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

Charlotte: metacomputing on the Web

To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

In summary, the essence of our proposition, presented in this chapter, is as follows: a distributed object-oriented programming model, smoothly extended to get a component-based programming model (in the form of a 100% Java library); moreover this model is “grid-aware” in the sense that it incorporates from the very beginning adequate mechanisms in order to further help in the deployment and runtime phases on all possible kind of infrastructures, notably secure grid systems. This programming framework is intended to be used for large scale grid applications. For instance, we have succeeded to apply it for a numerical simulation of electromagnetic waves propagation, a non embarrassingly parallel application [21], featuring visualization and monitor- ing capabilities for the user. To date, this simulation has successfully been deployed on various infrastructures, ranging from interconnected clusters, to an intranet grid composed of approxi- matively 300 desktop machines. Performances compete with a previous existing version of the application, written in Fortran MPI. The proposed object-oriented approach is more generic and features reusability (the component-oriented version is under development, which may further add dynamicity to the application), and the deployment is very flexible.

/pdf/programming-composing-deploying-for-the-grid-23rajv0waz.pdf

Programming, Composing, Deploying for the Grid

Design Issues in Building Web-Based Programming Environments.

World-wide virtual machine: a metacomputing environment integrating world wide web and high performance computing and communications technologies

This paper focuses on how recently announced modern programmable and smart residential thermostats for smart homes can contribute to better energy usage in households. It explores people’s motivation for using these technologies, how they actually use them and how much energy they can save based on the producer’s experimental measurements.

A review of modern residential thermostats for home automation to provide energy efficiency

During the last decade of the 20th century, Turkey was shocked with numerous disasters. As a result, central government or Turkey has decided to settle a national Crises Management Center. One of the main responsibilities of this center is to predict, track and minimize the effects of disasters in any part of the country. GIS-based disaster simulators are currently being developed for this purpose. In this paper, a subset of those simulators will be reviewed. Some technical details of the simulations, basic underlying models of simulations, and extent of required RS data will be presented.

Usage of GIS and RS technologies in disaster management

/pdf/benchmarking-the-computation-and-communication-performance-2z200a42dr.pdf

Kivanc Dincer

Papers

Design Issues in Building Web-Based Programming Environments.

World-wide virtual machine: a metacomputing environment integrating world wide web and high performance computing and communications technologies

A review of modern residential thermostats for home automation to provide energy efficiency

Usage of GIS and RS technologies in disaster management

Benchmarking the Computation and Communication Performance of the CM-5