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

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Integrating metaheuristics and ANN for improved stock price prediction.Both topology of ANN and the number of inputs are optimized.The number of the input variables is reduced to almost its half.HS-ANN has better generalization ability than GA-ANN model.Proposed methodologies outperformed both in statistical and financial terms. Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting.

Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic and critical review of about one hundred and twenty-two (122) pertinent research works reported in academic journals over 11 years (2007–2018) in the area of stock market prediction using machine learning. The various techniques identified from these reports were clustered into three categories, namely technical, fundamental, and combined analyses. The grouping was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modelling. The results revealed that 66% of documents reviewed were based on technical analysis; whiles 23% and 11% were based on fundamental analysis and combined analyses, respectively. Concerning the number of data source, 89.34% of documents reviewed, used single sources; whiles 8.2% and 2.46% used two and three sources respectively. Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.

A systematic review of fundamental and technical analysis of stock market predictions

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

There is no suspicion that IoT has added a new dimension to the living being by the link between smart objects. Thus making the link among any media and anything at any place and anytime was appreciable. Under the umbrella of the Internet of Things (IoT) the number of interconnected devices is expected to grow exponentially toward more than 34 billion devices until 2021. IoT will propose the unique identification of the objects and their virtual representation as the basis for autonomously development of applications and services. These will be characterized by enormous and self-governing data capture, incident transfer, network connectivity and interoperability. This technology has a lot of applications in heterogeneous fields. The IoT technology and applications are likely to be major drivers of investment and innovation in the communications sector, over the forthcoming years, delivering the valued advantage to citizens, client and industrial end-users. These will lead to the introduction of many new and modern services. It will permit data to be transmitted between many various types of devices, enhance the safety of transportation, and decrease the consumption of energy and enhance our health. In this paper, we are briefly discussing about the Internet of Things and applications in several fields. The IoT applications are using at the edge of the network sensors accumulate data on a computing and communicating device and actuators to perform distinguished tasks controlled by these devices.

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The Internet of Things (IoT) and its Application Domains

Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.

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Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm

This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system

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Neural Networks for Handwritten English Alphabet Recognition

Here, the goal of a character recognition system is to transform a hand written text document on paper into a digital format that can be manipulated by word processor software. The system is required to identify a given input character form by mapping it to a single character in a given character set. Each hand written character is split into a number of segments (depending on the complexity of the alphabet involved) and each segment is handled by a set of purpose built neural network. The final output is unified via a lookup table. Neural network architecture is designed for different values of the network parameters like the number of layers, number of neurons in each layer, the initial values of weights, the training coefficient and the tolerance of the correctness. The optimal selection of these network parameters certainly depends on the complexity of the alphabet.

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At present IoT is immensely a descriptive term of a vision that everything should be connected to the internet. IoT applications have been widely used in several fields of social living such as healthcare and social products, industrial automation and energy. In this scenario, there are more than 14 billion interconnected digital and electronic devices in operation worldwide, the equivalent of almost two devices for every human being on earth. The IoT connects different nonliving objects through the internet and enables them to share information with their community network to automate processes for human beings and makes their lives convenient. Subsequently, objects are being amalgamated with internet connectivity and powerful data analysis capabilities that promise to change the way we work and live. The Internet is a worldwide system of interconnected computer networks that use the standard Internet protocol suite (TCP/IP) to serve billions of users globally. The most vital characteristics of IoT include connectivity, active engagement, connectivity, sensors, artificial intelligence, and small device use. This paper provides an overview of existing Internet of Things (IoT), technical details, and applications in this new emerging area as well as we are thoroughly analyzing the layer about the IoT. However, this manuscript will give a better understanding for the new researchers, who want to do research in this field of Internet of Things.

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Yusuf Perwej

Papers

The Internet of Things (IoT) and its Application Domains

Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm

Neural Networks for Handwritten English Alphabet Recognition

Neural Networks for Handwritten English Alphabet Recognition

An Extended Review on Internet of Things (IoT) and its Promising Applications