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

Data Mining Practical Machine Learning Tools and Techniques

Theory in Practice: Increasing Professional Effectiveness

It sounds good when knowing the nature of managerial work in this website. This is one of the books that many people looking for. In the past, many people ask about this book as their favourite book to read and collect. And now, we present hat you need quickly. It seems to be so happy to offer you this famous book. It will not become a unity of the way for you to get amazing benefits at all. But, it will serve something that will let you get the best time and moment to spend for reading the book.

https://link.springer.com/content/pdf/10.1057%2Fjors.1974.92.pdf

The Nature of Managerial Work

The tourist: A new theory of the leisure class.

This context-aware emotion-based model can help design intelligent agents for group decision making processes. Experiments show that agents with emotional awareness reach agreement more quickly than those without it.

/pdf/context-aware-emotion-based-model-for-group-decision-making-3aa2nljgpf.pdf

Context-Aware Emotion-Based Model for Group Decision Making

Incorporating affective characteristics (such as personality, emotion, and mood) in an agent-based group decision-support system can help improve the negotiation process.

Personality, Emotion, and Mood in Agent-Based Group Decision Making

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

The proportion of the world’s population growing older is rapidly increasing over the last decades. With the recent progresses seen in information and communication technologies, there have been great concerns about developing personalized healthcare services that can ensure the living conditions and active aging of the elderly people. Among these technologies, we highlight and review in this work, the current state of gamification and related techniques applied to the elderly care context. Six research questions were defined to provide an overview on the current state in the development of gamified systems for elderly care through the identification of publication source types, research areas, target groups, game design elements and technologies employed and observed issues and benefits of using gamification in this context. Results have shown a great diversity in publication source types and research areas, even within the health domain. Different target groups were identified based on surrounding environment and physical and cognitive capabilities of the elderly person. Feedback, progression, rewards and social interaction enhancement are highlighted as the most relevant and frequently used game design elements for this context. Technological features observed include self-management systems, portable devices, physical robots, consoles and wearable technologies. The use of gamification techniques to support elderly people has proven to be beneficial to improve wellbeing as well as both physical, cognitive, social, and emotional state of the elderly person. Current challenges are most related with the need for traditional healthcare services to integrate gamification techniques to improve personalized healthcare and answer different necessities and adapt the support provided according to the individual capabilities of elderly people. The findings presented in this systematic literature review should be considered in the development of future personalized elderly care solutions by adapting the support provided according to interests, capabilities, necessities and contexts associated to the elderly person as a means to improve independence, health and wellbeing, capture interest and positive engagement, facilitate social interaction and decrease impact of many different medical conditions affecting older people.

A systematic review of gamification techniques applied to elderly care

Nowadays computing technology research is focused on the development of Smart Environments. Following that line of thought several Smart Rooms projects were developed and their appliances are very diversified. The appliances include projects in the context of workplace or everyday living, entertainment, play and education. These appliances envisage to acquire and apply knowledge about the environment state in order to reason about it so as to define a desired state for its inhabitants and perform adaptation adaptation to these desires and therefore improving their involvement and satisfaction with that environment.

/pdf/smart-offices-and-intelligent-decision-rooms-2rbueavrf7.pdf

Goreti Marreiros

Papers

Context-Aware Emotion-Based Model for Group Decision Making

Personality, Emotion, and Mood in Agent-Based Group Decision Making

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

A systematic review of gamification techniques applied to elderly care

Smart Offices and Intelligent Decision Rooms