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Speech Emotion Recognition Using Minimum Extracted Features
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Recognizing speech emotions is an important subject in pattern recognition. This work is about studying the effect of extracting the minimum possible number of features on the speech emotion recognition (SER) system. In this paper, three experiments performed to reach the best way that gives good accuracy. The first one extracting only three features: zero crossing rate (ZCR), mean, and standard deviation (SD) from emotional speech samples, the second one extracting only the first 12 Mel frequency cepstral coefficient (MFCC) features, and the last experiment applying feature fusion between the mentioned features. In all experiments, the features are classified using five types of classification techniques, which are the Random Forest (RF), k-Nearest Neighbor (k-NN), Sequential Minimal Optimization (SMO), Naïve Bayes (NB), and Decision Tree (DT). The performance of the system validated over Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of the experiments showed given good accuracy compared with the previous studies using a fusion of a few numbers of features with the RF classifier.

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Publication Date
Thu Jan 13 2022
Journal Name
Medical & Biological Engineering & Computing
An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs
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Publication Date
Tue Jun 09 2020
Journal Name
Article In Journal Of Engineering Science And Technology
English Numbers Recognition Based on Sign Language Using Line-Slope Features and PSO-DBN Optimization Method
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Publication Date
Mon Aug 01 2022
Journal Name
Telkomnika (telecommunication Computing Electronics And Control)
Dorsal hand veins features extraction and recognition by correlation coefficient
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Publication Date
Mon Oct 30 2023
Journal Name
Traitement Du Signal
A Comprehensive Review on Machine Learning Approaches for Enhancing Human Speech Recognition
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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
Mon Dec 31 2012
Journal Name
Al-khwarizmi Engineering Journal
Speech Compression Using Multecirculerletet Transform
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Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp

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Publication Date
Sun Jun 01 2008
Journal Name
Baghdad Science Journal
The Minimum Inhibitory Concentration (MIC) and Minimum fungicidal concentration(MFC) of extracted oil of lemon grass and thyme against Candida spp. and Bacteria Spp. Isolated from children with oral thrush
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120 samples were collected from children (ages between new born and 10 years) who infected with oral thrush. The results revealed that the Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC) of extracted oil of lemon grass against C.albicans, C.tropicalis, C.keyfr, C.glabrata and C.guilliermondii were 1.25,1.25,1.25,2.5 and 2.5µl/ml and 2.5, 2.5, 2.5, 5 and 5 µl /ml respectively. while the (MIC) and (MFC) for the extraction oil of thyme against C.albicans, C.tropicalis, C.keyfr, C.glabrata and C.guilliermondii were 0.6, 0.6, 1.25, 1.25, and 1.25µl/ml and 1.25, 1.25, 2.5, 2.5, and 2.5µl/ml respectively . While the value of (MIC) and (MFC) for Nystatin against Candida species were 32 and 64 µg

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system
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These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce

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Publication Date
Sun Mar 06 2011
Journal Name
Baghdad Science Journal
Numeral Recognition Using Statistical Methods Comparison Study
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The area of character recognition has received a considerable attention by researchers all over the world during the last three decades. However, this research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic numeral using the Statistical styles in two methods and making comparison study between them. First method Linear Discriminant function that is yield results with accuracy as high as 90% of original grouped cases correctly classified. In the second method, we proposed algorithm, The results show the efficiency of the proposed algorithms, where it is found to achieve recognition accuracy of 92.9% and 91.4%. This is providing efficiency more than the first method.

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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
Optical Character Recognition Using Active Contour Segmentation
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Document analysis of images snapped by camera is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text; this makes automatic analysis complicated. OCR is one of the image processing techniques which is used to perform automatic identification of texts. Existing image processing techniques need to manage many parameters in order to clearly recognize the text in such pictures. Segmentation is regarded one of these essential parameters. This paper discusses the accuracy of segmentation process and its effect over the recognition process. According to the proposed method, the images were firstly filtered using the wiener filter then the active contour algorithm could b

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