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

In order to solve combinatorial optimization problem are used mainly hybrid heuristics. Inspired from nature, both genetic and ant colony algorithms could be used in a hybrid model by using their benefits. The paper introduces a new model of Ant Colony Optimization using multiple colonies with different level of sensitivity to the ant’s pheromone. The colonies react different to the changing environment, based on their level of sensitivity and thus the exploration of the solution space is extended. Several discussion follows about the fuzziness degree of sensitivity and its influence on the solution of a complex problem.

A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony Optimization

This paper represents the control of a two-wheel self-balancing robot based on the theory of controlling the inverted pendulum. This paper dividing the system modeling into two main parts. The first part is the dc motor and the second part are the whole mechanical design and its characteristics as a function in the motor speed and the torque depending on the system, creating two control closed loops inner and outer. The study uses conventional proportional–integral–derivative (PID) and two degree of freedom PID controllers to obtain a robust controller for the system. The inner loop controls the motor speed use the motor speed feedback signal from the encoder. The outer loop keeps the robot always in the accepted vertical angle boundary, using a six-degree of freedom gyroscope and accelerometer as a feedback signal. A state space model is obtained considering some assumptions and simplifications. The results are verified through simulations and experiments. Numerical simulation results indicate that the 2-DOF PID controller is superior to the traditional PID controller.

Self-balancing Robot Modeling and Control Using Two Degree of Freedom PID Controller

Intrusion detection systems (IDSs) is an essential key for network defense. Many classification algorithms have been proposed for the design of network IDS. Data preprocessing is a common phase to the classification learning algorithm, which leads to improve the network IDS performance. One of the important data preprocessing steps is discretization, where continuous features are converted into nominal ones. This paper addresses the impact of applying discretization on building network IDS. Furthermore, it explores the impact of the quality of the classification algorithms when combining discretization with genetic algorithm (GA) as a feature selection method for network IDS. In order to evaluate the performance of the introduced network IDS, several classifiers algorithms; rules based classifiers (Ridor, Decision table), trees classifiers (REPTree, C 4.5, Random Forest) and Naive bays classifier are used. Several groups of experiments are conducted and demonstrated on the NSL-KDD dataset. Experiments show that discretization has a positive influence on the time to classify the test instances. Which is an important factor if real time network IDS is desired.

Improved Real-Time Discretize Network Intrusion Detection System

Artificial Neural Network for PWM Rectifier Direct Power Control and DC Voltage Control

According to its significance, robotics is always an area of interest for research and further development. While robots have varying types, design and sizes, the six degrees of freedom (DOF) serial manipulator is a famous robotic arm that has a vast areas of applications, not only in industrial application, but also in other fields such as medical and exploration applications. Accordingly, control and optimization of such robotic arm is crucial and needed. In this paper, different analyses are done on the chosen design of robotic arm. Forward kinematics are calculated and validated, then simulation using MSC ADAMS is done, followed by experimentation and tracking using Microsoft Kinect. Two approaches are used in this study: adaprive neuro fuzzy (ANF) system optimized by simulated annealing (SA) algorithm and convolutional neural networks (CNNs) optimized by adaptive moment estimation (Adam). The same inputs are given to both models and their results are compared in order to determine the best fit algorithm for higher precision in the given robotic model. The findings have shown that the accuracy of CNNs is higher. Furthermore, this advantage has a higher cost for the time of computation than for NFs with SA.

Ahmad Taher Azar

Papers

A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony Optimization

Self-balancing Robot Modeling and Control Using Two Degree of Freedom PID Controller

Improved Real-Time Discretize Network Intrusion Detection System

Artificial Neural Network for PWM Rectifier Direct Power Control and DC Voltage Control

Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning