An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

CYCLONE Unified Deployment and Management of Federated, Multi-Cloud Applications

Various Cloud layers have to work in concert in order to manage and deploy complex multi-cloud applications, executing sophisticated workflows for Cloud resource deployment, activation, adjustment, interaction, and monitoring. While there are ample solutions for managing individual Cloud aspects (e.g. network controllers, deployment tools, and application security software), there are no well-integrated suites for managing an entire multi cloud environment with multiple providers and deployment models. This paper presents the CYCLONE architecture that integrates a number of existing solutions to create an open, unified, holistic Cloud management platform for multi-cloud applications, tailored to the needs of research organizations and SMEs. It discusses major challenges in providing a network and security infrastructure for the Intercloud and concludes with the demonstration how the architecture is implemented in a real life bioinformatics use case

Patient's Feedback Platform for Quality of Services via “Free Text Analysis” in Healthcare Industry

Data analysis of social media posting continues to offer a huge variety of information about the health situation faced by an individual. Social networking or social media websites provide us a wealth of information generated by users in a variety of domains, that generated information are unstructured and unlabeled and are not captured in an exceedingly systematic manner, as info generated is not humanly possible to process due to its size. One traditional way of collecting patients experience is by conducting surveys and questionnaires, as these methods ask fixed questions and are expensive to administer. In this paper, a patient feedback platform (PFP) using free text sentiment analysis is developed to computationally identify and categorize the polarity expressed in a piece of text. Six machine learning latest algorithms have been used as key evaluation for evaluating accuracy of the developed (PFP) model. Results achieved have shown 88 % accuracy on the basis of which it is recommended that developed (PFP) patient feedback platform could be used to improve E-health care services indeed

Principles of Multimedia News Systems for Business Applications

In the past few years considerable demand for business oriented multimedia information systems has developed. A multimedia information system is one that can create, import, integrate, store, retrieve, edit, and delete two or more types of media materials in digital form, such as audio, image, full-motion video, and text information. Multimedia information systems play a central role in many business activities. They represent a very special class complex computing systems. This paper surveys a special type of multimedia information systems: multimedia news systems. Multimedia news systems deal with architectures to manage complex multimedia news databases, online presentation and distribution services or the integration of several existing services to meta-services using intelligent news retrieval engines. The leading presentation platform in multimedia news presentation is news networks providing television services and Internet content distribution. The primary focus is on advanced multimedia news systems infrastructure, document standards, application architecture and principles for multimedia news on the Web that suggest long-term trends in this increasingly important area.multimedia, information systems, business applications

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502