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 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

Applicability of artificial intelligence in e-commerce fashion platforms

A inovação tecnológica e a democratização da inteligência artificial (IA) têm vindo a alavancar o potencial de sucesso em todas as áreas que conhecemos hoje, com expectativas do que ainda está para vir. A presente dissertação propõe uma análise das aplicações da IA na indústria da moda, particularmente nas plataformas de marcas de moda do comércio eletrónico, e de que forma está a ter impacto na esfera pessoal do consumidor, particularmente no processo de tomada de decisão dos consumidores da Geração Z. O âmbito da IA tem vindo a evoluir de tal forma que permitiu às empresas não só melhorar a sua oferta e a procura dos clientes, como também proporcionar uma experiência de compra que vai para além da “seleção e compra” mecânica: os pontos de contacto impulsionados pela IA influenciam e enriquecem cada fase do processo de tomada de decisão, seja de forma mais positiva ou negativa. Em última análise, esta dissertação pretende proporcionar ao leitor um melhor conhecimento sobre a IA e o comércio eletrónico de moda, bem como delinear o seu impacto no comportamento online do consumidor.Technological innovation and democratization of artificial intelligence (AI) have been leveraging the potential success in every field we know today, while more is yet to come. The following dissertation proposes an analysis of AI achievements within the fashion industry, particularly in e-commerce fashion brand platforms, and how it is impacting the consumer personal sphere, particularly the decision-making process of Gen-Z consumers. The field of AI has been evolving in such a way that allows companies to not only improve their supply and customer demand, but also provide a shopping experience that goes beyond the mechanical “select and buy“: AI-driven touchpoints influence and enrich each stage of the decision-making process, whether more positively or negatively. Ultimately, this dissertation intends to provide the reader a better knowledge of AI and fashion e-commerce joining applications, and to delineate its impact on the online customer journey

AI in marketing, consumer research and psychology: A systematic literature review and research agenda

This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross-fertilization of theories used across fields, and neglected research topics

User centered neuro-fuzzy energy management through semantic-based optimization

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings

Big data and cloud computing: roles and relationships, techniques and tools

In the past years, the increase in data has been accompanied by rapid growth in various fields. It is difficult to analyze large volumes of data using traditional and relational database technology. Therefore, new databases have emerged, and for this reason, big data has become one of the new topics in IT and business today. Also, the cloud environment is increasingly used to store and process big data. Cloud processing refers to processing anything, including Big Data Analytics, on the "cloud". A "cloud" is a collection of high-powered servers from providers that can often view and query large data sets much faster than a regular computer. These two topics differ from each other in various aspects, including definition, collection references, usage method, form and format, and application. In this research, the dimensions and basic concepts, characteristics, tools and techniques, classification, and communication of data are examined. Big has been dealt with cloud computing, and in addition, storage systems, opportunities and challenges, and big data design principles in the cloud environment have been analyzed