Face detection is the task of detecting faces on photos, videos as well as the streaming data such as a webcam. Face detection, which is a specific type of general-purpose object detection, is a key prerequisite for many other artificial intelligence tasks such as face verification, face tagging and retrieval, and face tracking. In addition to that, nowadays, face detection is commonly used in daily routines such as social media, and camera software of smartphones. As a result of this necessity, several face detection tools have been proposed. In this study, an experimental performance comparison of well-known face detection tools in terms of (1) accuracy, and (2) elapsed time of detection, which has become even more critical criteria especially when the face detection mechanism is utilized for a real-time system, is proposed. As a result of this experimental study, it is aimed that shed light on the much-concerned query “which face detection tool provides the best performance?”. In addition to that, this study succeeds in showing that convolutional neural networks achieve great accuracy for face detection.

Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person’s interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI).In our research, we are going to use Machine Learning Algorithm specially focus on Linear Regression (LR), Three month Moving Average(3MMA), Exponential Smoothing (ES) and Time Series Forecasting using MS Excel as best statistical tool for graph and tabular representation of prediction results. We obtained data from Yahoo Finance for Amazon (AMZN) stock, AAPL stock and GOOGLE stock after implementation LR we successfully predicted stock market trend for next month and also measured accuracy according to measurements.

Industry 4.0 is the new industrial stage that is committed to greater automation, connectivity and globalization. The interrelation between the different areas has penetrated the industrial world thanks to the Internet of things and the world of Big Data. This amount of information available in plants is growing increasingly, also aided by the network computing services offered by cloud computing or edge computing. That is why it’s necessary to carry out complex fusion methods and data analysis using Machine Learning techniques to address specific industrial requirements and needs. The central challenge of industry 4.0 from the perspective of data science is to predict the history within the monitored processes, providing as much information as possible, avoiding them and stave off severe economic losses. This article will show a review of the application of Artificial Intelligence (AI) techniques such as Machine Learning (ML) immersed in multi-agent systems (MAS) in Industry 4.0. For this, a bibliographic search has been carried out in databases recognized as Science Direct, Google Scholar, Scopus or Springer, filtering the investigations from 2018 to the actually. The article concludes by pointing out the possible future lines and the importance of the transition towards the implementation of new technologies for the competitiveness of factories.

Global warming affects us all, that is why we must all act to stop it. It has been shown that this undoubted problem can be solved to a large extend if we make small individual efforts. How can we do this? Making prudent use of electricity. If we manage to make more efficient use of the energy we consume in our homes, we will contribute enormously in this common cause. With the help of virtual agents, we will get a better management of the energy we consume.

A multi-agent system is proposed that simulates a network of vehicle rental stations in a city. The paper studies the relationship between the agents and the client, analyses the casuistry associated with possible problems that may be encountered in the absence of transport in a given stop, as well as the decisions that could be taken by the interested party. Subsequently, an architecture capable of being scalable in terms of functionalities and the number of agents involved in it will be proposed. The aim of this paper is to revise the original paper, which is more focused on the possibility of studying a particular city, raising and solving the problems associated with public vehicle sharing services.

This paper builds a data mining tool via a classification method using Multi-Layer Perceptron (MLP) with Backpropagation learning method and an algorithm of feature selection along with biomedical testing values for diagnosing heart disease. Addition to that, developing a prototype for heart disease diagnosing with a friendly-user graphical interface (GUI). The purpose to construct this software is that; clinical prosopopoeia is done in any event by doctor’s experience. Despite that, some cases are reported negative diagnosis and treatment; therefore, patients are asked to take a number of tests for diagnosis. Moreover, not all the tests contribute towards an effective diagnosis of a disease, and by using data mining approach to diagnose heart disease that supports the doctors to make more efficient and subtle decisions.

This paper addresses the robust formation control of non-holonomic mobile robots with homogeneous system architecture and decentralized control structure. Therefore, it was necessary the mathematical modeling of mobile robots, from which, the Separation-Bearing variant of Leader-Following control strategy was implemented. The stability proof were based on the Lyapunov theory. The sliding mode control (SMC) strategy was used in the controller design to make the control robust to the incidence of uncertainties and disturbances. The Fuzzy Adaptive Formation Control is designed to eliminate the previous bounding knowledge of these uncertainties and disturbances. The proposed control effectiveness is demonstrated by results obtained with simulations in Matlab/Simulink. The pure kinematic and kinematic with disturbances is also analyzed. The results shows the controllers effectiveness to formation of multi-robots systems to the eight-shaped trajectory.

The increasing use and development of renewable energy sources and distributed generation, brought several changes to the power system operation. Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. This paper proposes a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, applied to load values, submitted to different types of normalization process, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage units

The human body has endogenous biological rhythms. One of those rhythms is the circadian rhythm, with a cycle of approximate 24 hours, that is responsible for regulating many important functions of human body. In this contribution, we propose a nonlinear coupled differential equation for modelling some agents responsible for the circadian rhythm under the influence of pain. We show results concerning the well-posedness of a solution as well as an analysis on the synchronization for each agents. We use a multi-agent simulation strategy of an corresponding model as an alternative interpretation of the pain influences in the behavior of the circadian rhythm.