Volume 2, Issue 2, 2012

By | September 4, 2018

A Novel Approach for Energy Optimization of Wireless Sensors Network by Adaptive Clustering

Rufaida Muhammad Shamroukh, Aryaf Abdullah Aladwan
Computer Engineering dept., Faculty of Engineering Technology, Al-Balqa’ Applied University, Amman , Jordan.
Ana’am Abdullah Aladwan, Information Systems & Technology dept., University of Banking and Financial Sciences, Amman , Jordan.

Abstract—Energy optimization has a major role in modern researches. While, energy optimization of wireless sensors network is the most important, because of the limitations of the battery energy of the wireless sensor. This paper concentrate on energy optimization by introducing a novel and an adaptive clustering algorithm that is fuzzy logic based. The result of our work minimizes the interval between the first node (sensor) death and the last one. The dead node interval minimization to a value near to zero increases the efficiency of energy and saves 93% of traditional clustering of wireless sensors network. This paper compares the result of this novel approach to LEACH, LEACH-M, and LEACH-L algorithms.

Keywords-Wireless Sensors Network; LEACH; Energy Efficient; Fuzzy Clustering.

A Novel Study of Biometric Speaker Identification Using Neural Networks and Multi-Level Wavelet Decomposition

Aryaf Abdullah Aladwan, Rufaida Muhammad Shamroukh
Computer Engineering dept., Faculty of Engineering Technology, Al-Balqa’ Applied University, Amman , Jordan.
Ana’am Abdullah Aladwan, Information Systems & Technology dept., University of Banking and Financial Sciences, Amman , Jordan.

Abstract — Researchers in voice and speaker recognition systems has been entered a new stage. The overall researches concern on trials to enhance the accuracy and precession of the developed system techniques, especially in intelligent systems. The use of Digital Signal Processing (DSP) with cooperation of Artificial Intelligence (AI) is common in such researches. But the main inertia in that is to developing the algorithm in trial and error in most cases. This research aims to find the hot spot points in merging specific techniques of DSP with AI. Neural networks based speaker recognition was been developed in order to test the results of the proposed algorithm and record the study results. Multi-level decomposition of wavelet transformation is adopted to extract the features of the speaker person. The feature extraction using wavelet transformation is studied and this paper determines the best level and condition of applying that technique.

Keywords-Wavelet Transform; Multi-Level Decomposition; Voice; Neural Networks; Speaker Identification; Biometrics.

Multiple Classifiers to verify the Online Signature

Mohammed J. Alhaddad
Department of Information Technology, Computer Science & Information Technology Collage, King Abdulaziz University, Saudi Arabia.

Abstract: Nowadays biometric increasingly used in many applications that has strong relation to our live; it’s a reliable mean as an alternative to the traditional methods of personal identification. As a behavioral biometric, an online signature still has some shortcomings because of that nature. Furthermore, features in online signature verification system can be either global or local; the techniques that can be used also variety. In this paper both global and local features were used. To classify the mentioned features; the back-propagation neural network (BPNN) technique was used to classify the local features, whereas, the global features was classified by the probabilistic model. Once the results obtained from the local classifier and global classifier, the “AND” fusion was used to combine the two classifiers for final decision. SVC2004 dataset was used to evaluate the proposed method in term of False Rejection Rate (FRR) and False Acceptance Rate (FAR). The obtained results for FRR and FAR were 0.3% and 0.5% respectively. These results are encouraging when compared with related existing studies.

Keywords : Online Signature; Probabilistic Modeling; Back-propagation Neural Network (BPNN).

Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

Surjeet Kumar Yadav, Research Scholar, Shri Venkateshwara University, J.P. Nagar, India.
Saurabh Pa, Head, Dept. Of MCA, VBS Purvanchal University, Jaunpur, India.

Abstract: Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students’ performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student’s performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student’s data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.

Keywords : Prediction; Educational data mining; Decision tree; C4.5 algorithm; ID3 algorithm; CART algorithm.

An Investigation into privacy and Security in Online Social Networking Sites among IIUM Students

Husein Osman Abdullahi, Abdirizaq said, Jamaludin Bin Ibrahim
Department Of Information System, Faculty of Information Communication Technology, IIUM, Jalan Gombak, 53100, Kuala Lumpur, Malaysia.

Abstract: The issues of privacy and security in online social networking sites have been the dual themes of utmost concern amongst many communities and IIUM community in particular. This article highlights the importance of online social networking sites such as: Face book, Google+, and Twitter and the issues of privacy and security in online interactions. The authors also argue that the online social networks have played a significant role on daily digital interaction for more than half billion users around the world today are bedeviling with the issues of privacy and security. This study employs a quantitative data analysis, a survey and a random sampling of (n=160) IIUM students from different Kulliyah were conducted. The result shows that the percentage responses of IIUM who seems to be actively involved in online social network pages are not aware of their privacy and security in online environment.

Keywords- Social network; Privacy Security.

Arabic Text Summerization Model Using Clustering Techniques

Ahmad Haboush, Maryam Al-Zoubi, Ahmad Momani, Motassem Tarazi
Computer Science Department, Jerash University, Jerash, Jordan.

Abstract— the current work investigates a developed automatic Arabic text summarization model. In this model, a technique of word root clustering is used as the major activity. Unlike the previously presented systems of Arabic text summarization in the extract based design field, the current model adopts cluster weight of word roots instead of the word weight itself.
The model is thoroughly illustrated through its different stages. Obviously, the general scheme follows traditional descriptive model of most of the system stages in literature with the exception of the ranking stage. This model with its developed technique has been subjected to a set of experiments. Various Arabic text examples are used for evaluation purposes. The efficiency of the summarization is calculated in terms of Precision and Recall measures. Result obtained actually is considered promising and competitive to the verb/noun categorization ranking method. This enhancement has been detected for Precision 76% and Recall 79% with the analogous values of 62% and 70% obtained in the verb/noun categorization method. The enhancement emerges in this tangible result is attributed to the implicit embedding of semantic capability of the developed model to expand the extract boundaries towards the abstract extremes of the design theme.

Keywords – Text Summarization; Clustering; Natural Language Processing Evaluation.

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