Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An Introduction to MultiAgent Systems.

Phenolics and polyphenolics in foods, beverages and spices: Antioxidant activity and health effects – A review

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Many real-world applications are mapped into combinatorial problems. An example of such problem is timetable scheduling. In this case, the two basic characteristics can be defined by its distributed and dynamic environment. One efficient solution to solve this problem could be provided by an agent-based approach. A timetable scheduling problem can be modelled as a multi-agent system that provides the final schedule by taken into account all the restrictions. In this paper it is presented a preliminary research work that involves the development of a multi-agent system for university course timetable scheduling, named MAS_UP-UCT. We focus on the architecture of the multi-agent system, and on the evaluation of the communication process by using the interaction diagrams.

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MAS_UP-UCT: A Multi-Agent System for University Course Timetable Scheduling

The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters that were computed are root mean square error (RMSE) and mean absolute error (MAE).

A comparative study of computational intelligence techniques applied to PM2.5 air pollution forecasting

The paper describes an environment quality analysis system based on a combination of some artificial intelligence techniques, artificial neural networks and rule-based expert systems. Two case studies of the system use are discussed: air pollution analysis and flood forecasting with their impact on the environment and on the population health. The system can be used by an environmental decision support system in order to manage various environmental critical situations (such as floods and environmental pollution), and to inform the population about the state of the environment quality.

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An Artificial Intelligence-Based Environment Quality Analysis System

The paper describes an ontology for air pollution analysis and control, AIR_POLLUTION_Onto, and presents its use in two case studies, an expert system, and a multiagent system, both dedicated to monitoring and control of air pollution in urban regions

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AIR_POLLUTION_Onto: an Ontology for Air Pollution Analysis and Control

The paper presents details on the development of an intelligent system for particulate matter (PM) air pollution monitoring, analysis and forecasting in two pilot cities, Ploiesti and Targoviste, in selected areas near schools, kindergartens and pediatric hospitals. The main purpose of the system is to provide expert early warnings to protect children with health problems. An in-situ PM 10 , PM 2.5 monitoring network was designed and an online PM 2.5 monitoring network is under development for each city. Two case studies of PM air pollution analysis and forecast for the Ploiesti city are described. The PM air pollution analysis, forecasting, and children health impact analysis were made by using techniques from statistics and artificial intelligence.

Mihaela Oprea

Papers

MAS_UP-UCT: A Multi-Agent System for University Course Timetable Scheduling

A comparative study of computational intelligence techniques applied to PM2.5 air pollution forecasting

An Artificial Intelligence-Based Environment Quality Analysis System

AIR_POLLUTION_Onto: an Ontology for Air Pollution Analysis and Control

On the development of an intelligent system for particulate matter air pollution monitoring, analysis and forecasting in urban regions