There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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 - Concepts and Techniques.

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Data Mining Practical Machine Learning Tools and Techniques

The issues of multitude of noisy, irrelevant, misleading features, and the capability to tackle inaccurate and inconsistent data in real world topics are the justification to turn into one of the most significant needs for feature selection. This paper proposes an intelligent hybrid approach using Rough Set Theory (RST), Chaos Theory and Binary Grey Wolf Optimization Algorithm (CBGWO) for feature selection problems. Ten different chaotic maps are used to estimate and tune GWO parameters. Experiments are applied on complex medical datasets with various uncertainty features and missing values. The performance of the proposed approach is extensively examined and compared with that of existent feature selection algorithms; such as ant lion optimization (ALO), chaotic ant lion optimization (CALO), bat optimization (BAT), whale optimization algorithm (WOA), chaotic whale optimization algorithm (CWOA), binary crow search algorithm (BCSA), and chaotic binary crow search algorithm (BCCSA) algorithms. The achievement of the proposed approach is analyzed using different evaluation criteria. The overall result indicates that the proposed approach delivers better performance, lower error, higher speed and shorter execution time.

Intelligent Hybrid Approach for Feature Selection

Fractional-order controller design and implementation for maximum power point tracking in photovoltaic panels

In this article, different control design approaches for parallel robot manipulators are presented with two distinguished classes of control strategies in the literature. These are the model-free control and the dynamic control strategy, which is mainly a model-based scheme, and is mostly the alternative when the control requirements are more stringent. The authors strongly believe that this paper will be helpful for researchers and engineers in the field of robotic systems.

Control design approaches for parallel robot manipulators: a review

Contrary to the popular belief that TQM is a poor fit in higher education sector, this research proposes a Rough Set Theory (RST) based model for grading educational institution using TQM parameters. It is a well established fact that TQM needs major reshaping before it can be effectively applied in higher education sector for quality assessment and improvement. This chapter takes a balance view by employing RST approach in TQM architecture and eliminating the much publicized shortcomings of TQM approach. RST theory has advantage of working on a small size of data containing vague and imprecise information which is widely prevalent in education sector. A carefully drafted questionnaire, containing nine attributes, is used for generating research data from the different stake holders in higher educational institutes of India. Nine modified condition attributes are selected on the basis of literature survey and expert views which are subsequently treated with RST analysis. One decision parameter ‘Grade’ depends on nine independent condition attributes. The resultant model contains only two significant attributes namely, ‘Effective Learning and Teaching’ and ‘Administrative Setup’ which can effectively determine the grading of educational institutions. Results of this study may be utilized to improve the higher education quality through appropriate grading mechanism based on self assessment of quality parameters by the different stakeholders of the education sector. The study confirms that TQM can be useful to enhance both quasi-academic areas such as ‘administrative setup’ along with core academic area ‘effective teaching and learning’.

A Rough Set Based Total Quality Management Approach in Higher Education

This book gathers the proceedings of the 2nd International Conference on Advanced Intelligent Systems and Informatics (AISI2016), which took place in Cairo, Egypt during October 2426, 2016. This international interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE) and sponsored by the IEEE Computational Intelligence Society (Egypt chapter) and the IEEE Robotics and Automation Society (Egypt Chapter). The books content is divided into four main sections: Intelligent Language Processing, Intelligent Systems, Intelligent Robotics Systems, and Informatics.

Ahmad Taher Azar

Papers

Intelligent Hybrid Approach for Feature Selection

Fractional-order controller design and implementation for maximum power point tracking in photovoltaic panels

Control design approaches for parallel robot manipulators: a review

A Rough Set Based Total Quality Management Approach in Higher Education

Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016