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A New Arabic Dataset for Emotion Recognition
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In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and Naïve Bayes achieving the highest results in terms of accuracy, precision, recall, and F-measure.

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Publication Date
Mon May 15 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Developing a Real Time Method for the Arabic Heterogonous DBMS Transformation
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   A common problem facing many Application models is to extract and combine information from multiple, heterogeneous sources and to derive information of a new quality or abstraction level. New approaches for managing consistency, uncertainty or quality of Arabic data and enabling e-client analysis of distributed, heterogeneous sources are still required. This paper presents a new method by combining two algorithms (the partitioning and Grouping) that will be used to transform information in a real time heterogeneous Arabic database environment

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Publication Date
Sat Nov 02 2019
Journal Name
Advances In Intelligent Systems And Computing
Spin-Image Descriptors for Text-Independent Speaker Recognition
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Building a system to identify individuals through their speech recording can find its application in diverse areas, such as telephone shopping, voice mail and security control. However, building such systems is a tricky task because of the vast range of differences in the human voice. Thus, selecting strong features becomes very crucial for the recognition system. Therefore, a speaker recognition system based on new spin-image descriptors (SISR) is proposed in this paper. In the proposed system, circular windows (spins) are extracted from the frequency domain of the spectrogram image of the sound, and then a run length matrix is built for each spin, to work as a base for feature extraction tasks. Five different descriptors are generated fro

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Publication Date
Sun Oct 29 2023
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Optimization Techniques for Human Multi-Biometric Recognition System
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Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa

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Publication Date
Wed Dec 01 2021
Journal Name
Journal Of Physics: Conference Series
Disc damage likelihood scale recognition for Glaucoma detection
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Abstract<p>Glaucoma is a visual disorder, which is one of the significant driving reason for visual impairment. Glaucoma leads to frustrate the visual information transmission to the brain. Dissimilar to other eye illness such as myopia and cataracts. The impact of glaucoma can’t be cured; The Disc Damage Likelihood Scale (DDLS) can be used to assess the Glaucoma. The proposed methodology suggested simple method to extract Neuroretinal rim (NRM) region then dividing the region into four sectors after that calculate the width for each sector and select the minimum value to use it in DDLS factor. The feature was fed to the SVM classification algorithm, the DDLS successfully classified Glaucoma d</p> ... Show More
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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Theoretical And Applied Information Technology
A Survey on the Vein Biometric Recognition Systems: Trends and Challenges
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Vascular patterns were seen to be a probable identification characteristic of the biometric system. Since then, many studies have investigated and proposed different techniques which exploited this feature and used it for the identification and verification purposes. The conventional biometric features like the iris, fingerprints and face recognition have been thoroughly investigated, however, during the past few years, finger vein patterns have been recognized as a reliable biometric feature. This study discusses the application of the vein biometric system. Though the vein pattern can be a very appealing topic of research, there are many challenges in this field and some improvements need to be carried out. Here, the researchers reviewed

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Publication Date
Wed Jan 01 2020
Journal Name
Imdc-sdsp
Emotion management among the headmaster of intermediate schools
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Publication Date
Fri Sep 01 2023
Journal Name
Journal Of Engineering
Iraqi Sentiment and Emotion Analysis Using Deep Learning
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Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

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Publication Date
Fri Jun 29 2018
Journal Name
Journal Of The College Of Education For Women
HandWritten Numerals Recognition System
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  Recognition is one of the basic characteristics of human brain, and also for the living   creatures. It is possible to recognize images, persons, or patterns according to their characteristics. This recognition could be done using eyes or dedicated proposed methods. There are numerous applications for pattern recognition such as recognition of printed or handwritten letters, for example reading post addresses automatically and reading documents or check reading in bank.

      One of the challenges which faces researchers in character recognition field is the recognition of digits, which are written by hand. This paper describes a classification method for on-line handwrit

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Publication Date
Sat Jan 01 2022
Journal Name
Proceedings Of International Conference On Computing And Communication Networks
Speech Gender Recognition Using a Multilayer Feature Extraction Method
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Publication Date
Thu Oct 01 2009
Journal Name
Journal Of The College Of Languages (jcl)
A RAISIN IN THE SUN: the dream of RECOGNITION
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Lorraine Hansberry’s A Raisin in the Sun (1959) appeared at the beginning of renewed political activity on the part of the blacks; it is a pamphlet about the dream of recognition of black people and the confusion of purposes and means to reach such recognition. It embodies ideas that have been uncommon on the Broadway stage in any period. Situations such as a black family moving into an all-white neighborhood were not familiar before this time; they were just beginning to emerge. In depicting this so realistically, Hansberry depends more on her personal experience as an African American embittered by social prejudices and discrimination. 

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