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.

Data Mining Practical Machine Learning Tools and Techniques

to many, however, it constantly appears as an instructional problem. The final two chapters serve as excellent summary statements. Chapter 13 emphasizes the application of previous text material to the classroom situation. Of great significance is the author's discussion of commonly defined independent, dependent, and moderator variables. The final chapter focuses on evaluation in the overall sense, particularly as it relates to programs of study. The principles of measurement and research design are applied to the evaluation process through several ex-

Educational Research: Competencies for Analysis and Application:

An Introduction to MultiAgent Systems.

Water is always a crucial part of everyday life. Due to global environmental situation, water management and conservation is vital for human survival. In recent times, there were huge needs of consumer based humanitarian projects that could be rapidly developed using Internet of Things (IoT) technology. In this paper, we propose an IoT based water monitoring system that measures water level in real-time. Our prototype is based on idea that the level of the water can be very important parameter when it comes to the flood occurrences especially in disaster prone areas. A water level sensor is used to detect the desired parameter, and if the water level reaches the parameter, the signal will be feed in realtime to social network like Twitter. A cloud server was configured as data repository. The measurement of the water levels are displayed in remote dashboard.

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Internet of Things (IoT) enabled water monitoring system

The knowledge acquisition process is a crucial stage in the technology of expert systems. However, this process is not well defined. One of the promising structured sources of learning can be found in the recent work on neural network technology. A neural network can serve as a knowledge base of expert systems that does classification tasks. Another way of learning is by using the rough set as a new mathematical tool to deal with uncertain and imprecise data. Two methods based on rough set analysis were developed and merged with the integration of neural networks and expert systems, forming a new hybrid architecture of expert systems called a rough neural expert system. The first method works as a pre-processor for neural networks within the architecture, and it is called a pre-processing rough engine, while the second one was added to the architecture for building a new structure of inference engine called a rough neural inference engine. Consequently, a new architecture of knowledge base was designed. This new architecture was based on the connectionist of neural networks and the reduction of rough set analysis. The performance of the proposed system was evaluated by an application to the field of medical diagnosis using a real example of hepatitis diseases. The results indicate that the new methods have improved the inference procedures of the expert systems, and have showed that this new architecture has some properties over the conventional architectures of expert systems.

Rough neural expert systems

Recently, many researchers have focused on data stream processing as an efficient method for extracting knowledge from big data. Data stream clustering is an unsupervised approach that is employed for huge data. The continuous effort on data stream clustering method has one common goal which is to achieve an accurate clustering algorithm. However, there are some issues that are overlooked by the previous works in proposing data stream clustering solutions; (1) clustering dataset including big segments of repetitive data, (2) monitoring clustering structure for ordinal data streams and (3) determining important parameters such as k number of exact clusters in stream of data. In this paper, DCSTREAM method is proposed with regard to the mentioned issues to cluster big datasets using the vector model and k-Means divide and conquer approach. Experimental results show that DCSTREAM can achieve superior quality and performance as compare to STREAM and ConStream methods for abrupt and gradual real world datasets. Results show that the usage of batch processing in DCSTREAM and ConStream is time consuming compared to STREAM but it avoids further analysis for detecting outliers and novel micro-clusters.

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Data stream clustering by divide and conquer approach based on vector model

The Internet is one of the fastest growing areas of intelligence gathering. During their navigation Web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Advanced mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one Web Usage Mining application. However, the accuracy of the prediction and classification in the current architecture of predicting users' future requests systems can not still satisfy users especially in huge Web sites. To provide online prediction efficiently, we advance an architecture for online predicting in Web Usage Mining system and propose a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests. The Excremental results show that the approach can improve accuracy of classification in the architecture.

/pdf/a-web-usage-mining-approach-based-on-lcs-algorithm-in-online-2g315aptk4.pdf

A Web Usage Mining Approach Based on LCS Algorithm in Online Predicting Recommendation Systems

The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification is the procedure of finding a model (or function) that depicts and distinguishes data classes or notions, with the end goal of having the ability to utilize the model to predict the crime labels. In this research classification is applied to crime dataset to predict the “crime category” for diverse states of the United States of America (USA). The crime data set utilized within this research is real in nature, it was gathered from socio-economic data from 1990 US census. Law enforcement data from 1990 US LEMAS survey, and from the 1995 FBI UCR. This paper compares two different classification algorithms namely - Naive Bayesian and Back Propagation (BP) for predicting “Crime Category” for distinctive states in USA. The result from the analysis demonstrated that Naive Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for group 2. This clearly indicates that Naive Bayesian calculation is supportive for prediction in diverse states in USA.

/pdf/improved-method-of-classification-algorithms-for-crime-1wffqqjhge.pdf

Nasir Sulaiman

Papers

Internet of Things (IoT) enabled water monitoring system

Rough neural expert systems

Data stream clustering by divide and conquer approach based on vector model

A Web Usage Mining Approach Based on LCS Algorithm in Online Predicting Recommendation Systems

Improved method of classification algorithms for crime prediction