Ahmad T. Al-Taani

A Fuzzy Decision Tree for Processing Satellite Images and Landsat Data

Abstract Satellite and airborne images, including Landsat, ASTER, and Hyperspectral data, are widely used in remote sensing and Geo- graphic Information Systems (GIS) to understand natural earth related processes, climate change, and anthropogenic activity. The nature of this type of data is usually multi or hyperspectral with individual spectral bands stored in raster file structures of large size and global coverage. The elevated number of bands (on the order of 200 to 250 bands) requires data processing algorithms capable of extracting information content, removing redundancy. Conventional statistical methods have been devised to reduce dimension- ality however they lack specific processing to handle data diversity. Hence, in this paper we propose a new data analytic technique to classify these complex multidimensional data cubes. Here, we use a well-known database consisting of multi-spectral values of pixels from satellite images, where the classification is associated with the central pixel in each neighborhood. The goal of our proposed approach is to predict this classification based on the given multi-spectral values. To solve this classification problem, we propose an improved decision tree (DT) algorithm based on a fuzzy approach. More particularly, we introduce a new hybrid classification algorithm that utilizes the conventional decision tree algorithm enhanced with the fuzzy approach. We propose an improved data classification algorithm that utilizes the best of a decision tree and multi-criteria classification. To investigate and evaluate the performance of our proposed method against other DT classifiers, a comparative and analytical study is conducted on well-known Landsat data.

Arabic Single-Document Text Summarization Using Particle Swarm Optimization Algorithm

Abstract In this research, we propose the use of Particle Swarm Optimization (PSO) algorithm for the extraction of summaries for single Arabic documents. The PSO approach is compared with evolutionary approaches that use Genetic Algorithms (GA) and Harmony Search (HS). The Essex Arabic Summaries Corpus (EASC) and the Recall-Oriented Understanding for Gisting Evaluation (ROUGE) tool are used to evaluate the proposed approach. Experimental results showed that the proposed approach achieved competitive and even higher ROUGE scores in comparison to HS and GA approaches in the state-of-the-art.

Secure LSB steganography for colored images using character-color mapping

Steganography is the science of embedding the secret messages inside other medium files in a way that hides the existence of the secret message at all. Steganography can be applied to text, audio, image, and video file types. In this study, we propose a new steganography approach for digital images in which the RGB coloring model was used. The efficiency of the proposed approach has been tested and evaluated. The experimental results show that the proposed approach produce high-quality stego images that resist against visual and statistical attacks.

An Adaptive Parser for Arabic Language Processing

In this study, we present a robust bottom-up Arabic parser that investigates the correctness of Arabic sentences by passing them through a set of predetermined states relying on their individual words. The major benefit of our approach is the reduced number of backup states tested when determining the grammatical structure of a given sentence. The proposed approach is optimized to tokenize the input sentences correctly since accurate tokenization is the essential step of the parser; this process also reduces the parsing time. Our proposed parser is extendable; hence, it allows new words to be added to the lexicon, i.e. the lexicon is built dynamically. Experimental results have demonstrated the effectiveness of our approach in checking correctly numerable sentences with different lengths. The accuracy was 85.88% when tested on a sample of 170 Arabic sentences taken from an existing Arabic text taught in k-12 grade levels.

An automated scoring approach for Arabic short answers essay questions

Evaluating and scoring essay questions is an exhausting, time consuming process and require a lot of effort. So, applying automated tools is essentially required to tackle these drawbacks. In this study, we propose an automated scoring approach for short answers to Arabic essay questions. The scoring process is based on the similarity between the students answer and model answer, cosine similarity measure is used for this purpose. Cosine similarity is a heuristic evolutionary measure that has succeeded to solve text to text similarity problems. Word root and synonyms for each keyword in the students answer and the model answer are used in order to achieve accurate results. Experimental results showed that the proposed approach achieved competitive scores in comparison with other approaches.

Hybrid-based Arabic single-document text summarization approach using genatic algorithm

Text summarization is one of the most paramount applications of the field of natural language processing. This paper presents a Hybrid-based Single-document Extractive Arabic Text Summarization approach based on Genetic Algorithm. The proposed summarization approach was evaluated using EASC corpus, and ROUGE evaluation method to determine the accuracy of the proposed approach. The result showed that our method outperformed many state-of-the art methods.

Poetry expert system

This paper aims at shedding light on an important field of literature which is poetry. The computer expert system merges its codes with some wide knowledge of several poems. Using linguistics and statistical stylistics facilitates dealing with any unexpected poem; hence the user is asked simply to count some repeated lines and stanzas, or to find out some canonical figures of speech; for instance: rhyme and similes; or even to search for some marked punctuations. Apparently, the methodology enables any person with a normal knowledge of poetry to go through some explicit questions in order to end up with a title and an authors name for the unseen chosen poem. The project aspires a prosperous interdisciplinary future of both computers and poetry and hopefully, users will be able to learn about any unknown poem to them easy.