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Subject Independent Facial Emotion Classification Using Geometric Based Features
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Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles and others relative geometric features for recognition give accuracy about 95.73% when the seven emotion classes are tested and 97.23% when the 6 classes (except normal class) are only tested. These rates are considered high when compared with the results of other newly published works.

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Publication Date
Wed Jan 01 2025
Journal Name
Iraqi National Journal Of Earth Science (injes)
The Effect of Satellite Image Fusion on the Classification Process by Using Multiple Sensors
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Publication Date
Mon Apr 01 2024
Journal Name
Telkomnika (telecommunication Computing Electronics And Control)
Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets
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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

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Publication Date
Sun Jul 26 2026
Journal Name
Discover Artificial Intelligence
Multi-classification of autism spectrum disorder behavior for children using explainable artificial intelligence techniques
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Precise and interpretable classification of autism-related behaviors is important for initial diagnosis, personalized intervention, and support arrangements. This study proposes an interpretable machine learning (ML) model using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to classify behavioral patterns into four categories (normal, mild, moderate, and severe) associated with Autism Spectrum Disorder (ASD) based on a custom 377-instance survey dataset from Iraqi parents and teachers of children aged 6-12. The model observes 16 key features across communication and social interaction, repetitive behaviors, language, and adaptive skills, preprocessed via interquartile range (IQR) outlier removal, me

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Rock facies classification and its effect on the estimation of original oil in place based on petrophysical properties data
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The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

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Publication Date
Mon Jun 15 2026
Journal Name
Biomedicines
EEG-ChTABNet: A Dual-Branch Channel-Wise Transformer with Gated Attention-Branch Network for EEG-Based Classification of Dementia
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Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to

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Publication Date
Sat Sep 01 2018
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The Ability of the Independent Auditor to Audit the Application Requirements of Electronic Commerce
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The purpose of this research is to determine the extent to which independent auditors can audit the requirements of e-commerce related to (infrastructure requirements, legislation and regulations, tax laws, and finally human cadres). To achieve this, a questionnaire was designed for auditors. Numerous statistical methods, namely arithmetic mean and standard deviation, have been used through the implementation of the Statistical Packages for Social Sciences (SPSS) program.

The research has reached several results, the most important of which are: There are noobstacles to enabling the auditor to audit the application of the e-commerce requirements as well as the respective(infrastructure requirements, legislation and regulations, t

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Publication Date
Thu Aug 01 2019
Journal Name
Int J Mol Cell Med
Caspase Dependent and Independent Anti-hematological Malignancy Activity of AMHA1 Attenuated Newcastle Disease Virus
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Hematological malignancies remain one of the leading causes of death worldwide despite advances in cancer therapeutics. Newcastle disease virus (NDV) is a member of Paramyxoviridae that elicits considerable interest as an anticancer agent because it can replicate up to 10 000 times faster in human cancer cells than in most normal cancer cells. Several NDV strains reportedly induce the cytolysis of cancerous cell lines. The attenuated Iraqi strain (AMHA1) of NDV is a novel oncolytic agent with promising antitumor characteristics, including apoptosis induction. This study aimed to evaluate the ability of the AMHA1 NDV strain to induce apoptotic cell death in hematological tumors through caspase-dependent or independent apoptotic pathways. The

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Noor's Curve, a New Geometric Form of Agnesi Witch, a Construction Method is Produced
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In this paper, a new form of 2D-plane curves is produced and graphically studied. The name of my daughter "Noor" has been given to this curve; therefore, Noor term describes this curve whenever it is used in this paper. This curve is a form of these opened curves as it extends in the infinity along both sides from the origin point. The curve is designed by a circle/ ellipse which are drawing curvatures that tangent at the origin point, where its circumference is passed through the (0,2a). By sharing two vertical lined points of both the circle diameter and the major axis of the ellipse, the parametric equation is derived. In this paper, a set of various cases of Noor curve are graphically studied by two curvature cases;

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Publication Date
Tue Oct 26 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Achieving urban sustainability by revitalizing the performance of Islamic geometric pattern on residential façades
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Publication Date
Sun Oct 31 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Achieving urban sustainability by revitalizing the performance of Islamic geometric pattern on residential façades
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Cultural awareness is becoming increasingly important in an effort to reinvigorate local urban heritage into modern production. Considering that, one of the most significant aspects of sustainability is preserving socio-cultural and environmental specificities and restoring local heritage. The aim is influencing the local identity by promoting sustainability through the revival of Islamic geometric patterns (IGP) and their performance as effective components in local urban facades. A set of indicators related to the purposes for reusing IGP in facades within three main performances (decorative, structural, and functional). Their sustainability was studied and extrapolated, and a checklist has developed and tested by Likert scale on

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