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Journal articles on the topic 'Edge artificial intelligence'

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Author: Grafiati

Published: 25 May 2024

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1

Deng, Shuiguang, Hailiang Zhao, Weijia Fang, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya. "Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence." IEEE Internet of Things Journal 7, no. 8 (August 2020): 7457–69. http://dx.doi.org/10.1109/jiot.2020.2984887.

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2

Edwards, Chris. "Shrinking artificial intelligence." Communications of the ACM 65, no. 1 (January 2022): 12–14. http://dx.doi.org/10.1145/3495562.

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3

Zhou, Zhi, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang. "Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing." Proceedings of the IEEE 107, no. 8 (August 2019): 1738–62. http://dx.doi.org/10.1109/jproc.2019.2918951.

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4

Songlin Chen, Songlin Chen, Hong Wen Songlin Chen, and Jinsong Wu Hong Wen. "Artificial Intelligence Based Traffic Control for Edge Computing Assisted Vehicle Networks." 網際網路技術學刊 23, no. 5 (September 2022): 989–96. http://dx.doi.org/10.53106/160792642022092305007.

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<p>Edge computing supported vehicle networks have attracted considerable attention in recent years both from industry and academia due to their extensive applications in urban traffic control systems. We present a general overview of Artificial Intelligence (AI)-based traffic control approaches which focuses mainly on dynamic traffic control via edge computing devices. A collaborative edge computing network embedded in the AI-based traffic control system is proposed to process the massive data from roadside sensors to shorten the real-time response time, which supports efficient traffic control and maximizes the utilization of computing resources in terms of incident levels associated with different rescue schemes. Furthermore, several open research issues and indicated future directions are discussed.</p> <p> </p>

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5

Sathish. "Artificial Intelligence based Edge Computing Framework for Optimization of Mobile Communication." Journal of ISMAC 2, no. 3 (July 9, 2020): 160–65. http://dx.doi.org/10.36548/jismac.2020.3.004.

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For improving the mobile service quality and acceleration of content delivery, edge computing techniques have been providing optimal solution to bridge the device requirements and cloud capacity by network edges. The advancements of technologies like edge computing and mobile communication has contributed greatly towards these developments. The mobile edge system is enabled with Machine Learning techniques in order to improve the edge system intelligence, optimization of communication, caching and mobile edge computing. For this purpose, a smart framework is developed based on artificial intelligence enabling reduction of unwanted communication load of the system as well as enhancement of applications and optimization of the system dynamically. The models can be trained more accurately using the learning parameters that are exchanged between the edge nodes and the collaborating devices. The adaptivity and cognitive ability of the system is enhanced towards the mobile communication system despite the low learning overhead and helps in attaining a near optimal performance. The opportunities and challenges of smart systems in the near future are also discussed in this paper.

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6

Michael, James Bret. "Security and Privacy for Edge Artificial Intelligence." IEEE Security & Privacy 19, no. 4 (July 2021): 4–7. http://dx.doi.org/10.1109/msec.2021.3078304.

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Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang, and Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence." Electronics 9, no. 7 (July 18, 2020): 1169. http://dx.doi.org/10.3390/electronics9071169.

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.

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8

Hu, Gang, and Bo Yu. "Artificial Intelligence and Applications." Journal of Artificial Intelligence and Technology 2, no. 2 (April 5, 2022): 39–41. http://dx.doi.org/10.37965/jait.2022.0102.

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Artificial intelligence and machine-learning are widely applied in all domain applications, including computer vision and natural language processing (NLP). We briefly discuss the development of edge detection, which plays an important role in representing the salience features in a wide range of computer vision applications. Meanwhile, transformer-based deep models facilitate the usage of NLP application. We introduce two ongoing research projects for pharmaceutical industry and business negotiation. We also selected five papers in the related areas for this journal issue.

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Foukalas, Fotis, and Athanasios Tziouvaras. "Edge Artificial Intelligence for Industrial Internet of Things Applications: An Industrial Edge Intelligence Solution." IEEE Industrial Electronics Magazine 15, no. 2 (June 2021): 28–36. http://dx.doi.org/10.1109/mie.2020.3026837.

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10

Debauche, Olivier, Meryem Elmoulat, Saïd Mahmoudi, Sidi Ahmed Mahmoudi, Adriano Guttadauria, Pierre Manneback, and Frédéric Lebeau. "Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (March 1, 2021): 11–17. http://dx.doi.org/10.5383/juspn.15.02.002.

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Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

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11

Schultz, Martin. "Artificial intelligence for air quality." Project Repository Journal 12, no. 1 (January 31, 2022): 70–73. http://dx.doi.org/10.54050/prj1218384.

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Artificial intelligence for air quality IntelliAQ is an ERC Advanced Grant project to explore the application of cutting-edge machine learning techniques to global air quality data in combination with high resolution geospatial and weather data. It combines novel data management and data science approaches to build the foundation for innovative air quality information services.

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12

Kumar, Sarvesh, Upasana Gupta, Arvind Kumar Singh, and Avadh Kishore Singh. "Artificial Intelligence." Journal of Computers, Mechanical and Management 2, no. 3 (August 31, 2023): 31–42. http://dx.doi.org/10.57159/gadl.jcmm.2.3.23064.

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As we navigate the digital era of the 21st century, cyber security has grown into a pressing societal issue that requires innovative, cutting-edge solutions. In response to this pressing need, Artificial Intelligence (AI) has emerged as a revolutionary instrument, causing a paradigm shift in cyber security. AI's prowess resides in its capacity to process and analyze immense quantities of heterogeneous cyber security data, thereby facilitating the efficient completion of crucial tasks. These duties, which include threat detection, asset prioritization, and vulnerability management, are performed with a level of speed and accuracy that far exceeds human capabilities, thereby transforming our approach to cyber security. This document provides a comprehensive dissection of AI's profound impact on cyber security, as well as an in-depth analysis of how AI tools not only augment, but in many cases transcend human-mediated processes. By delving into the complexities of AI implementation within the realm of cyber security, we demonstrate the potential for AI to effectively anticipate, identify, and preempt cyber threats, empowering organizations to take a proactive stance towards digital safety. Despite these advancements, it is essential to consider the inherent limitations of AI. We emphasize the need for sustained human oversight and intervention to ensure that cyber security measures are proportionate and effective. Importantly, we address potential ethical concerns and emphasize the significance of robust governance structures for the responsible and transparent use of artificial intelligence in cyber security. This paper clarifies the transformative role of AI in reshaping cyber security strategies, thereby contributing to a safer, more secure digital future. In doing so, it sets the groundwork for further exploration and discussion on the use of AI in cyber security, a discussion that is becoming increasingly important as we continue to move deeper into the digital age.

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Sun, Junlei. "The Legal Regulation of Artificial Intelligence and Edge Computing Automation Decision-Making Risk in Wireless Network Communication." Wireless Communications and Mobile Computing 2022 (March 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/1303252.

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This article is aimed at studying the legal regulation of artificial intelligence and edge computing automated decision-making risks in wireless network communications. The data under artificial intelligence is full of flexibility and vitality, which has changed the way of data existence in the whole society. Its core is various algorithm programs, which determine the existence of artificial intelligence. In this environment, society develops rapidly with unstoppable momentum. However, from a legal perspective, artificial intelligence has algorithmic discrimination, such as gender discrimination, clothing discrimination, and racial discrimination. It does not possess openness, objectivity, and accountability. The consequences are sometimes serious enough to endanger the public interest of the entire society, leading to market disorder, etc. Therefore, the problem of artificial intelligence algorithm discrimination remains to be solved. This article uses algorithms to adjust algorithm discrimination to reduce the harm caused by artificial intelligence algorithm discrimination to a certain extent. First of all, this article introduces a regulatory-based edge cloud computing architecture model. It is mentioned that distributed cloud computing can use subsystems to calculate various resources and storage resources and can make automated decisions when calculating certain data. In order to reduce the impact of algorithm discrimination and trigger data diversification to reduce the probability of discrimination, an edge computing network data capture system is designed. And this article mentions the BP neural network model. The BP neural network model is divided into input layer, output layer, and hidden layer. The training samples are passed from the input layer to the output layer through the hidden layer. If the output information does not meet expectations, the error will be back-propagated, and the connection weight will be adjusted continuously. This paper proposes a deep learning system model in real-time artificial intelligence driven by edge computing. When this model is applied to legal regulations, it can cooperate with edge computing and artificial intelligence algorithms to provide high-precision automated decision-making. Finally, this paper designs an artificial intelligence-assisted automated decision-making experiment based on the theory of legal computing. This paper proposes a Bayesian algorithm that uses edge algorithms to merge into artificial intelligence and verifies the feasibility of this hypothesis through experiments. The experimental results show that it has a certain ability to regulate algorithmic discrimination caused by artificial intelligence in legal regulations. It can improve the regulatory effects of laws and regulations to a certain extent, and the improved artificial intelligence Bayesian algorithm clustering effect of edge computing is increased by about 7.2%.

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14

Allawadi, Sidhant, Jayaty, Parmod Sharma, Kapil Rohilla, and Gopal Deokar. "Artificial intelligence: A cutting edge technology in agriculture." INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 17, no. 1 (January 15, 2021): 114–20. http://dx.doi.org/10.15740/has/ijas/17.1/114-120.

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Attention is currently being paid to the use of smart technologies. Agriculture has provided an important source of food for humans over thousands of years, including the development of appropriate farming methods for the cultivation of different crops. The emergence of new advanced technologies has the potential to monitor the agricultural environment to ensure high-quality produce. In this context, a systematic review that aimsto study the application of various technologies and algorithms in Artificial Intelligence (AI) with the latest solutions to make the farming more efficient remains one of the greatest imperatives. Artificial intelligence can be applied directly in the field of agriculture for various operations. Amid high expectations about how AI will help the common personand transform his mindset, thoughts and attitude towards the benefits that it may bring. There are certain concerns about the ill effects of such sophisticated technologies as well.This review also focuses on the activation of perceptive technologies and application of computer vision and machine learning in agriculture.

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15

Munir, Arslan, Erik Blasch, Jisu Kwon, Joonho Kong, and Alexander Aved. "Artificial Intelligence and Data Fusion at the Edge." IEEE Aerospace and Electronic Systems Magazine 36, no. 7 (July 1, 2021): 62–78. http://dx.doi.org/10.1109/maes.2020.3043072.

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16

Elmoulat, Meryem, Olivier Debauche, Saïd Mahmoudi, Sidi Ahmed Mahmoudi, Pierre Manneback, and Frédéric Lebeau. "Edge Computing and Artificial Intelligence for Landslides Monitoring." Procedia Computer Science 177 (2020): 480–87. http://dx.doi.org/10.1016/j.procs.2020.10.066.

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17

Huh, Jun-Ho, and Yeong-Seok Seo. "Understanding Edge Computing: Engineering Evolution With Artificial Intelligence." IEEE Access 7 (2019): 164229–45. http://dx.doi.org/10.1109/access.2019.2945338.

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18

Falcao, Gabriel, and Joseph Cavallaro. "Special Issue on Artificial Intelligence at the Edge." IEEE Micro 42, no. 6 (November 1, 2022): 6–8. http://dx.doi.org/10.1109/mm.2022.3203489.

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19

Chen, Ding, Zuli Wang, Juan Wang, Lei Shi, Minkang Zhang, and Yimin Zhou. "Detection of distracted driving via edge artificial intelligence." Computers and Electrical Engineering 111 (October 2023): 108951. http://dx.doi.org/10.1016/j.compeleceng.2023.108951.

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20

John, Lizy Kurian. "Artificial Intelligence at the Edge: Designs and Architectures for Pervasive Intelligence." IEEE Micro 42, no. 6 (November 1, 2022): 4–5. http://dx.doi.org/10.1109/mm.2022.3210497.

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21

Balador, Ali, Sima Sinaei, Mats Pettersson, and Ilhan Kaya. "DAIS Project - Distributed Artificial Intelligence Systems." ACM SIGAda Ada Letters 42, no. 2 (April 5, 2023): 96–98. http://dx.doi.org/10.1145/3591335.3591348.

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DAIS is a step forward in the area of artificial intelligence and edge computing. DAIS intends to create a complete framework for self-organizing, energy efficient and private-by-design distributed AI. DAIS is a European project with a consortium of 47 partners from 11 countries coordinated by RISE Research Institute of Sweden.

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22

Radanliev, Petar, David De Roure, Kevin Page, Max Van Kleek, Omar Santos, La’Treall Maddox, Pete Burnap, Eirini Anthi, and Carsten Maple. "Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars." Safety in Extreme Environments 2, no. 3 (October 2020): 219–30. http://dx.doi.org/10.1007/s42797-021-00025-1.

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AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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23

Castiglioni, Matteo, Diodato Ferraioli, Nicola Gatti, and Giulia Landriani. "Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition." Journal of Artificial Intelligence Research 71 (August 27, 2021): 1049–90. http://dx.doi.org/10.1613/jair.1.12826.

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We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent by seeds and spreading through the network according to the independent cascade model. We provide a comprehensive theoretical study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network and removing or adding edges in the social network given the set of the seeds and the information sent. In particular, we study a wide range of cases distinguishing in the number of candidates or the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in the worst-case, even when one accepts to get an approximation of the optimal margin of victory, except for the case of seeding when the number of hard-to-manipulate voters is not too large, and the number of uncertain voters is not too small, where we say that a voter that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases. More precisely, in the case of seeding, we show that the manipulation is hard even if the graph is a line and that a large class of algorithms, including most of the approaches recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such as undirected graphs with every node having a degree at most two or directed trees. In the case of edge removal or addition, our hardness results also apply to election manipulation when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary number of edges, and to the basic case of social influence maximization/minimization in the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold in a re-optimization variant, where the manipulator already knows an optimal solution to the problem and computes a new solution once a local modification occurs, e.g., the removal/addition of a single edge.

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24

Kumar, Kamal, Vinod Kumar, Seema, Mukesh Kumar Sharma, Akber Ali Khan, and M. Javed Idrisi. "A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems." Journal of Computer Networks and Communications 2024 (February 9, 2024): 1–15. http://dx.doi.org/10.1155/2024/9979371.

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Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.

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25

DeCotis, Paul A. "The Generative Artificial Intelligence Utility." Climate and Energy 40, no. 5 (November 2023): 21–26. http://dx.doi.org/10.1002/gas.22378.

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Generative Artificial Intelligence (Gen AI) is still in its infancy, yet it has started to transform the way people live, work, and play. The cutting‐edge technological innovation of Gen AI has unbounded potential and its contributions to the economy and society are still mostly hypothetical. In the short run we might overestimate its transformative impact and in the long run we might underestimate its impact. In either case, unprecedented change is upon us. Think of the changes to the ways in which we live, work, and play as technology evolved from wall‐mounted landline telephones to car phones to satellite phones to mobile cell phones to hand‐held smart devices, and the explosion of applications and services available on the device platform.

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26

Fuketa, Hiroshi, and Kunio Uchiyama. "Edge Artificial Intelligence Chips for the Cyberphysical Systems Era." Computer 54, no. 1 (January 2021): 84–88. http://dx.doi.org/10.1109/mc.2020.3034951.

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27

Corchado, Juan M., Sascha Ossowski, Sara Rodríguez-González, and Fernando De la Prieta. "Advances in Explainable Artificial Intelligence and Edge Computing Applications." Electronics 11, no. 19 (September 28, 2022): 3111. http://dx.doi.org/10.3390/electronics11193111.

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28

Lin, Bor-Shing, Tiku Yu, Chih-Wei Peng, Chueh-Ho Lin, Hung-Kai Hsu, I.-Jung Lee, and Zhao Zhang. "Fall Detection System With Artificial Intelligence-Based Edge Computing." IEEE Access 10 (2022): 4328–39. http://dx.doi.org/10.1109/access.2021.3140164.

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29

Zhang, Yingchen Y. C., and Marc Spieler. "Bringing Artificial Intelligence to the Grid Edge [Technology Leaders]." IEEE Electrification Magazine 10, no. 4 (December 2022): 6–9. http://dx.doi.org/10.1109/mele.2022.3210778.

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30

Lin, Cheng-Jui, Ying-Ying Chen, Hong-Mou Shih, and Chih Jen Wu. "WCN24-1035 BestShape, A Cutting-Edge Artificial Intelligence-Driven." Kidney International Reports 9, no. 4 (April 2024): S377—S378. http://dx.doi.org/10.1016/j.ekir.2024.02.792.

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31

Djemame, Safia, and Siham Fichouche. "A Novel Edge Detection Algorithm Based on Outer Totalistic Cellular Automata." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 19–30. http://dx.doi.org/10.18280/ria.360103.

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Edge detection is a key technique in image processing. The detected edge quality has a direct and significant impact on performance. There is a multitude of methods for edge detection but they are strongly associated with the application and the quality of the images. However, more precise outcomes and a reduced execution time remain the primary objectives for extracting edges. To address these issues, we propose a novel technique based on a complex system called Cellular Automata (CA). They are successfully applied in edge detection due to their simplicity and local interactions. This undertook shed new light on a novel method using Outer Totalistic Cellular Automata (OTCA) to perform efficiently edge detection. We have tested images from Berkeley dataset. RMSE and SSIM are used as fitness functions for estimating numerical performance of OTCA rules. Comparisons were made with classical edge detectors like: Canny, Scharr, Sobel, Roberts. Experimental results showed that OTCA rules provide excellent performance and outperforms other edge detectors in terms of precision and execution time, particularly when dealing with noisy images.

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Seng, Jasmine Kah Phooi, Kenneth Li-minn Ang, Eno Peter, and Anthony Mmonyi. "Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing." Electronics 11, no. 14 (July 18, 2022): 2239. http://dx.doi.org/10.3390/electronics11142239.

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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

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Akshay Agarwal,. "Implementing Artificial Intelligence in Salon Management: Revolutionizing Customer Relationship Management at PK Salon." Tuijin Jishu/Journal of Propulsion Technology 45, no. 02 (April 12, 2024): 1700–1712. http://dx.doi.org/10.52783/tjjpt.v45.i02.6151.

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Aim: This study aims to explore the implementation of artificial intelligence-driven customer relationship management (CRM) within the framework of PK Salon Management, investigating both the practical aspects and the challenges and opportunities associated with integrating artificial intelligence into the salon business. Methods: A qualitative research approach, primarily relying on secondary research methods, was employed to gather insights into the adoption of artificial intelligence in PK salons. Literature reviews and case studies related to artificial intelligence in salon management were examined and synthesized. Additionally, interviews with salon partners provided valuable perspectives on the practical implications of artificial intelligence adoption. Results: Evaluation findings indicate that PK Salons are actively embracing automation in appointment scheduling and marketing, alongside increased investment in artificial intelligence applications. However, challenges such as data security concerns and workforce readiness have been identified as barriers to effective integration. Qualitative experiences underscore the importance of overcoming implementation challenges while harnessing artificial intelligence's potential to enhance operations and improve customer engagement. Conclusion: Artificial intelligence-powered CRM systems hold significant potential to revolutionize salon management within the PK Salon context. Addressing challenges such as cost assessment and data security requires proactive measures and collaboration among stakeholders. By fostering a culture of innovation and investing in workforce training, salon establishments can leverage artificial intelligence technology to deliver enhanced customer experiences and gain a competitive edge in the market.

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Liu, Yang, Qingtian Wang, Haitao Liu, Jiaying Zong, and Fengyi Yang. "Edge Intelligence-Based RAN Architecture for 6G Internet of Things." Discrete Dynamics in Nature and Society 2022 (November 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/4955498.

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Edge Intelligence, which blends Artificial Intelligence (AI) with Radio Access Network (RAN) and edge computing, is recommended as a crucial enabling technology for 6G to accommodate intelligent and efficient applications. In this study, we proposed Edge Intelligent Radio Access Network Architecture (EIRA) by introducing new intelligence modules, which include broadband edge platforms that allow policies to interact with virtualized RAN for various applications. We also developed a Markov chain-based RAN Intelligence Control (RIC) scheduling policy for allocating intelligence elements. Experimental results justified that the virtualized RAN delivers on its performance promises in terms of throughput, latency, and resource utilization.

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Chopra, Ronit. "Artificial Intelligence in Robotics: (Review Paper)." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2345–49. http://dx.doi.org/10.22214/ijraset.2023.50635.

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Abstract: This article offers an overview of the dynamic intersection of artificial intelligence, robotics, and their impact on economic and organizational dynamics. We delve into the burgeoning research streams that explore the multifaceted consequences of these cutting-edge technologies in the fields of economics and management. Drawing from the diverse approaches adopted by scholars in this field, we provide insights into the implications of artificial intelligence, robotics, and automation for organizational design and firm strategy. We call for increased attention and involvement by organizational and strategy researchers in these areas and outline promising avenues for future research endeavors

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Lv, Baolong, Feng Liu, Yulin Li, Jianhua Nie, Fangfang Gou, and Jia Wu. "Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images." Diagnostics 13, no. 6 (March 10, 2023): 1063. http://dx.doi.org/10.3390/diagnostics13061063.

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Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

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Wiweko, Budi. "Cutting Edge of Reproductive Medicine." Fertility & Reproduction 01, no. 02 (June 2019): 78–87. http://dx.doi.org/10.1142/s2661318219300071.

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Background: Louise Brown’s delivery in 1978 was the mark of a successful IVF program that has now been in practice for more than 40 years. The technology has delivered more than 8 million babies. Many breakthrough innovations were established to answer the problem in ART services. Optimizing ART biomarkers and cross border reproductive care have become a rising issue in ART services. Disruptive innovation disrupts the existing condition and takes the lead in the new market, including to change our patient behavior in health services. National health services addressed new issues about the impact of 4.0 industrial revolution on health workforce and our daily practices. Every disruptive innovation today is enhanced by a combination of physical, digital, and biological domain. The advancement in the area of the internet of things, artificial intelligence, virtual reality, nanotechnology, cloud computing, big data, deep learning, machine learning, robotics, and gene editing could potentially support us to innovate. And to improve the quality and outcome of ART, the introduction of the latest technology, such as robotics and artificial intelligence, has become an essential approach. A recent study discovered that the use of artificial intelligence would remove the embryologist’s subjectivity and improve the way we choose the best embryo for implantation. The next challenging issue in ART is improving the success rate through optimizing noninvasive biomarkers development. Many biological products such as blood, tissue, organ fluid can be assessed and considered to be used as IVF biomarkers. Proteomic tools were used and are needed to analyze a sample from subjects before it was created as a biomarker for improving the IVF services quality. Conclusion: The development of IVF over 40 years has brought about many distinct achievements in the laboratory and in clinic. Industrial revolution 4.0 has generated many innovations that have helped improve the quality of ART services, including AUGMENT social egg freezing, artificial intelligence, and genome editing. In this era, precision medicine looks very promising for bridging the gap and increasing the accuracy and efficacy of promotive, preventive, diagnostic, and treatment approaches in reproductive medicine.

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Milton, Constance L. "Living on the Edge: Paradoxical Experiences With Ethics." Nursing Science Quarterly 36, no. 4 (October 2023): 333–35. http://dx.doi.org/10.1177/08943184231187849.

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Paradox is living phenomenon that provides insights into straight thinking and diverse human experiences important to the discipline of nursing from a nursing philosophical theory-based approach. Here, the author delves into the metaphorical experience of living on the edge and the paradoxical concepts that assist the discipline in its thinking about artificial intelligence. Possible ethical implications of utilizing artificial intelligence from a humanbecoming ethos of understanding are discussed. The metaphorical implications for future disciplinary priorities are presented.

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Liu, Ziheng. "The Evolution of Artificial Intelligence and its Collaboration with Brain Science." Highlights in Science, Engineering and Technology 1 (June 14, 2022): 31–39. http://dx.doi.org/10.54097/hset.v1i.424.

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The emergence of artificial intelligence is the product of the development of science and technology. At the same time, the development of science and technology has greatly improved the ability of the human brain and artificial intelligence. For example, scientists choose to train artificial intelligence with video games and improve the flexibility of the human brain. One example of artificial intelligence’s success and its rapid development is AlphaGo. Go has long been recognized as one of the most difficult games in the world, and it seems surprising that artificial intelligence could easily win a match against a human master. But, it wasn't easy, and the reason for AlphaGo's victory has a lot to do with machine learning. By mimicking humans and their own evolution to give themselves an edge in the game and win. At the same time, many people think that artificial intelligence is very dangerous, and they think that artificial intelligence will replace or eliminate human beings, but the fact is the same as people's imagination? The benevolent see benevolence and the wise see wisdom. After decades of development, artificial intelligence can reach the level of cooperation with humans. Basic cooperation can no longer meet people's needs, and more advanced cooperation projects need to be developed. Human beings and artificial intelligence help each other, especially in brain science and the application of CNN in image processing. In these studies, human beings provide inspiration for the research and development of artificial intelligence, and artificial intelligence provides convenience for human life. Therefore, we can know that artificial intelligence plays a very important role in the evolution of human beings.

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Urblik, Lubomir, Erik Kajati, Peter Papcun, and Iveta Zolotová. "Containerization in Edge Intelligence: A Review." Electronics 13, no. 7 (April 2, 2024): 1335. http://dx.doi.org/10.3390/electronics13071335.

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The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in software development and the benefits it brings. Certain applications run into obstacles when deployed on the cloud due to the latency it introduces or the amount of data that needs to be processed. These issues are addressed by edge intelligence. This paper describes edge intelligence, the deployment of artificial intelligence close to the data source, the opportunities it brings, along with some examples of practical applications. We also discuss some of the challenges in the development and deployment of edge intelligence solutions and the possible benefits of applying containerization in edge intelligence.

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Li, Zehao. "Application Scenarios of Edge Computing in Conjunction with Cloud Computing and Artificial Intelligence." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 527–33. http://dx.doi.org/10.54097/bky92374.

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The proliferation of mobile technology products and the development of wireless communication networks have given rise to numerous application scenarios that prioritize higher computing power, better privacy protection, lower latency, and lower energy consumption. Given these requirements, edge cloud computing has emerged as a critical component of computing platforms and a key enabler for communication technology. In this paper, the concept of edge computing and its synergies with cloud computing and artificial intelligence are explored. This paper outlines the various advantages that can be obtained through the collaboration of edge computing and cloud computing. Specifically, it focuses on existing and potential scenarios where this collaboration can be beneficial. These include the Internet of Things, mobile gaming, metaverse and extended reality. The potential for innovation in these fields is immense and the possibilities are vast. The paper also presents the definition of artificial intelligence and its opportunities in the field of communication networks. Particularly, a discussion of feasible models for integrating artificial intelligence with edge computing concludes the paper.

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Li, Hao. "Edge color difference detection of color image based on artificial intelligence technology." Journal of Computational Methods in Sciences and Engineering 21, no. 3 (August 2, 2021): 787–802. http://dx.doi.org/10.3233/jcm-215189.

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In order to solve the problems of the traditional methods in detecting color image edge chromatic aberration, such as the poor accuracy of detection and the poor detection effect, a color image edge chromatic aberration detection method based on artificial intelligence technology is proposed. The approximate principal component analysis method is used to segment the color image and smooth the image denoising; The linear gray-scale transformation is applied to the color image to enlarge the smaller gray-scale space to the larger gray-scale space according to the linear relationship and obtain the edge information of the color image; The artificial intelligence technology is used to locate the edge sub-pixel of the image to complete the edge color difference detection of the color image. The experimental results show that the detection accuracy of the proposed method is about 98%, and the detection effect is good, which is feasible.

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Carrasco Ramírez, José Gabriel, and Md Mafiqul Islam. "Utilizing Artificial Intelligence in Real-World Applications." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (February 7, 2024): 14–19. http://dx.doi.org/10.60087/jaigs.v2i1.p19.

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Artificial Intelligence (AI) stands as a pivotal innovation deeply ingrained in both our daily routines and industrial operations. Its rapid evolution promises transformative impacts across various sectors, from cutting-edge industries to the lives of ordinary individuals. AI constantly updates human experiences, shaping interactions and augmenting capabilities. For instance, contemporary educational institutions leverage AI algorithms for attendance tracking via facial recognition technology. Looking ahead, the advent of autonomous vehicles represents a pinnacle of AI application, where vehicles rely entirely on AI systems for navigation, detecting traffic signals, and navigating roads.

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Carrasco Ramírez, José Gabriel, and Md mafiqul Islam. "Application of Artificial Intelligence in Practical Scenarios." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (February 8, 2024): 14–19. http://dx.doi.org/10.60087/jaigs.v2i1.41.

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Artificial Intelligence (AI) stands as a pivotal innovation deeply ingrained in both our daily routines and industrial operations. Its rapid evolution promises transformative impacts across various sectors, from cutting-edge industries to the lives of ordinary individuals. AI constantly updates human experiences, shaping interactions and augmenting capabilities. For instance, contemporary educational institutions leverage AI algorithms for attendance tracking via facial recognition technology. Looking ahead, the advent of autonomous vehicles represents a pinnacle of AI application, where vehicles rely entirely on AI systems for navigation, detecting traffic signals, and navigating roads

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Ferràs, Xavier, Emma Louise Hitchen, Elisenda Tarrats-Pons, and Nuria Arimany-Serrat. "Smart Tourism Empowered by Artificial Intelligence." Journal of Cases on Information Technology 22, no. 1 (January 2020): 1–13. http://dx.doi.org/10.4018/jcit.2020010101.

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Artificial intelligence (AI) is changing the rules of the game in many industries. This case details how the combination of open innovation and artificial intelligence generates new opportunities in the tourism sector. Specifically, how to create new customer experiences through searching tools, social platforms and cognitive interfaces to make intelligent decisions. The authors show that it is possible to increase tourist satisfaction by offering a set of customized activities and experiences according to their personal characteristics and motivations. The combination of cutting-edge digital technologies makes it possible to design new services in an automated and cost-affordable manner. The experience has been carried out in Lanzarote (Canary Islands, Spain), with support of IBM's Watson system. This is a good example of AI-fueled innovation in services, which is adequate for courses on innovation, technology, entrepreneurship and competitive strategy.

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Kang, Zelun. "Research on risk prediction method of software robot based on artificial intelligence." Journal of Physics: Conference Series 2248, no. 1 (April 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2248/1/012003.

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Abstract Aiming at the problem of software robot’s recognition of life scenes, this paper studies the recognition and judgment method based on AI edge computing. Based on artificial intelligence methods and the theory of edge computing, through the analysis of the overall architecture of edge computing, the scene judgment rules and recognition algorithms are clarified. Mainly, the feature extraction and recognition part of the software robot image recognition function is arranged in the edge server, so that the judgment and recognition can be quickly realized and the system operation efficiency can be improved. The experimental verification shows that: under the same conditions, the software robot identification error of the method in this paper is lower, and the calculation time is shorter than that of other software robots, which is far superior to the traditional identification method. At the same time, by changing the comparison of data receiving methods, it can also be proved that the use of edge computing is more efficient, and the recognition problems in the work of software robots can be realized.

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OUACHA, Ali, and Mohamed EL Ghmary. "Virtual machine migration in MEC based artificial intelligence technique." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 244. http://dx.doi.org/10.11591/ijai.v10.i1.pp244-252.

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<span id="docs-internal-guid-bef24e0c-7fff-7c19-7865-2ba99054831c"><span>The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,...), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is to cut a virtual machine into several small pieces and then send them to an appropriate target node (Edge Server) using the ant colony algorithm. In order to test and prove the effectiveness of our approach, several simulations are made by NS3. The obtained results show that our approach is well adapted to mobile environments.</span></span>

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Bhatia, Harsimran, Anmol Bhatia, Chirag K. Ahuja, Arnavjit Singh, and Kushaljit S. Sodhi. "Artificial Intelligence: A Primer for the Radiologists." Indographics 01, no. 02 (December 2022): 215–21. http://dx.doi.org/10.1055/s-0042-1759863.

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AbstractArtificial intelligence (AI) has revolutionized almost every sphere of life today by providing cutting-edge tools aimed at improving the quality of life. The term AI refers to any operating system or a software that mimics human intelligence and performs functions like the human mind with minimal human intervention. The present review article focuses on the basics of AI and the terminology used in the field of AI. Flowcharts and figures to facilitate easy understanding of its impact and its potential applications have also been provided. It is meant to serve as a primer for the beginner.

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Kou, Jiaqing, and Tianbai Xiao. "Artificial intelligence and machine learning in aerodynamics." Metascience in Aerospace 1, no. 2 (2024): 190–218. http://dx.doi.org/10.3934/mina.2024009.

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<abstract><p>With the increasing availability of flow data from simulation and experiment, artificial intelligence and machine learning are revolutionizing the research paradigm in aerodynamics and related disciplines. The integration of machine learning with theoretical, computational, and experimental investigations unlocks new possibilities for solving cutting-edge problems. In this paper, we review the status of artificial intelligence and machine learning in aerodynamics, including knowledge discovery, theoretical modeling, numerical simulation, and multidisciplinary applications. Representative techniques and successful applications are summarized. Finally, despite successful applications, challenges still remain, which are discussed in the conclusion.</p></abstract>

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Wang, Shanshan. "Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing." International Journal of Distributed Systems and Technologies 13, no. 3 (July 1, 2022): 1–9. http://dx.doi.org/10.4018/ijdst.307994.

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In the internet era, huge data is generated every day. With the help of cloud computing, enterprises can store and analyze these data more conveniently. With the emergence of the internet of things, more hardware devices have accessed the network and produced massive data. The data heavily relies on cloud computing for centralized data processing and analysis. However, the rapid growth of data volume has exceeded the network throughput capacity of cloud computing. By deploying computing nodes at the edge of the local network, edge computing allows devices to complete data collection and preprocessing in the local network. Thus, it can overcome the problems of low efficiency and large transmission delay of cloud computing for massive native data. This paper designs a human trajectory training system for enterprise management. The simulation demonstrates that the system can support human trajectory tracing and prediction for enterprise management.

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