Yahya M. Tashtoush

Fuzzy Self-Clustering for Wireless Sensor Networks

This paper is proposing a new protocol for uniformly clustering distributed wireless sensor networks based on fuzzy inference system. The objective of this protocol is increasing the lifetime of the sensor network by uniformly distributing the clusters on the wireless sensor network by reclustering the whole network repeatedly. The uniform distribution of clusters on the network provides efficient coverage of that network. Efficient coverage means that the clusters are neither overlapped nor leaving nodes in the network unclustered. Each sensor node uses the energy level, local density within its sensing range and time as parameters for clustering, re-clustering, and merging existed clusters by using fuzzy logic technique. The proposed protocol requires neither a prior knowledge of the sensor locations nor a centralized controller for clustering the sensor nodes. A comparison was conducted and found that the new protocol distributes clusters uniformly as good as ACE protocol and prolong the network lifetime in magnitude of 100% more than ACE.

Fibonacci sequence based multipath load balancing approach for mobile ad hoc networks

This paper explores Fibonacci Multipath Load Balancing protocol (FMLB) for Mobile Ad Hoc Networks (MANETs). The FMLB protocol distributes transmitted packets over multiple paths through the mobile nodes using Fibonacci sequence. Such distribution can increase the delivery ratio since it reduces the congestion. The FMLB protocols responsibility is balancing the packets transmission over the selected paths and ordering them according to hops count. The shortest path is used more frequently than the other ones. The simulation results show that the FMLB protocol has achieved an enhancement on packet delivery ratio, up to 21%, as compared to the Ad Hoc On-demand Distance Vector routing protocol (AODV) protocol, and up to 11% over the linear Multiple-path routing protocol. Also the results show the effect of nodes pause time and speed on each of the data delivery ratio and End-to-End (E2E) delay transmission time. Finally, the simulation results are obtained by the well-known Glomosim Simulator, version 2.03, without any distance or location measurements devices.

Are emoticons good enough to train emotion classifiers of Arabic tweets

Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.

The Arabian E-government websites accessibility: A case study

Nowadays, most people are using the Internet to achieve their daily needs. One of these needs is the ability to access the government websites, to make the necessary transactions and procedures via the internet. Maintaining the transaction over the web arises the needs of building an available and accessible website for all users, even those with special needs. In this paper, we evaluate the accessibility for a set of Arabian E-government websites with two versions, English and Arabic, by using a set of automated tools. The results obtained from the two versions are compared to decide which is the best government website that achieved the least number of errors. The selected accessibility tools are supporting the Web Contents Accessibility Guidelines (WCAG) version 2.0 as an update of the previous version WCAG 1.0.

The noble quran Arabic ontology: Domain ontological model and evaluation of human and social relations

Recently, Arab and Muslim researchers have given a great attention to retrieve information and search about knowledge in the noble Quran. During the past years a lot of websites and applications offered a number of methods to search throughout the noble Quran, some offered a syntactic search, others offered a semantic search and some others offered both. The noble Quran has a special style and metaphorical nature. Furthermore, the Arabic language of the noble Quran has a complicated structure that needs an exceptional extra attention on the issues of searching as well as information retrieval rather than English or any other language. This paper proposes a new ontological modeling that models the human social relations in the noble Quran by employing Web Ontology Language (OWL) as well as Resource Description Framework (RDF). This paper methodology involves a descriptive identification of the human relations related concepts that are described in the Noble Quran with identifying the relations among them. The concept ontological model, in this work, mainly built to support Arabic, ArabEzi (popular chat language) and English languages. As a result, SPARQL queries and DL queries are used in the ontology model to retrieve Quran domains, concepts and Verses in Arabic language. Hence, this work will help in the noble Quran searching and retrieving information.

Geometric sequence based multipath routing protocol for multi-hop ad hoc networks

Purpose The purpose of this paper is to propose, a new multi-path routing protocol that distributes packets over the available paths between a sender and a receiver in a multi-hop ad hoc network. We call this protocol Geometric Sequence Based Multipath Routing Protocol (GMRP). Design/methodology/approach GMRP distributes packets according to the geometric sequence. GMRP is evaluated using GloMoSim simulator. The authors use packet delivery ratio and end-to-end delay as the comparison performance metrics. They also vary many network configuration parameters such as number of nodes, transmission rate, mobility speed and network area. Findings The simulation results show that GMRP reduces the average end-to-end delay by up to 49 per cent and increases the delivery ratio by up to 8 per cent. Originality/value This study is the first to propose to use of geometric sequence in the multipath routing approach.

A Business Classifier to Detect Readability Metrics on Software Games and Their Types

Readability metric is considered to be one of the most important factors that may affect games business in terms of evaluating games’ quality in general and usability in particular. As games may go through many evolutions and developed by many developers, code readability can significantly impact the time and resources required to build, update or maintain such games. This paper introduces a new approach to detect readability for games built in Java or C++ for desktop and mobile environments. Based on data mining techniques, an approach for predicting the type of the game is proposed based on readability and some other software metrics or attributes. Another classifier is built to predict software readability in games applications based on several collected features. These classifiers are built using machine learning algorithms (J48 decision tree, support vector machine, SVM and Naive Bayes, NB) that are available in WEKA data mining tool. A Business Classifier to Detect Readability Metrics on Software Games and Their Types

Decentralized fuzzy controlling for target classification using wireless sensor networks

Target classification is one of the applications of wireless sensor networks that aims to recognize the type of mobile targets that navigate within a sensing field. This paper presents a fuzzy-based controller module using MaxMin and MinMax Distributed K-Nearest Neighbors (DKNN) algorithms for ground vehicle classification in order to achieve efficient energy usage and better classification accuracy. This fuzzy module has embedded in an existing target classification system. The fuzzy-based controller module handles the wireless sensor nodes sensing rate (refresh rate) dynamically. A simulation-based study has carried out to test our approach and the simulation results have compared to well-known MaxMin and MinMax DKNN algorithms from literature. Simulation results show that our proposed approach prolongs the network lifetime and achieves better target classification accuracy.