Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Models and metaphors: complexity theory and through-life management in the built environment

Complexity thinking may have both modelling and metaphorical applications in the through-life management of the built environment. These two distinct approaches are examined and compared. In the first instance, some of the sources of complexity in the design, construction and maintenance of the built environment are identified. The metaphorical use of complexity in management thinking and its application in the built environment are briefly examined. This is followed by an exploration of modelling techniques relevant to built environment concerns. Non-linear and complex mathematical techniques such as fuzzy logic, cellular automata and attractors, may be applicable to their analysis. Existing software tools are identified and examples of successful built environment applications of complexity modelling are given. Some issues that arise include the definition of phenomena in a mathematically usable way, the functionality of available software and the possibility of going beyond representational modelling. Further questions arising from the application of complexity thinking are discussed, including the possibilities for confusion that arise from the use of metaphor. The metaphor of a 'commentary machine' is suggested as a possible way forward and it is suggested that an appropriate linguistic analysis can in certain situations reduce perceived complexity

An intelligent recommendation system framework for student relationship management

In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Psycho-Physiologically-Based Real Time Adaptive General Type 2 Fuzzy Modelling and Self-Organising Control of Operator's Performance Undertaking a Cognitive Task

—This paper presents a new modelling and control fuzzy-based framework validated with real-time experiments on human participants experiencing stress via mental arithmetic cognitive tasks identified through psycho-physiological markers. The ultimate aim of the modelling/control framework is to prevent performance breakdown in human-computer interactive systems with a special focus on human performance. Two designed modelling/control experiments which consist of carrying-out arithmetic operations of varying difficulty levels were performed by 10 participants (operators) in the study. With this new technique, modelling is achieved through a new adaptive, self-organizing and interpretable modelling framework based on General Type-2 Fuzzy sets. This framework is able to learn in real-time through the implementation of a re-structured performance-learning algorithm that identifies important features in the data without the need for prior training. The information learnt by the model is later exploited via an Energy Model Based Controller that infers adequate control actions by changing the difficulty level of the arithmetic operations in the human-computer-interaction system; these actions being based on the most current psycho-physiological state of the subject under study. The real-time implementation of the proposed modelling and control configurations for the human-machine-interaction under study shows superior performance as compared to other forms of modelling and control, with minimal intervention in terms of model re-training or parameter re-tuning to deal with uncertainties, disturbances and inter/intra-subject parameter variability

Baymax- Your Mental Health Care Companion - An Artificial Intelligence based Chat bot for Mental Health Care

Artificial intelligence (AI) technologies and techniques have useful purposes in every domain of mental health care including clinical decision-making, treatments, assessment, self-care, mental health care management and more. Recent technological innovations are highlighted to demonstrate capabilities and opportunities. This application involves an AI based Expert System which can significantly contribute to improving mental health of an individual to lead a better life without any stress or melancholy. The expert system provides expert advice and therapy to overcome negative thoughts. This app can also help to reduce the number of suicides caused due to extreme depression. It is about virtual human conversation with the system to support user’s interaction within a mental health care context. It provides private online healthcare guidance and support where the app can serve the role of a clinician or a psychotherapist.It uses Smartphone technology particularly relevant for applications in Mental health. Recent advances in artificial intelligence are providing an unprecedented ability of online mental health care research and clinical organizations to collect and analyse data that is broader in scope. This application provides a system capable of calculating the depression level using Fuzzy Logic Controller. It sends an alert message to user’s acquaintance thereby preventing the user from causing harm to himself. It tries to imbibe happy thoughts and optimism into the user. Thus, this system can have a meaningful impact on people’s lives by improving their mental health

Smart wearable stress monitoring device for autistic children

Vital sign monitoring is the process of recording human physiological signals in order to determine the mental stress level. High stress levels can prove tobe dangerous especially for certain individuals such as autistic children who are not able to express mounting levels of stress before it leads to a full anxiety attack. This paper presents the prototype design of a real-time embedded device that accurately measures heart rate and galvanic skin response (GSR) in a non-invasive and non-intrusive way which is then used by the intelligent decision making module that uses fuzzy logic to determine the stress level of the user. Such a device could be used with autistic children in order to give early warning of an impending anxiety attack and help adults to prevent it from happening. The prototype was designed using Arduino mega platform and tested with 35 clinical patients in three experimental settings targeted to induce low stress, medium stress and high stress response. Initial results have shown that the device is capable of detecting and displaying the various stress levels efficiently

Fuzzy Human Reliability Analysis: Applications and Contributions Review

The applications and contributions of fuzzy set theory to human reliability analysis (HRA) are reassessed. The main contribution of fuzzy mathematics relies on its ability to represent vague information. Many HRA authors have made contributions developing new models, introducing fuzzy quantification methodologies. Conversely, others have drawn on fuzzy techniques or methodologies for quantifying already existing models. Fuzzy contributions improve HRA in five main aspects: (1) uncertainty treatment, (2) expert judgment data treatment, (3) fuzzy fault trees, (4) performance shaping factors, and (5) human behaviour model. Finally, recent fuzzy applications and new trends in fuzzy HRA are herein discussed

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars