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 application of theory of planned behavior to predict young Indian consumers' green hotel visit intention

A multi-analytical approach to predict the Facebook usage in higher education

This study aims to identify green-hotel attributes and determinants that contribute to guests’ intention generation for visiting a green hotel and practicing pro-environmental actions during their ...

How do green attributes elicit pro-environmental behaviors in guests? The case of green hotels in Vietnam

The study analyses teachers’ perceptions of methods, teacher roles, success and evaluation in PBL and traditional classroom instruction. The analysis is based on empirical data collected in primary schools and vocational secondary schools. An analysis of 109 questionnaires revealed numerous differences based on degree of experience and type of school. In general, project-based methods were preferred among teachers, who mostly perceived themselves as facilitators and considered motivation and transmission of values central to their work. Teachers appeared not to capitalize on the use of ICT tools or emotions. Students actively participated in the evaluation process via oral evaluation.

/pdf/in-service-teachers-perceptions-of-project-based-learning-59smlbfq0t.pdf

In-service teachers' perceptions of project-based learning.

/pdf/a-study-on-student-preference-towards-the-use-of-edmodo-as-a-4i5abtr5av.pdf

A study on "Student preference towards the use of Edmodo as a learning platform to create responsible learning environment"

This review paper aims at understanding the importance of implementing sustainable green practices in the current hotel industry and the perception of the same from the point of view of the customers as well as the industry experts. Many hotels have benefited from green management such as enhanced reputation of the firm and more worth customers. For the business standing, it reduces business’s cost for posting advertisements and the clear hotel’s orientation shows hotels’ positive image which might increase employees’ recognition toward the business. Sustainability in business is the growth in lively processes which enable people to understand the potential to protect the Earth’s existent support systems. Well, looking to the future today’s green concerns will definitely become facet of more synchronized business environment, perhaps the concerns discussed in this study, may exchange a few words which hotels may consider in near future to widen awareness and improve business model. Keywords—Environmental Protection, Green Hotel Concept, Hotel Industry, Sustainability.

/pdf/connect-among-green-sustainability-and-hotel-industry-a-44eotr2meg.pdf

Connect among Green, Sustainability and Hotel Industry: A Prospective Simulation Study

Service Delivery Quality Improvement Models: A Review

http://academicscience.co.in/admin/resources/project/paper/f201407121405188083.pdf

Hotel managers perspective of managerial competency among graduating students of hotel management programme

The idea of Smart Cities has been one of the key driving factors for the urban transformation to a low carbon climate, sustainable economy and mobility in recent years because of the alarming situation of global warming. One of the industries with swift growth is hotel sector and hence is one of the key contributors to carbon emission and leaves environment footprints. The new emerging concept of sustainable tourism is envisaged as an important part of the Smart Cities paradigm. Improving sustainability by saving energy is becoming a primary task toady for many hotels. A great opportunity is provided by Artificial Intelligence (AI) and Internet of Things (IoT) to assimilate different systems on a platform by encouraging and assisting hotel guests to operate through single device and optimizing hotel operations. Current research focuses to identify the strategic positions of a hotel in terms of sustainability, AI and IoT technology. Components that will be considered by Hotels for the strategic intention of adopting AI and IoT for environmental sustainability. Different development and modification needed to be taken if management wants high sustainability readiness and/or IoT readiness. This conceptual paper constructs on the comprehensive study and systematic review of different area where the hotels can feasibly implement AI and IoT for improving sustainably.

Leena Fukey

Papers

A study on "Student preference towards the use of Edmodo as a learning platform to create responsible learning environment"

Connect among Green, Sustainability and Hotel Industry: A Prospective Simulation Study

Service Delivery Quality Improvement Models: A Review

Hotel managers perspective of managerial competency among graduating students of hotel management programme

Artificial Intelligence and Internet of Things readiness: inclination for hotels to support a sustainable environment