We thank Intelligent System Research Group (ISysRG) Universitas Sriwijaya, Indonesia for its full... more We thank Intelligent System Research Group (ISysRG) Universitas Sriwijaya, Indonesia for its full support of our research infrastructure.
Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness... more Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.
Currently there are tons of applications available on the Google Play Store. But some of these ap... more Currently there are tons of applications available on the Google Play Store. But some of these applications have been infiltrated by malware files. On Android, each program is contained in apk file, which contains all program code, resources, assets, certificates, and manifest files. Users can download applications from any website and copy the apk file to the device, which further increases the popularity of Android. In open the network still provides many possibilities for the attacker. For example, spread via email and phishing sites. This is the root of the problem if the application that has been infiltrated by the malware file has been installed on the Android user's device, then automatically the file on the Android device can be blocked by malware. The type of malware that is very dangerous and growing rapidly is the type of ransomware. Given their rapid growth, there is an urgent need to develop an effective countermeasure solution by classifying the ransomware using the DNN algorithm and optimizing it by initializing the weights of Xavier and He. The results were significant, with a score of 99.96% for training and testing accuracy, 99.95% for precision, 99.99% for sensitivity, 99.07% for specificity and 99.97% for F1-score. The greater the value obtained from the test results, the better the performance in the testing system.
Computer Engineering and Applications, Jun 1, 2022
The intensity of light will greatly affect every process carried out in image processing, especia... more The intensity of light will greatly affect every process carried out in image processing, especially facial images. It is important to analyze how the performance of each face detection method when tested at several lighting levels. In face detection, various methods can be used and have been tested. The FLP method automates the identification of the location of facial points. The Fisherface method reduces the dimensions obtained from PCA calculations. The LBPH method converts the texture of a face image into a binary value, while the WNNs method uses RAM to process image data, using the WiSARD architecture. This study proposes a technique for testing the effect of light on the performance of face detection methods, on an embedded platform. The highest accuracy was achieved by the LBPH and WNNs methods with an accuracy value of 98% at a lighting level of 400 lx. Meanwhile, at the lowest lighting level of 175 lx, all methods have a fairly good level of accuracy, which is between 75% to 83%.
Echocardiographic interpretation during the prenatal or postnatal period is important for diagnos... more Echocardiographic interpretation during the prenatal or postnatal period is important for diagnosing cardiac septal abnormalities. However, manual interpretation can be time consuming and subject to human error. Automatic segmentation of echocardiogram can support cardiologists in making an initial interpretation. However, such a process does not always provide straightforward information to make a complete interpretation. The segmentation process only identifies the region of cardiac septal abnormality, whereas complete interpretation should determine based on the position of defect. In this study, we proposed a stacked residual-dense network model to segment the entire region of cardiac and classifying their defect positions to generate automatic echocardiographic interpretation. We proposed the generalization model with incorporated two modalities: prenatal and postnatal echocardiography. To further evaluate the effectiveness of our model, its performance was verified by five car...
This paper studies a significant amount of residual evidence characterized by the historical flig... more This paper studies a significant amount of residual evidence characterized by the historical flight trajectory of PK-LQP (B737 MAX 8), which underwent an accident. Subsequently, this method is employed to generate novel safety-relevant knowledge based on existing flight data. At the beginning of this study, the method is applied by developing the hypothesis with the support of all data collected from online and offline reports, ADS-B data from flightradar24, and a statistical approach. This preliminary study employs Python as an essential program for the purpose of data collation and analysis. The results show that in the data offered by KNKT (The Indonesian National Transportation Safety Committee-Indonesia), the aircraft (PK-LQP) demonstrated unusual behaviour in comparison with the typical climbing phase that is produced by the dataset valid B737 MAX 8 (the ground truth data). The results also confirmed the hypothesis proposed in this study.
Open Access Macedonian Journal of Medical Sciences
BACKGROUND: In a cardiac septal defect, there is left-to-right shunt at the atrial, ventricle lev... more BACKGROUND: In a cardiac septal defect, there is left-to-right shunt at the atrial, ventricle level, or both. This causes clinical symptoms of heart failure, pulmonary hypertension, or malnutrition. NTproBNP is synthesized and released into the circulation by the ventricular myocytes in response to pressure, volume overload, and increase in myocardial wall stress. AIM: This study aims to evaluate relationship between NTproBNP levels and clinical symptoms of cardiac septal defect. PATIENTS AND METHODS: This cross-sectional study was conducted from April to August 2021 at Moh Husin Hospital, Palembang, Indonesia. The presence of heart failure was determined using the modified Ross score. Nutritional status was defined on anthropometric measurement, and data were plot to weight to height Z-score chart. The presence of pulmonary hypertension was measured by Doppler echocardiography. RESULTS: A total of 75 cardiac septal defect patients were included in this study. A similar plasma NTpro...
Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), 2020
Gacobot is garbage collection and deliver the garbage to the temporary dumpsite and a robot which... more Gacobot is garbage collection and deliver the garbage to the temporary dumpsite and a robot which is an integration of mobile robotic systems and non-mobile robot, that robot such as a car robot systems and robotic manipulator as garbage grabber system. GACOBOT system also integrates an ultrasonic sensor and navigation systems as GACOBOT support systems, process waste dumps by conditioning the front ultrasonic sensor, then adjust grabber garbage and put it in a trash receptacle while the robot is installed by robots. The process of garbage disposal at temporary dumpsite has managed to pass the barrier and to the point that has been set. The process of garbage disposal using pattern
Robot omnidirectional is a type of wheeled robot that can move efficiently in various directions.... more Robot omnidirectional is a type of wheeled robot that can move efficiently in various directions. Generally, this robot uses Omni wheels. In this study, the mobile robot omnidirectional was developed to move freely in reaching the target point. The robot uses four Omni wheels, where the direction of movement of the robot uses kinematic analysis. The method used for the movement of this robot is fuzzy logic control. The fuzzy logic controller accepts input from five proximity sensors, and the output produces the action of the movement of the robot. Fuzzy logic control in a robot is designed to work on two behaviors, namely avoiding obstacles and approaching the target. The obstacle avoidance behavior is active if the sensors detect an object or wall, while the behavior of approaching the target is based on the error value between the actual position and the reference. The experimental results show that the robot can navigate with good movement in avoiding obstacles and reaching the s...
Pap smears. Our clinic has provided services previously inaccessible to many women in the communi... more Pap smears. Our clinic has provided services previously inaccessible to many women in the community. Colposcopy is a technique which has been used in Europe for many years, but it is relatively new in the United States. The colposcope is a binocular microscope with a magnification of x13.5 and a light source of high intensity. With this instrument, the nurse can identify specific areas of cervical atypia and subsequently per-
International Journal of Computational Intelligence Systems
Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart dise... more Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart disease (CHD). A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet/Computing in Cardiology Challenge 2013, this study achi...
Background Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step fo... more Background Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm/beat. Method A computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach. Results The empirical results using both beat segmentation approaches, with a total of 14,588 beats were sho...
The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atr... more The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99...
Computer Engineering and Applications Journal, 2018
This paper presents a method to improve data integrity of individual-based bibliographic reposito... more This paper presents a method to improve data integrity of individual-based bibliographic repository. Integrity improvement is done by comparing individual-based publication raw data with individual-based clustered publication data. Hierarchical Agglomerative Clustering is used to cluster the publication data with similar author names. Clustering is done by two steps of clustering. The first clustering is based on the co-author relationship and the second is by title similarity and year difference. The two-step hierarchical clustering technique for name disambiguation has been applied to Universitas Sriwijaya Publication Data Center with good accuracy.
Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and... more Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrat...
The heart’s mitral valve is the valve that separates the chambers of the heart between the left a... more The heart’s mitral valve is the valve that separates the chambers of the heart between the left atrium and left ventricle. Heart valve disease is a fairly common heart disease, and one type of heart valve disease is mitral regurgitation, which is an abnormality of the mitral valve on the left side of the heart that causes an inability of the mitral valve to close properly. Convolutional Neural Network (CNN) is a type of deep learning that is suitable for use in image analysis. Segmentation is widely used in analyzing medical images because it can divide images into simpler ones to facilitate the analysis process by separating objects that are not analyzed into backgrounds and objects to be analyzed into foregrounds. This study builds a dataset from the data of patients with mitral regurgitation and patients who have normal hearts, and heart valve image analysis is done by segmenting the images of their mitral heart valves. Several types of CNN architecture were applied in this resea...
Background The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activi... more Background The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, th...
Artificial intelligence (AI) technologies continue to play significant roles during the Coronavir... more Artificial intelligence (AI) technologies continue to play significant roles during the Coronavirus 2019 (COVID-19) pandemic in the world. However, health is an area where the rules are stringent and inflexible. This can be justified because it deals with human life. Nevertheless, at the same time, a large number of tests, certifications, and panels will lead to innovations in AI for healthcare that are longer, more complex, and difficult to incorporate into real-world applications. Indonesia has a lot of AI research, which is challenging to commercialize in medicine. These researches are not yet effective due to several limitations in terms of (i) the readiness of a skilled workforce to develop and use AI, (ii) the readiness of regulations that regulate the ethics of using and utilizing responsibly, (iii) the readiness of computational infrastructure and supporting data for AI modeling, and (iv) readiness industry and the public sector in adopting AI innovations. In pandemic outbre...
Journal of Engineering Science and Technology Review
Due to its high population and growing middle class, Indonesia is a locale for a promising expans... more Due to its high population and growing middle class, Indonesia is a locale for a promising expansion of commercial aviation businesses. Furthermore, being a large archipelago, mobility of people and goods via air travel appears to be the most convenient option available. Nevertheless, aviation safety has remained an issue of concern in Indonesia since 2007, after the EU banned Indonesian aircraft flights in the European airspace. This was done as a response to the multiple accidents that earned Indonesian aviation a global reputation for being unsafe. We conducted the investigation based on a reputable report produced by KNKT, known as the National Transportation Safety Board, to establish a more comprehensive context of this situation. Since 1988, all accident data reports have been gathered and archived in the KNKT database, which was then utilized to further investigate the issues confronting Indonesian airlines by evaluating the data using the EDA approach. According to the results, Papua Island is the least safe region for aviation, with 140 accidents (21.9 percent of the total accidents) in Indonesia over the past 33 years. Moreover, the noteworthy results reveal that 45.71 percent of the accidents or incidents occur during the landing phase. This finding informed us regarding the overall geography of Papua, which lacks transportation roads, prompting the government to open the airport for mobilization. Papua has 217 airports, accounting for 42.3 percent of all Indonesian airports, with 196 being small airports at an average altitude of 452.15 meters. Human factors account for 63 percent of accidents in Papua, while technological factors account for 19 percent. The government may use the study's findings to pressure operators to enhance safety in addition to revitalizing aviation safety supporting technologies.
We thank Intelligent System Research Group (ISysRG) Universitas Sriwijaya, Indonesia for its full... more We thank Intelligent System Research Group (ISysRG) Universitas Sriwijaya, Indonesia for its full support of our research infrastructure.
Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness... more Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.
Currently there are tons of applications available on the Google Play Store. But some of these ap... more Currently there are tons of applications available on the Google Play Store. But some of these applications have been infiltrated by malware files. On Android, each program is contained in apk file, which contains all program code, resources, assets, certificates, and manifest files. Users can download applications from any website and copy the apk file to the device, which further increases the popularity of Android. In open the network still provides many possibilities for the attacker. For example, spread via email and phishing sites. This is the root of the problem if the application that has been infiltrated by the malware file has been installed on the Android user's device, then automatically the file on the Android device can be blocked by malware. The type of malware that is very dangerous and growing rapidly is the type of ransomware. Given their rapid growth, there is an urgent need to develop an effective countermeasure solution by classifying the ransomware using the DNN algorithm and optimizing it by initializing the weights of Xavier and He. The results were significant, with a score of 99.96% for training and testing accuracy, 99.95% for precision, 99.99% for sensitivity, 99.07% for specificity and 99.97% for F1-score. The greater the value obtained from the test results, the better the performance in the testing system.
Computer Engineering and Applications, Jun 1, 2022
The intensity of light will greatly affect every process carried out in image processing, especia... more The intensity of light will greatly affect every process carried out in image processing, especially facial images. It is important to analyze how the performance of each face detection method when tested at several lighting levels. In face detection, various methods can be used and have been tested. The FLP method automates the identification of the location of facial points. The Fisherface method reduces the dimensions obtained from PCA calculations. The LBPH method converts the texture of a face image into a binary value, while the WNNs method uses RAM to process image data, using the WiSARD architecture. This study proposes a technique for testing the effect of light on the performance of face detection methods, on an embedded platform. The highest accuracy was achieved by the LBPH and WNNs methods with an accuracy value of 98% at a lighting level of 400 lx. Meanwhile, at the lowest lighting level of 175 lx, all methods have a fairly good level of accuracy, which is between 75% to 83%.
Echocardiographic interpretation during the prenatal or postnatal period is important for diagnos... more Echocardiographic interpretation during the prenatal or postnatal period is important for diagnosing cardiac septal abnormalities. However, manual interpretation can be time consuming and subject to human error. Automatic segmentation of echocardiogram can support cardiologists in making an initial interpretation. However, such a process does not always provide straightforward information to make a complete interpretation. The segmentation process only identifies the region of cardiac septal abnormality, whereas complete interpretation should determine based on the position of defect. In this study, we proposed a stacked residual-dense network model to segment the entire region of cardiac and classifying their defect positions to generate automatic echocardiographic interpretation. We proposed the generalization model with incorporated two modalities: prenatal and postnatal echocardiography. To further evaluate the effectiveness of our model, its performance was verified by five car...
This paper studies a significant amount of residual evidence characterized by the historical flig... more This paper studies a significant amount of residual evidence characterized by the historical flight trajectory of PK-LQP (B737 MAX 8), which underwent an accident. Subsequently, this method is employed to generate novel safety-relevant knowledge based on existing flight data. At the beginning of this study, the method is applied by developing the hypothesis with the support of all data collected from online and offline reports, ADS-B data from flightradar24, and a statistical approach. This preliminary study employs Python as an essential program for the purpose of data collation and analysis. The results show that in the data offered by KNKT (The Indonesian National Transportation Safety Committee-Indonesia), the aircraft (PK-LQP) demonstrated unusual behaviour in comparison with the typical climbing phase that is produced by the dataset valid B737 MAX 8 (the ground truth data). The results also confirmed the hypothesis proposed in this study.
Open Access Macedonian Journal of Medical Sciences
BACKGROUND: In a cardiac septal defect, there is left-to-right shunt at the atrial, ventricle lev... more BACKGROUND: In a cardiac septal defect, there is left-to-right shunt at the atrial, ventricle level, or both. This causes clinical symptoms of heart failure, pulmonary hypertension, or malnutrition. NTproBNP is synthesized and released into the circulation by the ventricular myocytes in response to pressure, volume overload, and increase in myocardial wall stress. AIM: This study aims to evaluate relationship between NTproBNP levels and clinical symptoms of cardiac septal defect. PATIENTS AND METHODS: This cross-sectional study was conducted from April to August 2021 at Moh Husin Hospital, Palembang, Indonesia. The presence of heart failure was determined using the modified Ross score. Nutritional status was defined on anthropometric measurement, and data were plot to weight to height Z-score chart. The presence of pulmonary hypertension was measured by Doppler echocardiography. RESULTS: A total of 75 cardiac septal defect patients were included in this study. A similar plasma NTpro...
Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), 2020
Gacobot is garbage collection and deliver the garbage to the temporary dumpsite and a robot which... more Gacobot is garbage collection and deliver the garbage to the temporary dumpsite and a robot which is an integration of mobile robotic systems and non-mobile robot, that robot such as a car robot systems and robotic manipulator as garbage grabber system. GACOBOT system also integrates an ultrasonic sensor and navigation systems as GACOBOT support systems, process waste dumps by conditioning the front ultrasonic sensor, then adjust grabber garbage and put it in a trash receptacle while the robot is installed by robots. The process of garbage disposal at temporary dumpsite has managed to pass the barrier and to the point that has been set. The process of garbage disposal using pattern
Robot omnidirectional is a type of wheeled robot that can move efficiently in various directions.... more Robot omnidirectional is a type of wheeled robot that can move efficiently in various directions. Generally, this robot uses Omni wheels. In this study, the mobile robot omnidirectional was developed to move freely in reaching the target point. The robot uses four Omni wheels, where the direction of movement of the robot uses kinematic analysis. The method used for the movement of this robot is fuzzy logic control. The fuzzy logic controller accepts input from five proximity sensors, and the output produces the action of the movement of the robot. Fuzzy logic control in a robot is designed to work on two behaviors, namely avoiding obstacles and approaching the target. The obstacle avoidance behavior is active if the sensors detect an object or wall, while the behavior of approaching the target is based on the error value between the actual position and the reference. The experimental results show that the robot can navigate with good movement in avoiding obstacles and reaching the s...
Pap smears. Our clinic has provided services previously inaccessible to many women in the communi... more Pap smears. Our clinic has provided services previously inaccessible to many women in the community. Colposcopy is a technique which has been used in Europe for many years, but it is relatively new in the United States. The colposcope is a binocular microscope with a magnification of x13.5 and a light source of high intensity. With this instrument, the nurse can identify specific areas of cervical atypia and subsequently per-
International Journal of Computational Intelligence Systems
Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart dise... more Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart disease (CHD). A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet/Computing in Cardiology Challenge 2013, this study achi...
Background Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step fo... more Background Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm/beat. Method A computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach. Results The empirical results using both beat segmentation approaches, with a total of 14,588 beats were sho...
The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atr... more The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99...
Computer Engineering and Applications Journal, 2018
This paper presents a method to improve data integrity of individual-based bibliographic reposito... more This paper presents a method to improve data integrity of individual-based bibliographic repository. Integrity improvement is done by comparing individual-based publication raw data with individual-based clustered publication data. Hierarchical Agglomerative Clustering is used to cluster the publication data with similar author names. Clustering is done by two steps of clustering. The first clustering is based on the co-author relationship and the second is by title similarity and year difference. The two-step hierarchical clustering technique for name disambiguation has been applied to Universitas Sriwijaya Publication Data Center with good accuracy.
Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and... more Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrat...
The heart’s mitral valve is the valve that separates the chambers of the heart between the left a... more The heart’s mitral valve is the valve that separates the chambers of the heart between the left atrium and left ventricle. Heart valve disease is a fairly common heart disease, and one type of heart valve disease is mitral regurgitation, which is an abnormality of the mitral valve on the left side of the heart that causes an inability of the mitral valve to close properly. Convolutional Neural Network (CNN) is a type of deep learning that is suitable for use in image analysis. Segmentation is widely used in analyzing medical images because it can divide images into simpler ones to facilitate the analysis process by separating objects that are not analyzed into backgrounds and objects to be analyzed into foregrounds. This study builds a dataset from the data of patients with mitral regurgitation and patients who have normal hearts, and heart valve image analysis is done by segmenting the images of their mitral heart valves. Several types of CNN architecture were applied in this resea...
Background The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activi... more Background The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, th...
Artificial intelligence (AI) technologies continue to play significant roles during the Coronavir... more Artificial intelligence (AI) technologies continue to play significant roles during the Coronavirus 2019 (COVID-19) pandemic in the world. However, health is an area where the rules are stringent and inflexible. This can be justified because it deals with human life. Nevertheless, at the same time, a large number of tests, certifications, and panels will lead to innovations in AI for healthcare that are longer, more complex, and difficult to incorporate into real-world applications. Indonesia has a lot of AI research, which is challenging to commercialize in medicine. These researches are not yet effective due to several limitations in terms of (i) the readiness of a skilled workforce to develop and use AI, (ii) the readiness of regulations that regulate the ethics of using and utilizing responsibly, (iii) the readiness of computational infrastructure and supporting data for AI modeling, and (iv) readiness industry and the public sector in adopting AI innovations. In pandemic outbre...
Journal of Engineering Science and Technology Review
Due to its high population and growing middle class, Indonesia is a locale for a promising expans... more Due to its high population and growing middle class, Indonesia is a locale for a promising expansion of commercial aviation businesses. Furthermore, being a large archipelago, mobility of people and goods via air travel appears to be the most convenient option available. Nevertheless, aviation safety has remained an issue of concern in Indonesia since 2007, after the EU banned Indonesian aircraft flights in the European airspace. This was done as a response to the multiple accidents that earned Indonesian aviation a global reputation for being unsafe. We conducted the investigation based on a reputable report produced by KNKT, known as the National Transportation Safety Board, to establish a more comprehensive context of this situation. Since 1988, all accident data reports have been gathered and archived in the KNKT database, which was then utilized to further investigate the issues confronting Indonesian airlines by evaluating the data using the EDA approach. According to the results, Papua Island is the least safe region for aviation, with 140 accidents (21.9 percent of the total accidents) in Indonesia over the past 33 years. Moreover, the noteworthy results reveal that 45.71 percent of the accidents or incidents occur during the landing phase. This finding informed us regarding the overall geography of Papua, which lacks transportation roads, prompting the government to open the airport for mobilization. Papua has 217 airports, accounting for 42.3 percent of all Indonesian airports, with 196 being small airports at an average altitude of 452.15 meters. Human factors account for 63 percent of accidents in Papua, while technological factors account for 19 percent. The government may use the study's findings to pressure operators to enhance safety in addition to revitalizing aviation safety supporting technologies.
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