. © The Author(s) 2023.

关键词： Generative adversarial networks

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Multimodal Contrastive Learning for Protein-Protein Interaction Inhibitor Prediction

Multimodal Contrastive Learning for Protein-Protein Interact...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Zitong Wang, Zhixian Zhao, Lingling Wang, Junjie Wang, Chunyu Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350386226

Protein-protein interactions (PPIs) are crucial for various cellular activities and disease development, and modulating PPIs using small molecule inhibitors (PPIIs) has gradually become a promising therapeutic strategy. Recently, researchers have proposed several machine learning methods to screen PPIIs, but most of the works focused on unimodal representations of molecules or combining multimodal features in a naive splicing manner. Meanwhile, current research progress is being slowed by the lack of large-scale PPII datasets. To address these issues, we propose MCLPPII, a unified multimodal contrastive learning framework for PPII prediction. MCLPPII extracts comprehensive molecular information from four modalities and effectively combines them through an adaptive feature fusion method. Furthermore, we propose a three-stage training strategy to enhance the PPII prediction capability of MCLPPII by leveraging self-supervised pre-training on a large unlabeled dataset. We evaluate MCLPPII on nine PPI targets and two downstream tasks, including the PPI inhibitor identification task and potency prediction task. Experimental results show that MCLPPII achieves competitive performance. The source code and datasets are freely available at https://***/1zzt/MCLPPII. © 2024 IEEE.

关键词： Contrastive Learning

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AI-Powered Diagnostic Tool for Retinal Disease Detection Using Convolutional Neural Networks 5

AI-Powered Diagnostic Tool for Retinal Disease Detection Usi...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Moovendran, Manikandan Kumar, K. K. Dhanush Sashanth, J.M. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India Chennai603203 India

ISBN: (纸本)9798350389609

Accurate and timely detection of retinal disease helps prevent severe vision impairment, although traditional diagnostics take plenty of time and are demanding to perform. This led to the design of the advanced diagnostic tool, in which AI and Convolutional Neural Networks (CNNs) will enhance the identification of retinal disorders. This system will analyze a retina image to detect among others, diabetic retinopathy and age-related macular degeneration, among others. Advanced deep learning algorithms enable the tool to rapidly identify the subtle patterns and anomalies in retinal scans, which even an astute human analyst might not identify, providing insightful and diagnostic support to healthcare experts. This AI-based approach leads to the acceleration of the diagnostic workflow and the minimization of errors, which elevates overall diagnostic accuracy and allows earlier intervention. This technology will contribute to making high-quality retinal care more accessible and efficient, finally arriving at the benefit of a patient at an earlier time of early detection and treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics 22

End-to-End Optimized Pipeline for Prediction of Protein Fold...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Vijay Arvind, R. Haribharathi, S. Brindha, R. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu 603 203 India

ISBN: (纸本)9798350345346

Protein folding is the intricate process by which a linear sequence of amino acids self-assembles into a unique three-dimensional structure. Protein folding kinetics is the study of pathways and time-dependent mechanisms a protein undergoes when it folds. Understanding protein kinetics is essential as a protein needs to fold correctly for it to perform its biological functions optimally, and a misfolded protein can sometimes be contorted into shapes that are not ideal for a cellular environment giving rise to many degenerative, neuro-degenerative disorders and amyloid diseases. Monitoring at-risk individuals and de-tecting protein discrepancies in a protein's folding kinetics at the early stages could majorly result in public health benefits, as preventive measures can be taken. This research proposes an efficient pipeline for predicting protein folding kinetics with high accuracy and low memory footprint. The deployed machine learning (ML) model outperformed the state-of-the-art ML models by 4.8% in terms of accuracy while consuming 327x lesser memory and being 7.3% faster. © 2023 IEEE.

关键词： Bio-computing Logarithmic folding rate loT Machine learning algorithms

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