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.

International Journal of Computer and Applications

Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but, at the same time, a unique opportunity. Access to the Internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this article, a survey of technologies for providing connectivity to rural areas, which can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in this article, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost-efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas, and they are granted an important share of the discussions in this article. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for the future evolution of rural connectivity are outlined in this article. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

/pdf/a-key-6g-challenge-and-opportunity-connecting-the-base-of-385d3hceqi.pdf

A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

IEEE transactions on systems, man and cybernetics. Part B, Cybernetics

CuI catalyzed CS bond formation by using nitroarenes

Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. These classifiers first build a decision tree and then prune subtrees from the decision tree in a subsequent pruning phase to improve accuracy and prevent “overfitting”. In this paper, the different pruning methodologies available & their various features are discussed. Also the effectiveness of pruning is evaluated in terms of complexity and classification accuracy by applying C4.5 decision tree classification algorithm on Credit Card Database with pruning and without pruning. Instead of classifying the transactions either fraud or non-fraud the transactions are classified in four risk levels which is an innovative concept.

/pdf/evaluation-of-decision-tree-pruning-algorithms-for-lr97wg2rrl.pdf

Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy

There is a long list of words that describe depression; sadness, unhappiness, sorrow, dejection, low spirit, despondency, woe, gloom, pessimism, desolation, despair, hopelessness, moodiness, and a host of others. This study throws light upon the contribution EEG signal for depression analysis. In this paper, classification of depressed patients from normal subjects are identified by using EEG signal. Experimental results are carried out with the help of 13 depressed patients and 12 normal subjects. This paper tries to classify person's mental state either normal or depressed with the help of EEG signal using signal processing technique FFT and machine learning technique SVM. These noninvasive signal techniques are useful for detection of depression disorders through EEG signals. The proposed work is compared with the other methods. The diagnosis is done and appropriate remedies are taken according to scale of the depression in the patient. This work shows that linear analysis of EEG can be an efficient method for identifying depressed patients from normal subjects. It is recommended that this analysis may be a supporting aid for psychiatrists to identify severity level of depressed patients.

Non invasive EEG signal processing framework for real time depression analysis

In this paper, a novel watermarking technique is proposed for data authentication and integrity of Relational Database. For integrity verification of tables in the database, the watermark has to depend on a secret key and on the original copy of that table. It is important that the dependence on the key should be sensitive. The proposed method makes use of the concept of eigen values by constructing a tuple -Relation matrix for each tuple. The eigen values are used for generating the watermark for a record in the table. Watermark embedding is done by using eigen values in a non numeric attribute of a tuple. Detection of the watermark prove the authenticate and integrity of data. We will show that our approach leads to an effective technique that is robust against different forms of malicious attacks as well as benign updates to the data . General Terms Security Keywords Watermarking, Relational Database, Eigen values; 1. INTRODUCTION Digital images, video and audio are examples of digital assets which have become easily accessible by ordinary people around the world. However, the owners of such digital assets have long been concerned with the copyright of their digital products, since copying and distributing digital assets across the Internet was never easier and possible as its now a days. Digital watermarking technology was suggested lately as an effective solution for protecting the copyright of digital assets[2,3]. This technology provides ownership verification of a digital product by inserting imperceptive information into the digital product. Such 'right witness' information is called the watermark and it is inserted in such a way that the usefulness of the product remains, in addition to providing it with robustness against attempts to remove the watermark. A watermarking of database systems started to receive attention because of the increasing use of database systems in many real-life applications. There is a need to preserve originality, ownership and integrity of database systems in a way that cannot be identified by everyone. It in turn arises the need of developing a well secured watermark technique to protect the database systems against the piracies. Many researchers concentrated on watermarking numeric attribute relational database systems and also for non numeric attribute which stores image in it[1,2,6]. In this paper, we present an effective watermarking technique for non numeric relational data that is robust against various attacks. The main contributions of the paper are summarized as follows: We select non numeric low impact attributes which are used to watermark a relation. During watermark generation process, we design an algorithm for : 1. Secret Key generation using Eigen values of tuple-Relation matrix for a tuple. 2. Watermark insertion. 3. Watermark detection is the reverse process of watermark insertion. As any change to non-numeric attribute values of the tuple must be a change in vowel, consonant, numbers and special character of tuple-Relation matrix, we consider their individual counts to compute the weighted ASCII sum A for developing a new watermarking scheme. It provides a secret key to the authenticated user to verify the received database relation. If the database has undergone tampering during transmission, it can be recognized by the authenticated user.

/pdf/a-new-watermarking-approach-for-non-numeric-relational-11k5pvs9vz.pdf

A New Watermarking Approach for Non-numeric Relational Database

An efficient, microwave-assisted ligand-free, catalyst-free synthetic method for nonsymmetrical diaryl ethers has been developed by using nitroarenes. A variety of phenols and nitroarenes was scanned by using this method to produce nonsymmetrical aryl ethers. The newly developed method is an ecofriendly and cost-­effective approach to synthesize nonsymmetrical aryl ethers.

Vijay M. Wadhai

Papers

CuI catalyzed CS bond formation by using nitroarenes

Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy

Non invasive EEG signal processing framework for real time depression analysis

A New Watermarking Approach for Non-numeric Relational Database

Microwave-Assisted Synthesis of Nonsymmetrical Aryl Ethers Using Nitroarenes

Vijay M. Wadhai

Papers

CuI catalyzed CS bond formation by using nitroarenes

Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy

Non invasive EEG signal processing framework for real time depression analysis

A New Watermarking Approach for Non-numeric Relational Database

Microwave-Assisted Synthesis of Nonsymmetrical Aryl Ethers Using Nitro­arenes

Microwave-Assisted Synthesis of Nonsymmetrical Aryl Ethers Using Nitroarenes