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Introduction to the Issue on artificial intelligence in the Public Sector: Risks and Benefits of AI for Governments

Digital Government: Research and Practice 2024年第1期5卷 1-6页

作者： Mellouli, Sehl Janssen, Marijn Ojo, Adegboyega Université Laval 2320 rue des bibliothèques Quebec cityQCG1V 0A6 Canada Delft University of Technology TU Delft Postbus 5 Delb 2600 AA Netherlands Carleton University 5131 Richcrab Hall 1125 Colonel by Drive OttawaONK1S 5B6 Canada

artificial intelligence (AI) is increasingly adopted by public sector organizations to provide better public services and to transform their internal processes. AI is now considered a key enabler for digital innovation and transformation in the public sector. However, AI is still relatively a new research area in the field of digital government. The term, AI, captures a wide range of technologies, techniques, and tools such as machine/deep learning, natural language processing, robotics, computer vision, and more recently Generative AI. While these AI technologies afford different applications and benefits in the gov- ernment context, they also create social, ethical, and legal challenges. These challenges require solutions combining both technical (e.g., data and algorithmic solutions to minimize bias) and institutional (e.g., governance structures and processes) mechanisms. The special issue is a collection of articles that contribute to a better understanding of the issues associated with AI deployment in different areas of government operations. They cover AI applications in the areas of emergency re- sponse, policy analysis, public bids, and citizen participation. The contributions also address the challenge of realizing a legal transparency regime for AI in government and the effect of AI in bureaucratic decision-making. Copyright © 2024 held by the owner/author(s).

关键词： e-government

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Towards new-generation of intelligent welding manufacturing: A systematic review on 3D vision measurement and path planning of humanoid welding robots

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MEASUREMENT 2025年 242卷

作者： Chi, Peng Wang, Zhenmin Liao, Haipeng Li, Ting Wu, xiangmiao Zhang, Qin South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 511400 Peoples R China

In recent years, intelligent welding technology has emerged as a prominent focus within the welding domain, amalgamating a diverse array of sophisticated technologies, including robotics, computer vision, artificial intelligence, and sensor systems. This integration heralds unprecedented levels of automation in welding processes, endowing them with heightened efficiency, precision, and cognitive capabilities. Notably, scholarly attention has been dedicated to the realms of intelligent welding manufacturing and welding robotics. However, a discernible lacuna exists in the form of a comprehensive review elucidating the pivotal technologies underpinning welding robots, while research about autonomous mobile welding robots appears to have encountered a developmental impasse. In response, this study undertakes a systematic literature review to scrutinize the core technologies of humanoid welding robots (HWR), positing their elevated research prospects within the milieu of next-generation intelligent welding manufacturing. This study explores the hardware of humanoid welding robots as an emerging technology, drawing on current advancements in humanoid robotics. The key technologies relevant to both humanoid and welding robots are also examined, highlighting their integration and potential applications. Initially, the discourse delves into hand-eye calibration methodologies, delineating a multifaceted approach predicated upon a multi-coordinate system tailored to HWR. Subsequently, the significance of visual-based pose estimation and three-dimensional (3D) reconstruction techniques is underscored, given their instrumental role in furnishing HWR with environmental cognition, a discourse expounded meticulously. Additionally, meticulous scrutiny is accorded to mobile robot path planning and dual-robot trajectory planning methodologies, pivotal for orchestrating welding operation sequences tailored to HWR. To assess the job completion and potential applications of HWR, this pa

关键词： Humanoid welding robot 3D target measurement Hand-eye calibration Visual-based 3D reconstruction Trajectory planning

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Special Issue: artificial intelligence and Smart Sensor-Based Industrial Advanced Technology

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SENSORS 2024年第22期24卷 7391页

作者： Jia, Luyu Sun, Bairong Tan, Weilin Zhang, Shurong Zhang, Bin Zhu, Jianxiong Chongqing Univ Technol Sustainable Dev Res Grp Chongqing 400054 Peoples R China Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Southeast Univ Sch Mech Engn Suzhou Campus Suzhou 215125 Peoples R China Xiaomi Smart Home Appliances Wuhan Co Ltd Nanjing 210036 Peoples R China

With the rapid growth of smart sensors and industrial data, artificial intelligence (AI) technology (such as machine learning, machine vision, multi-sensor fusion, cloud computing, edge computing, digital twins, etc.) now exhibits the potential to significantly enhance manufacturing processes and industries [1]. In recent years, a large number of research organizations have focused on intelligent process and manufacturing technologies, resulting in extensive innovation and advancements in the sector [2,3,4]. Intelligent manufacturing technologies, which include smart sensor deployment [5], IoT sensor nodes [6], virtual reality [7], fussy systems [8], etc., may be modified to create process control systems and technologies for the prediction of quality prediction and the detection of defects [9,10,11]. Multi-sensor-based intelligent process control systems may eventually become a reality thanks to cutting-edge machine learning algorithms and enhanced computed performance [12], offering a wide range of exciting new applications in the coming era, including robotics [13], smart factories [14], smart homes [15], and decision support systems [16].

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Egocentric Behaviour Analysis Based on Object Relationship Extraction for Cognitive Rehabilitation Support 17th

Egocentric Behaviour Analysis Based on Object Relationship E...

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17th International Conference on Intelligent robotics and applications

作者： Besari, Adnan Rachmat Anom Rahmah, Syadza Atika Ardilla, Fernando Saputra, Azhar Aulia Obo, Takenori Kubota, Naoyuki Tokyo Metropolitan Univ Fac Syst Design Dept Mech Syst Engn Hachioji Tokyo Japan Politekn Elekt Negeri Surabaya Dept Informat & Comp Engn Surabaya Indonesia

ISBN: (纸本)9789819607853;9789819607860

Human behaviour recognition plays a significant role in early intervention for the cognitive rehabilitation of older people. While existing methods focus on improving third-person vision, human visual attention has been largely ignored in recognition, especially when humans interact with objects. This paper proposes an egocentric behaviour analysis (EBA) based on object relationships using multi-scopic approach. First, we use egocentric vision to extract features from hand posture, object detection, and visual attention movement at the microscopic level. Second, we develop the perception ability to validate the hand-object interaction (HOI) with visual attention at the mesoscopic level. Third, we implement the cognitive ability to describe human activity with multiple object interactions at the macroscopic level. We analyzed Graph Attention Networks (GAT) to evaluate the proposed method for categorizing some activities. The experimental results demonstrate that the GAT can successfully classify related objects with activities. Further research and development are expected to support the robotics application in our living laboratory.

关键词： first-person vision behaviour recognition environmental context human attention episodic memory

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artificial intelligence Algorithms for Robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st International Conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

Robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, artificial intelligence, and machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： machine vision

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Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第2期63卷 594-627页

作者： Balasubramanian, Sreejith Shukla, Vinaya Islam, Nazrul Upadhyay, Arvind Duong, Linh Middlesex Univ Dubai Chair Res Comm & Head Ctr Supply Chain Excellence Dubai U Arab Emirates Middlesex Univ London Dept Business & Management London England Univ East London London England London Metropolitan Univ Guildhall Sch Business & Law London England Univ West England Bristol Business Sch Bristol England Middlesex Univ Dubai Chair Res & Head Ctr Supply Chain Excellence POB 500697 Dubai U Arab Emirates

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like artificial intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the 'what', 'where' and 'how' of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE's healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI's capacity to enhance healthcare's operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges.

关键词： artificial intelligence Industry 4.0 healthcare COVID-19 UAE

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SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

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IEEE ACCESS 2023年 11卷 47410-47419页

作者： Kothadiya, Deep R. Bhatt, Chintan M. Rehman, Amjad Alamri, Faten S. Saba, Tanzila Charotar Univ Sci & Technol CHARUSAT Chandubhai S Patel Inst Technol CSPIT Fac Technol FTE U&PU Patel Dept Comp Engn Changa 388421 India Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Pandit Deendayal Energy Univ Sch Engn & Technol Dept Comp Sci & Engn Gandhinagar 382007 Gujarat India Princess Nourah bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia

Deep learning has significantly aided current advancements in artificial intelligence. Deep learning techniques have significantly outperformed more than typical machine learning approaches, in various fields like Computer vision, Natural Language Processing (NLP), robotics Science, and Human-Computer Interaction (HCI). Deep learning models are ineffective in outlining their fundamental mechanism. That's the reason the deep learning model mainly consider as Black-Box. To establish confidence and responsibility, deep learning applications need to explain the model's decision in addition to the prediction of results. The explainable AI (xAI) research has created methods that offer these interpretations for already trained neural networks. It's highly recommended for computer vision tasks relevant to medical science, defense system, and many more. The proposed study is associated with xAI for Sign Language Recognition. The methodology uses an attention-based ensemble learning approach to create a prediction model more accurate. The proposed methodology used ResNet50 with the Self Attention model to design ensemble learning architecture. The proposed ensemble learning approach has achieved remarkable accuracy at 98.20%. In interpreting ensemble learning prediction, the author has proposed SignExplainer to explain the relevancy (in percentage) of predicted results. SignExplainer has illustrated excellent results, compared to other conventional Explainable AI models reported in state of the art.

关键词： Deep learning artificial intelligence Computational modeling Predictive models Assistive technologies Computer vision Gesture recognition computer vision explainable AI SignExplainer classification sign language technological development

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Detection and defense of cyberattacks on the machine learning control of robotic systems

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JOURNAL OF DEFENSE MODELING AND SIMULATION-applications METHODOLOGY TECHNOLOGY-JDMS 2024年第2期21卷 181-203页

作者： Clark, George W. Andel, Todd R. McDonald, J. Todd Johnsten, Tom Thomas, Tom Spring Hill Coll 4000 Dauphin St Mobile AL 36608 USA Univ S Alabama Mobile AL USA

Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks.

关键词： Cybersecurity machine learning robotics artificial intelligence simulation

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Advances in diffusion models for image data augmentation: a review of methods, models, evaluation metrics and future research directions

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artificial intelligence REVIEW 2025年第4期58卷 1-55页

作者： Alimisis, Panagiotis Mademlis, Ioannis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Panagiotis Papadopoulos, Georgios Th. Harokopio Univ Athens Dept Informat & Telemat Thiseos 70Attiki Athens 17676 Greece Univ Western Macedonia Dept Elect & Comp Engn Act Urban Planning Zone Kozani 50150 Kozani Greece K3Y Vitosha QuarterBl 9 BG-1700 Sofia Bulgaria

Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets;thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative artificial intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.

关键词： Image data augmentation Diffusion models Generative artificial intelligence Evaluation metrics

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Linking Robots to Human Thought: Bridging Minds and machines

Linking Robots to Human Thought: Bridging Minds and Machines

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2024 IEEE International Conference on Big Data and machine Learning, ICBDML 2024

作者： Singh, Akhilesh Joshi, Priyanka Kothari, Yogesh VMSB Uttarakhand Technical Univ. Dept of Electrical Engg. 262502 India Graphic Era Hill University Dept of CSE Bhimtal India VMSB Uttarakhand Technical Univ. Dept of CSE 262502 India

ISBN: (纸本)9798350374100

This extensive report provides a comprehensive overview of artificial intelligence (AI), including its history, definitions, classification, philosophy, applications, and benefits and drawbacks. It begins by tracing the development of AI from its inception to the present, highlighting significant milestones and obstacles. The document also examines AI's mythological antecedents. The document classifies AI into four categories, ranging from reactive machines to self-aware systems, and provides an overview of its present capabilities and potential future developments. It focuses on the operations of artificial intelligence in robotization, machine literacy, machine vision, natural language processing, robotics, and independent vehicles. An assessment of AI's development since 2010 is presented, highlighting significant developments and ethical considerations. The document outlines and emphasizes the impact of 18 critical AI applications across a variety of industries. The document concludes with a discussion of AI's benefits, including error reduction, safety enhancement, and innovation, while acknowledging concerns such as employment displacement and privacy concerns and emphasizing the need for ethical guidelines. This document provides an exhaustive overview of AI's history, applications, and societal implications, as well as its advantages and disadvantages. © 2024 IEEE.

关键词： Computer vision

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