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Ensemble Multifeatured Deep Learning Models and Applications: A Survey

IEEE Access 2023年 11卷 107194-107217页

作者： Abimannan, Satheesh El-Alfy, El-Sayed M. Chang, Yue-Shan Hussain, Shahid Shukla, Saurabh Satheesh, Dhivyadharsini Mumbai410206 India King Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence Information and Computer Science Department Dhahran34464 Saudi Arabia King Fahd University of Petroleum and Minerals Interdisciplinary Research Center of Intelligent Secure Systems Information and Computer Science Department Dhahran34464 Saudi Arabia National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan School of Business MaynoothW23 F2H6 Ireland Lucknow226002 India Vellore632014 India

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models. © 2013 IEEE.

关键词： Computer vision

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Robust Visual Servoing Algorithm-based Cucumis-melo Harvesting Robot

Robust Visual Servoing Algorithm-based Cucumis-melo Harvesti...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Woojun Park Yonghyun Park Changjo Kim Hyoung Il Son Department of Convergence Biosystems Engineering Chonnam National University Gwangju Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National University Gwangju Korea Research Center for Biological Cybernetics Chonnam National University Gwangju Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Harvesting robots are performed in unpredictable and unstructured environments. Visual servoing is being researched and developed to enhance the ability to perform tasks in real-time within such environments. It is essential to accurately estimate the pose and move to the desired location to harvest the fruit. In this study, we utilized image stabilization(IS) to obtain stable images and proposed a curvature-based modeling method for the fruit and pedicel to apply it to various fruits and vegetables. The main objective of this study is to perform detection and 6D pose estimation that can be applied to various fruits using deep learning-based fruit detection. The proposed algorithm will be validated by evaluating its performance on cucumis-melo in a laboratory environment. Through this evaluation, it has been confirmed that it can be applied to cucumis-melo, and if the proposed system is used, it can be applied to other fruits. The experimental results will serve as foundational data for assessing the possibility of applying this system in real farms in the future.

关键词： Automation Pose estimation Laboratories Feature extraction Control systems Visual servoing Real-time systems Robots

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Workforce Development and Training Needs for Behavioral Health Telehealth Use in the Post COVID-19 Era

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Journal of Technology in Behavioral Science 2024年第1期9卷 131-139页

作者： Fleddermann, Kathryn Chwastiak, Lydia Fortier, Ashley Gotham, Heather Murphy, Ann Navarro, Rachel Tapscott, Stephanie Vjorn, Olivia Molfenter, Todd Center for Health Enhancement Systems Studies Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI United States Department of Psychiatry and Behavioral Sciences University of Washington School of Medicine Seattle WA United States Western Interstate Commission for Higher Education (WICHE) Boulder CO United States Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA United States Department of Psychiatric Rehabilitation and Counseling Professions Rutgers University New Brunswick NJ United States Department of Education Health and Behavior Studies Counseling Psychology Ph.D. Program University of North Dakota Grand Forks ND United States Department of Health and Policy Management Rollins School of Public Health Emory University Atlanta GA United States

Temporary regulatory changes early in the COVID-19 pandemic facilitated telehealth use, but with an increased return to in-person care in some settings, understanding provider attitudes about the practice and benefits of telehealth may help to inform policy and practice. This mixed methods study seeks to identify areas of training needed for effective telehealth provision in this new era. An online survey was distributed by five Mental Health Technology Transfer Center (MHTTC) regional service centers, which provide workforce capacity-building training and technical assistance, and the MHTTC Network Coordinating Office’s national listserv. Three hundred and sixty-five respondents from 43 states and Puerto Rico participated. The majority of respondents were clinical providers (69.3%). Eighty-five percent of respondents indicated they provided at least one telehealth service. Most indicated telehealth has improved their organizations’ ability to meet patient needs and increased access for underserved populations but suggested significant needs for continued training on telehealth use. These needs were consistent across organization locations and provider types. Qualitative analysis of free response questions identified key areas for ongoing training, including administrative topics (e.g., billing, documentation);integration of telehealth with existing systems;telehealth use with specific populations, especially children;and methods to increase access to technology for less-connected populations. Despite many providers receiving exposure to telehealth use during COVID-19, training in telehealth administration is still needed and may better serve those in certain roles and areas. While ongoing training may effectively address many needs identified, other significant concerns facing behavioral health providers may not be easily addressed via training (e.g., increasing technology access) and may require other approaches to facilitate the continued use of telehealth. © 202

关键词： Behavioral health Digital divide Telehealth Training

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Prescribed Time Convergence Analysis for Continuous Action Iterated Dilemma with Lyapunov Stability Theory

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Dynamic Games and Applications 2025年 1-18页

作者： Amrr, Syed Muhammad Zaery, Mohamed Suhail Hussain, S. M. Abido, Mohammad A. Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Dhahran Saudi Arabia K. A. CARE Energy Research & Innovation Center KFUPM Dhahran Saudi Arabia Electrical Engineering Department KFUPM Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia

This paper proposes a new approach for convergence analysis of continuous action iterative dilemma (CAID) to reach a stable consensus outcome within the prescribed time. Unlike usual game theory, where players can only choose between two options, i.e., cooperation or defection, the CAID model lets players pick from varying options and make more nuanced decisions. In the proposed strategy, a prescribed function, which is tunable by the user, adapts the learning rate of the player’s strategy. This new method guarantees that players will eventually agree on a single strategy, regardless of where they started initially, and it achieves this agreement in a predefined time set by the user. The Lyapunov analysis guarantees the convergence of players’ strategies to a consensus within a prescribed time. The simulation results of the proposed scheme with two evolutionary game examples under different communication networks demonstrate faster convergence and fewer iterations compared to the state-of-the-art method.

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A GNN-based Decentralized Path Planning for Agricultural Robot Team: Work in Progress

A GNN-based Decentralized Path Planning for Agricultural Rob...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Yuseung Jo Hyoung Il Son Department of Convergence Biosystems Engineering Chonnam National University Gwangju Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National University Gwangju Korea Research Center for Biological Cybernetics Chonnam National University Gwangju Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In a wide, dense, and unstructured agricultural environment, the deployment of autonomous mobile robots is an attractive option. In such the environment, large robot systems are subject to physical limitations such as communication distance and sensor measurements. This limitation is solved effectively with distributed path planning and coordination. A graph neural networks (GNNs) are an effective approach for efficient communication of multi-robot systems. In this paper, we propose a GNN-based decentralized path planning framework for agricultural robot team. The proposed model used a graph neural network for responsiveness to dynamic environmental changes, scalability, and efficient local information exchange among the adjacent agents. A graph neural network takes as input the observable features (e.g., states, subgoal, obstacle) of each agent for a partial observation scenario. As the action policy to predict the behavior of the agents, the model trained the tradition optimal multi-agent pathfinding algorithm, conflict-based search algorithm. Through the simulation-based validation, the model was confirmed to have performance comparable to existing expert algorithms, responsiveness to dynamic environments, and scalability.

关键词： Agricultural robots Adaptation models Heuristic algorithms Computational modeling Scalability Prediction algorithms Robot sensing systems Graph neural networks Path planning Testing

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations

An Examination of the Possible Application of Artificial Int...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Wahyu Sardjono Dewi Sagita Pranata Muhammad Isamesal Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster England Master of Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. This study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. The findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. These developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation.

关键词： Productivity Ethics Data analysis Reviews Data integrity Transforms Natural language processing Teamwork Stakeholders Artificial intelligence

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Developing a Floating Robot for Mechanical Control of Water Hyacinth

Developing a Floating Robot for Mechanical Control of Water ...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Mostafa E.A. Elsayed Ahmed K.A. Mohamed Mahmoud Shaaban Mohamed A. Abdelrahman Mohamed E. A. E. Ahmed Mostafa R. Rashed Mechanical Engineering Department Automation and Energy Technology Lab. Shoubra Faculty of Engineering Benha University Cairo Egypt Department of Agricultural Engineering Agricultural Power and Machinery Faculty of Agriculture Benha University Kalubia Egypt Smart Engineering Systems Center. Mechanical Engineering Program. School of Engineering and Applied Sciences Nile University Giza Egypt Mechanical Engineering Department Combustion and Energy Technology Lab. shoubra Faculty of Engineering Benha University Cairo Egypt Mechanical Engineering Department Shoubra Faculty of Engineering Benha University Cairo Egypt Mechanical Engineering Department Faculty of Engineering Modern University for Technology and Information Cairo Egypt

Water hyacinth has become widespread in freshwater bodies in several countries, leading to various issues, particularly its substantial water consumption and the resulting need to control its growth. Among the different methods of controlling water hyacinth, mechanical control stands out as the most effective and environmentally friendly approach. A proposed harvester robot has been developed, capable of efficiently cutting and collecting water hyacinth from the water's surface. The robot boasts several advantages, including its ability to operate in narrow water paths and lakes, use clean energy sources to preserve the water ecology, employ a GPS module for safety and error detection, and generate maps for evaluating work and predicting annual growth. When compared to other technologies dealing with water hyacinth, this innovative water hyacinth harvesting robot presents a significantly lower cost of 0.01 $/kg, making it an optimal solution for addressing water hyacinth problems.

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Visual Scene Understanding for Efficient Cooperative Control of Agricultural Dual-Arm Robots

Visual Scene Understanding for Efficient Cooperative Control...

引用

International Conference on Control, Automation and systems ( ICCAS)

作者： Yonghyun Park Hyoung Il Son Department of Convergence Biosystems Engineering Chonnam National University Gwangju Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National University Gwangju Korea Research Center for Biological Cybernetics Chonnam National University Gwangju Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This study introduces an system designed to cooperative control for agricultural dual-arm robots through visual scene understanding. Leveraging images, the system constructs scene graphs and assigns weights to various object attributes, enabling the identification of the most efficient sequence for performing harvesting tasks. Key factors, such as object ripeness, size, and accessibility, are considered to enhance the precision and productivity of robotic harvesting operations. Experimental validation demonstrates the potential of the proposed system in robotic harvesting and promoting advancements in smart agriculture. Future work will be focused on integrating deep learning models for improved object detection, coupling the system with control mechanisms for dual-arm robots to enable seamless task execution, and extending its application to various agricultural tasks beyond harvesting.

关键词： Deep learning Smart agriculture Productivity Couplings Visualization Object detection Control systems Resource management Object recognition Robots

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S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Cho, Daesol Shim, Dongseok Kim, H. Jin Department of Aerospace Engineering Seoul National University Korea Republic of Interdisciplinary Program in AI Seoul National University Korea Republic of

Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from the logged experience and augments the predicted state transition to extend the data distribution. For exploiting such benefit also on the image-based RL, we firstly propose a generative model, S2P (State2Pixel), which synthesizes the raw pixel of the agent from its corresponding state. It enables bridging the gap between the state and the image domain in RL algorithms, and virtually exploring unseen image distribution via model-based transition in the state space. Through experiments, we confirm that our S2P-based image synthesis not only improves the image-based offline RL performance but also shows powerful generalization capability on unseen tasks. © 2022, CC BY-NC-SA.

关键词： Reinforcement learning

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Revisiting a hidden order at an ferromagnet/antiferromagnet interface and its switching dynamics

Revisiting a hidden order at an ferromagnet/antiferromagnet ...

引用

Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Chao-Yao Yang Sheng-Huai Chen Chih-Huang Lai Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu Taiwan

Study on the exchange bias (H ex ) switching in a ferromagnet (FM)/antiferromagnet (AFM) bilayer driven by the spin-orbit torque (SOT) has been an explicit research field and current focus has been devoted to determining the approaches to drive the H ex switching along with specific SOT applications. H ex is magnetically dominant but only reveals the partial facet of IrMn, which is only responsible for the ferromagnetic component at the interface of AFM. Therefore, the complete facet of AFM and its interaction with SOT at an FM/AFM interface is still not fully uncovered. This research reports an SOT-driven hidden order transition behind H ex in a heavy metal (Pt)/FM (Co)/AFM (IrMn) trilayer along with the issue of switching activity at Co/IrMn interface. The exchange coupling across Co/IrMn interface allows a long-range spin order in the form of an exchange-spring configuration, yielding two kinds of order in IrMn: a canting-induced ferromagnetic order for hosting H ex and a hidden Néel order for preserving the antiferromagnetic nature. The former appears to be strongly coupled to the magnetization state of Co during the SOT switching, so it can be reversibly switched with Co on the H ex switching. On the contrary, the hidden Néel order transition re-orientated to the current direction appears to be irreversibly driven by SOT and independent on the current polarity, which can reflect on the magnetoresistance (MR) measurement and X-ray magnetic linear dichroism (XMLD) spectroscopy. Finally, a micromagnetic simulation reveals the dynamic IrMn spins are mainly based on a canting type interface instead of an uncompensated type via an exchange-spring coupling with Co.

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