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

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A Break in the Clouds: Towards a Cloud Definition

The Grid 2: Blueprint for a New Computing Infrastructure

The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

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Internet of Things: Architectures, Protocols, and Applications

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Every physical sensor is pre-defined to act on purpose. Likewise, a virtual sensor is destined to act on application needs, perhaps do more for a single sensor device. This paper explores the conceptual form of a fundamental virtual sensor and also provides experimental result so that it can be deployed as cloud based service.

Poster: Virtual Sensor: The Purpose and Applications

This paper studies different popular international standards in the field of Electronic Health Record and finds out the ease and difficulties that arise at the time of implementation of such standards in a country like India. A remote health service provisioning system is being developed based on a sensor-cloud environment. This paper presents a data model which is suitable for countries like India and for similar situations and can be implemented in the sensor cloud environment. The data model is prepared following Indian standard for Electronic Health Records. This paper also proposes that incremental or phased approach is needed to implement Electronic Health Record and Electronic Health Record System in India.

Standards of EHR and their scope of implementation in a sensor-cloud environment: In Indian context

Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are host...

Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure

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Nandini Mukherjee

Papers

Poster: Virtual Sensor: The Purpose and Applications

Standards of EHR and their scope of implementation in a sensor-cloud environment: In Indian context

Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure

A Survey on Application of Machine Learning to Resource Management in Grid Environment

Security in WSN