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

An integrated project of fishery and poultry

Social media has given customers more power over sharing their knowledge, opinions and experiences with each other. Tourists as customers of destinations are also using text ads on social media and websites to share their experiences. The purpose of this study is to find out the factors which have affect on the decisions of tourists towards the most popular destinations in Tehran, Isfahan and Shiraz of Iran.,Netnography methodology has been applied to 2,852 comments showing travelers’ experiences through TripAdvisor.com. As a result, ten major factors have been discovered. According to these factors, a questionnaire has been designed and distributed among 449 tourists. In the second step, the collected data are used by rough set theory to discover the rules of destination recommendation based on the factors discovered before. Finally, eight main rules are determined to further analysis.,The findings confirm that beauty, cultural attractions, safety, welfare, costs and dealing with passengers are more important than other observed dimensions.,In this study, first the factors affecting consumer behavior in the tourism industry have been investigated. Based on this, the comments of tourists who have traveled to one of the cities Shiraz, Isfahan or Teheran and shared their experiences on TripAdvisor.com are studied. Further, the rules are discovered based on the rough set theory, and owing to the large number of objects (449 customer), the Rosetta software has been used.

Application of rough set and netnography in tourism marketing analysis

In this article, an imperfect production inventory model consisting of a manufacturer and a retailer with quality improvement effort and the promotional effort sensitive demand pattern is investigated under a two-tier credit policy. We study the model for deteriorating items where the deterioration occurs at different rates in the manufacturer’s and the retailer’s level considering a fixed lifetime of the product. Discussing the six possible cases of credit policy analytically, we analyze the behavior of the model under an integrated system concerning production lot-size, quality effort and promotional effort such that the integrated average profit is maximum. To obtain the optimal solutions of the model, we design an operative solution algorithm. A numerical example is provided to test feasibility of the model, and the effect of the variation of the key parameters is also studied. The outcomes of this proposed model show that the manufacturer and the retailer have to be more careful about their offered credit periods in aspect of the profit. It is observed that the integrated profit is maximum when both credit periods provided by the manufacturer and the retailer belong to the manufacturer’s cycle. Moreover, we identify that the extended product lifetime does not meet with higher profit all times. This study directs that quality effort and promotional effort stimulate the market demand while it is not always economically profitable for the supply chain.

https://www.rairo-ro.org/articles/ro/pdf/2021/03/ro200445.pdf

Two-phase deteriorated supply chain model with variable demand and imperfect production process under two-stage credit financing

Flexible manufacturing systems as technological and automated structures have a high complexity for scheduling. The decision-making process is made difficult with interruptions that may occur in the system and these problems increase the complexity to define an optimal schedule. The research proposes a three-stage hybrid algorithm that allows the rescheduling of operations in an FMS. The novelty of the research is presented in two approaches: first is the integration of the techniques of Petri nets, discrete simulation, and memetic algorithms and second is the rescheduling environment with machine failures to optimize the makespan and Total Weighted Tardiness. The effectiveness of the proposed Soft computing approaches was validated with the bottleneck of heuristics and the dispatch rules. The results of the proposed algorithm show significant findings with the contrasting techniques. In the first stage (scheduling), improvements are obtained between 50 and 70% on performance indicators. In the second stage (failure), four scenarios are developed that improve the variability, flexibility, and robustness of the schedules. In the final stage (rescheduling), the results show that 78% of the instances have variations of less than 10% for the initial schedule. Furthermore, 88% of the instances support rescheduling with variations of less than 2% compared to the heuristics.

/pdf/soft-computing-approaches-for-rescheduling-problems-in-a-3ylau521lm.pdf

Soft-computing approaches for rescheduling problems in a manufacturing industry

Customer’s recognition, classification, and selecting the target market are the most important success factors of a marketing system. ABC classification of the customers based on axiomatic design exposes the behavior of the customer in a logical way in each class. Quite often, missing data is a common occurrence and can have a significant effect on the decision- making problems. In this context, this proposed article determines the customer’s behavioral rule by incomplete rough set theory. Based on the proposed axiomatic design, the managers of a firm can map the rules on designed structures. This study demonstrates to identify the customers, determine their characteristics, and facilitate the development of a marketing strategy.

Shib Sankar Sana

Papers

An integrated project of fishery and poultry

Application of rough set and netnography in tourism marketing analysis

Two-phase deteriorated supply chain model with variable demand and imperfect production process under two-stage credit financing

Soft-computing approaches for rescheduling problems in a manufacturing industry

ABC analysis of the customers using axiomatic design and incomplete rough set