Zied Kechaou

An enhanced healthcare system in mobile cloud computing environment

Mobile cloud computing (MCC) is a new technology for mobile web services. Accordingly, we assume that MCC is likely to be of the heart of healthcare transformation. MCC offers new kinds of services and facilities for patients and caregivers. In this regard, we have tried to propose a new mobile medical web service system. To this end, we implement a medical cloud multi-agent system (MCMAS) solution for polyclinic ESSALEMA Sfax—TUNISIA, using Google’s Android operating system. The developed system has been assessing using the CloudSim Simulator. This paper presents initial results of the system in practice. In fact the proposed solution shows that the MCMAS has a commanding capability to cope with the problem of traditional application. The performance of the MCMAS is compared with the traditional system in polyclinic ESSALEMA which showed that this prototype yields better recital than using usual application.

Swarm intelligence and multi agent system in healthcare

The domain of Healthcare is characterized by difficulty, dynamism and variety. In the 21st century healthcare represents different challenges (the increasing cost of care and the growing of populations). For that, Agent Technology can provide better healthcare than the traditional medical system. In the hospital, several types of medical problems can be solved by agents. As examples of problems, which emerge in the hospital, we mention: collaboration between hospital wards, elaborations of diagnostics, the collection of information about patients etc. The adaptation of cooperative Multi Agent System (MAS) can solve these problems. In this regard, this study proposes a general architecture that integrates Swarm Intelligence into Multi Agent healthcare System in order to make care as efficient as possible.

Mobile Cloud Computing in Healthcare System

Mobile Cloud Computing (MCC) is a potential technology for mobile web services. Accordingly, we assume that MCC is likely to be of great avail to healthcare domain. MCC offers new kinds of services and facilities for patients and caregivers. In this regard, we have tried to propose a new mobile medical web service system. The proposed system called Medical Cloud Multi Agent System is a complex system which integrates MCC and Multi Agent System in healthcare with view to improving healthcare system.

Towards an Offloading Framework based on Big Data Analytics in Mobile Cloud Computing Environments

Abstract Mobile Cloud Computing (MCC) is the combination between cloud computing and mobile devices. The challenge for mobile devices is to provide solutions for their limited resources, and it would be possible through cloud computing by running memory intensive operations on distant servers. This paper proposes a framework for code offloading based on big data analytics in mobile cloud environments.

A new-arabic-text classification system using a Hidden Markov Model

The Recent years have witnessed a rapid growth in the quantity of Arabic-formulated information available in electronic format on both the Internet and corporate intranet. As a result, the user turns out to be overwhelmed by such a huge mass of information, with an arising question of how to locate or retrieve the desired information they need. For this end, several automatic classification systems have been developed both on the Internet, and within companies. With respect to the present paper, a special attempt is made to present a thorough examination of the effectiveness of applying a specific machine-learning technique relevant to help solve the Arabic text related classification problem. In addition, we undertake to explore and identify the major Hidden Markov Model (HMM) classifier benefits with regard to Arabic text classification procedure based on our newly-designed stemming approach. On the basis of the reached experimental results, one might well notice that our conceived HMM-based model has managed to achieve a high-classification accuracy with regard to Arabic-electronic text corpuses.

A MULTI-AGENT BASED SYSTEM FOR SENTIMENT ANALYSIS OF USER-GENERATED CONTENT

The recent years have been marked by a rapid growth in the World Wide Web 2.0 applications such as blog posts, forums, mailing lists, and product-review websites. As a result, a special sentiment analysis field has sprung up relevant to the issue of peoples responses to the diversity of available subjects. Hence, one might well wonder: how do people feel and react when dealing with certain topics? In this paper, a new automatic sentiment-processing model has been advanced, whereby the current problems faced by the prevalent existing models can be deciphered and more properly treated. The suggested approach consists in developing a multi-agent system based on a thorough linguistic analysis, meanwhile highlighting the major contributions provided by such a study in combination with the syntactic, semantic, and subjective analyses. Actually, the newly-devised framework enables to resolve the ambiguities and complexities of the natural evaluative language and to strengthen, as well as consolidate, the result...

A new linguistic approach to sentiment automatic processing

With the growth of the Web2.0, e-commerce has become very popular in use, many websites offer the opportunity to make sales online and give the opportunity to get own an online review about objects, persons, and products. New opportunities and challenges arise as people can now actively use information technologies to seek and understand other peoples opinions (sentiments) when to making their choices. In this paper, we propose a new two-step approach for an automatic sentiment processing, consisting a first step dealing with extract the subjective portions of reviews and a second one which determines the review overall sentiment by identifying whether a review appreciates (positive) or desappreciates (negative) its purpose. This approach consists of a four levels processing chain operating in a parallel way (i.e syntactic, semantic, linguistic and sentiment analysis).

Towards a Medical Intensive Care Unit Decision Support System Based on Intuitionistic Fuzzy Logic

Intensive Care Unit (ICU) medical processes can be so complex and unpredictable that physicians sometimes must make decisions based on perception. Both decision support system and Intuitionistic Fuzzy Logic (IFL) techniques can assist doctors to handle this complexity in a safe, harmless and efficient manner. To this end, we propose a prototype called Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS) based on IFL and the Modified Early Warning Score (MEWS) standard score. Moreover, the experimental results have been shown the efficiency of the proposed system.

Cloud computing and mobile devices based system for healthcare application

Nowadays the emergence of mobile devises and Cloud Computing can change the culture of healthcare from direct care services into Mobile Cloud computing (MCC) services. For that, the application targeted at mobile devices becoming copious with others systems in healthcare sector. Consequently, we assume that this technology have a potential effect in healthcare domain. MCC offers new kinds of services and facilities medical tasks. In this regard, we have tried to propose a new mobile medical web service system. The proposed system called Medical Mobile Cloud Multi Agent System (2MCMAS) is a hybrid system which integrates MCC and Multi Agent System in healthcare in order to make efficiency care.

Enhanced Deep Learning Models for Sentiment Analysis in Arab Social Media

Over the last few years, the amount of Arab sentiment rich data as appearing on the web has been marked with a rapid surge, owing mainly to the remarkable increase noticed in the number of social media users. In this respect, various companies are now turning to online forums, blogs, and tweets with the aim of getting reviews of their products, as drown from customers. Hence, sentiment analysis turns out to lie at the heart of social media associated research, targeted towards detecting people opinion as embedded within the wide range of texts while attempting to capture their pertaining polarities, whether positive or negative.