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

The social network, by the name which has popularised in today's world and growing rapidly at all times and controlling over mankind. The social networks like Twitter, Facebook, and LinkedIn, etc., have become a regular and daily usage of many people. It becomes a good mediator for the people who would like to share some posts, are some of their own videos, or some messages. But there has been major issues that the particular user of the social networks like Twitter and Facebook have the problem of indiscipline actions which we call as spam, by the third person who is knowingly doing this to spoil their intention and good opinion upon each other. Also, these spams help to steal information about the people who using social networks. In this paper, we study and analyse about the spam in social networks and machine learning algorithms to detect such kind of spams. This paper also focuses on the ML algorithms detection rate and false positive rate over different datasets.

Detecting spams in social networks using ML algorithms - a review

The internet contains a large amount of data, to exploit it a special treatment of its content should be done, and a particular web information system should be developed. As a critical step of those systems, the web page classification is proposed. In this paper, we present the characteristics of web page classification, we reviewed the different machine learning algorithms used to categorize web pages. Finally, we track some assumptions from the studied methods.

A Review of Machine Learning Algorithms for Web Page Classification

Explainable context-aware IoT framework using human digital twin for healthcare

Abstract Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques. 

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

Random surfers spend very little time on a web page. If the most important web page content fails to attract his attention within the short time span, he will move away to some other page, thus defeating the purpose of the web page designer. In order to predict if the contents of a web page will catch a random surfer's attention or not, we propose a machine learning based approach to classify web pages into “bad” and “not bad” classes, where the “bad” class implies poor attention drawing ability. We propose to divide web page contents into “objects”, which are coherent regions of web page conveying the same information, to develop the classifier approach. We surveyed 100 web pages sampled from the Internet to identify the type and frequency of objects used in web page design. From our survey, we identified six types of objects that are most important in determining the class of a web page, in terms of its attention drawing capability. We used the WEKA tool to implement the machine learning approach. Two different strategies of percentage split and three different strategies of cross validation are used to check for accuracy of the classifier. We have experimented with 65 algorithms supported by WEKA and found that the algorithms RBF network and Random subspace, among the 65, gives the best performance, with about 83% accuracy.

Classification of web pages on attractiveness: A supervised learning approach

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Classification of web pages on attractiveness: A supervised learning approach