Approaches of Artificial Intelligence and Machine Learning in Smart Cities: Critical Review

Varshney, Harshit; Khan, Rizwan Ahmed; Khan, Uzair; Verma, Ram U.

doi:10.1088/1757-899x/1022/1/012019

Cited by 14 publications

(6 citation statements)

References 51 publications

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“…Reliable monitoring and management systems are required for processing vast amounts of data at the vehicle level. Deep Learning approaches can improve the quality of ITS data [20]. In addition, in for testing or simulation of microgrid connection for electricity distribution in smart cities, it also implements AI and ML to enhance the microgrid connectivity via a microgrid model.…”

Section: Smart City Developmentmentioning

confidence: 99%

AI and ML in IR4.0: A Short Review of Applications and Challenges

Sannasy Rao,

Chong Peng Lean,

et al. 2024

Malaysian J. Sci. Adv. Tech.

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Artificial intelligence and machine learning are essential for the development of IR4.0 due to their ability to analyse vast amounts of data, automate processes, and drive innovation across various sectors. These technologies enable intelligent decision-making, predictive analytics, and automation, leading to increased efficiency, productivity, and competitiveness in the digital age. In IR4.0, AI and ML power smart systems and connected devices, transforming industries. They facilitate the integration of digital, physical, and biological systems, enabling the creation of personalized medicine and medical diagnosis smart manufacturing, self-autonomous driving vehicles, smart cities, and smart home. Hence, this review aims to address the contribution of AI and ML in the development of medical diagnosis, smart manufacturing, smart cars, smart cities, and smart homes as well as to highlight the existing challenges faced by AI and ML in these fields. This review also showcases the relevant prospects of AI and ML applications in the fields mentioned.

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Section: Smart City Developmentmentioning

confidence: 99%

AI and ML in IR4.0: A Short Review of Applications and Challenges

Sannasy Rao,

Chong Peng Lean,

et al. 2024

Malaysian J. Sci. Adv. Tech.

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“…ML, on the other hand, is a subset of AI that is focused on the creation and refinement of algorithms and statistical models that can effectively learn from historical data and thus accomplish tasks (such as making decisions or predictions) without explicit instructions/programming tailored to the specific task at hand [19,22]. The fundamental principles and methodologies of ML include supervised learning, unsupervised learning, and reinforcement learning [31,32].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Gbashi,

Njobeh

2024

Applied Sciences

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Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.

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“…Machine learning, one of the most prominent subfields of AI, deals with the design and creation of algorithms for the recognition of complex patterns and decision making based on experimental data [ 10 ]. Problems handled with ML methods can be broadly categorized into (i) supervised, (ii) unsupervised, and (iii) reinforcement learning methods.…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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Approaches of Artificial Intelligence and Machine Learning in Smart Cities: Critical Review

Cited by 14 publications

References 51 publications

AI and ML in IR4.0: A Short Review of Applications and Challenges

AI and ML in IR4.0: A Short Review of Applications and Challenges

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Computer Vision Applications in Intelligent Transportation Systems: A Survey

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