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

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

A survey of mobile cloud computing: architecture, applications, and approaches

http://mail.im.tku.edu.tw/~myday/teaching/992/SEC/S/992SEC_T4_Paper_20110520_What%20drives%20mobile%20commerce.pdf

What drives mobile commerce? An empirical evaluation of the revised technology acceptance model

Wireless sensor networks for healthcare: A survey

Hawaii International Conference on System Sciences

With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, the wireless infrastructure can support many current and emerging healthcare applications. This could fulfill the vision of "Pervasive Healthcare" or healthcare to anyone, anytime, and anywhere by removing locational, time and other restraints while increasing both the coverage and the quality. In this paper, we present applications and requirements of pervasive healthcare, wireless networking solutions and several important research problems. The pervasive healthcare applications include pervasive health monitoring, intelligent emergency management system, pervasive health-care data access, and ubiquitous mobile telemedicine. One major application in pervasive healthcare, termed comprehensive health monitoring is presented in significant details using wireless networking solutions of wireless LANs, ad hoc wireless networks, and, cellular/GSM/3G infrastructure-oriented networks. Many interesting challenges of comprehensive wireless health monitoring, including context-awareness, reliability, and, autonomous and adaptable operation are also presented along with several high-level solutions. Several interesting research problems have been identified and presented for future research.

Pervasive healthcare and wireless health monitoring

A fundamental problem in many disciplines is the classification of objects in a domain of interest into a taxonomy. Developing a taxonomy, however, is a complex process that has not been adequately...

https://link.springer.com/content/pdf/10.1057%2Fejis.2012.26.pdf

A method for taxonomy development and its application in information systems

Advances in e-commerce have resulted in significant progress towards strategies, requirements, and development of e-commerce applications. However, nearly all e-commerce applications envisioned and developed so far assume fixed or stationary users with wired infrastructure. We envision many new e-commerce applications that will be possible and significantly benefit from emerging wireless and mobile networks. To allow designers, developers, and researchers to strategize and create mobile commerce applications, we propose a four-level integrated framework for mobile commerce. Since there are potentially an unlimited number of mobile commerce applications, we attempt to identify several important classes of applications such as mobile financial applications, mobile inventory management, proactive service management, product location and search, and wireless re-engineering. We discuss how to successfully define, architect, and implement the necessary hardware/software infrastructure in support of mobile commerce. Also, to make mobile commerce applications a reality, we address networking requirements, discuss support from wireless carriers, and present some open research problems.

Mobile commerce: framework, applications and networking support

Mobile advertising will become more pervasive and profitable, but not before addressing key technical and business challenges.

Challenges and business models for mobile location-based services and advertising

W ith the increasing use of small portable computers, wireless networks, and satellites, a trend to support computing on the move has emerged—this trend is known as mobile computing or nomadic computing [3]. Also referred to as anytime/anywhere computing, mobile computing has several interesting and important applications for business (such as instant claim processing and e-commerce), telecommunications and personal communications, national defense (tracking troop movements), emergency and disaster management, real-time control systems, remote operation of appliances, and in accessing the Internet. Since a user may not maintain a fixed position in such environments, the mobile and wireless networking support allowing mobile users to communicate with other users (fixed or mobile) becomes crucial. A possible scenario may involve several different networks that can support or can be modified to support mobile users. When dealing with different wireless networks, a universal mobile device should be able to select the network (LAN, the Internet, PCS, or satellite) that best meets user requirements. Wireless and mobile networks have provided the flexibility required for an increasingly mobile workforce. As shown in Figure 1(a), the worldwide number of cellular, GSM, and PCS subscribers increased from 140 million in 1996 to over 300 million in 1999 and is expected to grow to 650 million by 2001 (see www.gsmdata.com). In the U.S., capital investment increased from $6.3 billion in 1990 to $66.8 billion in 1999 and service revenues were up from $4.5 billion to $38.7 billion in 1999 (see www.wow-com.com) as shown in Figure 1(b). During the same period, the average local monthly bill diminished from $80 to $39 as shown in Figure 1(c), indicating the technological maturity and the tremendous competition among service providers. Many general remarks can be made about wireless systems. First, the channel capacity typically available in wireless systems is much lower than what is Upkar Varshney and Ron Vetter

Upkar Varshney

Papers

Pervasive healthcare and wireless health monitoring

A method for taxonomy development and its application in information systems

Mobile commerce: framework, applications and networking support

Challenges and business models for mobile location-based services and advertising

Emerging mobile and wireless networks