Computer
Afrah Salman Dawood
Abstract
Recently, the burgeoning disciplines of Machine Learning (ML) and Deep Learning (DL) have experienced considerable integration across diverse scientific domains. Of significant note is their integration into the medical sector, specifically in the intricate methodologies of pathological categorization. ...
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Recently, the burgeoning disciplines of Machine Learning (ML) and Deep Learning (DL) have experienced considerable integration across diverse scientific domains. Of significant note is their integration into the medical sector, specifically in the intricate methodologies of pathological categorization. Present-day innovations underscore the pivotal role of Deep Convolutional Neural Networks (DCNN) in mediating the tasks of image-based taxonomies and prognostications within this domain. In this research, a new DCNN with different modified intelligent architectures like CNN, modified VGG-16, VGG-19, ResNet50, and DenseNet121, besides the newly added classification layer, was implemented and tested for the detection and classification of Alzheimer’s disease. The evaluation and performance metrics are accuracy, loss, f1-score, precision, and recall. Experiments were made on Kaggle-based dataset and test results show that the CNN-based model is the most accurate model, with the highest accuracy of 96% and the lowest loss of 9.92%. Finally, the average performance percentage of the overall proposed model is as follows: accuracy is 91%, loss is 19.75%, precision is 89.4%, F1- score is 88.83%, and recall is 90%.
Computer
Raja’a M. Mohammed; Suhad M. Kadhem
Abstract
Sign language (SL) is Non-verbal communication and a way for thedeaf and mute to communicate without words. A deaf and mute person's hands,face, and body shows what they want to say. Since the number of deaf and dumbpeople is increasing, there must be other ways to learn sign language orcommunicate with ...
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Sign language (SL) is Non-verbal communication and a way for thedeaf and mute to communicate without words. A deaf and mute person's hands,face, and body shows what they want to say. Since the number of deaf and dumbpeople is increasing, there must be other ways to learn sign language orcommunicate with deaf and dumb people. One of these ways is using advancedtechnology to produce systems that help the deaf/dumb, such as creatingrecognition and sign language translators. This paper presents an applicationthat works on the computer for machine translation of Iraqi sign language intwo directions from sign language to Arabic language (text/speech) and fromArabic language(text) to Iraqi sign language. The proposed system uses aConvolution Neural Network (CNN) to classify sign language based on itsfeatures to predicate the sign meaning. The sign language to Arabiclanguage(text/speech) part of the proposed system has an accuracy of 99.3% forletters.
H. Saeed Essad; Hanaa Mohsin Ahmed
Abstract
Due to the fact that the risk factor in the international border is very high, it causes threats affecting soldiers’ lives, border military facility and state security. In fields where there are difficulties for people to go or where human life may be endangered (such as places that contain the ...
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Due to the fact that the risk factor in the international border is very high, it causes threats affecting soldiers’ lives, border military facility and state security. In fields where there are difficulties for people to go or where human life may be endangered (such as places that contain the harmful gases and explosive things). Human guards may be substituted by a robot system that is designed for the purpose of taking care of the dangerous tasks of surveillance. The main objective of this paper is to build an intelligent surveillance robot with high accuracy to detect intrusions, easy to use and inexpensive. This paper includes a new contribution by integrating intelligent algorithms into monitoring systems and robotics technology, which is a strong addition to the research through the accuracy of the system. The system provides a modern monitoring method for detecting and recognizing faces using a robot equipped with a pi camera, sensors and a control panel. The result of the proposal is a system that uses face detection and recognition by utilizing HAAR algorithm, and CNN algorithm, the system percentage accuracy becomes 99.87%.and the loss is 0.013. The proposed have high accuracy, effective, easy to use, with low cost, can be used to guard critical infrastructures, large facilities, and national borders.
Computer
shayma Ashor; Hanaa Mohsin Ahmed
Abstract
In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image ...
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In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%.
Zainab Mohammed Resan; Muayad Sadik Croock
Abstract
Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, ...
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Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, if it wanted to raise the precision ofindoor classification, the number of image dataset of the indoor environment should be aslarge as possible to satisfy and cover the underlying area. In this work, a smartphonecamera is used to build the image-based dataset of the investigated building. In addition,captured images in real time are taken to be processed with the proposed model as a testset. The proposed indoor localization includes two phases the first one is the offlinelearning phase and the second phase is the online processing phase. In the offline learningphase, here we propose a convolutional neural network (CNN) model that sequences thefeatures of image data from some classis's dataset composed with a smartphone camera.In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appointthe expected indoor location. The proposed system has been tested over variousexperiments and the obtained experimental results show that the accuracy of the predictionis almost 98.0%.
