Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2909
Fallah H. Najjar, Safa Riyadh Waheed, Duha Amer Mahdi
In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.
{"title":"Coronavirus Classification based on Enhanced X-ray Images and Deep Learning","authors":"Fallah H. Najjar, Safa Riyadh Waheed, Duha Amer Mahdi","doi":"10.11113/mjfas.v19n3.2909","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2909","url":null,"abstract":"In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"5 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91091640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.3018
Wayan Dasna, H. W. Wijaya, Faaza’izzahaq, Setta Putra, Hakam Abdulloh, A. Taufiq
The synthesis of copper(II) or Nickel(II) complex with bis-2,4-dimethoxy-1,3,5-triazapentadiene ligand has been reported using a direct reaction and reflux method. These methods take a relatively long synthesis time, so it is necessary to develop a faster synthesis method. This study reports a solvothermal method to synthesize bis-2,4-dimethoxy-1,3,5-triazapentadiene copper(II) and nickel(II) as in situ with sodium dicyanamide and methanol that produces single crystals in a day and two days respectively. XRD analysis of both single crystals from solvothermal results showed a monoclinic crystal lattice and a P21/n space group which was not different from previous studies. The Hirshfeld analysis indicates that the interactions with the most prevelant contributions in both of complexes are H—H, O—H/H—O, and N—H/N—H.
{"title":"Highly Efficient Synthesis of Complex bis-2,4-dimethoxy-1,3,5-triazapentadienemetal(II) (metal = Cu, Ni) with Hirshfeld Surface Analysis","authors":"Wayan Dasna, H. W. Wijaya, Faaza’izzahaq, Setta Putra, Hakam Abdulloh, A. Taufiq","doi":"10.11113/mjfas.v19n3.3018","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.3018","url":null,"abstract":"The synthesis of copper(II) or Nickel(II) complex with bis-2,4-dimethoxy-1,3,5-triazapentadiene ligand has been reported using a direct reaction and reflux method. These methods take a relatively long synthesis time, so it is necessary to develop a faster synthesis method. This study reports a solvothermal method to synthesize bis-2,4-dimethoxy-1,3,5-triazapentadiene copper(II) and nickel(II) as in situ with sodium dicyanamide and methanol that produces single crystals in a day and two days respectively. XRD analysis of both single crystals from solvothermal results showed a monoclinic crystal lattice and a P21/n space group which was not different from previous studies. The Hirshfeld analysis indicates that the interactions with the most prevelant contributions in both of complexes are H—H, O—H/H—O, and N—H/N—H.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88221659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2900
Safa Riyadh Waheed, S. M. Saadi, Mohd Shafry Mohd Rahim, Norhaida Mohd Suaib, Fallah H Najjar, M. M. Adnan, Ali Aqeel Salim
Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.
{"title":"Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms","authors":"Safa Riyadh Waheed, S. M. Saadi, Mohd Shafry Mohd Rahim, Norhaida Mohd Suaib, Fallah H Najjar, M. M. Adnan, Ali Aqeel Salim","doi":"10.11113/mjfas.v19n3.2900","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2900","url":null,"abstract":"Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"42 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74376365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2991
Noor M. Hashem, H. K. Abbas, H. Mohamad
The process development of the image processing can solve the problem of detection and recognition of the license plate by taking pictures of the cars and then recognizing them. Most traffic applications rely on automatic vehicle plate detection in parking lots, border control, speed control, etc. In this study, a smart visual system was presented to identify car plates in the College of Science for Girls - University of Baghdad parking lot. The work included distinguishing the car plate and identifying cars, whether they belonged to the college or not. This process was based on the Cascade Classifier method based on the Viola-Jones algorithm, and a database for all car plate features was stored in a file using the proposed method. The recognized car was compared with the characteristics of the database using Oriented FAST and Rotated BRIEF then features were extracted using Histograms of Oriented Gradients. The license plate is recognized when matching features are employed using the matching feature’s function. The results of congruence and discrimination were excellent and very highly efficient. The luminous intensity dependence is considered, as the work is based on the red band of the car's image.
{"title":"Optical System to Recognize Car Plate Ownership","authors":"Noor M. Hashem, H. K. Abbas, H. Mohamad","doi":"10.11113/mjfas.v19n3.2991","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2991","url":null,"abstract":"The process development of the image processing can solve the problem of detection and recognition of the license plate by taking pictures of the cars and then recognizing them. Most traffic applications rely on automatic vehicle plate detection in parking lots, border control, speed control, etc. In this study, a smart visual system was presented to identify car plates in the College of Science for Girls - University of Baghdad parking lot. The work included distinguishing the car plate and identifying cars, whether they belonged to the college or not. This process was based on the Cascade Classifier method based on the Viola-Jones algorithm, and a database for all car plate features was stored in a file using the proposed method. The recognized car was compared with the characteristics of the database using Oriented FAST and Rotated BRIEF then features were extracted using Histograms of Oriented Gradients. The license plate is recognized when matching features are employed using the matching feature’s function. The results of congruence and discrimination were excellent and very highly efficient. The luminous intensity dependence is considered, as the work is based on the red band of the car's image.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"20 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84935671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2993
Mustafa Qahtan Alsudani, Mushtaq Talb Tally, Israa Fayez Yousif, Ali Abdullhussein Waad, Safa Riyadh Waheeda, M. M. Adnan
Unmanned aerial vehicles (UAV) and cellular networks are growing closer to being integrated in the realm of wireless communications, which will improve service quality even further. In this study, we investigate a wireless communication system in which two types of base stations—in the air and on the ground—serve separate groups of users. We analyze the effect of the aerial base station (ABS) height and transmit power on the system's downlink and uplink data rates while accounting for the reciprocal interference between the Aerial and terrestrial communication lines. The findings demonstrate that in many cases the best ABS altitude and transmit Power are either the highest or lowest values attainable. The distance between the ABS, the ABS user (AU), and the terrestrial base station user, among other factors, may affect how well they all communicate (TU). In this article we will discuss the following topics: unmanned aerial vehicle (UAV), terrestrial base station (BTS), transmit power optimization (TPO), interference (I), downlink (DL), and uplink (UL).
{"title":"Positioning Optimization of UAV (Drones) Base Station in Communication Networks","authors":"Mustafa Qahtan Alsudani, Mushtaq Talb Tally, Israa Fayez Yousif, Ali Abdullhussein Waad, Safa Riyadh Waheeda, M. M. Adnan","doi":"10.11113/mjfas.v19n3.2993","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2993","url":null,"abstract":"Unmanned aerial vehicles (UAV) and cellular networks are growing closer to being integrated in the realm of wireless communications, which will improve service quality even further. In this study, we investigate a wireless communication system in which two types of base stations—in the air and on the ground—serve separate groups of users. We analyze the effect of the aerial base station (ABS) height and transmit power on the system's downlink and uplink data rates while accounting for the reciprocal interference between the Aerial and terrestrial communication lines. The findings demonstrate that in many cases the best ABS altitude and transmit Power are either the highest or lowest values attainable. The distance between the ABS, the ABS user (AU), and the terrestrial base station user, among other factors, may affect how well they all communicate (TU). In this article we will discuss the following topics: unmanned aerial vehicle (UAV), terrestrial base station (BTS), transmit power optimization (TPO), interference (I), downlink (DL), and uplink (UL).","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90094830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2903
Israa Fayez Yousif, Mustafa Qahtan Alsudani, Safa Riyadh Waheed, Z. N. Khudhair, M. M. Adnan, Ameer Al-khaykan
In this article, we looked at how to go about creating a CNC pen or drawing machine of your own. Inkscape, which translates graphics and text into g- code format, was utilized as the controller for this project, with the microcontroller serving as the interface between the computer and the language of the CNC machine. The g-code transmits a series of x, y, and z coordinates to the motors; the servo motor controls the pen's movement in response to the Z coordinates; stepper motor 1 controls the rail's horizontal motion; and stepper motor 2 controls the rail's vertical motion in response to the X coordinate. The laser machine employs industrial applications to expedite manufacturing and perform engraving and cutting, resulting in a superior and expertly finished output. The carbon laser beam emitted by the laser engraving machine may be used for engraving, cutting, and shaping a wide variety of materials and end products.
{"title":"Automatic Laser Engraving Machine for Different Materials based on Microcontroller","authors":"Israa Fayez Yousif, Mustafa Qahtan Alsudani, Safa Riyadh Waheed, Z. N. Khudhair, M. M. Adnan, Ameer Al-khaykan","doi":"10.11113/mjfas.v19n3.2903","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2903","url":null,"abstract":"In this article, we looked at how to go about creating a CNC pen or drawing machine of your own. Inkscape, which translates graphics and text into g- code format, was utilized as the controller for this project, with the microcontroller serving as the interface between the computer and the language of the CNC machine. The g-code transmits a series of x, y, and z coordinates to the motors; the servo motor controls the pen's movement in response to the Z coordinates; stepper motor 1 controls the rail's horizontal motion; and stepper motor 2 controls the rail's vertical motion in response to the X coordinate. The laser machine employs industrial applications to expedite manufacturing and perform engraving and cutting, resulting in a superior and expertly finished output. The carbon laser beam emitted by the laser engraving machine may be used for engraving, cutting, and shaping a wide variety of materials and end products.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"10 43 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87906195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2911
Fallah H. Najjar, K. A. Kadhim, Munaf Hamza Kareem, Hanan Abbas Salman, Duha Amer Mahdi, Horya M Al-Hindawi
As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical texture features and then trains two machine learning classifiers - K-Nearest Neighbor and Support Vector Machine - on these features. The method was tested on the COVID-19 Radiography Database and was compared to a state-of-the-art method, delivering highly efficient results with impressive sensitivity, accuracy, precision, F1-score, specificity, and Matthew's correlation coefficient. The proposed approach offers a promising new way to classify COVID-19 infections in chest X-ray images and has the potential to play a crucial role in the ongoing fight against the pandemic.
{"title":"Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning","authors":"Fallah H. Najjar, K. A. Kadhim, Munaf Hamza Kareem, Hanan Abbas Salman, Duha Amer Mahdi, Horya M Al-Hindawi","doi":"10.11113/mjfas.v19n3.2911","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2911","url":null,"abstract":"As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical texture features and then trains two machine learning classifiers - K-Nearest Neighbor and Support Vector Machine - on these features. The method was tested on the COVID-19 Radiography Database and was compared to a state-of-the-art method, delivering highly efficient results with impressive sensitivity, accuracy, precision, F1-score, specificity, and Matthew's correlation coefficient. The proposed approach offers a promising new way to classify COVID-19 infections in chest X-ray images and has the potential to play a crucial role in the ongoing fight against the pandemic.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"74 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80970733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2901
Karrar A. Kadhim, Fallah H Najjar, Ali Abdullhussein Waad, Ibrahim H. Al-Kharsan, Z. N. Khudhair, Ali Aqeel Salim
Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.
{"title":"Leukemia Classification using a Convolutional Neural Network of AML Images","authors":"Karrar A. Kadhim, Fallah H Najjar, Ali Abdullhussein Waad, Ibrahim H. Al-Kharsan, Z. N. Khudhair, Ali Aqeel Salim","doi":"10.11113/mjfas.v19n3.2901","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2901","url":null,"abstract":"Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"114 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81049968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2992
Riyam A. Hasan, J. E. Jamaluddin
Forecasting in pandemics and disasters is one of the means that contribute to reducing the damage of this pandemic, and the Corona virus is reportedly the most dangerous pandemic that the entire world is suffering from. As a result, we aim to use a deep learning algorithm to predict confirmed and new cases of Covid-19 in our study. This paper identifies the most essential deep learning techniques. Long short-term memory (LSTM) and gated recurrent unit (GRU) were shown to forecast verified Covid-19 fatalities in Malaysia, Egypt, and the U.S. using time series data from 1 January 2021 to 14 May 2022. The first section of this study examines a comparison of prediction models, while the second section examines how prediction and performance analysis may be enhanced using mean absolute error (MAE), mean absolute error percentage (MAPE), and root mean squared error (RMSE) Metrics. On the basis of the regression curves of two two-layer models, the data were split into training sets of 80% and test sets of 20%. The conclusion is that the outputs of the training model and the original data greatly converged. The findings of the study indicated that, for predicting Covid-19 cases, the GRU model in the three nations is superior than the LSTM model.
{"title":"Prediction of Covid-19 Cases for Malaysia, Egypt, and USA using Deep Learning Models","authors":"Riyam A. Hasan, J. E. Jamaluddin","doi":"10.11113/mjfas.v19n3.2992","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2992","url":null,"abstract":"Forecasting in pandemics and disasters is one of the means that contribute to reducing the damage of this pandemic, and the Corona virus is reportedly the most dangerous pandemic that the entire world is suffering from. As a result, we aim to use a deep learning algorithm to predict confirmed and new cases of Covid-19 in our study. This paper identifies the most essential deep learning techniques. Long short-term memory (LSTM) and gated recurrent unit (GRU) were shown to forecast verified Covid-19 fatalities in Malaysia, Egypt, and the U.S. using time series data from 1 January 2021 to 14 May 2022. The first section of this study examines a comparison of prediction models, while the second section examines how prediction and performance analysis may be enhanced using mean absolute error (MAE), mean absolute error percentage (MAPE), and root mean squared error (RMSE) Metrics. On the basis of the regression curves of two two-layer models, the data were split into training sets of 80% and test sets of 20%. The conclusion is that the outputs of the training model and the original data greatly converged. The findings of the study indicated that, for predicting Covid-19 cases, the GRU model in the three nations is superior than the LSTM model.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"8 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90288129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.11113/mjfas.v19n3.2906
Safa Riyadh Waheed, Ammar AbdRoba Sakran, Mohd Shafry Mohd Rahim, Norhaida Mohd Suaib, Fallah H Najjar, Karrar A. Kadhim, Ali Aqeel Salim, M. M. Adnan
The Internet of Things (IoT) is a cutting-edge innovation that facilitates the cost-effective development of smart system architectures. Although current regulations necessitate installing an analog fire alarm system, such a system lacks the intelligence to instantly notify the appropriate parties in a timely fashion. In addition, since people are not always present, an analog fire alarm will not be able to prevent immediate danger or damage in the event of a fire. Therefore, the incident must be reported as soon as possible to the appropriate party in order to lessen the impact of a fire. In this study, we suggest a smart fire-alarm system made of a fire sensor and a sound sensor that can both detect fire and noise as well as the status of the analog fire alarm system to ascertain whether the analog fire alarm system is operational. We first tested our proposed smart fire alarm system to determine its effectiveness before putting it into use. From there, we ran experiments to determine how well it worked. The outcomes show that the system is trustworthy in a range of scenarios.
{"title":"Design a Crime Detection System based Fog Computing and IoT","authors":"Safa Riyadh Waheed, Ammar AbdRoba Sakran, Mohd Shafry Mohd Rahim, Norhaida Mohd Suaib, Fallah H Najjar, Karrar A. Kadhim, Ali Aqeel Salim, M. M. Adnan","doi":"10.11113/mjfas.v19n3.2906","DOIUrl":"https://doi.org/10.11113/mjfas.v19n3.2906","url":null,"abstract":"The Internet of Things (IoT) is a cutting-edge innovation that facilitates the cost-effective development of smart system architectures. Although current regulations necessitate installing an analog fire alarm system, such a system lacks the intelligence to instantly notify the appropriate parties in a timely fashion. In addition, since people are not always present, an analog fire alarm will not be able to prevent immediate danger or damage in the event of a fire. Therefore, the incident must be reported as soon as possible to the appropriate party in order to lessen the impact of a fire. In this study, we suggest a smart fire-alarm system made of a fire sensor and a sound sensor that can both detect fire and noise as well as the status of the analog fire alarm system to ascertain whether the analog fire alarm system is operational. We first tested our proposed smart fire alarm system to determine its effectiveness before putting it into use. From there, we ran experiments to determine how well it worked. The outcomes show that the system is trustworthy in a range of scenarios.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"21 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88643926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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