A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges. 

/pdf/data-integration-challenges-for-machine-learning-in-1we2j1aq.pdf

Data Integration Challenges for Machine Learning in Precision Medicine

Big data is working hand in hand with cloud computing. Cloud computing can enable real-time processing and analysis at a bigger scale and efficiently. This paper aims to understand the use of big data analytics in the cloud infrastructure. The discussion begins with various use cases and applications that are implemented via cloud computing in big data analytics. This research also discusses the organization's challenges during the implementation and utilization of cloud computing. Cloud deployment models, cloud services and feature are also part of the discussion. Finally, a comparison between three big cloud providers is drawn to critically review the services offered by these providers with respect to their offerings for big data analytics. 

A Comparative Investigation on the Use of Cloud Computing for Big Data Analytics

Despite the benefits of standardization, the customization of Software as a Service (SaaS) application is also essential because of the many unique requirements of customers. This study, therefore, focuses on the development of a valid and reliable software customization model for SaaS quality that consists of (1) generic software customization types and a list of common practices for each customization type in the SaaS multi-tenant context, and (2) key quality attributes of SaaS applications associated with customization. The study was divided into three phases: the conceptualization of the model, analysis of its validity using SaaS academic-derived expertise, and evaluation of its reliability by submitting it to an internal consistency reliability test conducted by software-engineer researchers. The model was initially devised based on six customization approaches, 46 customization practices, and 13 quality attributes in the SaaS multi-tenant context. Subsequently, its content was validated over two rounds of testing after which one approach and 14 practices were removed and 20 practices were reformulated. The internal consistency reliability study was thereafter conducted by 34 software engineer researchers. All constructs of the content-validated model were found to be reliable in this study. The final version of the model consists of 6 constructs and 44 items. These six constructs and their associated items are as follows: (1) Configuration (eight items), (2) Composition (four items), (3) Extension (six items), 4) Integration (eight items), (5) Modification (five items), and (6) SaaS quality (13 items). The results of the study may contribute to enhancing the capability of empirically analyzing the impact of software customization on SaaS quality by benefiting from all resultant constructs and items.

Development of a valid and reliable software customization model for SaaS quality through iterative method: perspectives from academia

Traffic congestion, air pollution, fuel wastage, and car accidents are all exacerbated by increased traffic. Thus, vehicular communications, which refer to information transmission between cars, pedestrians, and infrastructures, have lately gained popularity and been extensively explored due to their enormous potential to enable intelligent transportation and various safety applications. Manually piloted cars and automated vehicles can acquire relevant information via vehicular communications to enhance traffic security and boost entertainment services. The basic concept of automobile clouds was originally published in the literature not long ago, and several suggested structural approaches have been presented in this study thus far. Several academics have concentrated on the structural layout to address various problems and, as a result, satisfy user expectations in order to give dependable services. We examined various vehicular cloud topologies in this study. We also offered a complete summary of current network layer research on allowing efficient vehicle communications and examined specific security, architectural, and reliability concerns in vehicular clouds. Also, the taxonomy of vehicular networks was discussed in terms of the service link between vehicular networks and cloud computing. Ultimately, we discussed the research prospects available. The results showed that security and privacy challenges are among the most important challenges.

Dependable and reliable cloud‐based architectures for vehicular communications: A systematic literature review

Cloud accounting current form of manifestation of digital accounting

Software as a Service (SaaS) is one of the delivery model of cloud computing in which service are provided by the service providers and client make use of it through Internet. Here we present a saas in pcloud. pcloud is a distributed system which contains a number of cooperated nodes. In pcloud, distribute the programming software to a number of nodes so that queries can be executed in parallel manner. In this distributed system, communication overhead between the co-operated nodes is very low. Programming code is send as a user queries to pcloud where it is processed and retrieved through PIR protocol. The concept used by the PIR protocol is the way that accesses of sequential segments are made to different physical storage devices. To minimize the effect of node failures, pCloud includes data replication. The Load Balancing can be achieved by loosely coupled and nearest neighbor property of clouds. Key terms: pCloud, software as a service, PIR, Stripping technique, Load Balancing.

http://irdindia.in/journal_ijraet/pdf/vol8_iss4/1.pdf

Software as a Service in Cloud Computing

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