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Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

DicomWorks is freeware software for reading and working on medical images [digital imaging and communication in medicine (DICOM)]. It was jointly developed by two research laboratories, with the feedback of more than 35,000 registered users throughout the world who provided information to guide its development. We detail their occupations (50% radiologists, 20% engineers, 9% medical physicists, 7% cardiologists, 6% neurologists, and 8% others), geographic origins, and main interests in the software. The viewer's interface is similar to that of a picture archiving and communication system viewing station. It provides basic but efficient tools for opening DICOM images and reviewing and exporting them to teaching files or digital presentations. E-mail, FTP, or DICOM protocols are supported for transmitting images through a local network or the Internet. Thanks to its wide compatibility, a localized (15 languages) and user-friendly interface, and its opened architecture, DicomWorks helps quick development of non proprietary, low-cost image review or teleradiology solutions in developed and emerging countries.KEY WORDS: Computers, digital imaging and communication in medicine (DICOM), DICOM viewer, teleradiology BACKGROUND

Nowadays, many industries and government can exploit Big Data to extract valuable insight. Such insight can help decision makers to enhance their strategies and optimize their plans. It helps the organization to gain a competitive advantage and provides added value for many economic and social sectors. In fact, several governments have launched programs, with important funds, in order to enhance research and development in the field of Big Data. Private sector has also made many investments to maximize profits and optimize resources. This article presents several Big Data projects, opportunities, examples and models in many sectors such as healthcare, commerce, tourism and politics. It gives also examples of technologies and solutions developed to face Big Data challenges.

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