Data Mining - Concepts and Techniques.

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature.

Binary grasshopper optimisation algorithm approaches for feature selection problems

In the recent past, self learning plays a significant role in enhancing knowledge of students. The mobile learning environment (m-learning) enables the students to achieve more in education even without the direct assistance of a teacher. The m-learning is a method of electronic learning (e-learning). Learning is very essential to everyone to satisfy their needs and wants. Continuous learning can improve sustainable growth of the individual and nation. In some occasions, students are not able to learn their subject with direct contact of teachers at schools or colleges due to various personal and family reasons such as health problems, migration of family, etc. This problem is addressed by m-learning using mobile application. In m-learning approach-based education, the course contents are uploaded to the cloud server by the teacher. Then, the students those who are not able to attend school or college can download the course content through internet using an m-learning mobile application and learn them. Once the course content is downloaded to the mobile devices through mobile application, the student can learn even without internet connectivity. This paper presents a mobile application for m-learning for the students those who are not able to attend the classes at engineering colleges. 

Keywords: Mobile application; m-learning; e-learning; web-application; self-learning; mobile application for e-learning;

http://www.ijarcs.info/index.php/Ijarcs/article/download/3005/2988

Mobile Application for m-Learning

Mobile Ad hoc networks are a class of dynamic networks without any centralized administration. A major problem in Mobile Ad hoc networks (MANETs) is the anonymity threat arises among the nodes usually when they mobile. Many researches are concentred on the part of avoiding anonymity threat in MANET with different approaches using simulators. This paper presents an implementation of (ATARPA) Anonymity Threat Avoidance Routing Protocol Architecture in order to agonist the anonymity threat such as traceability, intrusion, observability and linkability. The ATARPA has been designed with the principle of reactive routing protocol. It provides strong security with anonymity protection and scalability.

IATARPA: Implementation of anonymity threat avoidance routing protocol architecture for MANET

Nowadays in the new era of Information technology, the use of visual aids in teaching and learning process is inevitable. Image processing is an interesting field that studies about various processing techniques for digital images, which is the basis of the emerging visual communication. Teaching the image processing has been a challenge since it requires imagination and creativity to some extent for the students to understand the concepts of image processing. MATLAB is a computing platform that is suitable for developing and testing a number of applications. The major advantage of using MATLAB is the graphical user interface (GUI) which can contribute positively to understand the concepts with ease. The pictorial illustrations confer better understanding of the concepts with ease.  This article addresses a novel method of teaching the concepts of image processing with MATLAB.  It also provides an insight to some of the basic image processing techniques namely image restoration, image denoising, image segmentation and edge detection with examples using MATLAB.

https://ejournal.ukm.my/ajtlhe/article/download/11909/3767

E. Jebamalar Leavline

Papers

Mobile Application for m-Learning

IATARPA: Implementation of anonymity threat avoidance routing protocol architecture for MANET

A practical approach for teaching digital image processing with matlab

IHNS: A Pragmatic Investigation on Identifying Highly Noisy Subband in FMDFB for Fixing Threshold to Deteriorate Noise in Images

Design of FinFET based low power, high speed hybrid decoder for SRAM