An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms are proposed to solve problems in various fields of medical treatment, which is able to reduce the workload of the medical system. Due to excellent learning ability, AI has played an important role in drug development, epidemic forecast, and clinical diagnosis. This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.     

Expert process planning system with solid model interface

Artificial intelligence (AI) techniques provide various tools for use in the development of automated process planning systems. AI can be utilized for automated reasoning about the shape, features and relationship between features and for development of expert systems for creating the process plan itself. Most of the previous work on AI in process planning deals with one specific application. This paper presents an integrated hierarchical framework of a process planning system with a CAD interface. The objective of the project discussed in the paper is to integrate design with process planning using AI techniques. The development of a CAD interface is discussed with respect to automated feature recognition, determination of tool approach direction and deciding the precedence relationship between the features. Sample results from the CAD interface are presented. The expert system for the process planning module is discussed with the part representation and knowledge base and the plan generation procedure. The module uses hierarchically organized frames for both part representation and the knowledge base. Some initial results are presented from the process planner to demonstrate the current capability of the system.     

Strategies for Development of Process Control Models for Hot Metal Desulfurization: Conventional and AI Techniques

Process control models can be developed by using both conventional and AI approaches. The conventional approaches include regression models and process kinetic models, and the artificial intelligence (AI) approaches are based on artificial neural nets (ANN), genetic algorithm (GA), and fuzzy rule-based expert systems FRBES. Plant data on hot metal desulfurization, carried out by injecting calcium carbide, is analyzed to test and tune different types and combinations of models and then evaluate their relative performance. Although the control models based on process fundamentals provide a fillip to the new ideas for technological developments and improvements, the combination of conventional and AI approaches may be a better option for process control on the shop floor. It is advisable to first develop and test all models and then decide about the best strategy of using them.     

An intelligent hybrid system for initial process parameter setting of injection moulding

Currently, determination of the initial process parameter settings for injection moulding is mainly performed by moulding personnel, and the effectiveness of the parameter setting is largely dependent on the experience of these personnel. In this paper, an intelligent hybrid system, called HSIM, is described, which is used to determine a set of initial process parameters for injection moulding based on the artificial intelligence (AI) techniques, case-based reasoning (CBR), and hybrid neural network (NN) and genetic algorithm (GA). HSIM can determine a set of initial process parameters for injection moulding quickly, without relying on expert moulding personnel, from which moulded parts free from major moulding defects can be produced.     

基于SpringMvc架构模式的远程居家医疗系统设计

目的基于现实意义的基础上,利用SpringMvc架构模式建立软件程序,将医疗系统中的定性问题与患有慢性病的老年人用户需求结合起来,建立比较完善的远程就医系统,通过相应的媒介,解决患有慢性病的老年人日常体检和就医困难问题。方法通过针对性人群的问卷调查分析和采集数据分析,以患有慢性病老年人的体征数据为依据,挖掘其内在需求,发现健康医疗智能设备的问题,采用慢性病数据模型分析,提出具有现实意义的远程医疗系统设计。结论通过前期调研和设计实践证实,在我国现实医疗水平和科技水平的基础上,能够实现远程居家医疗系统与现代信息物理系统技术的结合,有效解决慢性病老年人的日常看病就医问题。     

Application of expert systems and pattern recognition methodologies to facilities layout planning

This paper deals with two basic concepts of artificial intelligence (AI), from a facilities layout problem domain perspective. In this work, the facilities layout problem is treated as a multi-objective situation. From conventional multi-objective perspective, the philosophy underlying this work is not a different one. However, the qualitative constraints are handled via a symbolic manipulation structure. The two conceptualizations are: (a) an expert system and (b) a pattern recognition system. In the expert system, the heuristics used are based on the augmented transition networks of natural language processing. In the pattern recognition system, the use of productions rules to capture the expert knowledge is illustrated. For both the systems example problems are given.     

Pulmonary Diseases Decision Support System Using Deep Learning Approach

Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of our knowledge, this is the first published study to ever attempt to distinguish all four cases normal, pneumonia, COVID-19 and lung opacity from Chest-X-Ray (CXR) images. All models were trained using Chest-X-Ray (CXR) images, and statistically tested using 5-fold cross validation. Using individual models, XceptionNet outperformed all other models with a 94.775% accuracy and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) of 99.84%. On the other hand, DarkNet19 represents a good compromise between accuracy, fast convergence, resource utilization, and near real time detection (0.33 s). Using a collection of models, the 97.79% accuracy achieved by Ensemble Features was the highest among all surveyed methods, but it takes the longest time to predict an image (5.68 s). An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time, such a dependable system can be used in rural areas and various healthcare sectors.     

面向老年人的社区慢性病管理服务设计研究

目的 探索如何为社区慢性病老人提供更好的健康管理服务,构建出本土化的社区慢性病管理服务模式.方法 首先通过文献分析了解国内外社区慢性病管理的研究现状,其次以一位老年慢性肾脏病患者为目标用户进行个案研究.运用半参与式观察、深度访谈等用户研究方法,理解与分析慢性病老人在社区慢性病管理活动中的外显行为与内隐需求,最后依据目标用户的主要需求与痛点,综合各方服务人员进行概念设计.结论 提出了5种面向老年人社区慢性病管理的功能服务模块:健康档案共享模块、协同诊疗服务模块、药物分装管理模块、药物配送服务模块和健康学习分享模块,构建了面向老年慢性肾脏病患者的综合护理服务系统,为应对我国老龄化和老年慢性病管理问题提供了新的思考与路径.     

An application of an expert system in layout compaction of VLSI design

Abstract

Recent research in knowledge‐based expert systems of VLSI design tools has concentrated on placement, routing, and cell generation. This paper presents an alternative application for artificial intelligence (AI) techniques on compaction design for a VLSI mask layout‐expert compactor. In order to overcome the shortcomings of iterative search through a large problem space within a working memory, and therefore, to speed‐up the runtime of compaction, a set of rule‐based region query operations and knowledge‐based techniques for the plane sweep method are proposed in this system. Experimental results have explored the possibility of using expert system technology (EST) to automate the compaction process by “reasoning” out the layout design and applying sophisticated expert rules to its knowledge base.     

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.     

Drone-based AI/IoT Framework for Monitoring,Tracking and Fighting Pandemics

Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two main systems; AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing. The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking (SDN). The proposed work is built based on a thermal camera mounted in a face-shield, or on a helmet that can be used by people during pandemics. The detected images, thermal images, are processed by the developed AI algorithm that is built based on the convolutional neural network (CNN). The drone system can be called, by the IoT system connected to the helmet, once infected cases are detected. The drone is used for sterilizing the area that contains multiple infected people. The proposed framework employs a single centralized SDN controller to control the network operations. The developed system is experimentally evaluated, and the results are introduced. Results indicate that the developed framework provides a novel, efficient scheme for monitoring and fighting COVID-19 and other future pandemics.     

Incorporating sustainability in decision-making for medical device development

The development and commercialization of contemporary medical devices are inherently multidisciplinary. Consequently, they have to undergo a stringent regulatory compliance procedure in conformity with an ever increasingly fierce and competitive business environment. Throughout the product life cycle, medical devices would significantly consume renewable as well as non-renewable resources and as a result exert a substantial social, economic and environmental impact(s). Sustainability from an overall perspective in terms of social, economic and environmental domains is crucial for decision-making during product development; nevertheless they have rarely been incorporated simultaneously. Both public and private institutions only focused towards economic and environmental sustainability without acknowledging the critical role of social sustainability that needs to be addressed concurrently so as to uphold the other two. Accordingly, it is imperative to consider the criteria of the aforementioned domains of sustainability in the initial phases of product development. The proposed conceptual multifaceted framework comprehensively explores a broader scope of sustainable product development, mainly from the pragmatic standpoint of systems engineering in comparison to the contemporary evaluation and development approaches. The underpinnings of the proposed framework encompass the critical role of a MultiCriteria Hierarchical Model (MCHM), which is in fact an extensive revision of the analytical hierarchy process decision making model. The MCHM mainly functions across the idea screening phase (Stage 2) up to the business and feasibility analysis phase (Stage 4). Moreover, unlike its predecessors, the MultiCriteria Hierarchical Model is less dependent upon numerical scores allotted by expert opinion and apparently broader in its scope of application. Furthermore, the proposed framework elucidates the active participation of the MCHM in product design and development by conjoining with an artificial intelligence based computer system known as expert systems. The principal objective of the proposed conceptual framework is to deliver a thorough assessment and a feasible roadmap for the development of sustainable medical devices.     

体医融合视角下慢性病移动医疗设计策略研究

目的在工业化、老龄化、生态环境恶化各种现实背景下,构思当代慢性病移动医疗服务的创新策略,重构慢性病患者的移动医疗服务体验。方法系统地研究了慢性病移动医疗服务的现状,分析慢性病治疗以及慢性病移动医疗平台在国内发展存在的各种问题,结合慢性病治疗本身独特的特点,在系统创新思维和服务设计理念的基础上,从体医融合的视角提出了具有针对性的慢性病移动医疗服务系统创新策略。结论指出当代慢性病移动医疗服务系统创新策略在于整合性的动态解决方案,站在时间和空间的纬度上,将体育与医疗资源跨界整合,缓解慢性病治疗面临的资源不足、断层、同质化等问题,构建新的服务流程系统与治疗体验,为慢性病移动医疗的发展,提供一种新的解决思路与发展方向。     

高层建筑结构初步设计的专家系统

本文通过对结构初步设计本质和内容的分析，提出了高层建筑结构初步设计的专家系统HIPRED的原型系统．它是一个综合集成系统：通过人工神经元网络（ANN）技术存贮专家的设计经验来确定高层建筑的结构体系；利用能连续化地反映高层建筑结构参数分布的基于悬臂梁的结构简化模型，并结合常微分方程求解器COLSYS很好地从速度和精度两方面的匹配来进行力学反应分析；通过人机交互和知识库的引入，系统能够对设计的结构反应参数（位移、周期和地震为）做出评价及对结构做出相应的修改．HIPRED能够帮助设计者在一个较高层次上进行高层建筑结构的初步设计。HIPRED是在Windows环境下，采用面向对象的编程技术（OOP），用C＋＋编写的。     

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Artificial intelligence adoption in the physical sciences,natural sciences,life sciences,social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960–2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI “springs” and “winters”. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.     

Supervised Machine Learning-Based Prediction of COVID-19

COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and overwhelming spread has become one of the greatest challenges for the world. As yet, no satisfactory vaccine or medication has been developed that could guarantee its mitigation, though several efforts and trials are underway. Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment. In this regard, healthcare experts, researchers and scientists have delved into the investigation of existing as well as new technologies. The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease. The state-of-the-art research in Artificial intelligence (AI), Machine learning (ML) and cloud computing have encouraged healthcare experts to find effective detection schemes. This study aims to provide a comprehensive review of the role of AI & ML in investigating prediction techniques for the COVID-19. A mathematical model has been formulated to analyze and detect its potential threat. The proposed model is a cloud-based smart detection algorithm using support vector machine (CSDC-SVM) with cross-fold validation testing. The experimental results have achieved an accuracy of 98.4% with 15-fold cross-validation strategy. The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.     

Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm

In recent days, the gigantic generation of medical data from smart healthcare applications requires the development of big data classification methodologies. Medical data classification can be utilized for visualizing the hidden patterns and finding the presence of disease from the medical data. In this article, we present an efficient multi-kernel support vector machine (MKSVM) and fruit fly optimization algorithm (FFOA) for disease classification. Initially, FFOA is employed to choose the finest features from the available set of features. The selected features from the medical dataset are processed and provided to the MKSVM for medical data classification purposes. The proposed chronic kidney disease (CKD) classification method has been simulated in MATLAB. Next, testing of the dataset takes place using the own benchmark CKD dataset from UCI machine learning repositories such as Kidney chronic, Cleveland, Hungarian, and Switzerland. The performance of the proposed CKD classification method is elected by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate. The investigational outcome specifies that the proposed CKD classification method achieves maximum classification precision value of 98.5% for chronic kidney dataset, 90.42904% for Cleveland, 89.11565% for Hungarian, and 86.17886% for Switzerland dataset than existing hybrid kernel SVM, fuzzy min-max GSO neural network, and SVM methods.     

An expert system for the design and selection of ball bearing parameters

Artificial Intelligence (AI) is an emerging technology. Research in Al is focused on developing computational approaches to intelligent behaviour.The computer programs with which AI could be associated are primarily processes associated with complexity, ambiguity, indecisiveness, and uncertainty. One of these computer programs is referred to as Knowledge-based Expert System (KBES) as it represents knowledge acquired from various experts in a particular field of interest to the user. The expert system emulates human behaviour in solving problems thought to require experts for their solution by utilizing computer programs that incorporate experts' heuristic reasoning. In this paper, the application of KBES to aid the design of ball and roller bearing is discussed. The precision rolling-element bearing of the twentieth century is a product of exacting technology and sophisticated science. A bearing supports radial and axial loads, at the same time allowing relative motion between two elements of a machine. Various requirements and steps in the design of ball and roller bearings are discussed. Equations are developed for the relevant design parameters and input into the expert system shell called VP-Expert. The expert system rules are also provided.