Jihad Mohamad Al Ja'am

A pictorial mobile-based communication application for non-verbal people with autism

Non-verbal people with autism are usually unable to communicate normally using natural languages. They can, however, learn to communicate through specific symbols and images. Special education instructors have adopted this method of communication to teach non-verbal people with autism. They introduce the symbols and images to them through different methodologies. This learning process appeared to be effective but it is very long. The process is carried out manually and requires a lots of times, dedication, and resources. The instructors should find the materials in different formats and circumstances. They should repeat the lessons several times and normally in a face-to-face framework. We propose in this paper a mobile-based application that allows non-verbal people with autism to learn and communicate with their surroundings using a smart device. They can then be taught to use specific symbols and images through the smart mobile phones. They can form simple words and sentences to express their feelings and needs. The application is flexible and allows the addition of new contents very easily. To assess the progress of the users, different exercises and puzzles are proposed. These allow the users to improve their skills and to continue learning outside the classrooms.

Automatic diacritics restoration for modern standard arabic text

In this paper, the problem of missing diacritic marks in most of Arabic written resources is investigated. Our aim is to implement a scalable and extensible platform to automatically restore missing diacritic marks for Modern Standard Arabic text. Different rule-based and statistical techniques are proposed. These include: morphological analyzer-based, maximum likelihood estimate, and statistical n-gram models. Diacritization accuracy of each technique was evaluated based on Diacritic Error Rate (DER) and Word Error Rate (WER). The proposed platform includes helper tools for text preprocessing and encoding conversion. It yielded a WER of 7.1% and DER of 3.9%. When the case ending was ignored, the platform yielded a WER and DER of 5.1% and 2.7%, respectively.

A native Arabic eLearning mobile application to transpose Arabic text to illustrations

Mobile eLearning are capable to avail children in improving learning capabilities, self-consciousness living, and profession growth. They can inspire the perfection of eLearning with the diverse subservient smart handled devices. Most available software applications that are attainable by children are done without taking in consideration their needs and specific deficiency. We have investigated Arabic mobile educational system that presents illustrations automatically to characterize content of Arabic stories for children. In order to display these illustrations, different sections have been implemented which comprise: the processing of natural Arabic language, applying semantic queries on expanded multi-domain educational ontology, word-to-word based relationship extraction, and querying different online search-engines. The fundamental destination of our proposed work is to improve children educational skills which include observation, comprehension, realization, and deduction.

An Arabic mobile educational system

Digital devices and computer software can assist children in improving their learning capabilities and comprehension skills. However most of these items are built without taking into consideration the effective needs and background of Arab children. We propose in this paper an Arabic-based mobile educational system that processes natural questions, extract pertinent answers from animal domain ontology, and illustrate pictures automatically. It combines the transformation of natural language questions into SPARQL queries and finds solutions for multi-phased based questions. In order to process questions, different phases are carried out which include extracting concepts from the Arabic questions, determining the pattern, extraction of word-to-word relationships, extracting knowledge from the ontology, and finding answers through direct or indirect linked data. The aim of our proposed system is to generate substantial pedagogic answers, for asked questions in the natural language, in a specific domain.

An arabic ontology-based learning system for children with intellectual challenges

Children with intellectual challenges (IC) are growing up with extensive exposure to computer technology. Computer software and assistive devices have the potential to help these children in education, career development, and independent living. However, most of the software, tools and web sites that these children interact with are designed without consideration of their special needs, making these elements less effective or completely inaccessible for them. This paper proposes an assistive education system that dynamically generates multimedia tutorials for children with IC in the state of Qatar. We use several techniques to generate the tutorials which include: Arabic text processing, entities relationship extraction, multimedia-based ontology, and online retrieval of multimedia contents. The main aim of our system is to enhance those children learning capabilities, understanding, communications, thinking and memorization skills through multimedia.

Arabic Named Entity Recognition—A Survey and Analysis

As Arabic digital data has been increasing in abundance; the need for processing this information is growing. Named entity recognition (NER) is an information extraction technique that is vital to the processes of natural language processing (NLP). The ambiguous characteristics of the Arabic language make tasks related to NER and NLP very challenging. In addition to that, work related to Arabic NER is rather limited and under-studied. In this study, we survey previous works and methodologies and provide an analysis and discussion on the feature sets used, evaluation tools and advantages and disadvantages of each technique.

An Arabic-Based Tutorial System for Children with Special Needs

Abstract In spite of the current proliferation of the use of computers in education in the Arab world, complete suites of solutions for students with special needs are very scarce. This paper presents an assistive system managing learning content for children with moderate to mild intellectual disabilities. The system provides educational multimedia contents, inspired from the local environment, in different subjects such as math, science, religion, daily life skills, and others to target specific learning goals suitable for this group of learners. The system tracks the individual student progress against the student individualized learning plan assigned by the specialized teacher and according to the learner abilities. Upon completion of learning a particular task, the system will test the learner to order a set of sub-tasks in its logical sequence necessary to successfully accomplish the main task. The system also facilitates deploying intelligent tutoring algorithms to automatically correct mistakes af...