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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://cyberleninka.org/article/n/854933.pdf

Social media analytics – Challenges in topic discovery, data collection, and data preparation

Using online social networks to track a pandemic: A systematic review.

Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.

/pdf/survey-on-ai-based-multimodal-methods-for-emotion-detection-16kedanj54.pdf

Survey on AI-Based Multimodal Methods for Emotion Detection

The quantity of medical publications in the media such a tweets, blogs, and another content in social media is growing at an exponential rate. Exploring and analyzing this content has become a necessity. The objective of this paper is to discuss the variety of issues and challenges surrounding the perspectives regarding the use of Social Network Analyses and Text Mining methods for applications in E health and medecine. The article will first look at the directions taken in Social Media and Text Mining for medical science. First we present a review of the literature in Social Media and Text Mining analysis for medical purposes and then describe the work done for our prediction system for collecting and manipulating and Twitter data.

Social media analysis for e-health and medical purposes

This paper describes the functions of a system designed for the assessment of movie reviews. Such a system enables the automatic collection, evaluation and rating of film criticspsila opinions of movies. First the system searches and retrieves probable movie reviews from the Internet, especially those expressed by prolific reviewers. Subsequently the system carries out an evaluation and rating of those movie reviews. Finally the system automatically associates a numerical mark to each review, this is the objective of the system. This data constitutes the input to the cognitive engine. Our system uses three different methods for classifying opinions in criticspsila reviews. We introduce two new methods based on linguistic knowledge. Results are then compared with the overall statistical method using Bayes classifier. The last step is to combine the results obtained in order to make the final assessment as accurately as possible.

An Autonomous System Designed for Automatic Detection and Rating of Film Reviews

This paper describes the functions of a system proposed for the music tube recommendation from social network data base. Such a system enables the automatic collection, evaluation and rating of music critics, the possibility to rate music tube by auditors and the recommendation of tubes depended from auditor's profiles in form of regional internet radio. First, the system searches and retrieves probable music reviews from the Internet. Subsequently, the system carries out an evaluation and rating of those reviews. From this list of music tubes the system directly allows notation from our application. Finally the system automatically create the record list diffused each day depended form the region, the year season, day hours and age of listeners. Our system uses linguistics and statistic methods for classifying music opinions and data mining techniques for recommendation part needed for recorded list creation. The principal task is the creation of popular intelligent radio adaptive on auditor's age and region - IA-Regional-Radio.

/pdf/social-network-an-autonomous-system-designed-for-radio-2f3b195ax1.pdf

Social Network - An Autonomous System Designed for Radio Recommendation

This paper describes the part of recommender system designed for movies' critics recognition. Such a system allows the automatic collection, evaluation and rating of critics and opinions of the movies. First the system searches and retrieves texts supposed to be movies' reviews from the Internet. Subsequently the system carries out an evaluation and rating of movies' critics. Finally the system automatically associates a numerical mark to each critic. The goal of system is to give the score of critics associated to the users' who wrote them. All of this data are the input to the cognitive engine. Data from our base allow making correspondences which are required for cognitive algorithms to improve advanced recommending functionalities for e-business and e-purchases websites. Our sesystem uses three different methods for classifying opinions from reviews critics. In this paper we describe the part of system which is based on automatically identifying opinions using natural language processing knowledge.

/pdf/tool-of-the-intelligence-economic-recognition-function-of-4ojlpkvbf9.pdf

Tool of the Intelligence Economic: Recognition Function of Reviews Critics. Extraction and linguistic Analysis of sentiments.

This paper describes the part of a recommendation system designed for the recognition of film reviews (RRSS). Such a system allows the automatic collection, evaluation and rating of reviews and opinions of the movies. First the system searches and retrieves texts supposed to be movie reviews from the Internet. Subsequently the system carries out an evaluation and rating of the movie reviews. Finally, the system automatically associates a digital assessment with each review. The goal of the system is to give the score of reviews associated with the user who wrote them. All of this data is the input to the cognitive engine. Data from our base allows the making of correspondences, which are required for cognitive algorithms to improve, advanced recommending functionalities for e-business and e-purchase websites. In this paper we will describe the different methods on automatically identifying opinions using natural language knowledge and techniques of classification.

/pdf/rrss-rating-reviews-support-system-purpose-built-for-movies-6yz8r70l5t.pdf

Grzegorz Dziczkowski

Papers

Social media analysis for e-health and medical purposes

An Autonomous System Designed for Automatic Detection and Rating of Film Reviews

Social Network - An Autonomous System Designed for Radio Recommendation

Tool of the Intelligence Economic: Recognition Function of Reviews Critics. Extraction and linguistic Analysis of sentiments.

RRSS - Rating Reviews Support System Purpose Built for Movies Recommendation