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

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

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

PUBLIC OPINION ANALYSIS BASED ON PROBABILISTIC TOPIC MODELING AND DEEP LEARNING

With the rapid development of Internet, especially the social media technologies, the public have gradually published their perception of social events online through social media. In Web2.0 era, with the concept of extensive participation of public in social-event-related information sharing, the effective content analysis and better results presentation for these media published online thus possesses significant importance for public opinion analysis and monitoring. In view of this, this paper proposes a novel method for public opinion analysis on social media website. First, the probabilistic topic model of Latent Dirichlet Allocation (LDA) is adopted to extract the public ideas about the distinct topics of certain event, and then the deep learning model named word2vec is used to calculate the emotional intensity for each text. Next, the underlying themes in the whole as well as the events of emotional intensity are investigated, and the variation trend of public’s emotion intensities is tracked based on time series analysis. Finally, the rationality and effectiveness of the method are verified with the analysis of a real case

A Comparative Emotions-detection Review for Non-intrusive Vision-Based Facial Expression Recognition

Affective computing advocates for the development of systems and devices that can recognize, interpret, process, and simulate human emotion. In computing, the field seeks to enhance the user experience by finding less intrusive automated solutions. However, initiatives in this area focus on solitary emotions that limit the scalability of the approaches. Further reviews conducted in this area have also focused on solitary emotions, presenting challenges to future researchers when adopting these recommendations. This review aims at highlighting gaps in the application areas of Facial Expression Recognition Techniques by conducting a comparative analysis of various emotion detection datasets, algorithms, and results provided in existing studies. The systematic review adopted the PRISMA model and analyzed eighty-three publications. Findings from the review show that different emotions call for different Facial Expression Recognition techniques, which should be analyzed when conducting Facial Expression Recognition. Keywords: Facial Expression Recognition, Emotion Detection, Image Processing, Computer Visio

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced