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 enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes’ degrees remains constant.

/pdf/towards-enhancing-the-robustness-of-scale-free-iot-networks-3u43cy32.pdf

Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism

The selection of an algorithm to tackle a certain problem is a vital undertaking that necessitates both time and knowledge. Non-functional needs, such as the size, quality, and nature of the data, must frequently be taken into account. To develop a generalized machine learning model for any domain, the most relevant features must be chosen because noisy and irrelevant characteristics degrade data mining performance. However, the selection of the dominating features is still dependent on the search technique. When there are a high number of input features, stochastic optimization can be applied to the search space. In this research, we investigate the Ant Lion Optimization (ALO), a nature-inspired algorithm that mimics the hunting process of ant lions and is further stimulated to identify the smallest reducts. We also investigate Rough Set based ant lion optimizer for feature selection. The actual results reveal that the antlion-based rough set reduct selects a better feature subset and classifies them more accurately.

Robust Feature Selection Using Rough Set-Based Ant-Lion Optimizer for Data Classification

In medical field, diagnosis of diseases competently carried out by using the image processing. So that to retrieve the relevant data from the amalgamation of resulting image is too difficult. Here the segmentation done by semi supervised learning then the result is tuned by using Deep Learning Neural Network. Higher tuning of results will leads to efficient detection of disease. The experiment done by using retinal image data sets in order to predict any disease affected or not. The aim of this paper is to keenly predict diseased image or not by the efficient tuning of image. Keywords—segmentation; semi supervised learning; Neural network; Deep learning neural network. I. INTRODUCTION Nowadays diseases related to eye are increasing and many people fell in to blindness. Image processing is the area which leads with image analysis and which involves the study of feature extraction, segmentation and classification. The process of recognizing the patterns that are used to the performance of an image .Diabetic retinopathy one of the complicated disease which affects to the retina and outcome is the total blindness. The technique segmentation is the method of identifying regions of pixels in an image so as to find out the correlation with objects. The programmed mining of blood vessels in retinal images is one of the important step in computer aided diagnosis and treatment of diabetic retinopathy, glaucoma, arteriosclerosis, obesity, retinal artery occlusion and hypertension. Segmentation can be done by supervised, unsupervised or semi supervised. Here using semi supervised segmentation method because of easy to use labeled data and unlabeled data together. Semi supervised segmentation have much applications in medical image data sets. To efficient tuning purpose, the segmented output classified under the neural network. Neural Network has good performance in non linear data bases such a way that multi-layer neural network could accurately estimated to any linear or non-linear capacity. Through tuning the incomplete data , can achieve the estimate prediction of the whole data with the generalization capability of neural network. By using neural network easy to store information of the continuous quantity ,by using adjacent sampling points relationship. Neural network store the continuous quantity so as to reduce the amount of storage space. For analysis of medical images , we can consider the ideal

Deep Learning Neural Network with Semi supervised Segmentation for Predicting Retinal and Cancer Cell Diseased Images.

Hemodialysis (HD) is one of the treatments included in what is called the Renal Replacement Therapy (RRT). As every treatment, hemodialysis has its dose. How quantify this hemodialysis dosage was one of the results of the National Dialysis Cooperative Study (NCDS) published in 1983. A formula based in the Urea Kinetic Modeling (UKM) was developed. This formula was the dimensionless equation Kt/V where K is the dialyzer clearance rate of urea (or volume of plasma cleared), t is the duration of the dialysis session and V is the urea distribution volume (the total body water volume). Because of the complexity of urea kinetic modeling, a number of shortcut methods of estimating Kt/V have been proposed. The aims of this chapter are twofold: 1) to give an overview of single pool urea kinetic modeling and 2) to introduce concepts and methods needed to manage the approaches available to estimate the single pool Kt/V.

Single Pool Urea Kinetic Modeling

https://www.techscience.com/cmc/v71n1/45455/pdf

Ahmad Taher Azar

Papers

Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism

Robust Feature Selection Using Rough Set-Based Ant-Lion Optimizer for Data Classification

Deep Learning Neural Network with Semi supervised Segmentation for Predicting Retinal and Cancer Cell Diseased Images.

Single Pool Urea Kinetic Modeling

An Improved Sparrow Search Algorithm for Node Localization in WSN