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 accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth So researches focus on the nature of knowledge processing in the mind This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase Each of those phases is mathematically formulated using the notions of real-time process algebra The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets The acquired datasets are divided into training and testing data that are encoded using concept matrix Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets This representation will be available in a usable form when it is needed in the future The inference stage uses the rule set to obtain the classes of the encoded testing datasets Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules

A computational knowledge representation model for cognitive computers

This paper presents a robust kinematic control of unmanned UAV aerial vehicles with non-holonomic constraints. The studied system consists of a 2D UAV non-holonomic kinematic model represented as a driftless system with its state and control inputs. The first part of this study consists of the evidence that the model being studied is non-holonomic in view of its involutivity properties. The second part of this study consists of the design of a robust kinematic controller for an unmanned aerial vehicle (UAV) in which the states of the systems are used for feedback control and the desired angular and linear velocities are precisely tracked by the proposed controller approach. This control strategy is achieved by designing the appropriate Lyapunov functional to meet the robust stability conditions and by finding the switching gains to track the desired profile. The control strategy obtained is tested in the proposed 2D mathematical model of the unmanned aerial vehicle and it is confirmed that the system variables track the desired profile while keeping the angular and linear velocity bounded. This study concludes with a discussion of the results and the respective conclusions.

Robust Kinematic Control of Unmanned Aerial Vehicles with Non-holonomic Constraints

Mechanical systems such as hyperjerk dynamical systems and their control constitute an attractive research trend in the literature. This chapter proposes a new 4-D chaotic hyperjerk system with a hyperbolic sinusoidal nonlinearity. In this chapter, we aim to control the chaotic behavior of the new hyperjerk mechanical chaotic system with multi-stability and coexisting attractors. We discuss the qualitative behavior of the new hyperjerk system in detail. Backstepping control is applied to control and synchronize the chaos in the proposed hyperjerk system with hyperbolic sinusoidal nonlinearity. A circuit design of the new chaotic hyperjerk dynamical system has been worked out using Multisim to aid the practical implementation of the proposed chaotic hyperjerk system.

A new 4-D chaotic hyperjerk system with coexisting attractors, its active backstepping control, and circuit realization

Tuning process which is used to find the optimum values of the proportional integral derivative (PID) parameters, can be performed automatically using meta-heuristics algorithms such as BA (Bat Algorithm), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony). This paper presented a theoretical and practical implementation of a drawing robot using BA to tune the PID controller governing the robotic arm which is a non linear system difficult to be controlled using classical control. In line with the above and in order to achieve this aim and meet high performance feedback and robust dynamic stability of the system, the PID controller is designed considering the realistic constraints. For faster tuning of the controller parameters, ten individuals and five iterations have been selected. BA, ABC and PSO have been compared and it’s noticed that BA is the best choice to achieve good performance control. In the proposed design, MATLAB was used for trajectory reckoning. Afterwards, the value of coordinate position of the shape to be drawn is translated into a joint angle by applying the inverse kinematics to control the two DC motors through the ATMEGA 2560 microcontroller. The suggested technique reveals via simulations and hardware implementation the high efficiency of the applied algorithm. The PID controller approach presents an impressive stability and robustness results. The achieved results demonstrated that high performance can be obtained by tuning a PID controller using nature inspired based algorithms.

Optimal Design of PID Controller for 2-DOF Drawing Robot Using Bat-Inspired Algorithm

The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. This paper presents an intelligent diagnosis system using hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Wavelet-transform is used for effective feature extraction and ANFIS is considered for the classifier model. It can parameterise the incoming ECG signals and then classify them into eight major types for health reference: left bundle branch block (LBBB), normal sinus rhythm (NSR), pre-ventricular contraction (PVC), atrial fibrillation (AF), ventricular fibrillation (VF), complete heart block (CHB), ischemic dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). The inclusion of adaptive neuro-fuzzy interface system (ANFIS) in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical problem domains. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95% is achieved which is a significant improvement.

Ahmad Taher Azar

Papers

A computational knowledge representation model for cognitive computers

Robust Kinematic Control of Unmanned Aerial Vehicles with Non-holonomic Constraints

A new 4-D chaotic hyperjerk system with coexisting attractors, its active backstepping control, and circuit realization

Optimal Design of PID Controller for 2-DOF Drawing Robot Using Bat-Inspired Algorithm

Neuro-fuzzy system for cardiac signals classification