Facial Expression Recognition (FER) has been an active topic of papers that were researched durin... more Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.
The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the ... more The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.
Indonesian Journal of Electrical Engineering and Computer Science
Facial exprestion recognition as a recently developed method in computer vision is founded upon t... more Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest.
The rapid advancement in the Internet of things applications generates a considerable amount of d... more The rapid advancement in the Internet of things applications generates a considerable amount of data and requires additional computing power. These are serious challenges that directly impact the performance, latency, and network breakdown of cloud computing. Fog Computing can be depended on as an excellent solution to overcome some related problems in cloud computing. Fog computing supports cloud computing to become nearer to the Internet of Things. The fog's main task is to access the data generated by the IoT devices near the edge. The data storage and data processing are performed locally at the fog nodes instead of achieving that at cloud servers. Fog computing presents high-quality services and fast response time. Therefore, Fog computing can be the best solution for the Internet of things to present a practical and secure service for various clients. Fog computing enables sufficient management for the services and resources by keeping the devices closer to the network edg...
2022 International Conference on Computer Science and Software Engineering (CSASE)
The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the ... more The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.
Internet of Thing (IoT) is a system of interconnected calculating equipment, electronically and m... more Internet of Thing (IoT) is a system of interconnected calculating equipment, electronically and mechanically and digital equipment delivered with Unique Identifiers and capability of data-transmission through a system without needful of Human-to-Human or Human-to-Computer communication. However, IoMT considered as IoT-program-implementation aimed at medicinal besides healthcare requirements, information gathering also investigation to be studied and observed. This led to proposing extensive scope of fascinating prospective consequences for enterprises: vehicles mileage-sensitivity as well auto-strategy support otherwise prepare it strongly establish in addition description predicted alighting periods towards taking-up travelers. This is due to that its standards are as of now being connected to improve access to the mind, increment the quality of care also, above all decrease the cost of care. An efficient IoT healthcare system aims to give continuous remote checking of patient heal...
One of the most important subject which many researchers depending on it by applying many algorit... more One of the most important subject which many researchers depending on it by applying many algorithms and methods is Cloud Computing. Some of these methods were used to enhance performance, speed, and advantage of task level parallelism and some of these methods used to deal with big data and scheduling. Many others decrease the computation’s quantity in the process of implementation; specially decrease the space of the memory. Parallel data processing is one of the common applications of infrastructure, which is classified as a service in cloud computing. The purpose of this paper is to review parallel processing in cloud. However, the results and methods are inconsistent; therefore, the scheduling concepts give easy method to use the resources and process the data in parallel and decreasing the overall implementation time of processing algorithms. Overall, this review give us and open new doors for using the suitable technique in parallel data processing filed. As a result our work...
Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s... more Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.
2019 International Conference on Advanced Science and Engineering (ICOASE), 2019
Facial Expression Recognition (FER) has been an active topic of papers that were researched durin... more Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.
Asian Journal of Research in Computer Science, 2021
Optical fibers are utilized widely for data transmission systems because of their capacity to car... more Optical fibers are utilized widely for data transmission systems because of their capacity to carry extensive information and dielectric nature. Network architectures utilizing multiple wavelengths per optical fiber are used in central, metropolitan, or broad‐area applications to link thousands of users with a vast range of transmission speeds and capacities. A powerful feature of an optical communication link is sending several wavelengths through the 1300‐to‐1600‐ nm range of a fibre simultaneously. The technology of integrating several wavelengths onto a similar fiber is called wavelength division multiplexing (WDM). The principle of WDM utilized in concurrence with optical amplifiers has an outcome in communication links that permit rapid communications among users in the world's countries. This paper presents an overview of the challenges of fibre optic communication. This paper offers an outline of the areas to be the most relevant for the future advancement of optical com...
Semantic web and cloud technology systems have been critical components in creating and deploying... more Semantic web and cloud technology systems have been critical components in creating and deploying applications in various fields. Although they are selfcontained, they can be combined in various ways to create solutions, which has recently been discussed in depth. We have shown a dramatic increase in new cloud providers, applications, facilities, management systems, data, and so on in recent years, reaching a level of complexity that indicates the need for new technology to address such tremendous, shared, and heterogeneous services and resources. As a result, issues with portability, interoperability, security, selection, negotiation, discovery, and definition of cloud services and resources may arise. Semantic Technologies, which has enormous potential for cloud computing, is a vital way of re-examining these issues. This paper explores and examines the role of Semantic-Web Technology in the Cloud from a variety of sources. In addition, a "cloud-driven" mode of interacti...
This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in... more This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of waiting lines analysis. The main parameters used to measure the performance of the systems are the length of the queue line, utilization of the server, and the delays for arrivals. This study aims to avoid others to expose from epidemics such as (nCovid-19), because of becoming a big global problem today in the world. Also, to know the length for the expected and actual waiting times to diagnosis the arrivals to Duhok city. Collection and execution are done within one week with one activity healthcare team's (HCT) in the main entry Duhok city, port. Data was collected utilize individual conceptions and records from Saturday through to Thursday, which they are the most critical time setting. The main interest in this study was calculating the average waiting time ...
The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths wo... more The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths worldwide. It causes a devastating impact on the lifestyle, public health, and the global economy. This motivates the researchers to invent and develop innovative and automated methods to detect COVID-19 at its early stages. It is necessary to isolate the positive cases quickly to prevent this epidemic and treat affected patients. Many diagnosis methods are proposed to perform accurate and fast detection for COVID-19, such as Reverse Transcription-Polymerase Chain Reaction (RT -PCR). The clinical studies indicate that the severity of COVID-19 cases depends on the virus's amount within infected lungs. Chest X-ray (CXR) and Computed Tomography (CT) images are useful imaging methods for diagnosing COVID-19 cases. Deep Convolutional Neural Network (DCNN) is a machine learning technique usually used in computer vision applications. This review focuses on utilizing the DCNN methods for build...
Asian Journal of Research in Computer Science, 2021
Optical fibers are utilized widely for data transmission systems because of their capacity to car... more Optical fibers are utilized widely for data transmission systems because of their capacity to carry extensive information and dielectric nature. Network architectures utilizing multiple wavelengths per optical fiber are used in central, metropolitan, or broad-area applications to link thousands of users with a vast range of transmission speeds and capacities. A powerful feature of an optical communication link is sending several wavelengths through the 1300-to-1600-nm range of a fibre simultaneously. The technology of integrating several wavelengths onto a similar fiber is called wavelength division multiplexing (WDM). The principle of WDM utilized in concurrence with optical amplifiers has an outcome in communication links that permit rapid communications among users in the world's countries. This paper presents an overview of the challenges of fibre optic Review Article Kareem et al.; AJRCOS, 7(4): 48-58, 2021; Article no.AJRCOS.67395 49 communication. This paper offers an outline of the areas to be the most relevant for the future advancement of optical communications. The invention of integrated optics and modern optical fibers takes place in the field of optical equipment and components.
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Facial exprestion recognition as a recently developed method in computer vision is founded upon t... more Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and K-Nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers' performance. CK+ is used as the research's dataset. random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. Keywords: Base function Facial expression recognition Chi-square feature selection K-Nearest neighbor Multi-layer perceptron This is an open access article under the CC BY-SA license.
Facial Expression Recognition (FER) has been an active topic of papers that were researched durin... more Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.
The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the ... more The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.
Indonesian Journal of Electrical Engineering and Computer Science
Facial exprestion recognition as a recently developed method in computer vision is founded upon t... more Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest.
The rapid advancement in the Internet of things applications generates a considerable amount of d... more The rapid advancement in the Internet of things applications generates a considerable amount of data and requires additional computing power. These are serious challenges that directly impact the performance, latency, and network breakdown of cloud computing. Fog Computing can be depended on as an excellent solution to overcome some related problems in cloud computing. Fog computing supports cloud computing to become nearer to the Internet of Things. The fog's main task is to access the data generated by the IoT devices near the edge. The data storage and data processing are performed locally at the fog nodes instead of achieving that at cloud servers. Fog computing presents high-quality services and fast response time. Therefore, Fog computing can be the best solution for the Internet of things to present a practical and secure service for various clients. Fog computing enables sufficient management for the services and resources by keeping the devices closer to the network edg...
2022 International Conference on Computer Science and Software Engineering (CSASE)
The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the ... more The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.
Internet of Thing (IoT) is a system of interconnected calculating equipment, electronically and m... more Internet of Thing (IoT) is a system of interconnected calculating equipment, electronically and mechanically and digital equipment delivered with Unique Identifiers and capability of data-transmission through a system without needful of Human-to-Human or Human-to-Computer communication. However, IoMT considered as IoT-program-implementation aimed at medicinal besides healthcare requirements, information gathering also investigation to be studied and observed. This led to proposing extensive scope of fascinating prospective consequences for enterprises: vehicles mileage-sensitivity as well auto-strategy support otherwise prepare it strongly establish in addition description predicted alighting periods towards taking-up travelers. This is due to that its standards are as of now being connected to improve access to the mind, increment the quality of care also, above all decrease the cost of care. An efficient IoT healthcare system aims to give continuous remote checking of patient heal...
One of the most important subject which many researchers depending on it by applying many algorit... more One of the most important subject which many researchers depending on it by applying many algorithms and methods is Cloud Computing. Some of these methods were used to enhance performance, speed, and advantage of task level parallelism and some of these methods used to deal with big data and scheduling. Many others decrease the computation’s quantity in the process of implementation; specially decrease the space of the memory. Parallel data processing is one of the common applications of infrastructure, which is classified as a service in cloud computing. The purpose of this paper is to review parallel processing in cloud. However, the results and methods are inconsistent; therefore, the scheduling concepts give easy method to use the resources and process the data in parallel and decreasing the overall implementation time of processing algorithms. Overall, this review give us and open new doors for using the suitable technique in parallel data processing filed. As a result our work...
Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s... more Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.
2019 International Conference on Advanced Science and Engineering (ICOASE), 2019
Facial Expression Recognition (FER) has been an active topic of papers that were researched durin... more Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.
Asian Journal of Research in Computer Science, 2021
Optical fibers are utilized widely for data transmission systems because of their capacity to car... more Optical fibers are utilized widely for data transmission systems because of their capacity to carry extensive information and dielectric nature. Network architectures utilizing multiple wavelengths per optical fiber are used in central, metropolitan, or broad‐area applications to link thousands of users with a vast range of transmission speeds and capacities. A powerful feature of an optical communication link is sending several wavelengths through the 1300‐to‐1600‐ nm range of a fibre simultaneously. The technology of integrating several wavelengths onto a similar fiber is called wavelength division multiplexing (WDM). The principle of WDM utilized in concurrence with optical amplifiers has an outcome in communication links that permit rapid communications among users in the world's countries. This paper presents an overview of the challenges of fibre optic communication. This paper offers an outline of the areas to be the most relevant for the future advancement of optical com...
Semantic web and cloud technology systems have been critical components in creating and deploying... more Semantic web and cloud technology systems have been critical components in creating and deploying applications in various fields. Although they are selfcontained, they can be combined in various ways to create solutions, which has recently been discussed in depth. We have shown a dramatic increase in new cloud providers, applications, facilities, management systems, data, and so on in recent years, reaching a level of complexity that indicates the need for new technology to address such tremendous, shared, and heterogeneous services and resources. As a result, issues with portability, interoperability, security, selection, negotiation, discovery, and definition of cloud services and resources may arise. Semantic Technologies, which has enormous potential for cloud computing, is a vital way of re-examining these issues. This paper explores and examines the role of Semantic-Web Technology in the Cloud from a variety of sources. In addition, a "cloud-driven" mode of interacti...
This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in... more This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of waiting lines analysis. The main parameters used to measure the performance of the systems are the length of the queue line, utilization of the server, and the delays for arrivals. This study aims to avoid others to expose from epidemics such as (nCovid-19), because of becoming a big global problem today in the world. Also, to know the length for the expected and actual waiting times to diagnosis the arrivals to Duhok city. Collection and execution are done within one week with one activity healthcare team's (HCT) in the main entry Duhok city, port. Data was collected utilize individual conceptions and records from Saturday through to Thursday, which they are the most critical time setting. The main interest in this study was calculating the average waiting time ...
The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths wo... more The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths worldwide. It causes a devastating impact on the lifestyle, public health, and the global economy. This motivates the researchers to invent and develop innovative and automated methods to detect COVID-19 at its early stages. It is necessary to isolate the positive cases quickly to prevent this epidemic and treat affected patients. Many diagnosis methods are proposed to perform accurate and fast detection for COVID-19, such as Reverse Transcription-Polymerase Chain Reaction (RT -PCR). The clinical studies indicate that the severity of COVID-19 cases depends on the virus's amount within infected lungs. Chest X-ray (CXR) and Computed Tomography (CT) images are useful imaging methods for diagnosing COVID-19 cases. Deep Convolutional Neural Network (DCNN) is a machine learning technique usually used in computer vision applications. This review focuses on utilizing the DCNN methods for build...
Asian Journal of Research in Computer Science, 2021
Optical fibers are utilized widely for data transmission systems because of their capacity to car... more Optical fibers are utilized widely for data transmission systems because of their capacity to carry extensive information and dielectric nature. Network architectures utilizing multiple wavelengths per optical fiber are used in central, metropolitan, or broad-area applications to link thousands of users with a vast range of transmission speeds and capacities. A powerful feature of an optical communication link is sending several wavelengths through the 1300-to-1600-nm range of a fibre simultaneously. The technology of integrating several wavelengths onto a similar fiber is called wavelength division multiplexing (WDM). The principle of WDM utilized in concurrence with optical amplifiers has an outcome in communication links that permit rapid communications among users in the world's countries. This paper presents an overview of the challenges of fibre optic Review Article Kareem et al.; AJRCOS, 7(4): 48-58, 2021; Article no.AJRCOS.67395 49 communication. This paper offers an outline of the areas to be the most relevant for the future advancement of optical communications. The invention of integrated optics and modern optical fibers takes place in the field of optical equipment and components.
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Facial exprestion recognition as a recently developed method in computer vision is founded upon t... more Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and K-Nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers' performance. CK+ is used as the research's dataset. random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. Keywords: Base function Facial expression recognition Chi-square feature selection K-Nearest neighbor Multi-layer perceptron This is an open access article under the CC BY-SA license.
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