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Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.

In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.

This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.

As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.

Web services play vital role in the development of emerging technologies such as Cloud computing and Internet of Things. Although, there is a close relationship among the discovery, selection and composition tasks of Web services, research community has treated these challenges at individual level rather to focus on them collectively for developing efficient solution, which is the purpose of this work. This paper aims to propose an approach to integrate the service discovery, selection and composition of Semantic Web services on runtime basis.

The proposed approach defined as a quality of service (QoS)-aware approach is based on QoS model to perform discovery, selection and composition tasks at runtime to enhance the user satisfaction and quality guarantee by incorporating non-functional parameters such as response time and throughput with the Web services and user request. In this paper, the proposed approach is based on ontology for semantic description of Web services, which provides interoperability and automation in the Web services tasks.

This work proposed an integrated framework of Web service discovery, selection and composition which supports end user to search, select and compose the Web services at runtime using semantic description and non-functional requirements. The proposed approach is evaluated by various data sets from the Web Service Challenge 2009 (WSC-2009) to show the efficiency of this work. A use case scenario of Healthcare Information System is implemented using proposed work to demonstrate the usability and requirement the proposed approach.

The main contribution of this paper is to develop an integrated approach of Semantic Web services discovery, selection and composition by using the non-functional requirements.

The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making.

In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score.

The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset.

Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

In December 2015, 196 countries will meet in Paris to reach a new global climate change agreement. This case looks at the climate negotiation process from the eyes of India's environment minister Mr Prakash Javadekar. In India's context, the energy sector has a big role to play in emission reduction as it is the largest emitter of the GHGs. When compared to US and China, India's per-capita emissions are miniscule but they are expected to rise substantially as the GoI is investing heavily in the infrastructure sector which has a substantial carbon footprint. The case discusses the mandatory emission cuts that India will announce considering the fulfillment of sustainable development goals. The case also points out, the government's promise of providing 24*7 electricity by 2019 which it feels can be an impediment in setting an aggressive emission cut target. The case questions if changes in the portfolio mix can be a part of the solution.

The purpose of this exploratory study is to look at how the Internet was used by political parties and candidates during the Indian parliamentary elections of 2009.

A total of 31 web sites belonging to political parties and their candidates in the Indian states of Maharashtra and Gujarat were examined for how they were used to mobilize volunteers and voters. An online questionnaire and in‐depth interviews were administered to the web site coordinators/designers and politicians.

The study found that sites were not used to their maximum potential but instead, merely for publicity, online presence, and to explore the new medium. There was greater reliance by most candidates on traditional media such as rallies and face‐to‐face interaction. The reason may be due to the limited Internet penetration in India, which also means the Internet may have less influence on voters. Some candidates have shown the way to the potential use of the medium for fund raising and recruiting volunteers. But Indian politicians will likely continue to be cautious in using the Internet.

This study was limited to the states of Maharashtra and Gujarat and did not consider the impact or the effectiveness of the Internet.

This is the first such study of the use of web sites for electioneering in India. It also documents the development in the use of the new medium for campaigning in 2009 as compared with the elections of 2004.