Review Of Artificial Intelligence Techniques Research Articles

A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production

A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Review of artificial intelligence techniques in green/smart buildings

The numerous benefits offered by green and/or intelligent buildings have led to an increase in their construction. In parallel, various artificial intelligence (AI) techniques have been recognized as relevant for these types of buildings to rationalize, optimize and innovate their operation. The amalgamation of green and smart buildings with AI techniques holds remarkable potential to ensure greater structural stability and minimize negative ecological impacts on the environment and society. Although research on the application of AI of green and smart buildings exists, our understanding of the utility and benefits of its integration remain fragmented. To address this gap, this investigation aims to examine the relevance and benefits of the convergence of AI techniques in green and smart buildings. Using Web of Science and Scopus databases, this study used bibliometric analysis to present an exhaustive review of the field’ publication productivity, most influential articles and most relevant actors (i.e., authors, research centers, and countries). Additionally, using content analysis, this investigation sheds light on the field’ cognitive structure, which is based on five thematic clusters. The study concludes with a temporal keyword analysis focusing on the Triple Bottom Line benefits obtained through the application of AI techniques in green or smart buildings. The most noteworthy contribution of this study is that it is the first study that performs a bibliometric analysis on Artificial Intelligence Techniques in green and smart Buildings, which is an important endeavor to help interested scholars and professionals to gain a scientific overview of existing research in the field.

Future trends in organic flour milling: the role of AI

<abstract> <p>The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.</p> </abstract>

Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease.

Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. The resulting articles were assessed and organized into four basic contextual categories, namely: i) 'COPD diagnosis', ii) 'COPD prognosis', iii) 'Patient classification', iv) 'COPD management', and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.

A systematic review of artificial intelligence techniques for collaborative learning over the past two decades

This systematic review focuses on publications related to studies of the use of artificial intelligence (AI) for collaborative learning. The use of AI for collaborative learning is a recent phenomenon and a systematic review of such studies is lacking. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 41 journal articles were shortlisted. These articles were analysed in terms of the contexts of study (year, group size, platforms of interaction, and types of learners). Using a thematic approach, two broad foci and five sub-categories of use of AI for collaborative learning were identified: (1) learning outcomes – (a) collective performances, and (b) content of learning; (2) social interactions and processes – (c) sentiments and emotions, (d) discourse patterns and talk moves, and (e) learner characteristic and behaviours. The AI techniques were coded according to three broad purposes (discovering, learning and reasoning) and nine techniques: clustering, ensemble, regression algorithms, deep learning, decision trees, natural language processing, instance-based, fuzzy logic, and agents. A Sankey diagram was used to depict the relationships among the nine AI techniques to the five sub-categories of how AI was used to support collaborative learning. The gaps in the selected articles and limitations of the current review were discussed to suggest future areas of study.

Review of artificial intelligence techniques used in IoT networks

Review of artificial intelligence techniques used in IoT networks

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

A review of artificial intelligence techniques for selection & evaluation

Today, supplier selection &evaluation has been an important issue of strategic planning for a long time. Various artificial intelligence (AI) methods have been applied in the methodology of supplier section & evaluation from different researchers. This paper provides a review of past various artificial intelligence methods (AI) from 2010 to 2018 in the area supplier selection & evaluation problems. The review report is based on the literature of fifty articles include six review papers. Our survey presents and tries to find out the most useful and prominent AI method recommendable for the future in the problem of supplier selection &evalution.

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19.

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

Review of various Artificial Intelligence Techniques and its applications

With the upgrading needs of automation and prediction requirements in the industries, automation for improving the quality of the product and prediction of the product cycle to make product more economical. Now a days the use of artificial intelligence techniques becoming crucial to apply in the industries which makes the products more reliable, robust and economical. This paper deals and explains the different techniques of artificial intelligence. Also covers the review of the different applications of these techniques in the industries. This paper covers the review of Artificial intelligence techniques applied in the different fields like civil engineering, biomedical engineering, mechanical engineering, electrical and electronics engineering and many more.

TÉCNICAS DE INTELIGENCIA ARTIFICIAL EN MODELADO Y CONTROL DEL AMBIENTE DE INVERNADEROS: ESTADO DEL ARTE, PARTE 1

Evolutionary Algorithms, Bio-inspired Algorithms and Hybrid Algorithms, considered as Artificial Intelligence techniques have gained great importance due to their capacity to solve complex and nonlinear problems that conventional methods cannot solve. In the last decade the application of these techniques has been applied to the development of intelligent algorithms to identify, predict, optimize and control the weather for crops in protected environments (greenhouses) and to generate near optimal climatic conditions for biosystems for growth and crop development, and to obtain higher yields and product quality. The current paper shows a review of Artificial Intelligence Techniques applied in agricultural engineering, mainly to modeling and control of greenhouse environment, especially those algorithms inspired by the collective behavior of biological systems, evolution and natural genetics.

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

Knowledge based systems for desalination processes

Although artificial intelligence is an old field of computer science which dates back to the 50s, it didn't reach a deserved state of prominence till the mid-80s. Its application was originally aimed at: ill structured problems where there is no rigorous mathematical model, the knowledge of the problem is incomplete or uncertain, or when general algorithmic methods lead to combinatorial explosion problems. A brief review of artificial intelligence techniques is presented below. In this paper the introduction of artificial intelligent techniques to MSF desalination plants is investigated. They could be involved in designing, operating, or optimizing plant performance. The purpose is achieving better design, improved efficiency, and increased operational safety. There are many opportunities for introducing artificial intelligence into MSF desalination plants; examples of important and pervasive application areas are also presented.