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.

/pdf/the-theory-of-industrial-organization-19bejph0ej.pdf

The Theory of Industrial Organization

https://findresearcher.sdu.dk:8443/ws/files/116118561/reverse_logistics.pdf

Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future

The work is giving estimations of the discrepancy between solutions of the initial and the homogenized problems for a one{dimensional second order elliptic operators with random coeecients satisfying strong or uniform mixing conditions. We obtain several sharp estimates in terms of the corresponding mixing coeecient. Abstract. In the theory of homogenisation it is of particular interest to determine the classes of problems which are stable on taking the homogenisation limits. A notable situation where the limit enlarges the class of original problems is known as memory (nonlocal) eeects. A number of results in that direction has been obtained for linear problems. Tartar (1990) innitiated the study of the eeective equation corresponding to nonlinear equation: @ t u n + a n u 2 n = f: Signiicant progress has been hampered by the complexity of required computations needed in order to obtain the terms in power{series expansion. We propose a method which overcomes that diiculty by introducing graphs representing the domain of integration of the integrals in each term. The graphs are relatively simple, it is easy to calculate with them and they give us a clear image of the form of each term. The method allows us to discuss the form of the eeective equation and the convergence of power{series expansions. The feasibility of our method for other types of nonlinearities will be discussed as well.

Applied Mathematics and Computation

The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using data mining approaches shows an integral part of e-health system. However, medical databases are highly imbalanced, voluminous, conflicting and complex in nature, and these can lead to erroneous diagnosis of diseases (i.e. detecting class-values of diseases). In literature, numerous standard disease decision support system (DDSS) have been proposed, but most of them are disease specific. Also, they usually suffer from several drawbacks like lack of understandability, incapability of operating rare cases, inefficiency in making quick and correct decision, etc.,Addressing the limitations of the existing systems, the present research introduces a two-step framework for designing a DDSS, in which the first step (data-level optimization) deals in identifying an optimal data-partition (Popt) for each disease data set and then the best training set for Popt in parallel manner. On the other hand, the second step explores a generic predictive model (integrating C4.5 and PRISM learners) over the discovered information for effective diagnosis of disease. The designed model is a generic one (i.e. not disease specific).,The empirical results (in terms of top three measures, namely, accuracy, true positive rate and false positive rate) obtained over 14 benchmark medical data sets (collected from https://archive.ics.uci.edu/ml) demonstrate that the hybrid model outperforms the base learners in almost all cases for initial diagnosis of the diseases. After all, the proposed DDSS may work as an e-doctor to detect diseases.,The model designed in this study is original, and the necessary parallelized methods are implemented in C on Cluster HPC machine (FUJITSU) with total 256 cores (under one Master node).

An e-healthcare system for disease prediction using hybrid data mining technique

Digital Marketing strategies are sets of controllable e-marketing variables that organizations combine to achieve marketing goals and to meet customers’ needs. These strategies are the most important factors that electronic-marketing managers pay attention to the best strategy in order to achieve sales and profitability. This study aims to investigate the effect of these strategies on the buying behavior of customers in online shopping stores in Tehran. For this purpose, five best-selling online stores in Tehran are selected and 79 samples are taken from each of them. For data collection, a 2-tuple fuzzy linguistic representation model is used in order to no lose the linguistic information obtained from customers. For data analyzing and extracting proper rules, two approaches of the rough set theory are used. Based on the results provided by Rosetta software, five rules governing customer behavior are identified as the most important factors affecting buying behavior in online shopping. To evaluate the result, a comparison is carried out between the extracted rules using the proposed rough set technique and the tree diagram of the data obtained by Rapidminer software. Almost all provided rules are confirmed through this comparison along with the opinions of experts. Some of key results according to the obtained rules indicate that the most important digital marketing strategy is the search engine optimization. Moreover, the social media marketing and recommender engine play as second important issue of the marketing management.

The impact of digital marketing strategies on customer’s buying behavior in online shopping using the rough set theory

This paper develops a structural Economic Order Quantity (EOQ) model that evaluates the impact of a reduction rate in the selling price when the capacity of an Own Warehouse (OW) is limited and a Rented Warehouse (RW) is used, if needed. In many practical situations, there exists many factors to consider such as temporary price discounts, the limited capacity of OW houses, and transportation problems. Retailers need to build a new warehouse or rent other warehouses. However, from an economical point of view, they usually choose to rent other warehouses. However, in industries such as the textile and footwear industry or the food processing industry, after an certain time, the items decay or deteriorate. When this happens, a good manager wants to clear the stock by selling the huge items at reduced prices. In this paper the demand rate up to the start of deterioration is considered to be fixed and after that time, the new demand rate is considered to be a nonlinear increasing power function of the...

Two-warehouse inventory model on pricing decision

Joint project of fishery and poultry – A bioeconomic model

This research presents a mathematical model for a collaborative planning of the supply chain involving four echelons (supplier, production plants, distribution, retails, or clients). The model seeks to maximize profit (utility) when all members of the chain share information related to demand. It is developed for the aggregate consolidation of different raw materials in cement production. The novelty of the model is the consideration of products that deteriorate in the process and thus it has effect on the production times in the plant and lead time. In this supply chain, quality and compliant products and the return of deteriorated products are two flows. The considerations are lead time, inventories with shortages and excesses, production times in normal and extra days, and subcontracting, among others. A mixed integer linear programming with demand scenario analysis is used to optimize and analyze the uncertainty that is consistent with the performance of the construction sector. The model is formed considering two suppliers, two production plants, two distributors, two retailers and two end customers. Four manufacturing inputs (raw materials) are considered for the manufacture of two types of products. A case study of the cement production supply chain of Cartagena (Colombia) is illustrated. The shared benefit is generated around 5 billion pesos (COP) for all members of the chain in a period of 6 months.

/pdf/production-system-in-a-collaborative-supply-chain-3hyfupyj9n.pdf

Shib Sankar Sana

Papers

An e-healthcare system for disease prediction using hybrid data mining technique

The impact of digital marketing strategies on customer’s buying behavior in online shopping using the rough set theory

Two-warehouse inventory model on pricing decision

Joint project of fishery and poultry – A bioeconomic model

Production system in a collaborative supply chain considering deterioration