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 molecular representations of coal – A review

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Metrics and Models in Software Quality Engineering

Our study examines the literature that has been published in important journals on supply chain disruptions, a topic that has emerged the last 20 years, with an emphasis in the latest developments in the field. Based on a review process important studies have been identified and analyzed. The content analysis of these studies synthesized existing information about the types of disruptions, their impact on supply chains, resilience methods in supply chain design and recovery strategies proposed by the studies supported by cost–benefit analysis. Our review also examines the most popular modeling approaches on the topic with indicative examples and the IT tools that enhance resilience and reduce disruption risks. Finally, a detailed future research agenda is formed about SC disruptions, which identifies the research gaps yet to be addressed. The aim of this study is to amalgamate knowledge on supply chain disruptions which constitutes an important and timely as the frequency and impact of disruptions increase. The study summarizes and builds upon the knowledge of other well-cited reviews and surveys in this research area.

/pdf/supply-chain-disruptions-and-resilience-a-major-review-and-3ea73xziu3.pdf

Supply chain disruptions and resilience: a major review and future research agenda

The simulation technique on qualitative models is one of the powerful tools for the fault diagnosis. However, since a qualitative model is expressed with simple causal relations, enormous number of behaviors are possible even pruning off improbable behaviors by some real measured values patterns. Therefore, it is hard to apply it to practical scale system as it is. To cope with this problem, a stochastic qualitative reasoning is proposed in this paper. In this method, all of the behaviors have existence probabilities. The behaviors with relatively small probability are eliminated. We have applied this method to diagnosis of the air conditioning system. If a trouble is reported, the qualitative models of the target system in normal and failure are simulated and compared with the real measured values in the target plant. Agreement degrees with the model and the measured values are calculated with existence probabilities, and the cause of failure is found out. Through this application, we have confirmed the effectiveness of the method. >

Stochastic qualitative reasoning and its application to diagnosis of air conditioning system

Proposes an algorithm which detects structures of deadlock which have deadlock occurrence possibility. This deadlock detection algorithm finds out all deadlock structures in the business processes workflow model by using deadlock patterns which express deadlock structures by combinations of nodes. There is the problem that some deadlock structures cannot be identified with some deadlock pattern because the deadlock structures cross each other when the algorithm finds deadlock patterns in the business processes workflow model. The problem can be solved by detecting the deadlock structures not by using each deadlock pattern but by using each of the unit into which the deadlock pattern is integrated.

A deadlock detection algorithm for business processes workflow models

A diagnosis using qualitative reasoning is applicable to the diagnosis of building air conditioning systems such that their configuration is various. In this technique, a target system is modeled qualitatively, and possible patterns of behaviour of the target model are generated by the qualitative reasoning. By comparing the results of reasoning with the real measured values, a faulty part is identified. We propose a stochastic qualitative reasoning method. In this method, the existence probability is introduced to prevent the increasing possible behaviours of the model. Through the qualitative reasoning process, the states having negligible probability are eliminated. We have applied this method to fault diagnosis of a building air conditioning system and confirmed its effectiveness. >

https://ieeexplore.ieee.org/iel2/3189/9057/00402018.pdf

Diagnosis of an air conditioning system by stochastic qualitative reasoning

This work discusses a help desk support system that helps operators at help desk process inquiry e-mails and build up FAQ (Frequently Asked Questions) on their Web pages. Recently there have been increasing number of interactions between users and operators online. Therefore operators must take great deal of time to process inquiries from customers through the Internet. Operator tasks are mainly divided into two parts; replying to inquiries and managing typical question and answer sets to build up their FAQ pages. As for replying to inquiries, an automated replying method using existing FAQ and a replying support method that helps operators write e-mails are proposed. As for managing typical provided question and answer sets, an automated generation method of candidate FAQs is proposed. An experimental result proved by the proposed method is effective.

A help desk support system with filtering and reusing e-mails

A qualitative simulation system has been developed for understanding the behavior of a system modeled by a structural model. In decision making, it is necessary to understand the influence flow, namely how the model derives the behavior of the concerning node in the model rather than the behavior of each node. This paper presents a mechanism of scenario generating for explaining the influence flow to the concerning node. The mechanism consists of the following processes: 1) by tracing the structural model, all paths from the input node, which triggers the initial change of the system, to the concerning node are derived, and using similarity of behavior, the influence flow to the concerning node is selected out of all paths; 2) important nodes are selected according to the type of nodes; and 3) the scenario document is generated along the selected flow using the sentence template and the word dictionary.

http://ieeexplore.ieee.org/iel3/4021/11539/00537748.pdf

Scenario generator for qualitative simulation system

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