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.
This study aims to know how and what is the media processing presented by the television talk shows for the religious extremism topics in terms of topics, hosted personalities, and ways to address this global phenomenon.
The study is based on descriptive research, and the researcher used the analytical-survey method, analyzing the episodes of (Awkar Al Dhalam) T.V Show which was presented on Al-Iraqiya News Channel, and (Islam Hur) T.V Show which was presented on Al-Hurra in 2019 with 25 episodes from each Show, The sample and research community was chosen with the intent to cover the research problem and its
The study reached several conclusions, including:
- The various dialogs in the episo
In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
... Show MoreThe Comedy of patterns dramas that gave Representative possibilities of advanced functionality and wide being dependent on the diversity performance piece , which is intended to provoke laughter at the receiver, through the diversity of genres performing between dancing and singing and Albantomaim and other methods that have made them distinct from the species dramas other being enabled Representative in dealing with all roles across mature and adaptation of his tools of physical and vocal , as the comedy discusses the societal issues many have a factor of psychological and behind their platforms as a tool face is not directly give way to vent citizen injustice and disadvantages of humans and these conditions need to techniques of physic
... Show MoreBackground: Squamous cell carcinoma is a disease of elderly peopleand it is uncommon in people with less than 40 years old; however many literatures revealed that tumor developing in patients younger than forty years appears more aggressive at the time of diagnosis. The purpose of the present study was to focus on the clincopathological features of the oral SCC in different age groups. Material and methods: In this study thirty five cases of paraffin embedded tissue blocks of oral squamous cell carcinoma were studied. The age range was from 16 to 80 years. The clinicopathological data were recorded for evaluating the tumor characters according to age of patients. Results : The age was not significantly correlated to the clinicopathological
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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