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Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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Publication Date
Sat Sep 30 2023
Journal Name
Wasit Journal Of Computer And Mathematics Science
Real time handwriting recognition system using CNN algorithms
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Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition

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Publication Date
Sat Oct 01 2011
Journal Name
Journal Of Engineering
MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

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Publication Date
Mon Oct 17 2011
Journal Name
Journal Of Engineering
MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Engineering
Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq
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The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge

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Publication Date
Mon Oct 01 2018
Journal Name
Conference: First International Conference On Water Resources
Modeling BOD of the Effluent from Abu-Ghraib Diary Factory using Artificial Neural Network October 2018
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The proper operation, and control of wastewater treatment plants, is receiving an increasing attention, because of the rising concern about environmental issues. In this research a mathematical model was developed to predict biochemical oxygen demand in the waste water discharged from Abu-Ghraib diary factory in Baghdad using Artificial Neural Network (ANN).In this study the best selection of the input data were selected from the recorded parameters of the wastewater from the factory. The ANN model developed was built up with the following parameters: Chemical oxygen demand, Dissolved oxygen, pH, Total dissolved solids, Total suspended solids, Sulphate, Phosphate, Chloride and Influent flow rate. The results indicated that the constructed A

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Publication Date
Mon Jan 01 2018
Journal Name
Applied Science Reports
Facial Acne Therapy by Using Pumpkin Seed Oil with Its Physicochemical Properties
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The herbal remedy individually or in combination with standard medicines has been used in diverse medical treatises for the cure of different diseases. Pumpkin seed oil is one of the recognized edible oil and has substantial medicinal properties due to the presence of unique natural edible substances. Inflammation is an adaptive response that is triggered by noxious stimuli and conditions, which involves interactions amongst many cell types and mediators, and underlies many pathological processes. Unsaturated fatty acids (UFAs) can influence inflammation through a variety of mechanisms, and have been indicated as alternative anti-inflammatory agents to treat several inflammatory skin disorders. Pumpkin seed oil is rich in (UFAs), that its t

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Publication Date
Mon Nov 11 2019
Journal Name
Day 3 Wed, November 13, 2019
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
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Abstract<p>The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the</p> ... Show More
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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
Hybrid Cipher System using Neural Network
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The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

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Publication Date
Fri May 01 2015
Journal Name
Journal Of Engineering
Using Spatial Videos, Google Earthtm and Geographic Information System to Dynamically Monitor Built Environment Changes In a Challenging Environment: Baghdad, Iraq.
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Urban expansion and its environmental and safety effects are one of the critical information needed for future development planning, safety considerations and environmental management. This work used two methods to monitor urban expansion and it's environmental and safety effects, the first is based on Google Maps for the years 2002 and 2010, and the second was the usage of spatial videos for the year 2013. Although the usage of satellite images is critical to know and investigate the general situation and the total effects of the expansion on a large piece of area, but the Spatial videos do a very detailed fine scale investigation, site conditions regarding both environmental and safety cannot be easily distinguished fr

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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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