Search results

The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China.

This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen.

The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications.

This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

This paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot. Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots.

Descriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017. Four scopes considered to classify the articles as technology, service, user and resource. It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes.

A review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management. Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies. In this researches, librarian robots mostly designed for detection and replacing books on the shelf. Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research. Our conclusion is that we need to develop research in the area of smart resources.

This study has a new approach to the literature review in this area. We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific.

The increased utilization of artificial intelligence-enabled applications (AI-ETs) across the customer journey has transformed customer experience (CX), introducing entirely new forms of the concept. This paper aims to explore existing academic research on the AI-enabled customer experience (AICX), identifying gaps in literature and opportunities for future research in this domain.

A systematic literature review (SLR) was conducted in March 2022. Using 16 different keyword combinations, literature search was carried across five databases, where 98 articles were included and analysed. Descriptive analysis that made use of the Theory, Characteristics, Context, Methods (TCCM) framework was followed by content analysis.

This study provides an overview of available literature on the AICX, develops a typology for classifying the identified AI-ETs, identifies gaps in literature and puts forward opportunities for future research under five key emerging themes: definition and dynamics; implementation; outcomes and measurement; consumer perspectives; and contextual lenses.

This study establishes a fresh perspective on the interplay between AI and CX, introducing the AICX as a novel form of the experience construct. It also presents the AI-ETs as an integrated and holistic unit capturing the full range of AI technologies. Remarkably, it represents a pioneering review exclusively concentrating on the customer-facing dimension of AI applications.

随着人工智能应用程序 (AI-ET)在旅途中的使用不断增加, 消费者体验 (CX)得以转变, 引入了全新的概念形式。 本文旨在探索有关人工智能客户体验（AICX）的现有学术研究, 从中找出文献中的空白以及该领域未来研究的机会。

本系统性文献综述（SLR）于2022 年 3 月开工。基于16 个不同的关键词组合, 本综述统共收录并分析了来自 5 个数据库98 篇文献, 采用理论-特征-背景-方法 (TCCM) 框架先后进行描述性分析和内容分析。

该研究概述了 AICX 的现有文献, 开发了对已识别的 AI-ET 进行分类的类型学, 确定了现有文献中的空白, 并在 5 个关键新兴主题下提出了未来研究的机会：1. 定义和动态, 2 . 实施, 3. 结果和衡量, 4. 消费者视角, 5. 情境视角。

本研究建立了全新的视角看待 AI 和 CX 之间的相互作用, 引入了 AICX 这种新颖的体验构造形式, 还将 AI-ET 展示为一个集成了全方位人工智能技术的整体单元。 值得一提的是, 本文代表了一项专门关注人工智能应用面向客户维度的开创性综述。

La creciente utilización de aplicaciones habilitadas por inteligencia artificial (AI-ET) a lo largo del recorrido del cliente han transformado la experiencia del cliente (CX), introduciendo formas totalmente nuevas del concepto. Este artículo pretende explorar la investigación académica existente sobre la experiencia del cliente a través de la IA (AICX), identificando las lagunas en la literatura y las oportunidades para futuras investigaciones en este ámbito.

En marzo de 2022 se llevó a cabo una revisión bibliográfica sistemática (SLR). Utilizando 16 combinaciones diferentes de palabras clave, se realizó una búsqueda bibliográfica en 5 bases de datos en las que se incluyeron y analizaron 98 artículos. El análisis descriptivo que hizo uso del marco Teoría, Características, Contexto, Métodos (TCCM) fue seguido del análisis de contenido.

El estudio ofrece una visión general de la bibliografía disponible sobre la AICX, desarrolla una tipología para clasificar las AICX detectadas, identifica lagunas en la literatura y plantea oportunidades para futuras investigaciones bajo cinco temas emergentes claves: 1. Definición y dinámica, 2. Implementación, 3. Resultados y medición, 4. Perspectivas del consumidor, 5. Lentes contextuales.

El estudio establece una nueva perspectiva sobre la interacción entre la IA y la CX, introduciendo la AICX como una forma novedosa del constructo experiencia. También presenta las AICX como una unidad integrada y holística que capta toda la gama de tecnologías de la IA. Notablemente, representa una revisión pionera que se concentra exclusivamente en la dimensión orientada al cliente de las aplicaciones de la IA.

To avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.

The proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.

Results reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.

The proposed system was applied only to the forging process but could be extended to other manufacturing processes.

This paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

Unstructured knowledge management (UKM) becomes indispensable for the support of knowledge work. However, unstructured knowledge is inconvenient and difficult for sharing, organizing and acquisition. This paper seeks to present the development and implementation of a multi‐facet taxonomy system (MTS) for effective management of unstructured knowledge.

Multi‐facet taxonomy is a multi‐dimensional taxonomy which allows the classification of knowledge assets under multiple concepts at any levels of abstraction. The MTS system is based on five components: multi‐dimensional taxonomy structure, thesaurus model, automatic classification mechanism, intelligent searching, and self‐maintenance of taxonomy, respectively. Artificial intelligence (AI) and natural language process (NLP) technologies are used in the development of the MTS.

With the successful development of the MTS, the accuracy of categorization of unstructured knowledge is significantly improved. It also allows an organization to capture the valuable tacit knowledge embedded in the unstructured knowledge assets. This helps an organization to explore business opportunities for continuous business improvement.

The implementation of the MTS system not only dramatically reduces the human effort, time and cost for UKM but also allows an organization to capture valuable knowledge embedded in unstructured knowledge assets.

As the knowledge work and task become more complex and are dynamically changing with time and involve multiple concepts, the MTS addresses the inadequacy of conventional single dimensional taxonomy for managing unstructured knowledge. The self‐maintenance capability of the MTS ensures that the taxonomy is up‐to‐date and new knowledge is classified automatically for better knowledge sharing and acquisition.

One of the contributions of artificial intelligent (AI) in modern technology is emotion recognition which is mostly based on facial expression and modification of its inference engine. The facial recognition scheme is mostly built to understand user expression in an online business webpage on a marketing site but has limited abilities to recognise elusive expressions. The basic emotions are expressed when interrelating and socialising with other personnel online. At most times, studying how to understand user expression is often a most tedious task, especially the subtle expressions. An emotion recognition system can be used to optimise and reduce complexity in understanding users' subconscious thoughts and reasoning through their pupil changes.

This paper demonstrates the use of personal computer (PC) webcam to read in eye movement data that includes pupil changes as part of distinct user attributes. A custom eye movement algorithm (CEMA) is used to capture users' activity and record the data which is served as an input model to an inference engine (artificial neural network (ANN)) that helps to predict user emotional response conveyed as emoticons on the webpage.

The result from the error in performance shows that ANN is most adaptable to user behaviour prediction and can be used for the system's modification paradigm.

One of the drawbacks of the analytical tool is its inability in some cases to set some of the emoticons within the boundaries of the visual field, this is a limitation to be tackled within subsequent runs with standard techniques.

The originality of the proposed model is its ability to predict basic user emotional response based on changes in pupil size between average recorded baseline boundaries and convey the emoticons chronologically with the gaze points.

In recent years, railway systems worldwide have faced challenges such as the modernization of engineering projects, efficient management of intelligent digital railway equipment, rapid growth in passenger and freight transport demands, customized transport services and ubiquitous transport safety. The transformation toward intelligent digital transformation in railways has emerged as an effective response to the formidable challenges confronting the railway industry, thereby becoming an inevitable global trend in railway development.

This paper, therefore, conducts a comprehensive analysis of the current state of global railway intelligent digital transformation, focusing on the characteristics and applications of intelligent digital transformation technology. It summarizes and analyzes relevant technologies and applicable scenarios in the realm of railway intelligent digital transformation, theoretically elucidating the development process of global railway intelligent digital transformation and, in practice, providing guidance and empirical examples for railway intelligence and digital transformation.

Digital and intelligent technologies follow a wave-like pattern of continuous iterative evolution, progressing from the early stages, to a period of increasing attention and popularity, then to a phase of declining interest, followed by a resurgence and ultimately reaching a mature stage.

The results offer reference and guidance to fully leverage the opportunities presented by the latest wave of the digitalization revolution, accelerate the overall upgrade of the railway industry and promote global collaborative development in railway intelligent digital transformation.

This study social based on cognitive theory (SCT), aims to better understand how transformational leadership affects team-level knowledge sharing and absorptive ability in the construction industry. It also examines the moderating influence of the AI-based virtual assistant on the indirect relationship between transformational leadership and team innovation through knowledge sharing and absorptive ability at the team level.

This study used a simple random sample approach to gather data from several small and medium-sized construction firms in Anhui Province, China. A total of 407 respondents, including 89 site engineers and 321 team members, provided their responses on a five-point Likert scale questionnaire.

The findings showed that AI-based virtual assistants significantly moderated the direct and indirect association between transformational leadership and knowledge sharing, and subsequently with team innovation. Unexpectedly, the findings showed that AI-based virtual assistant did not moderate the direct relationship between transformational leadership and team-level absorptive capacity.

This study adds a fresh perspective to the literature on construction management by examining team innovation driven by transformational leadership through an underlying mechanism. It is unique in that it uses the team adaptation theory to investigate the understudied relationship between transformational leadership and team innovation in the construction industry.

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

The emerging markets are facing a lot of risks and disruptions across their supply chains (SCs) due to the deadly coronavirus disease 2019 (COVID-19) pandemic. To mitigate the significant post-COVID-19 consequences, organizations should modify their existing strategies and focus more on the key flexible sustainable SC (SSC) strategies. Still now, a limited number of studies have highlighted about the flexible strategies what firms should adopt to reduce the rampant effects in the context of emerging markets.

This study presents an integrated approach including Delphi method, Bayesian, and the Best-Worst-Method (BWM) to identify, assess and evaluate the importance of the key flexible SSC strategies for the footwear industry in the emerging market context.

The results found the manufacturing flexibility through automation integration as the most important flexible SSC strategy to improve the flexibility and sustainability of modern SCs. Also, developing omni-channel distribution and retailing strategies and increasing the level of preparedness by using artificial intelligent are crucial strategies for overcoming the post-COVID-19 impacts.

The novelty of this research is that the research connects a link among flexible strategies, SCs sustainability, and the impacts of the COVID-19 pandemic. Moreover, the research proposes a novel and intelligent framework based on Delphi and Bayesian-BWM to identify and analyze the key flexible SSC strategies to build up sustainable and robust SCs which can withstand in the post-COVID-19 world.