Big data analytics correlation taxonomy

Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries

Big data characteristics (V’s) in industry

In the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from

Blockchain-empowered AI for 6G-enabled internet of vehicles

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network

A new vision of a simple 1D Convolutional Neural Networks (1D-CNN) with Leaky-ReLU function for ECG abnormalities classification

Artificial Intelligence (AI) is increasingly impacting the healthcare field, due to its computational power that reduces time, cost and efforts for both healthcare professionals and patients. Diagnosing cardiac abnormalities using AI represents a very attractive subject for both medical and technical professionals. Cardiac abnormalities are characterized by the ECG signal, which is known by its variable morphology and intense affection by noises and artifacts. In this context, the presented study aims to propose a simple yet efficient version of Convolutional Neural Networks (CNN) to classify those abnormalities. This version increases the ability to detect several heart rate arrhythmias and severe cardiac abnormalities based only on the original 1D format of the ECG signal, which reserve the main feature of this signal and can be very suitable for ready-to-use and real-time applications. The main used training datasets are the MIT-BIH arrhythmias and the PTB databases. The proposed architectures are mainly inspired by the most recent CNN models and introduce several modifications on functions and layers, such as the use of the Leaky-ReLU instead of the ReLU activation function. The results of the proposed model are varying from an accuracy of 97%–99% in classifying Normal (n), Supraventricular (s), Ventricular (v), Fusion of ventricular and normal (f), and noisy (q) beats, in addition to the Myocardial Infarction (MI) case. A continuous performance was achieved while testing the model on real data, and after its migration to real mobile devices

Ontologias para deteção de fraude em meio académico

Este trabalho tem como objetivo principal o desenvolvimento de ontologias para a deteção de fraude académica e a aplicação de métodos quantitativos indicadores do desenvolvimento de um modelo que caminhe no sentido correto para a resolução do problema. Inicialmente e pelo facto de se tratar de um tema pouco explorado, é feita uma extensiva introdução conceptual e histórica, passando pelos principais conceitos da Web Semântica, por forma a fazer uma interiorização (pessoal e também ao leitor) das bases necessárias para desenvolver e compreender o trabalho desenvolvido, respetivamente. A abordagem teórica passa por um estado da arte, onde são discutidos trabalhos e conceitos relacionados, seguindo depois para a explicação do funcionamento geral da própria Web Semântica, com grande foco em “graph databases“ e nos seus conceitos. É construído um modelo inicial, uma “graph database“ populada manualmente, fazendo uso da ferramenta “Neo4j“ e depois ajustado conforme as necessidades do problema. Por fim são aplicados algoritmos sobre esse modelo, obtendo resultados objetivos e que estão intrínsecamente ligados aos objetivos propostos inicialmente, cumprindo-os de uma forma geral, dado que foram encontrados os elementos com mais influência relativamente a outros elementos do sistema, sendo um princípio fundamental constituinte de fraude académica.The main objective of this work is the development of ontologies for the detection of academic fraud and the application of quantitative methods that are indicators of the development of a model that moves in the right direction to solve the problem. Initially, and due to the fact that it is a little explored theme, an extensive conceptual and historical introduction is made, passing through the main concepts of the Semantic Web, in order to make an internalization (personal and also to the reader) of the necessary bases to develop and understand the work developed, respectively. The theoretical approach goes through a state of the art, where works and related concepts are discussed, followed by an explanation of the general functioning of the Semantic Web itself, with great focus on “graph databases“ and its concepts. An initial model is built, a manually populated “graph database“, using the “Neo4j“ tool and then adjusted according to the needs of the problem. Finally, algorithms are applied on this model, obtaining objective results that are intrinsically linked to the initially proposed objectives, fulfilling them in a general way, since the elements with more influence over other elements of the system were found, being a fundamental principle constituent of academic fraud

Sistema de modelos para la predicción de pago de cuotas anticipadas a préstamos / modelos de “mejor próxima oferta” en productos bancarios

RESUMEN PRÁCTICA PROFESIONAL I Uno de los problemas que afrontan las entidades financieras al otorgar créditos es el de la posibilidad de que el cliente cancele anticipadamente el préstamo que se le haya concedido, o que realice pagos anticipados. Por ello, como una forma de prevenir dicho problema, en este estudio se proponen diversos métodos que permiten ajustar modelos matemáticos útiles para estimar el riesgo de que se presenten esos casos. Además, se identifican aquellas variables que captan los sistemas de información de las entidades financieras y que se relacionan con los clientes a quienes se les señale esa práctica. Entre los modelos ajustados se tienen los modelos de supervivencia, para el caso de los clientes que efectúan la cancelación anticipada de sus préstamos, y los modelos de regresión logística para los que hacen pagos anticipados. Para estos últimos se obtuvieron tasas de clasificación correcta de hasta 89,4%. A la vez se identificaron grupos con un riesgo de cancelación y de pago anticipado altos, lo cual le permite a la entidad financiera saber cómo minimizar la cantidad de pagos o de cancelaciones anticipadas de las operaciones comerciales; o, en algunos escenarios, buscar cómo colocar el dinero en otras operaciones para de esta forma obtener un mejor rendimiento.RESUMEN PRÁCTICA PROFESIONAL II Las entidades bancarias, como cualquier negocio en la actualidad, tienen la necesidad de rentabilizar a sus clientes y una forma de lograrlo es mediante la venta cruzada de productos, la cual se puede optimizar a partir de modelos de recomendación, permitiendo a cada cliente priorizar el producto a recomendar, basado en la probabilidad de su aceptación. Este método puede aprovechar los momentos de contacto con clientes, para realizar una venta cruzada de los mismos. El estudio realizado busca establecer un sistema de recomendación de productos a clientes bancarios que permitan desarrollar un proceso de ventas eficiente en futuras implementaciones, con las que se busque mejorar la práctica de ventas cruzada en la entidad financiera que lo requiera. Para lograr esto se analizan diferentes aproximaciones, entre las que se considera una adaptación del modelo XGBoost y filtrados colaborativos basados en contenido e ítems mediante un caso de estudio en el ambiente bancario, tomando datos disponibles en línea de la página de Kaggle Inc (2021). Este caso considera productos pasivos, activos y otros servicios financieros que tradicionalmente se ofrecen en las entidades bancarias, además de una gamma de características del cliente bancario que se aprovechan para determinar similitudes entre ellos, lo que aporta a la recomendación. Se identifican tres métodos que pueden ser utilizados como sistema de recomendación, de los cuales el algoritmo propuesto en esta investigación, “Próxima mejor oferta” y “recommenderlab” de Hahsler (2021) ofrecen una solución atractiva para el caso específico de estudio.UCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ciencias Sociales::Maestría Profesional en Estadístic

A decision-making tool for real-time prediction of dynamic positioning reliability index

PhD ThesisThe Dynamic Positioning (DP) System is a complex system with significant levels of integration between many sub-systems to perform diverse control functions. The extent of information managed by each sub-system is enormous. The sophisticated level of integration between sub-systems creates an array of possible failure scenarios. A systematic analysis of all failure scenarios would be time-consuming and for an operator to handle any such catastrophic situation is hugely demanding. There are many accidents where a failure in a DP system has resulted in fatalities and environmental pollution. Therefore, the reliability assessment of a DP system is critical for safe and efficient operation. The existing methods are time-consuming, involving a lot of human effort which imposes built-in uncertainty and risk in the system during complex operation. This thesis has proposed a framework for a state-of-the-art decision-making tool to assist an operator and prevent incidents by introducing a new concept of Dynamic Positioning – Reliability Index (DP-RI). The DP-RI concept covers three phases, leading to technical suggestions for the operator during complex operations, which are defined as Data, Knowledge, Intelligence, and Action. The proposed framework covers analytics including descriptive, diagnostic, predictive and prescriptive analytics. The first phase of the research involves descriptive and diagnostic analytics by performing big data analytics on the available databases to identify the sub-systems which play critical roles in DP system functionality. The second phase of the research involves a novel approach where predictive analytics are used for the weight assignment of the sub-systems, dynamic reliability modelling and offline and realtime forecasting of DP-RI. The third phase introduces innovative prescriptive analytics to provide possible technical solutions to the operator in a short time during failures in the system to enable them to respond quickly and prevent DP incidents. Thus, the DP-RI acts as an innovative state-of-the-art decision-making tool which can suggest possible solutions to the DPO by using analytics on the knowledge database. The results proved that it is a useful tool if implemented on an actual vessel with diligent integration with the DP control system.Singapore Economic Development Board (EDB) and DNV GL Singapore Pte Ltd