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

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

In this paper, a heuristics algorithm to solve unconstrained optimisation problems (UOPs) in two dimensions is proposed. This algorithm is called as: circular local search (CLS); where it is an efficient local search. The algorithm starts with an arbitrarily chosen point in the search domain. Secondly, a radius of CLS is defined around the current search point; where any point in this region is feasible. Finally, by an angle with a decay step length, CLS can move from current search point to obtain a new base point. The radius and the angle of CLS are modified during the search. CLS is tested on evaluated by many benchmark problems taken from the literature. According to the obtained numerical results, the proposed method show that its robustness and effectiveness. 

Circular local search for unconstrained optimisation problems

Evaluation of the performance of a FONN-based MPPT control for a photovoltaic watering system

The need for energy is always increasing as civilization evolves. Renewable energy sources are crucial for meeting energy demands as conventional fuel resources are slowly running out. Researchers are working to extract the most amount of power possible from renewable resources. Numerous resources are in demand, including solar, wind, biomass, tidal, and geothermal resources. Solar energy outperformed all the aforementioned resources in terms of efficiency, cleanliness, and pollution freeness. Intermittency, however, is the resource’s main shortcoming. Maximum power point tracking algorithm (MPPT) integration is required for the system to achieve continuous optimum power by overcoming the feature of intermittency. However, generating electrical energy from solar energy has presented a significant problem in ensuring the output power’s quality within a reasonable range. Total harmonic distortion (THD), a phenomenon, may have an impact on the power quality. Depending on the properties of the load, variables like power factor, voltage sag/swell, frequency, and unbalancing may occur. The quality of power and its criterion exhibits a non-linear connection. The article’s primary objective is to analyze the PV interface grid-linked system’s qualitative and quantitative performance. With respect to varying solar irradiation conditions, partial shading conditions, and solar power quality within the acceptable dimension, a novel intelligent multiple-objective horse herd optimization (HHO)-based adaptive fractional order PID (HHO-AFOPID) controller is used to achieve this goal. Adaptive fractional order PID (AFOPID), conventional FOPID, and PID controllers were used to evaluate the performance of the suggested controller, which was then validated using a commercially available PV panel in MATLAB/Simulink by varying the productivity of non-conventional resources, the inverter’s level of uncertainty, and the potential at the grid’s end. In order to realize the features of the system, sensitivity examination is also carried out for solar energy’s sensitive parameters. The stability analysis of the proposed control topology is also carried out in terms of the integral absolute error (IAE) and integral time absolute error (ITAE). The examination of the sensitivity of variations in solar radiation in kilowatt per square meter per day is based on the total net present cost (TNPC) and levelized cost of energy (LCOE), as optimal dimension and energy cost are both aspects of priority. The suggested control methodology is an approach for the qualitative and quantitative performance analysis of a PV interface grid-oriented system.

Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach

The COVID-19 outbreak has impacted network operators and data centers in terms of congestion and high traffic that lead to outages and significant pressure on the network. The overhead traffic is generated from web, voice calls, and Internet activity. In this paper, we are investigating data center congestion control for Software Defined Networks (SDN) network data centers. A Software-Defined (SDN) data center is an emerging networking paradigm that simplifies the network architecture by decentralizing plane functionality into a single with centralized decision capabilities. Along with the SDN paradigm, there is a crucial part that is responsible for forwarding packet called OpenFlow switching engine. In a typical SDN environment, the rules are initiated by the SDN controller and pushed to the OpenFlow switches. The traditional OpenFlow switch has no forwarding decision and depends on the incoming policies from the controller’s southbound interface. Additionally, the flow of traffic is initiated from different sources that are assigned to a specific route. However, this significant flow of traffic due to COVID-19 can lead to congestion and degradation of network performance in terms of delay and interruption. To be precise, a single OpenFlow switch could receive a capacity of traffic that floods its forwarding table and lead to link flaps and outages. In order to optimize the OpenFlow switch with regards to how much traffic it can host and to adjust routing capabilities for dynamic changes in the network, we propose an optimized OpenFlow congestion control and fault prediction framework for inbound traffic to overcome the inefficient route planning in the network. The proposed developed optimization algorithm is based on Genetic Evolutionary Algorithm criteria and adds intelligence to the OpenFlow switch by the adoption of Fuzzy Logic prediction capabilities. The experimental evaluation shows that the proposed optimization method adds significant intelligence and optimization to OpenFlow operation. The testbed was implemented experimentally using Raspberry Pi (RPI)cluster with customized SDN and OpenFlow deployment. The probability of the best fitness was 14.11% for Gen 999. The proposed approach adds intelligence and prediction into the OpenFlow switch to overcome the unstable flows of traffic and to predict faults to enhance the traffic capacity levels and manage flows into an entirely uninterrupted production environment.

Ahmad Taher Azar

Papers

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Circular local search for unconstrained optimisation problems

Evaluation of the performance of a FONN-based MPPT control for a photovoltaic watering system

Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach

Intelligent OpenFlow Switch for SDN Networks Based on COVID-19’s High Network Traffic using Heuristic GA-Fuzzification Control Approach