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

An investigation into pilot and system response to critical in-flight events. Volume 2: Appendix

Materials relating to the study of pilot and system response to critical in-flight events (CIFE) are given. An annotated bibliography and a trip summary outline are presented, as are knowledge surveys with accompanying answer keys. Performance profiles of pilots and performance data from the simulations of CIFE's are given. The paper and pencil testing materials are reproduced. Conditions for the use of the additive model are discussed. A master summary of data for the destination diversion scenario is given. An interview with an aircraft mechanic demonstrates the feasibility of system problem diagnosis from a verbal description of symptoms and shows the information seeking and problem solving logic used by an expert to narrow the list of probable causes of aircraft failure

The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance

Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision.  Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness.  The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively.  A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process

Fake Review Detection using Data Mining

Online spam reviews are deceptive evaluations of products and services. They are often carried out as a deliberate manipulation strategy to deceive the readers. Recognizing such reviews is an important but challenging problem. In this work, I try to solve this problem by using different data mining techniques. I explore the strength and weakness of those data mining techniques in detecting fake review. I start with different supervised techniques such as Support Vector Ma- chine (SVM), Multinomial Naive Bayes (MNB), and Multilayer Perceptron. The results attest that all the above mentioned supervised techniques can successfully detect fake review with more than 86% accuracy. Then, I work on a semi-supervised technique which reduces the dimension- ality of the input features vector but offers similar performance to existing approaches. I use a combination of topic modeling and SVM for the implementation of the semi-supervised tech- nique. I also compare the results with other approaches that consider all the words of a dataset as input features. I found that topic words are enough as input features to get similar accuracy compared to other approaches where researchers consider all the words as input features. At the end, I propose an unsupervised learning approach named as Words Basket Analysis for fake re- view detection. I utilize five Amazon products review dataset for an experiment and report the performance of the proposed on these datasets

An investigation into pilot and system response to critical in-flight events, volume 2

Critical in-flight event is studied using mission simulation and written tests of pilot responses. Materials and procedures used in knowledge tests, written tests, and mission simulations are include

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Temporal Dynamism in Country-of-Origin Effect: The Malleability of Italians’ Perceptions Regarding the British Sixties

© Emerald Publishing Limited 2019. This accepted manuscript is deposited under the Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0). Any reuse is allowed in accordance with the terms outlined by the licence, here: https://creativecommons.org/licenses/by-nc/4.0/. To reuse the AAM for commercial purposes, permission should be sought by contacting [email protected]: The purpose of this paper is to enrich country of origin (COO) effect in international marketing theory by adding the understanding of temporal dynamism into COO research. Design/methodology/approach: Utilizing a qualitative and interdisciplinary phenomenological approach, this paper analyses historical and contemporary sources triangulated with contemporary primary interview data. The example of how perceptions of Italians about the values typical of the British Sixties varied over time periods is presented. Findings: COO perceptions are both malleable and in evolution. Results show that values from earlier peak periods of appeal can be combined and recombined differently over time due to the varying historical and contemporary resonances of COO values. Research limitations/implications: This study focuses on COO applied to two product areas, fashion and music, over a limited time period, in a two-country study and so the findings are not fully generalizable, but rather are transferable to similar contexts. Practical implications: The fact that COO is neither static nor atemporal facilitates a segmented approach for international marketing managers to review and renew international brands. This enriched COO theory provides a rich and variable resource for developing and revitalizing brands. Originality/value: The major contribution of this paper is that temporal dynamism, never before discussed in international marketing theory, renders COO theory more timeless; this addresses some critiques recently made about its relevance and practicality. The second contribution is the original research design that models interdisciplinary scholarship, enabling a thorough historical look at international marketing.Peer reviewe