CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Crowdsourcing traffic data for travel time estimation

Travel time estimation is a fundamental measure used in routing and navigation applications, in particular in emerging intelligent transportation systems (ITS). For example, many users may prefer the fastest route to their destination and would rely on real-time predicted travel times. It also helps real-time traffic management and traffic light control. Accurate estimation of travel time requires collecting a lot of real-time data from road networks. This data can be collected using a wide variety of sources like inductive loop detectors, video cameras, radio frequency identification (RFID) transponders etc. But these systems include deployment of infrastructure which has some limitations and drawbacks. The main drawbacks in these modes are the high cost and the high probability of error caused by prevalence of equipment malfunctions and in the case of sensor based methods, the problem of spatial coverage.;As an alternative to traditional way of collecting data using expensive equipment, development of cellular & mobile technology allows for leveraging embedded GPS sensors in smartphones carried by millions of road users. Crowd-sourcing GPS data will allow building traffic monitoring systems that utilize this opportunity for the purpose of accurate and real-time prediction of traffic measures. However, the effectiveness of these systems have not yet been proven or shown in real applications. In this thesis, we study some of the current available data sets and identify the requirements for accurate prediction. In our work, we propose the design for a crowd-sourcing traffic application, including an android-based mobile client and a server architecture. We also develop map-matching method. More importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key role in estimation in the last two decades. They are proved by providing better accuracy in estimation and in classification. However, employing a machine learning technique in any application requires creative modeling of the system and its sensory data. In this thesis, we model the road network as a graph and train different models for different links on the road. Modeling a road network as graph with nodes and links enables the learner to capture patterns occurring on each segment of road, thereby providing better accuracy. To evaluate the prediction models, we use three sets of data out of which two sets are collected using mobile probing and one set is generated using VISSIM traffic simulator. The results show that crowdsourcing is only more accurate than traditional statistical methods if the input values for input data are very close to the actual values. In particular, when speed of vehicles on a link are concerned, we need to provide the machine learning model with data that is only few minutes old; using average speed of vehicles, for example from the past half hour, as is usually seen in many web based traffic information sources may not allow for better performance

Data mining in computational finance

Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset

Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

An Empirical Analysis of the Forecast of Corporate Financial Distress in the European Energy Sector

openExploring the causes of corporate financial distress has been a topic of extensive discussion and research in the field of finance. Over the years, scholars and experts have dedicated their efforts to unraveling the intricacies behind financial struggles faced by businesses. The enduring interest in this subject can be attributed to the profound consequences that corporate financial distress can bring. When a company finds itself in a state of financial distress, it often marks a critical turning point that could lead to insolvency or even bankruptcy. This represents the ultimate failure of the company and has wide-ranging impacts that go beyond its immediate boundaries. Employees are affected by potential job losses, stakeholders face financial losses, connected companies may experience disruptions in their operations, and the overall economy can suffer. The costs associated with corporate financial distress are substantial and can take different forms. Direct costs include expenses related to legal proceedings, asset liquidation, and settling outstanding debts. Indirect costs can arise from the erosion of the company's reputation, diminished investor confidence, restricted access to credit, and the ripple effect felt throughout the supply chain. Given the prevalence and far-reaching consequences of corporate financial distress, researchers and experts have delved into the topic with great fervor. Their aim is to develop models, methodologies, and strategies that can help identify early warning signs of financial distress and enable proactive measures to be taken. By doing so, they seek to protect companies from the brink of failure and promote stability and growth in the broader economy. The study of corporate financial distress has yielded valuable insights into the various factors that contribute to these challenges. Researchers have examined aspects such as poor financial management practices, ineffective governance structures, unfavorable economic conditions, industry-specific challenges, and vulnerabilities unique to individual companies. Ultimately, the research conducted in this field not only sheds light on the causes and consequences of corporate financial distress but also strives to provide guidance for companies, investors, and policymakers. By understanding the dynamics of financial distress, stakeholders can make informed decisions, implement preventive measures, and contribute to the resilience and success of businesses in the face of adversity.Exploring the causes of corporate financial distress has been a topic of extensive discussion and research in the field of finance. Over the years, scholars and experts have dedicated their efforts to unraveling the intricacies behind financial struggles faced by businesses. The enduring interest in this subject can be attributed to the profound consequences that corporate financial distress can bring. When a company finds itself in a state of financial distress, it often marks a critical turning point that could lead to insolvency or even bankruptcy. This represents the ultimate failure of the company and has wide-ranging impacts that go beyond its immediate boundaries. Employees are affected by potential job losses, stakeholders face financial losses, connected companies may experience disruptions in their operations, and the overall economy can suffer. The costs associated with corporate financial distress are substantial and can take different forms. Direct costs include expenses related to legal proceedings, asset liquidation, and settling outstanding debts. Indirect costs can arise from the erosion of the company's reputation, diminished investor confidence, restricted access to credit, and the ripple effect felt throughout the supply chain. Given the prevalence and far-reaching consequences of corporate financial distress, researchers and experts have delved into the topic with great fervor. Their aim is to develop models, methodologies, and strategies that can help identify early warning signs of financial distress and enable proactive measures to be taken. By doing so, they seek to protect companies from the brink of failure and promote stability and growth in the broader economy. The study of corporate financial distress has yielded valuable insights into the various factors that contribute to these challenges. Researchers have examined aspects such as poor financial management practices, ineffective governance structures, unfavorable economic conditions, industry-specific challenges, and vulnerabilities unique to individual companies. Ultimately, the research conducted in this field not only sheds light on the causes and consequences of corporate financial distress but also strives to provide guidance for companies, investors, and policymakers. By understanding the dynamics of financial distress, stakeholders can make informed decisions, implement preventive measures, and contribute to the resilience and success of businesses in the face of adversity

Identification and characterization of diseases on social web

[no abstract