•We theoretically describe the expert systems.•We investigate the fuzzy, medical and wearable expert system variations.•We highlight the expert systems advantages and issues.•We emphasize the ...importance of expert system validation.•We describe in depth some expert system applications in the medical field.
The aim of this review is to provide a broad overview of the state-of-the-art works mainly published in the last ten years on expert systems applied in different medical domains.
Being able to support and sometimes substitute experts, an expert system may be a precious ally for medical diagnoses. Medical expert system applications provide physicians and patients with an immediate access to knowledge and advice, rooting their flexibility into their knowledge bases, rule sets and graphical interfaces. To be trusted by their users, medical expert systems should follow some criteria, which we investigate along with their different realization, from fuzzy logic to wearable solutions for out-of-clinical-environment care. We also consider the advantages of approaching diagnoses and alert systems through an artificial intelligence counterpart, without forgetting the importance of a good validation to assess the system functionality.
Therefore, we show the heterogeneity of the solutions proposed by the literature, bounded to the specific needs a medical expert system is called to answer, the common lack of a system validation and the possible benefits deriving from these systems application.
► Describes hybrid expert system approaches specifically connectionist and neuro-fuzzy system. ► Classifies 91 articles published between 1988 and 2010. ► Evaluation on system structure, algorithms, ...applications and building/implementation tools.
This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems’ areas. We also observe that many new industrial applications are developed using hybrid expert systems recently.
The article analyzes the company's need to create a prototype for the development of expert systems. Expert systems and tools for their creation were examined, functions and software for the creation ...of an expert system that met the needs of the company were determined. The expert system design and programming processes, system performance testing and system documentation preparation, installation of the system on the server are described.
Artificial intelligence (AI)—defined as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through ...flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades ...where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models ...have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late‐stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147–172. doi: 10.1002/wcms.1240
This article is categorized under:
Computer and Information Science > Chemoinformatics
Computer and Information Science > Databases and Expert Systems
Computer and Information Science > Computer Algorithms and Programming
•Automated detection of pain from facial expressions is a challenge in medical care.•A new deep neural network algorithm designed to detection pain intensity effectively.•A new feature extraction ...algorithm developed to speed up the algorithm.•The new enhanced deep learning algorithm can detect pain in multi level effectively.
Automated detection of pain intensity from facial expressions, especially from face images that show a patient's health, remains a significant challenge in the medical diagnostics and health informatics area. Expert systems that prudently analyse facial expression images, utilising an automated machine learning algorithm, can be a promising approach for pain intensity analysis in health domain. Deep neural networks and emerging machine learning techniques have made significant progress in both the feature identification, mapping and the modelling of pain intensity from facial images, with great potential to aid health practitioners in the diagnosis of certain medical conditions. Consequently, there has been significant research within the pain recognition and management area that aim to adopt facial expression datasets into deep learning algorithms to detect the pain intensity in binary classes, and also to identify pain and non-pain faces. However, the volume of research in identifying pain intensity levels in multi-classes remains rather limited. This paper reports on a new enhanced deep neural network framework designed for the effective detection of pain intensity, in four-level thresholds using a facial expression image. To explore the robustness of the proposed algorithms, the UNBC-McMaster Shoulder Pain Archive Database, comprised of human facial images, was first balanced, then used for the training and testing of the classification model, coupled with the fine-tuned VGG-Face pre-trainer as a feature extraction tool. To reduce the dimensionality of the classification model input data and extract most relevant features, Principal Component Analysis was applied, improving its computational efficiency. The pre-screened features, used as model inputs, are then transferred to produce a new enhanced joint hybrid CNN-BiLSTM (EJH-CNN-BiLSTM) deep learning algorithm comprised of convolutional neural networks, that were then linked to the joint bidirectional LSTM, for multi-classification of pain. The resulting EJH-CNN-BiLSTM classification model, tested to estimate four different levels of pain, revealed a good degree of accuracy in terms of different performance evaluation techniques. The results indicated that the enhanced EJH-CNN-BiLSTM classification algorithm was explored as a potential tool for the detection of pain intensity in multi-classes from facial expression images, and therefore, can be adopted as an artificial intelligence tool in the medical diagnostics for automatic pain detection and subsequent pain management of patients.
•We used wavelet packet coefficients to extract features from faulty bearings.•We proposed an Improved Range Overlap's method for feature selection.•The reduced feature set is well-suited to build ...the fuzzy expert system.•Findings on localized and distributed bearing faults.
Bearing fault diagnosis represents the core of induction machines condition monitoring. This paper presents an application of fuzzy expert system (FES) to bearing faults diagnosis. Here, fuzzy rules are automatically induced from numerical data using the Similarity partition method. Data of faulty bearings presents high noise level. Thus, an Improved Range Overlaps method (IRO) is proposed to select input feature vectors by giving them validity degrees. The Similarity method partition was found confused with features presenting range overlap. Consequently, the new proposed Improved Range Overlaps method is found quite suitable for improving the classifier accuracy. The model validity and efficiency were proved using experimental bearing faults data from Case Western Reserve University database and the NSF I/UCR Center on Intelligent Maintenance Systems (IMS) database.
•A review of the consensus processes in social network group decision making is presented.•Two approaches are identified: consensus based on trust relationships and based on opinion ...evolution.•Challenges and research future fields are presented.
In social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research.
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