There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

行銷硏究 : Marketing research

This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

The Oxford Handbook of Innovation

An Evaluation of Drivers in Implementing Sustainable Manufacturing in India: Using DEMATEL Approach

Purpose: Covid-19 pandemic is a unique and extraordinary situation for the globe, which has potentially disrupted almost all aspects of life. In this global crisis, the tourism and hospitality sector has collapsed in almost all parts of the world, and the same is true for India. Therefore, this paper aims to investigate the impact of Covid-19 on the Indian tourism industry. Design/methodology/approach: This study develops an appropriate model to forecast the expected loss of foreign tourist arrivals (FTAs) in India for 10 months. Since the FTAs follow a seasonal trend, seasonal autoregressive integrated moving average (SARIMA) method has been employed to forecast the expected FTAs in India from March 2020 to December 2020. The results of the proposed model are then compared with the ones obtained by Holt-Winter's (H-W) model to check the robustness of the proposed model. Findings: The SARIMA model seeks to manifest the monthly arrival of foreign tourists and also elaborates on the progressing expected loss of foreign tourists arrive for the next three quarters is approximately 2 million, 2.3 million and 3.2 million, respectively. Thus, in the next three quarters, there will be an enormous downfall of FTAs, and there is a need to adopt appropriate measures. The comparison demonstrates that SARIMA is a better model than H-W model. Originality/value: Several studies have been reported on pandemic-affected tourism sectors using different techniques. The earlier pandemic outbreak was controlled and region-specific, but the Covid-19 eruption is a global threat having potential ramifications and strong spreading power. This work is one of the first attempts to study and analyse the impact of Covid-19 on FTAs in India. © 2021, Md Ozair Arshad, Shahbaz Khan, Abid Haleem, Hannan Mansoor, Md Osaid Arshad and Md Ekrama Arshad.

/pdf/understanding-the-impact-of-covid-19-on-indian-tourism-45ycbwl91y.pdf

Understanding the impact of Covid-19 on Indian tourism sector through time series modelling


Purpose
The development of new communication technology such as 5G is now a solid choice for Industry 4.0. 5G in the fifth generation replaces the presently used mobile telecommunications networks. In every sector of modern life, this technology is designed to resolve the issue of the enormous rise in daily gadgets. 5G allows rapid data transfer and significantly improves the Internet of Things (IoT), which contains billions of devices. The purpose of this paper is to study the potential applications of 5G for Industry 4.0.


Design/methodology/approach
Relevant research publications from Scopus, Web of Science and Google Scholar were identified to accomplish the objectives of this paper. 5G for Industry 4.0 enables crucial communications such as wireless management of machines and robotics to unleash Industry 4.0’s full potential, including the considerable proliferation of IoT devices in 5G. This technology enhances quality control, increasing inspections that rely on real-time analysis for rapid and early detection.


Findings
In Industry 4.0, 5G technology provides high speed and flexibility connectivity which helps to enhance the entire manufacturing system. This paper briefs about 5G and different network technologies used in 5G. Advancements, associated features and specialties, and significant enablers of 5G for Industry 4.0 are discussed. Finally, the paper identifies and discusses eighteen 5G applications for Industry 4.0. 5 G-enabled robots are used in manufacturing to carry out extensive work to enhance connectivity.


Originality/value
5G is the next-generation communication technology, allowing numerous examples of inventive usage, including Industry 4.0. In line with its long-term vision of digitisation, 5G benefit the whole value chain, including consumers and businesses.


Progressive schema of 5G for Industry 4.0: features, enablers, and services

Civilizations have witnessed a long list of diseases that have made a devastating impact on humankind's working in almost all aspects of life. At the start, COVID-19 bought the world to a standstill. Today lakhs have lost their lives, many are still struggling on the death bed, and large numbers have lost their jobs. The world's conventional education system seems to come to a halt with the physical closure of all schools and institutions. Understanding the losses that occurred due to several diseases, the present world has to prepare a backup strategy to reduce the economic and human losses. The paper aims to identify the measures required for minimizing the losses caused by COVID-19 to human evolution. Further, this study proposes a working mechanism for several affected sectors during the disease. The paper also discusses the current challenges from the COVID-19 pandemic crisis and possible make-over in the working platform. With the help of this sustainable working platform, the affected sectors from COVID-19 can be helped. Further, we can reset specific sectors and sustainably reshape the world.

Make-over in the sustainable working platform during COVID-19 pandemic

Innovation management (IM) has increasingly been identified as an important research area to consider new ideas from different perspective and to improve the outcomes of business processes. Important contributions, mostly from the last five years, in field of IM have been included in this study. A model has also been presented to show the key theme areas of IM research. Literature has been reviewed to identify seven critical success factors (categorising 49 factors); nine benefits; and three perspectives of risks (categorising 11 risks). A statistical survey has been carried out to reveal: customer’s perspective and organisation perspective as most important CSFs; long term competitive advantage and improved efficiency as most beneficial; and operational risk as most critical area in IM. Analysis of CSFs, benefits and risks will help organisations to prioritise and manage them effectively and efficiently to draw advantage from IM.

Abid Haleem

Papers

An Evaluation of Drivers in Implementing Sustainable Manufacturing in India: Using DEMATEL Approach

Understanding the impact of Covid-19 on Indian tourism sector through time series modelling

Progressive schema of 5G for Industry 4.0: features, enablers, and services

Make-over in the sustainable working platform during COVID-19 pandemic

Contributions in research in the field of innovation management: analysis of critical success factors, benefits and risks