Abstract: Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge.

Abstract: With the ubiquity of the Internet, platforms such as: Google, Wikipedia and the like can provide insights pertaining to firms’ financial performance as well as capture the collective interest of traders through search trends, number of web page visitors and/or financial news sentiment. Information emanating from these platforms can significantly affect, or be affected by, changes in the stock market. The overarching goal of this paper is to develop a financial expert system that incorporates these features to predict short term stock prices. Our expert system is comprised of two main modules: a knowledge base and an artificial intelligence (AI) platform. The “knowledge base” for our expert system captures: (a) historical stock prices; (b) several well-known technical indicators; (c) counts and sentiment scores of published news articles for a given stock; (d) trends in Google searches for the given stock ticker; and (e) number of unique visitors for pertinent Wikipedia pages. Once the data is collected, we use a structured approach for data preparation. Then, the AI platform trains four machine learning ensemble methods: (a) a neural network regression ensemble; (b) a support vector regression ensemble; (c) a boosted regression tree; and (d) a random forest regression. In the cross-validation phase, the AI platform picks the “best” ensemble for a given stock. To evaluate the efficacy of our expert system, we first present a case study based on the Citi Group stock ($C) with data collected from 01/01/2013 - 12/31/2016. We show the expert system can predict the 1-day ahead $C stock price with a mean absolute percent error (MAPE) ≤ 1.50% and the 1–10 day ahead with a MAPE ≤ 1.89%, which is better than the reported results in the literature. We show that the use of features extracted from online sources does not substitute the traditional financial metrics, but rather supplements them to improve upon the prediction performance of machine learning based methods. To highlight the utility and generalizability of our expert system, we predict the 1-day ahead price of 19 additional stocks from different industries, volatilities and growth patterns. We report an overall mean for the MAPE statistic of 1.07% across our five different machine learning models, including a MAPE of under 0.75% for 18 of the 19 stocks for the best ensemble (boosted regression tree).

Abstract: Multiple attributes from IP flows are combined to detect anomalous events.GA metaheuristic used for Digital Signature of Network Segment using Flow Analysis.Unsupervised training technique applied efficiently for network traffic profiling.Fuzzy Logic improved accuracy and false positives compared to state of art. Due to the sheer number of applications that uses computer networks, in which some are crucial to users and enterprises, network management is essential. Therefore, integrity and availability of computer networks become priorities, making it a fundamental resource to be managed. In this work, a scheme combining Genetic Algorithm and a Fuzzy Logic for network anomaly detection is discussed. The Genetic Algorithm is used to generate a Digital Signature of Network Segment using Flow Analysis, where information extracted from network flows data is used to predict the networks traffic behavior for a given time interval. Furthermore, a Fuzzy Logic scheme is applied to decide whether an instance represents an anomaly or not, differing from some approaches present in the literature. Indeed, it is proposed an expert system with the capability to monitor the networks traffic with IP flows while expected behaviors are generated in a regular time interval basis, issuing alarms when a possible problem is present. The proposed anomaly detection system exposes network problems autonomously. The results acquired from applying the proposed approach in a real network traffic flows achieve an accuracy of 96.53% and false positive rate of 0.56%. Moreover, our method succeeds in achieving higher performance compared to several other approaches.

Abstract: Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage.In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze?remember when it seemed plausible that IBM's Watson could cure cancer??to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the ?moonshot? (curing cancer, or synthesizing all investment knowledge); look for the ?low-hanging fruit? to make your company more efficient.Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed?important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (?analytics on steroids?), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise.Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI.A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review.

Abstract: Many organizations and institutions are implementing accurate and practical tools to accelerate decision-making process. In this regard, hospitals and healthcare centers are not exceptions, in particular, because they directly impact the health and well-being of the community. When it comes to disease diagnosis, practitioners may have different opinions, which lead to different decisions and actions. On the other hand, the amount of available information, even in a case of a typical disease is so vast that rapid and accurate decision-making may be difficult. For example, practitioners may prescribe several expensive tests in order to diagnose a heart disease whereas many of those tests might not even be required. Accordingly, a Clinical Decision Support System (CDSS) can be very helpful here. In particular, such a CDSS can be developed as an expert system for those patients who have a high likelihood of developing heart diseases. This study develops an expert system based on Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Inference System in order to evaluate the condition of patients who are being examined for heart diseases. The Fuzzy AHP is used to calculate weights for different criteria that impact developing heart diseases, and the Fuzzy Inference System is used to assess and evaluate the likelihood of developing heart diseases in a patient. The developed system has been implemented in a hospital in Tehran. The outcomes show efficiency and accuracy of the developed approach.

Abstract: This work presents an assistive system for child with autism spectrum disorder (C-ASD). The main objective of this system is to reduce dependency on the caregiver and parent and thereby assisting them to make independent. Fuzzy logic based expert system is designed for the assisting system which will help in intervention strategies. The system collects data from four different sensors, such as GPS, heart beat, accelerometer and sound, and generates required notification for the parent, caregiver and C-ASD. The wearables-specifically smart watches- can be used to implement such system. A case study shows the proposed expert system is able to help the C-ASD to restore dysfunction.

Abstract: An expert system as an expert knowledge-based information system has been widely used by interested parties to consult their problems. Therefore, an expert system should be well designed so as to meet the requirements for a software to store knowledge, explain problems, and recommend solutions. The purpose of the present study is to develop a systematic design of an expert system using Unified Modeling Language (UML), which is one of popular object oriented modeling languages. The research method was a literature review as the main source for analyzing the expert system requirements. The developed design was then clarified, verified, and validated in a Focus Group Discussion (FGD). To evaluate the model, a review of some related studies was conducted. The research respondents were informatics lecturers at UIN Sunan Gunung Djati Bandung. The results of the study revealed that the systematic design of expert system was well-developed because it was user-friendly, flexible, easy to be modified, and able to propose solutions for various problems.

Abstract: In the era of social media, exponential growth of information generated by online social media and e-commerce applications demands expert and intelligent recommendation systems It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting friends, items, products, jobs etc according to users’ interests Recommendation uses Collaborative Filtering as one of the most popular approaches but the major limitations of this approach are sparsity and cold-start issues Mostly existing recommendation systems focus on a single domain, on the other end cross-domain collaborative filtering is able to alleviate the degree of sparsity and cold-start problems to a better extent To avoid these problems, cross domain evolution comes in limelight and has become an emerging topic of research nowadays This paper mainly discusses the notion of cross-domain recommendation, its techniques and proposes a generalized Cross Domain- Multi Dimension Tensor Factorization (CD-MDTF) approach to trade off influence among domains optimally Cross Domain recommendation system employs knowledge from source domain and commingles it to target domain which covers the aspect of intelligent behavior and brings it to the category of an expert system Finally, to evaluate the proposed CD-MDTF approach, experiments are performed on two real-world datasets, Movie-Lens and Book-Crossing Results validate that sparsity and cold start problem is reduced by 16% and 25% respectively in comparison to single-domain recommendation systems Further, the proposed CD-MDTF recommendation system accuracy is validated using precision and recall as evaluation performance metrics which shows an improvement of 41% in precision and 21% in recall The results show that embedding of multiple domains and multiple dimensions for recommendation helps in result improvement, thereby augmenting the recommendation system performance like an expert and intelligent system

Abstract: Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

Abstract: Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model.

Abstract: This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time Using an ensemble classifier, Random Forest, we demonstrated 984% accuracy in TB antigen-specific antibody detection on the mobile platform Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase

Abstract: This research involved the design of an initial expert system which helps farmers and specialists diagnose and provide appropriate advice on banana diseases. The management of knowledge used in the expert system was also discussed. One of the key elements of this research was to find the appropriate language to diagnose the disease and the current situation in the knowledge base. Expert systems enable effective consultation. Production rules were used to capture knowledge. The expert system was developed using CLIPS with the Delphi 10.2 as user interface. The expert system produced good results in analyzing cases of tested banana disease and enabling the system to determine the correct diagnosis in all cases.

Abstract: A socially intelligent robot must be capable to extract meaningful information in real-time from the social environment and react accordingly with coherent human-like behaviour. Moreover, it should be able to internalise this information, to reason on it at a higher abstract level, build its own opinions independently and then automatically bias the decision-making according to its unique experience. In the last decades, neuroscience research highlighted the link between the evolution of such complex behaviour and the evolution of a certain level of consciousness, which cannot leave out of a body that feels emotions as discriminants and prompters. In order to develop cognitive systems for social robotics with greater human-likeliness, we used an "understanding by building" approach to model and implement a well-known theory of mind in the form of an artificial intelligence, and we tested it on a sophisticated robotic platform. The name of the presented system is SEAI (Social Emotional Artificial Intelligence), a cognitive system specifically conceived for social and emotional robots. It is designed as a bio-inspired, highly modular, hybrid system with emotion modelling and high-level reasoning capabilities. It follows the deliberative/reactive paradigm where a knowledge-based expert system is aimed at dealing with the high-level symbolic reasoning, while a more conventional reactive paradigm is deputed to the low-level processing and control. The SEAI system is also enriched by a model which simulate the Damasio’s theory of consciousness and the theory of Somatic Markers. After a review of similar bio-inspired cognitive systems, we present the scientific foundations and their computational formalisation at the basis of the SEAI framework. Then, a deeper technical description of the architecture is disclosed underlining the numerous parallelisms with the human cognitive system. Finally, the influence of artificial emotions and feelings, and their link with the robot's beliefs and decisions have been tested in a physical humanoid involved in Human-Robot Interaction (HRI).

Abstract: Analysis of informational provision level of complex environmental monitoring system in surveillance zones of Ukrainian NPPs was carried out. It was established that different subsystems are used for solution of monitoring tasks. The systems are separated, heterogeneous, hardware-software incompatible, and aimed at observation and state assessment of specific components of the environment and natural resources. Such situation is not in compliance with the up-to-date European requirements and standards for environmental monitoring information systems in areas of influence of man-made facilities. It is demonstrated that solution of this problem is possible by developing an information and analytical expert system for evaluation of NPP environmental impact on the environment (EcoIES). The main tasks that will be solved by EcoIES and its specific functions during emergencies or corresponding emergency exercises were described. The main requirements for the system are consistency, openness, standardization and adaptation. Specific requirements are the completeness and hierarchy of information, comprehensive integration and rational use, semantic unity, compatibility of system components, integrated security. Three options of conceptual approaches to creation of EcoIES have been developed, each of which is characterized by its structure, level of hardware-software provision and organization of information exchange. The option, which to major extent is in compliance with the European requirements has been substantiated, and which allows to fully solve radiation and environmental safety tasks, as well as civil protection of population, territories and the environment in the surveillance zones of Ukrainian NPPs. Therefore, this approach is recommended for further practical implementation at NPPs in Ukraine. The basic scheme of structural organization and interconnections between the EcoIES and other subjects of environmental monitoring that are part of the State environmental monitoring system has been developed.

Abstract: The research implements a Case-based Reasoning method into an expert system to help mechanical team in an automobile service station in relation with making a specific decision to address customer complaints. A number of mechanistic criteria and potential alternatives are designed, using knowledge-based as a system backbone which is elaborated into four main general phases known as retrieving similar problems, reusing knowledge, revising solution and retaining experiences to fortify a best solution. Technically, an on line application system are constructed by Object-oriented Software Engineering (OOSE) model to serve end-users interactively. The result of this study indicates an on line expert system where the application not only can be effectively utilized by specialized team but also be able to serve as a knowledge bridge for other.

Abstract: Expert System is a system that seeks to adopt human knowledge to the computer, so that the computer can solve problems which are usually done by experts. The purpose of medical expert system is to support the diagnosis process of physicians. It considers facts and symptoms to provide diagnosis. This implies that a medical expert system uses knowledge about diseases and facts about the patients to suggest diagnosis. The aim of this research is to design an expert system application for diagnosing eye diseases using forward chaining method and to figure out user acceptance to this application through usability testing. Eye is selected because it is one of the five senses which is very sensitive and important. The scope of the work is extended to 16 types of eye diseases with 41 symptoms of the disease, arranged in 16 rules. The computer programming language employed was the PHP programming language and MySQL as the Relational Database Management System (RDBMS). The results obtained showed that the expert system was able to successfully diagnose eye diseases corresponding to the selected symptoms entered as query and the system evaluation through usability testing showed the expert system for diagnosis eye diseases had very good rate of usability, which includes learnability, efficiency, memorability, errors, and satisfaction so that the system can be received in the operational environment.

Abstract: Apparel manufacturing is labor-intensive, which is characterized by low-fixed capital investment; a wide range of product designs and, hence, input materials; variable production volumes; high competitiveness; and often high demand on product quality. To cater these demands, the labor-intensive processes should be converted into automated processes accomplished by the use of computers, models, digital components, and artificial intelligence (AI). AI is the field of study that deals with the synthesis and analysis of computational agents that act intelligently. In this chapter various applications of AI in apparel manufacturing have been described. This chapter also includes different types of AI such as expert systems, neural network, fuzzy logic, genetic algorithm, and other approaches used in garment manufacturing.

Abstract: Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.

Abstract: Fault diagnosis is an important step for software-intensive manufacturing system and process. But because of the increased scale and complexity, as well as the uncertain condition of the running environment, embedded software fault diagnosis is still an open challenge for industry application. In this research, an optimized hybrid model is proposed by integrating a case-based reasoning (CBR) method and a BN-based diagnosis method. Initially, a FMEA-style case-based reasoning (F-CBR) method is proposed by collecting and formalizing the existed fault cases for the low-level similarity searching guided diagnosis. Then, by adopting a new designed algorithm, F-CBR can be further transferred to a deep-level Bayesian diagnosis network for the dynamic multi-fault diagnosis with uncertainty. Based on this framework, we implement a prototype of hybrid expert system for the diagnosis of embedded software by integrating CBR with Bayesian network (BN) through F-CBR by the corresponding failure spectra as the bridge. The feasibility and benefits of this hybrid diagnosis strategy are verified by examples and case studies in real industry applications with great promising results for different kinds of multi-level diagnosis.

Abstract: Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundation...

Abstract: US manufacturers’ capabilities have consistently been degrading over the past few decades with no end in sight. To ebb this tide and assist the decision-makers in designing better configurations an...

Abstract: This study proposes an integrative safety performance evaluation framework using both scoring and fuzzy expert systems for offshore oil and gas platforms in Malaysia. Fourteen safety factors are used as the basis of performance evaluation. To generate the safety score of each safety factor, weights of the indicators grouped under the safety factor are first multiplied with the compliance levels assigned to the respective indicators. The products of weights and compliance levels are then summed up, yielding the safety score. The scores of safety factors serve as inputs of the fuzzy expert system to determine the overall safety performance of an offshore oil and gas platform. The scoring and fuzzy expert systems are tested on ten offshore oil platforms in Malaysia. All the platforms tested meet the status of ‘compliant’ defined by the rules of the expert system. The crisp outputs correspond well with the total safety scores of the platforms. However, irregularities are observed in the crisp outputs when tested with various compliance scenarios, indicating that rules set by the experts have larger effect on the outputs than the total safety scores obtained. All scenarios of membership function used for the inputs and outputs do not demonstrate significant difference in the R-squared values obtained from the plots of total safety scores against crisp outputs. This study contributes to identification of the most appropriate set-up of fuzzy expert system which can be integrated with this framework to permit integrative safety performance evaluation for early warning provision and performance benchmarking. This paper presents a novel use of fuzzy expert system in safety performance measurement of offshore platforms which contributes significantly to the management and retention of experts' knowledge in decision-making pertaining to safety performance.