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

Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs

Industry 4.0: Adoption challenges and benefits for SMEs

The purpose of this paper is to present a conceptual model that defines the essential components shaping the new Digital Supply Chains (DSCs) through the implementation and acceleration of Industry 4.0.,The scope of the present work exposes a conceptual approach and review of the key literature from 1989 to 2019, concerning the evolution and transformation of the actors and constructs in logistics and Supply Chain Management (SCM) by means of examining different conceptual models and a state-of-the-art review of Industry 4.0’s concepts and elements, with a focus on digitization in supply chain (SC) processes. A detailed study of the constructs and components of SCM, as defined by their authors, resulted in the development of a referential and systematic model that fuses the inherent concepts and roles of SCM, with the new technological trends directed toward digitization, automation, and the increasing use of information and communication technologies across logistics global value chains.,Having achieved an exploration of the different conceptual frameworks, there is no compelling evidence of the existence of a conceptual SCM that incorporates the basic theoretical constructs and the new roles and elements of Industry 4.0. Therefore, the main components of Industry 4.0 and their impact on DSC Management are described, driving the proposal for a new conceptual model which addresses and accelerates a vision of the future of the interconnectivity between different DSCs, grouped in clusters in order to add value, through new forms of cooperation and digital integration.,This research explores the gap in the current SCM models leading into Industry 4.0. The proposed model provides a novel and comprehensive overview of the new concepts and components driving the nascent and current DSCs. This conceptual framework will further aid researchers in the exploration of knowledge regarding the variables and components presented, as well as the verification of the newly revealed roles and constructs to understand the new forms of cooperation and implementation of Industry 4.0 in digitalized SCs.

Digital supply chain model in Industry 4.0

The adoption of digital technologies in supply chains: Drivers, process and impact

Overcooling indoor spaces in hot-and-humid regions indicate excessive usage of air conditioner (AC). Understanding the occupants' thermal perception in AC settings helps navigate the cooling energy required. This study investigated thermal comfort in biased and non-biased environments and examined the occupants’ preferences. Four set-point temperature conditions (Original, Original ±2 °C, and MS Standard) were implemented in a semi-controlled field study done in 19 offices. 628 samples were taken from 42 occupants via thermal measurements and questionnaire surveys. The indoor air temperature in the typical AC settings (Original set-point) was 23.1 °C, denoting non-compliance to the local guideline. The results showed that occupants generally felt more comfortable when the indoor air temperature was increased. The mean comfort temperature was 24.6 °C, and the proportion of comfort votes depletes when the operative temperature reaches 26 °C. The preferred temperature was estimated at 23.9 °C, and the linear relationship with comfort temperatures revealed that occupants preferred a cooler environment despite being thermally comfortable. The findings suggest that occupants could tolerate higher AC settings well, but thermal preference may be a critical factor in estimating the comfort temperature limits. 

Associating thermal comfort and preference in Malaysian universities’ air-conditioned office rooms under various set-point temperatures

Purpose




This paper aims to investigate the application of unsupervised machine learning in the international location decision (ILD). This paper addresses the need for a fast, quantitative and dynamic location decision framework.




Design/methodology/approach




Unsupervised machine learning technique, i.e. k-means clustering, is used to carry out the analysis. In total, 24 different indicators of 94 countries, categorized into five groups, have been used in the analysis. After the clustering, the clusters have been compared and scored to select the feasible countries.




Findings




A new framework is developed based on k-means clustering that can be used in ILD. This method provides a quantitative output without personal subjectivity. The indicators can be easily added or extracted based on the preferences of the decision-makers. Hence, it was found out that the unsupervised machine learning, i.e. k-means clustering, is a fast and flexible decision support framework that can be used in ILD.




Research limitations/implications




Limitations include the generality of selected indicators and clustering algorithm used. The use of other methods and parameters may lead to alternate results.




Originality/value




The framework developed through the research intends to assist the decision-makers in deciding on the facility locations. The framework can be used in international and national domains. It provides a quantitative, fast and flexible way to shortlist the potential locations. Other methods can also be used to further decide on the specific location.

/pdf/using-k-means-clustering-in-international-location-decision-2a9g9o5f99.pdf

Using k-means clustering in international location decision

The United Nations (UN) has introduced 17 Sustainable Development Goals (SDGs) intending to achieve a conventional trio of social, economic, and environmental prosperity across the world. Pakistan is making a fair contribution to SDG achievement under its ‘Vision 2025’. However, the introduced strategy for SDG implementation is a sectoral based approach which leads to severe resource inefficiency. The objective of this study is the development and implementation of a nexus strategy of SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action) in Pakistan due to their indepth interlinkage and interdependence, to achieve sustainability in Energy, Water, and Environment. This study comprises an onfield Energy-Water-Sustainability assessment of 24 public sector premises across Punjab to create a pilot model for future implementation. The Pearson correlation analysis of the SDGs substantiates the argument of the nexus approach. The results of this work show the energy conservation potential of approximately 12.8 MW and renewable energy installation potential of 40.4 MW to achieve SDG 7. Moreover, rainwater harvesting is calculated to be 0.035 million cm3 per annum, contributing to approximately 7% reduction in water use to achieve SDG 6 and minimizing the environmental impact on cities by reducing 14.8 million tons of Greenhouse Gases (GHGs) per annum to achieve SDG 11 and 13. The results show that due to indepth interlinkage, the nexus implementation approach enhances the execution process while eliminating resource duplication to reduce inefficiency. 

Nexus implementation of sustainable development goals (SDGs) for sustainable public sector buildings in Pakistan

Toward a framework for a maintenance architecture

It is important to be able to compare and evaluate different solutions early in development. This paper proposes a method for structuring historical data into a data model that can support the evaluation of new design concepts. The data is contextualized by linking it to a hierarchical decomposition of existing products. Two case studies were conducted to evaluate the value of using historical data when evaluating new concepts. The cases confirm that the proposed method is useful for evaluation of new concepts.

/pdf/structuring-and-contextualizing-historical-data-for-decision-wp4nt8qqph.pdf

Structuring and contextualizing historical data for decision making in early development

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Waqas Khalid

Author Tools

Chat about Author