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Articles de revues sur le sujet « AI control safety »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Abu, N. S., W. M. Bukhari, M. H. Adli, Hari Maghfiroh, and Alfian Ma’arif. "Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments." Journal of Robotics and Control (JRC) 5, no. 3 (2024): 613–35. https://doi.org/10.18196/jrc.v5i3.21114.

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This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and hig

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Jonsson, Roberth. "Avoiding AI Accidents." Industrial Vehicle Technology International 28, no. 1 (2020): 10. http://dx.doi.org/10.12968/s1471-115x(23)70471-6.

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Sharma,, Siddharth. "Revolutionizing Sports Bikes with Artificial Intelligence: Safety, Performance, and Design Innovations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30618.

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This study examines how artificial intelligence (AI) is incorporated into sports bikes and examines the significant impacts this has on ride quality, user experience, and safety. Adaptive cruise control and collision avoidance systems, two AI-driven technologies that improve rider safety, and engine performance improvements and predictive maintenance algorithms that improve overall bike performance and dependability. Furthermore, bike design processes are revolutionized by AI-driven design techniques, which allow for quick iterations and customisation. This study offers insights into how artif

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Krainiuk, O., Yu Buts, N. Didenko, V. Barbashyn, and O. Trishyna. "METROLOGICAL CONTROL OF SENSORS FOR MONITORING WORKING CONDITIONS USING ARTIFICIAL INTELLIGENCE." Municipal economy of cities 3, no. 184 (2024): 216–22. http://dx.doi.org/10.33042/2522-1809-2024-3-184-216-222.

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Metrological control plays a vital role in ensuring the accuracy and reliability of the data collected as part of the working conditions monitoring. It helps to prevent potential errors and guarantee the quality of the results, which is critical for the efficient assessment and management of occupational health and safety. The article aims to investigate and analyse the role and importance of metrological control of sensors in the system for monitoring working conditions at production facilities using artificial intelligence. The article examines the possibilities of using artificial intellige

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Duarte, Ana Beatriz. "THE CASE FOR CONVERGENCE IN AI SAFETY." IA Policy Brief Series 11, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.15073811.

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This policy brief emphasizes the need for a unified, evidence-based approach to AI safety. Given the inability to fully explain AI decisions, the global community has a growing consensus about the need to prioritize AI safety. However, discussions about AI safety often focus on different topics. While some scholars are alert to the dangers of cyberattacks and even the possibility of AI taking over, civil society experts claim for now-and-here issues such as the military use of autonomous weapons and other present-day risks. Common to the discourse of both groups is the concern with control, di

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Kamala Aliyeva, Rashid Jafarov, Kamala Aliyeva, Rashid Jafarov. "SAFETY CONTROL SYSTEMS FOR ETHYLENE PRODUCTION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 35, no. 12 (2023): 206–13. http://dx.doi.org/10.36962/pahtei35122023-206.

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Ethylene production is a cornerstone of the petrochemical industry, with a global demand that continues to rise. Ensuring efficient, safe, and environmentally responsible ethylene production processes is paramount. Ethylene production control systems, encompassing a range of technologies and strategies, play a pivotal role in achieving these objectives. This abstract provides a concise overview of key aspects of ethylene production control systems, their challenges, and their critical importance. Ethylene production control systems serve as the backbone of modern ethylene plants, orchestrating

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Ourzik, Victoria Yousra. "Security and safety concerns in the age of AI." International Conference on AI Research 4, no. 1 (2024): 329–37. https://doi.org/10.34190/icair.4.1.3142.

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Artificial Intelligence (AI) is transforming industries at an astonishing rate, reshaping how we live, work, and interact with technology. Yet, as AI becomes more pervasive, it brings urgent questions about security and safety. This article explores these critical issues, drawing a clear distinction between AI security and AI safety—two concepts that are often misunderstood but are crucial for responsible AI deployment. AI security focuses on protecting systems from external threats like data breaches, adversarial attacks, and unauthorized access. As AI systems increasingly handle sensitive da

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Vishal Goyal, Kamal Sharma, Amit Jain,. "A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems." Journal of Electrical Systems 20, no. 4s (2024): 486–507. http://dx.doi.org/10.52783/jes.2060.

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Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these

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Schöning, Julius, and Hans-Jürgen Pfisterer. "Safe and Trustful AI for Closed-Loop Control Systems." Electronics 12, no. 16 (2023): 3489. http://dx.doi.org/10.3390/electronics12163489.

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In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining

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Turchin, Alexey, David Denkenberger, and Brian Green. "Global Solutions vs. Local Solutions for the AI Safety Problem." Big Data and Cognitive Computing 3, no. 1 (2019): 16. http://dx.doi.org/10.3390/bdcc3010016.

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There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. On

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Rupnawar, Shivam, Puja Deokate, and Mohit Bhandari. "Advancements in Bicycle Safety: Integrating Control Sensors and Artificial Intelligence for Enhanced Airbag Innovation." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5053–74. http://dx.doi.org/10.22214/ijraset.2024.61181.

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Abstract: Motorcycle safety is a critical concern in traffic systems worldwide. This paper introduces a novel approach to enhancing rider safety through the integration of airless tires and advanced airbag systems, underpinned by artificial intelligence (AI) and control sensors. Airless tires contribute to vehicle stability by eliminating the risk of sudden deflation, while AI-driven airbag systems promise dynamic protection for riders during collisions. The study begins with an analysis of airless tire technology, emphasizing its impact on motorcycle stability and safety. It then transitions

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Silva Neto, Antonio V., Henrique L. Silva, João B. Camargo, Jorge R. Almeida, and Paulo S. Cugnasca. "Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System." Electronics 12, no. 24 (2023): 4903. http://dx.doi.org/10.3390/electronics12244903.

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With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application

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Davahli, Mohammad Reza, Waldemar Karwowski, Krzysztof Fiok, Thomas Wan, and Hamid R. Parsaei. "Controlling Safety of Artificial Intelligence-Based Systems in Healthcare." Symmetry 13, no. 1 (2021): 102. http://dx.doi.org/10.3390/sym13010102.

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In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, d

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Ukandu, Chidiebere Promise, O. Blessing Emoghene, and E. Thomas Boye. "Occupational Hazards Identification, Risk Evaluation and Mitigation in Contemporary Nigeria Society: The Application of Artificial Intelligence (AI)." International Journal of Research and Innovation in Social Science VII, no. XI (2023): 1344–56. http://dx.doi.org/10.47772/ijriss.2023.7011104.

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This paper examines the current state and potential future developments of Artificial Intelligence (AI) applications in Occupational Health and Safety (OHS) in Nigeria. OHS is a critical concern in the world and particularly in Nigeria, as workplace hazards pose risks to the health, safety, and well-being of workers. AI offers promising solutions to enhance hazard identification, risk evaluation, and control measures in OHS practices. The paper begins by providing an overview of AI, its subfields, and techniques commonly used in OHS. It then explores the real-time applications of AI in hazard

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Ravi, Aravind, and Chirag Vinalbhai Shah. "Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI (Revision-1)." International Journal of Engineering and Computer Science 13, no. 01 (2024): 26033–50. http://dx.doi.org/10.18535/ijecs/v13i01.4796.

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As the automobile industry moves toward greater efficiency, safety, and autonomous operation, the demand for Electronic Control Units (ECUs) - the microprocessors controlling everything from engine functions to infotainment systems - has exploded globally. Currently, there are more than 200 million ECUs in the world, and that number is expected to rise to 700 million by 2030, necessitating enhanced ECU performance, reliability, and safety. However, the growing complexity of ECUs has ironically led to increased false alarms and failures, which in some cases have endangered user safety and priva

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Pompi., R. Boro. "Artificial Intelligence in Meat Industry." Science world a Monthly e magazine 5, no. 4 (2025): 6700–6702. https://doi.org/10.5281/zenodo.15166754.

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The growing global demand for animal-derived food products has sparked significant concerns regarding the hygiene and overall quality of these products. In the animal food production industry, conventional methods of monitoring, inspection, and quality control are often labour-intensive, time-consuming, and prone to human error. In this context, Artificial Intelligence (AI) has emerged as a transformative technology, offering innovative approaches to enhance the safety and quality of animal food products. This review presents an overview of AI applications within the animal food sector, emphas

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Tsopa, V. А., S. І. Cheberiachko, О. V. Deryugin, and O. V. Tereshchuk. "THE TROLLEY DILEMMA IN ARTIFICIAL INTELLIGENCE SOLUTIONS FOR AUTONOMOUS VEHICLE SAFETY." Labour protection problems in Ukraine 41, no. 1-2 (2025): 39–44. https://doi.org/10.36804/nndipbop.41-1-2.2025.39-44.

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The issue of choosing a solution using artificial intelligence (AI) to control an autonomous vehicle to ensure passenger safety in dangerous conditions is considered. To determine the best solution, use the utility function l(x) to characterize losses, where l(x) ≠ 0. It is proposed to resolve the conflict between the two main ethical approaches, which are represented by the trolley dilemma, when using AI in autonomous vehicles to adhere to five universal ethical rules: damage to property is better than harming a person; AI is prohibited from classifying people by any criteria; the manufacture

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Yin, Binfeng, Gang Tan, Rashid Muhammad, Jun Liu, and Junjie Bi. "AI-Powered Innovations in Food Safety from Farm to Fork." Foods 14, no. 11 (2025): 1973. https://doi.org/10.3390/foods14111973.

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Artificial intelligence is comprehensively transforming the food safety governance system by integrating modern technologies and building intelligent control systems that provide rapid solutions for the entire food supply chain from farm to fork. This article systematically reviews the core applications of AI in the orbit of food safety. First, in the production and quality control of primary food sources, the integration of spectral data with AI efficiently identifies pest and disease, food spoilage, and pesticide and veterinary drug residues. Secondly, during food processing, sensors combine

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Joel Frenette. "Ensuring human oversight in high-performance AI systems: A framework for control and accountability"." World Journal of Advanced Research and Reviews 20, no. 2 (2023): 1507–16. https://doi.org/10.30574/wjarr.2023.20.2.2194.

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As AI systems increasingly outperform humans in specialized tasks such as medical diagnosis, financial analysis, and strategic decision-making, ensuring human oversight becomes a critical challenge. This paper explores frameworks and mechanisms that allow humans to maintain control over AI-driven agents without hindering their efficiency. We examine case studies where AI has demonstrated superior performance, analyze the risks of over-reliance, and propose governance strategies to ensure AI remains a tool for augmentation rather than replacement. The findings suggest that maintaining a balance

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Sahoo, Sushil Kumar, and Bibhuti Bhusan CHOUDHURY. "AI ADVANCES IN WHEELCHAIR NAVIGATION AND CONTROL: A COMPREHENSIVE REVIEW." Journal of process management and new technologies 11, no. 3-4 (2023): 115–32. http://dx.doi.org/10.5937/jpmnt11-45181.

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This paper presents a systematic review of the literature on integrating artificial intelligence (AI) to improve wheelchair navigation and control for people with mobility impairments. The review covers a range of AI-based approaches including computer vision, machine learning, and path planning algorithms. The paper highlights the potential benefits of integrating AI into wheelchair technology, including increased safety, autonomy, and personalized control. The review discusses the limitations and challenges of current wheelchair navigation and control systems, and how AI can address these li

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Kirwan, Barry. "Human Factors Requirements for Human-AI Teaming in Aviation." Future Transportation 5, no. 2 (2025): 42. https://doi.org/10.3390/futuretransp5020042.

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The advent of Artificial Intelligence in the cockpit and the air traffic control centre in the coming decade could mark a step-change improvement in aviation safety, or else could usher in a flush of ‘AI-induced’ accidents. Given that contemporary AI has well-known weaknesses, from data biases and edge or corner effects, to outright ‘hallucinations’, in the mid-term AI will almost certainly be partnered with human expertise, its outputs monitored and tempered by human judgement. This is already enshrined in the EU Act on AI, with adherence to principles of human agency and oversight required i

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Temilade Abass, Esther Oleiye Itua, Tabat Bature, and Michael Alurame Eruaga. "Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 823–28. http://dx.doi.org/10.30574/wjarr.2024.21.3.0719.

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The concept paper explores the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing food quality control processes. In response to the growing challenges faced by the food industry in ensuring consistent quality and safety standards, this paper proposes leveraging advanced technologies to enhance predictive analysis. The traditional methods of food quality control are often reactive and time-consuming, leading to inefficiencies and increased risks of contamination or spoilage. By harnessing AI and ML algorithms, businesses can shift towards proactive strategie

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Temilade, Abass, Oleiye Itua Esther, Bature Tabat, and Alurame Eruaga Michael. "Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 823–28. https://doi.org/10.5281/zenodo.14060627.

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The concept paper explores the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing food quality control processes. In response to the growing challenges faced by the food industry in ensuring consistent quality and safety standards, this paper proposes leveraging advanced technologies to enhance predictive analysis. The traditional methods of food quality control are often reactive and time-consuming, leading to inefficiencies and increased risks of contamination or spoilage. By harnessing AI and ML algorithms, businesses can shift towards proactive strategie

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Thirunagalingam, Arun kumar. "Generative AI Ethics: a Comprehensive Safety and Regulation Framework." International Journal of Security, Privacy and Trust Management 13, no. 4 (2024): 01–13. https://doi.org/10.5121/ijsptm.2024.13401.

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The quick development of generative AI technology has brought about revolutionary new possibilities for decision-making, design, and content production. But these developments have also raised serious ethical questions, emphasizing the need for an all-encompassing framework to guarantee the security and control of generative AI systems. To address the ethical issues raised by generative AI, this study puts forth a paradigm that centers on the concepts of accountability, transparency, and governance. Strategies for reducing the dangers associated with AI-generated content are provided by this p

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Mahajan, Gulshan, and Bhagirath S. Chauhan. "Screening of Herbicides for Rice Seedling Safety and Echinochloa colona Management under Australian Conditions." Agronomy 12, no. 6 (2022): 1273. http://dx.doi.org/10.3390/agronomy12061273.

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Different herbicides are currently required for sustainable weed management in aerobic rice. Three pot experiments were conducted using different herbicides to evaluate rice safety and for the control of Echinochloa colona, a major weed of aerobic rice. Among the pre-emergence (PRE) herbicides, it was found that pendimethalin (594 g ai ha−1) and flumioxazin (60 g ai ha−1) were relatively safe herbicides for rice and provided 100% control of E. colona. All other PRE herbicides, such as atrazine, cinmethylin, clomazone, dimethenamid-P, isoxaflutole, metribuzin, prosulfocarb + S-metolachlor, pyro

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Shah, Chirag Vinalbhai. "Vehicle Control Systems: Integrating Edge AI and ML for Enhanced Safety and Performance." International Journal of Scientific Research and Management (IJSRM) 10, no. 04 (2022): 871–86. http://dx.doi.org/10.18535/ijsrm/v10i4.ec10.

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AI-driven dynamic trajectory planning and control systems are the keys to enhancing safety and performance for the next generation of autonomous vehicles. The growing demand for autonomous vehicles is pushing relevant companies to deploy the latest AI/ML methods to build better, more reliable, and versatile control systems. Current software architectures supporting the deployment and execution of AI in vehicles rely on centralized or decentralized control. Centralized approaches optimize performance over specific tasks, but they are poorly scalable. Decentralized approaches target scalability

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Srivastava, Siddharth. "Unifying Principles and Metrics for Safe and Assistive AI." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15064–68. http://dx.doi.org/10.1609/aaai.v35i17.17769.

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The prevalence and success of AI applications have been tempered by concerns about the controllability of AI systems about AI's impact on the future of work. These concerns reflect two aspects of a central question: how would humans work with AI systems? While research on AI safety focuses on designing AI systems that allow humans to safely instruct and control AI systems, research on AI and the future of work focuses on the impact of AI on humans who may be unable to do so. This Blue Sky Ideas paper proposes a unifying set of declarative principles that enable a more uniform evaluation of arb

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Zhou, Zhiyu. "Technological Control Tool of Everyday life? Six Questions on the Design Ethics of Artificial Intelligence." Journal of Design Service and Social Innovation 1, no. 1 (2023): 36–43. http://dx.doi.org/10.59528/ms.jdssi2023.0614a5.

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Artificial intelligence (AI) continues to expand into different areas of social life, bringing design ethics and public rights under challenge. Instead of stopping the research application of AI, it is better to urgently study some of the practical problems that AI technology may bring about and promptly formulate corresponding laws to regulate them. This paper discusses the following issues: 1. Security and privacy of face recognition; 2. Political and economic applications of AI; 3. Emotional learning of AI; 3. Human-computer development of brain-computer interface; 5. Ethical supervision of

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Noviati, Nuraini Diah, Fengki Eka Putra, Sadan Sadan, Ridhuan Ahsanitaqwim, Nanda Septiani, and Nuke Puji Lestari Santoso. "Artificial Intelligence in Autonomous Vehicles: Current Innovations and Future Trends." International Journal of Cyber and IT Service Management 4, no. 2 (2024): 97–104. https://doi.org/10.34306/ijcitsm.v4i2.161.

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Artificial Intelligence (AI) has become a cornerstone in advancing autonomous vehicles, enabling realtime decision making, object detection, and automation in driving systems. This study aims to explore key AI innovations, including Machine Learning (ML) algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles. A qualitative approach} was adopted to analyze both current applications and future innovations of AI in autonomous vehicles. The study highlights various current AI applications in autonomous vehicles, such as automated safety f

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Xie, Aoyu. "Intelligent technologies in traffic flow control: a review." Applied and Computational Engineering 126, no. 1 (2025): 16–21. https://doi.org/10.54254/2755-2721/2025.20047.

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With the acceleration of global urbanization, traffic congestion has become a major bottleneck in the development of modern cities, leading to serious economic losses and a decline in the quality of life. To address this challenge, traffic flow prediction techniques have received much attention. This paper reviews the application of three key technologies, including 5G, artificial intelligence (AI), and vehicular networking (V2X) in intelligent traffic management. 5G technology provides a communication foundation with high speed rates, low latency, and large-scale device connectivity, which ef

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Zhang, Xuanning. "Artificial Intelligence in Intelligent Traffic Signal Control." Applied and Computational Engineering 118, no. 1 (2025): 113–20. https://doi.org/10.54254/2755-2721/2025.20846.

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With the rapid urbanization and the increasing traffic demand in cities, traffic congestion and accidents have become significant challenges for urban transportation systems. Traditional traffic signal control systems, which rely on fixed signal cycles, often fail to adapt to real-time traffic conditions, leading to inefficiencies and resource waste. This paper explores the application of Artificial Intelligence (AI) in intelligent traffic signal control systems. Specifically, it focuses on the use of Deep Reinforcement Learning (DRL), particularly the Deep Q-Network (DQN) model, for optimizin

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Ashrafi, Negin, Sahar Yousefi, Guy Roger Aby, et al. "AI-driven solutions to improve safety and health: Application of the REDECA framework for agricultural tractor drivers." PLOS Global Public Health 5, no. 6 (2025): e0003543. https://doi.org/10.1371/journal.pgph.0003543.

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Introduction Despite tremendous efforts, including research, teaching, and extension, toward improving the safety of agricultural tractor drivers, the number of incidents related to agricultural tractor drivers has not declined. This evidence points out an urgent need to explore artificial intelligence (AI) solutions to improve the safety of tractor drivers. Methods This paper uses 171 Fatality Assessment and Control Evaluation (FACE) reports related to tractor drivers and a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) to identify existing AI so

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Yu, Pan, Hui Wan, Bozhi Zhang, et al. "Review on System Identification, Control, and Optimization Based on Artificial Intelligence." Mathematics 13, no. 6 (2025): 952. https://doi.org/10.3390/math13060952.

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Control engineering plays an indispensable role in enhancing safety, improving comfort, and reducing fuel consumption and emissions for various industries, for which system identification, control, and optimization are primary topics. Alternatively, artificial intelligence (AI) is a leading, multi-disciplinary technology, which tries to incorporate human learning and reasoning into machines or systems. AI exploits data to improve accuracy, efficiency, and intelligence, which is beneficial, especially in complex and challenging cases. The rapid progress of AI facilitates major changes in contro

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Sathian, Brijesh, Edwin van Teijlingen, Israel Junior Borges Do Nascimento, et al. "Urgent need for better quality control, standards and regulation for the Large Language Models used in healthcare domain." Nepal Journal of Epidemiology 14, no. 2 (2024): 1310–12. http://dx.doi.org/10.3126/nje.v14i2.69361.

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Current methodologies for ensuring AI technology safety and efficacy may be adequate for earlier AI iterations predating generative artificial intelligence (GAI). However, governing clinical GAI may necessitate the development of novel regulatory frameworks. As AI technology advances, researchers, academic institutions, funding bodies, and publishers should continue to examine its impact on scientific inquiry and revise their understanding, ethical guidelines, and regulations accordingly.

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Vasileiou, Marios, Leonidas Sotirios Kyrgiakos, Christina Kleisiari, et al. "Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning." Crop Protection 176 (February 1, 2024): 106522. https://doi.org/10.1016/j.cropro.2023.106522.

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In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation,

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Durlik, Irmina, Tymoteusz Miller, Ewelina Kostecka, Polina Kozlovska, and Wojciech Ślączka. "Enhancing Safety in Autonomous Maritime Transportation Systems with Real-Time AI Agents." Applied Sciences 15, no. 9 (2025): 4986. https://doi.org/10.3390/app15094986.

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The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, efficiency, and sustainability of autonomous maritime systems. Following a structured literature review, we examine the architecture of real-time AI agents, including sensor integration, communication systems, and computational infrastructure. We distinguish maritime AI agents from conventional systems by emphasizing their specialized functions, rea

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SKRABACZ, Aleksandra, Magdalena BSOUL-KOPOWSKA, and Bartosz KOZICKI. "THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN ROAD TRAFFIC MANAGEMENT AND ITS SAFETY IMPROVEMENT." Transport Problems 19, no. 4 (2024): 5–16. https://doi.org/10.20858/tp.2024.19.4.01.

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Artificial intelligence (AI) is used in many aspects of life, from personal voice assistants to product recommendations in online stores to advanced diagnostic systems in medicine. All these applications show how AI technology is becoming an increasingly integral part of everyday life. Understanding the basics of AI is key to recognizing its role in traffic management. AI can be used to monitor and control road traffic, prevent traffic jams and road crashes, support vehicle diagnostics, and optimize response times for emergency services and roadside assistance. Particular attention should be p

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Izquierdo-Bueno, Inmaculada, Javier Moraga, Jesús M. Cantoral, María Carbú, Carlos Garrido, and Victoria E. González-Rodríguez. "Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management." Applied Sciences 14, no. 22 (2024): 10277. http://dx.doi.org/10.3390/app142210277.

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This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implemen

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Yao, Keyu. "AI-driven innovations in automation and urban management." Applied and Computational Engineering 57, no. 1 (2024): 160–65. http://dx.doi.org/10.54254/2755-2721/57/20241327.

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This paper examines the transformative impact of Artificial Intelligence (AI) across diverse sectors, particularly focusing on industrial automation, smart homes, and intelligent cities. It delves into how AI-driven technologies enhance predictive maintenance, quality control, and supply chain optimization in industrial settings, contribute to energy management, security enhancement, and health monitoring in smart homes, and improve traffic management, waste management, and public safety in intelligent cities. Through detailed analyses and quantitative assessments, the study showcases the effi

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Alvarado Contreras, Rocio E., Lizbeth Escobedo, and Jessica Beltrán. "AI Companions in Ride-Hailing: Enhancing Sense of Safety with Voice Chatbots." Avances en Interacción Humano-Computadora 9, no. 1 (2024): 11–15. https://doi.org/10.47756/aihc.y9i1.138.

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In this paper, we present the development and formative evaluation of AI Companion, a voice chatbot based on Artificial Intelligence Large Language Models designed to enhance users' perception of safety and control during transportation using ride-hailing apps or similar services. We conducted a study with 10 participants to understand their travel experiences and evaluate the AI Companion's ability to maintain coherent conversations, simulating a phone call. Our findings indicate that participants generally liked the system. However, we provide several recommendations for significant improvem

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Nanban, Desiya, Jennifer Selvan, A. T. Ashmi Christus, and Muhammad Al Amin. "Enhancing Air Traffic Management: The Transformative Role of Artificial Intelligence in Modern Air Traffic Control." FMDB Transactions on Sustainable Intelligent Networks 1, no. 2 (2024): 72–84. http://dx.doi.org/10.69888/ftsin.2024.000210.

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Since the Wright brothers' December 17, 1903 flight, the aviation business has grown quickly alongside IT. Growth is concentrated in aircraft development, airport infrastructure, and air traffic control. AI will revolutionize each of these fields. AI optimizes fuel usage, structural designs, and avionics in aircraft development, making them more efficient and modern. AI streamlines airport check-in, luggage processing, and airport security, improving efficiency and passenger experience. The heart of aviation, ATC, coordinates take-offs, landings, and en-route traffic through Aerodrome Control,

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Pishgar, Maryam, Salah Fuad Issa, Margaret Sietsema, Preethi Pratap, and Houshang Darabi. "REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health." International Journal of Environmental Research and Public Health 18, no. 13 (2021): 6705. http://dx.doi.org/10.3390/ijerph18136705.

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Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that A

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Revelou, Panagiota-Kyriaki, Efstathia Tsakali, Anthimia Batrinou, and Irini F. Strati. "Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods." Foods 14, no. 6 (2025): 922. https://doi.org/10.3390/foods14060922.

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Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing advanced data analysis capabilities and have proven to be powerful tools for assessing the safety of Animal-Source Foods (ASFs). Studies that link ML with HACCP monitoring in ASFs are limited. The present review provides an overview of ML, feature extraction, and selection algorithms employed for food safety. Several non-destructive techniques are

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Jadhav, N. R., Sunil Bhutada, S. R. Sagavkar, Rohit Pawar, Archana Bajirao Kanwade, and Purva Mange. "AI-driven pharmaceutical manufacturing : Revolutionizing quality control and process optimization." Journal of Statistics and Management Systems 27, no. 2 (2024): 405–16. http://dx.doi.org/10.47974/jsms-1265.

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The Integrating AI into pharmaceutical production processes represents a paradigm change for the pharmaceutical sector. This article examines the role of AI in pharmaceutical production, focusing on its potential to improve productivity, cut costs, and guarantee the highest standards of product quality and safety in quality control and process optimization. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly being employed to analyze large volumes of data generated throughout the pharmaceutical manufacturing lifecycle. Risks related t

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Adebayo, Abiodun Sunday, Olanrewaju Oluwaseun Ajayi, and Naomi Chukwurah. "AI-Driven Control Systems for Autonomous Vehicles: A Review of Techniques and Future Innovations." International Journal of Future Engineering Innovations 1, no. 1 (2024): 22–28. https://doi.org/10.54660/ijfei.2024.1.1.22-28.

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This review paper explores the current state of AI-driven control systems in autonomous vehicles (AVs), focusing on key techniques such as reinforcement learning (RL), Proportional-Integral-Derivative (PID) control, and hybrid approaches that combine traditional and AI-driven methods. While these techniques have enabled significant AV technology advancements, safety, reliability, scalability, and real-time decision-making challenges persist. The paper proposes several future innovations, including advanced RL techniques, integration of machine learning with traditional control systems, Model P

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Abbas, Zaigham, Fouzia Amin, and Muhammad Mashhood Khan. "Integration of Artificial Intelligence in Nuclear Command and Control Systems (NC2): Assessing Cold- War Paradigm." Journal of Social Sciences Review 5, no. 1 (2025): 180–88. https://doi.org/10.62843/jssr.v5i1.476.

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Artificial Intelligence (AI) is increasingly permeating critical decision-making domains, including nuclear command and control (NC2) systems. This study examines the strategic and ethical dimensions of AI integration into NC2 structures, emphasizing its potential to enhance decision-making speed, accuracy, and resilience while mitigating human cognitive limitations. The research introduces the concept of "Intelligentization Syndrome," a theoretical framework explaining resistance to AI adoption in high-risk environments. By contextualizing historical technological resistance and contemporary

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Anupama A, Ramya S Yamikar, and Renuka K M. "AI-driven gesture control for industrial robots: A vision-based approach for enhancing human-robot collaboration." World Journal of Advanced Research and Reviews 20, no. 2 (2023): 1498–506. https://doi.org/10.30574/wjarr.2023.20.2.2254.

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The integration of artificial intelligence (AI) with vision-based gesture control systems has significantly transformed human-robot interaction in industrial environments. This paper explores recent advancements in AI-driven gesture recognition for industrial robots, focusing on its role in improving human-robot collaboration, efficiency, and safety. AI-powered gesture control enables intuitive and contactless operation, reducing the need for physical controllers and enhancing ergonomics in industrial workflows. The study examines key components of AI-driven gesture recognition, including deep

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Hutchinson, Pamela J. S., Charlotte V. Eberlein, and Dennis J. Tonks. "Broadleaf Weed Control and Potato Crop Safety with Postemergence Rimsulfuron, Metribuzin, and Adjuvant Combinations." Weed Technology 18, no. 3 (2004): 750–56. http://dx.doi.org/10.1614/wt-03-172r1.

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The effects of postemergence rimsulfuron, metribuzin, and adjuvant combinations on potato crop safety and weed control were evaluated in field studies conducted at the University of Idaho Aberdeen Research and Extension Center in 1999 and 2000. Rimsulfuron at 26 g ai/ha plus metribuzin at 0, 140, or 280 g ai/ha was combined with nonionic surfactant (NIS), crop oil concentrate (COC), or methylated seed oil (MSO) in a 3 by 3 factorial with two controls. Under cool, cloudy conditions in 1999, initial ‘Russet Burbank’ potato injury was greater when metribuzin was included in the tank mixture than

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FAHMI, C. "Rescue Vision: AI Insight for Emergencies." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48934.

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Abstract— To improve safety procedures in a variety of locations, such as workplaces, public areas, and industrial sites, the project aims to create a comprehensive visual surveillance system that integrates fire detection with density counts of people. The system uses computer vision, deep learning, and artificial intelligence (AI) to analyze surveillance camera footage in order to identify fire dangers and count people in real time. It also provides dual alarms to increase fire safety and security. Even under difficult circumstances with congestion or obstacles, the system recognizes human f

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Zhong, Bingzhuo, Hongpeng Cao, Majid Zamani, and Marco Caccamo. "Towards Safe AI: Sandboxing DNNs-Based Controllers in Stochastic Games." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15340–49. http://dx.doi.org/10.1609/aaai.v37i12.26789.

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Nowadays, AI-based techniques, such as deep neural networks (DNNs), are widely deployed in autonomous systems for complex mission requirements (e.g., motion planning in robotics). However, DNNs-based controllers are typically very complex, and it is very hard to formally verify their correctness, potentially causing severe risks for safety-critical autonomous systems. In this paper, we propose a construction scheme for a so-called Safe-visor architecture to sandbox DNNs-based controllers. Particularly, we consider the construction under a stochastic game framework to provide a system-level saf

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