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 following study examines how various classification algorithms perform on the problem of handwritten digit recognition. The classifiers discussed are k-Nearest Neighbour (k-NN), Single Classification Decision Trees and Bagged Decision Trees. These algorithms were evaluated with the use of information from the United States Postal Service (USPS). This study’s results show that the k-NN classifier had the fastest performance while the bagged decision trees were the slowest. In terms of classification performance, the bagged decision tree method was found to have the fewest misclassifications and outperformed k-NN and single classification trees in all of the considered metrics.

Machine Learning Techniques for Handwritten Digit Recognition

Renewable energy displayed an important source for producing electricity, decreasing greenhouse gas emissions, and reducing nonrenewable energy dependence. This chapter highlights the necessity for clean and efficient energy sources through the development of advanced optimization methods. The hybrid system using solar–battery–wind and electric vehicle are meant to maximize energy generation. Complexity arises when determining the optimum design, functionality, and contribution of each system as they are combined to ensure system reliability. The development of the optimization method is generated to find the optimal solution of the hybrid system. In addition, the multiobjective analysis has been involved in achieving economic benefit by overcoming the grid’s generation costs. Reliance demand-side management, a control technique, is developed to schedule the hybrid system’s energy properly. The purpose of the aimed work is to maximize renewables’ use in various generation situations such as weather fluctuation and grid support.

Multiobjective optimization-based energy management system considering renewable energy, energy storage systems, and electric vehicles

This paper presents a fault tolerant scheme employing adaptive non-singular fixed-time terminal sliding mode control (AFxNTSM) for the application of robotic manipulators under uncertainties, external disturbances, and actuator faults. To begin, non-singular fixed-time terminal sliding mode control (FxNTSM) is put forth. This control method uses non-singular terminal sliding mode control to quickly reach fixed-time convergence, accomplish satisfactory performance in tracking, and produce non-singular and non-chatter control inputs. Then, without knowing the upper bounds beforehand, AFxNTSM is used as a reliable fault tolerant control (FTC) to estimate actuator faults and unknown dynamics. The fixed-time stability of the closed-loop system is established by the theory of Lyapunov analysis. The computer simulation results of the position tracking, control inputs, and adaptive parameters are presented to verify and illustrate the performance of the proposed strategy.

/pdf/adaptive-fault-tolerant-non-singular-sliding-mode-control-265ob0yq.pdf

Adaptive Fault Tolerant Non-Singular Sliding Mode Control for Robotic Manipulators Based on Fixed-Time Control Law

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis. Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily. These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues. To achieve dimensionality reduction for huge data sets, this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS. A novel hybrid strategy based on the Mayfly algorithm (MA) and the rough set (RS) is proposed in particular. The performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the literature. The simulation results and comparison with common reduction methods demonstrate the proposed MA-RS algorithm’s capacity to handle a wide range of data sets. Finally, the rough set approach, as well as the hybrid optimization techniques PSO-RS and MA-RS, were applied to deal with the massive data problem. MA-hybrid RS’s method beats other classic dimensionality reduction techniques, according to the experimental results and statistical testing studies. 

https://www.techscience.com/cmc/v73n1/47834/pdf

Rough Sets Hybridization with Mayfly Optimization for燚imensionality燫eduction

Background: The Kt/V value demonstrates the dose of hemodialysis (HD). However, because of several existing methods for calculating delivered dialysis dose, Kt/V values can, in fact, be different for the same set of pre-/post-dialysis blood urea concentrations. Methods: In the study presented here, another formula was derived for calculating Kt/V from the pre- and post-dialysis BUN. We prospectively compared the Kt/V values obtained using this new formula and the Kt/V values obtained via the other existing formulae to see whether reliance on the latter approach was likely to lead to errors in over- or underprescribing dialysis regimens. Data were processed on 268 dialysis patients. Results: The estimated Kt/V (Kt/V est ) values were statistically different (p < 0.05) from the calculated Kt/V values from other models, except for those Kt/V values calculated according to the lowrie (P = 0.112), Keshaviah (P = 0.069), Daugirdas First Generation (P = 0.059), Basile (P = 0.102), Ijely (P = 0.286) and Daugirdas Second Generation (P = 0.709). The best correlations were seen with the Daugirdas second generation formula (R = 0.958 and R 2 = 0.919). Conclusion: Since the best correlations were seen between Kt/V est and the Daugirdas second generation Kt/V we can demonstrate that these two models are more accurate than the other models.

https://journals.sagepub.com/doi/pdf/10.4137/CMU.S771

Ahmad Taher Azar

Papers

Machine Learning Techniques for Handwritten Digit Recognition

Multiobjective optimization-based energy management system considering renewable energy, energy storage systems, and electric vehicles

Adaptive Fault Tolerant Non-Singular Sliding Mode Control for Robotic Manipulators Based on Fixed-Time Control Law

Rough Sets Hybridization with Mayfly Optimization for燚imensionality燫eduction

Estimation of Accurate and New Method for Hemodialysis Dose Calculation