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

As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.

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Vision based hand gesture recognition for human computer interaction: a survey

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance.

/pdf/max-pooling-convolutional-neural-networks-for-vision-based-3adx83uq6g.pdf

Max-pooling convolutional neural networks for vision-based hand gesture recognition

A smartphone based technique to monitor driving behavior using DTW and crowdsensing

With the rapid growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits. Forged online accounts crack up every day. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are harder to identify. Spammers are the users who send unsolicited messages to a large audience with the intention of advertising some product or to lure victims to click on malicious links or infecting user’s system just for the purpose of making money. A lot of research has been done to detect spam profiles in OSNs. In this paper we have reviewed the existing techniques for detecting spam users in Twitter social network. Features for the detection of spammers could be user based or content based or both. Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.

/pdf/techniques-to-detect-spammers-in-twitter-a-survey-479h813i93.pdf

Techniques to Detect Spammers in Twitter- A Survey

Social spam is a huge and complicated problem plaguing social networking sites in several ways. This includes posts, reviews or blogs containing product promotions and contests, adult content and general spam. It has been found that social media websites such as Twitter is also acting as a distributor of pornographic content, although it is considered against their own stated policy. In this paper, we have reviewed the case of Twitter and found that spammers contributing to pornographic content follow legitimate Twitter users and send URLs that link users to pornographic sites. Behavioral analysis of such type of spammers has been conducted using graph-based as well as content-based information fetched using simple text operators to study their characteristics. In the present study, about 74,000 tweets containing pornographic adult content posted by around 18,000 users have been collected and analyzed. The analysis shows that the users posting pornographic content fulfill the characteristics of spammers as stated by the rules and guidelines of Twitter. It has been observed that the illegitimate use of social media for spreading social spam has been spreading at a fast pace, with the network companies turning a blind eye toward this growing problem. Clearly, there is an immense requirement to build an effective solution to remove objectionable and slanderous content as stated above from social networking websites to promote and protect public decency and the welfare of children and adults. It is also essential so as to enhance public experience of genuine users using social media and protect them from harm to their public identity on the World Wide Web. Further in this paper, classification of pornographic spammers and genuine users has also been performed using machine learning technique. Experimental results show that Random Forest classifier is able to predict pornographic spammers with a reasonably high accuracy of 91.96 %. To the best of our knowledge, this is the first attempt to analyze and categorize the behavior of pornographic users in Twitter as spammers. So far, the work has been done for identifying spammers but they are not specifically targeting pornographic spammers.

Behavioral analysis and classification of spammers distributing pornographic content in social media

Automated latent fingerprint identification system: A review.

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activis...

Sanjeev Sofat

Papers

A smartphone based technique to monitor driving behavior using DTW and crowdsensing

Techniques to Detect Spammers in Twitter- A Survey

Behavioral analysis and classification of spammers distributing pornographic content in social media

Automated latent fingerprint identification system: A review.

Who is Who on Twitter–Spammer, Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time