Computer Engineering and Applications Journal (ComEngApp, Universitas Sriwijaya)
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102 research outputs found
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Development Of A Cloud-Based Condition Monitoring Scheme For Distribution Transformer Protection
No full textDistribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer’s health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days. The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre
Optimization of Deep Neural Networks with Particle Swarm Optimization Algorithm for Liver Disease Classification
No full textLiver disease has affected more than one million new patients in the world. which is where the liver organ has an important role function for the body\u27s metabolism in channeling several vital functions. Liver disease has symptoms including jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, enlarged spleen and gallbladder and has abnormalities that are very difficult to detect because the liver works as usual even though some liver functions have been damaged. Diagnosis of liver disease through Deep Neural Network classification, optimizing the weight value of neural networks with the Particle Swarm Optimization algorithm. The results of optimizing the PSO weight value get the best accuracy of 92.97% of the Hepatitis dataset, 79.21%, Hepatitis 91.89%, and Hepatocellular 92.97% which is greater than just using a Deep Neural Network
Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory
No full textCardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long ShortTerm Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture\u27s performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients
Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method
No full textThe brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1- score values on the epilepsy signal EEG data than the LeNet-5 architecture
Leaders and Followers Algorithm for Balanced Transportation Problem
No full textLeaders and Followers algorithm is a metaheuristic algorithm which uses two sets of solutions and avoid comparison between random exploratory sample solutions and the best solutions. In this paper, it is used to solve the balanced transportation problem. There are some modifications in the proposed algorithm in order to fit the algorithm to the problem. The proposed algorithm is evaluated using 138 problems. The results are better than the results obtained by other algorithm from previous studies. Overall, Leaders and Followers algorithm has no difficulty in finding optimal solution, even in problems that have large dimension, number of supply and number of demands
Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network
No full textSkin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298
Voice Recognition Systems for The Disabled Electorate: Critical Review on Architectures and Authentication Strategies
No full textAn inevitable factor that makes the concept of electronic voting irresistible is the fact that it offers the possibility of exceeding the manual voting process in terms of convenience, widespread participation, and consideration for People Living with Disabilities. The underlying voting technology and ballot design can determine the credibility of election results, influence how voters felt about their ability to exercise their right to vote, and their willingness to accept the legitimacy of electoral results. However, the adoption of e-voting systems has unveiled a new set of problems such as security threats, trust, and reliability of voting systems and the electoral process itself. This paper presents a critical literature review on concepts, architectures, and existing authentication strategies in voice recognition systems for the e-voting system for the disabled electorate. Consequently, in this paper, an intelligent yet secure scheme for electronic voting systems specifically for people living with disabilities is presented
Nonparametric Regression Analysis of BE4DBE2 Relationship with n and z Variables using Naive Bayes and SVM Classification on Nuclear Data
No full textThis research article describes several analyses of nuclear data using various statistical methods. The first analysis uses linear regression to investigate the relationship between the independent variables (n and z) and the response variable (BE4DBE2). The second analysis uses a nonparametric regression model to overcome the assumptions of normality and linearity in the data. The third analysis uses the Naive Bayes method to classify nuclear data based on variables n and z. The fourth analysis uses a decision tree to classify nuclear data based on the same variables. Finally, the article describes an SVM analysis and a K-means analysis to classify and group nuclide data. The article presents clear and organized descriptions of each analysis, including visual representations of the results. The findings of each analysis are discussed, providing valuable insights into the relationships between the variables and the response variable. The article demonstrates the usefulness of statistical methods in analyzing nuclear data
Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications
No full textCOVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%
Classification of Covid-19 Diseases Through Lung CT-Scan Image Using the ResNet-50 Architecture
No full textCovid-19 is a disease of the respiratory tract caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus. One way to diagnose Covid-19 can be done by examining lung abnormalities on the results of a Computed Tomography Scan (CT-Scan) of the lungs. However, the determination of the diagnostic results obtained requires high accuracy and quite a long time. For this reason, an automatic system is needed to make it easier for medical personnel to diagnose Covid-19 disease quickly and accurately. One way to do this with the help of a computer is pattern recognition. In this study, pattern recognition techniques were used which were divided into three stages, namely pre-processing, feature extraction and classification. The methods used in the pre-processing stage are grayscale and Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve image quality and contrast. The extraction stage uses the Principal Component Analysis (PCA) method, because it can reduce data dimensions without eliminating important features in the data. For the classification stage, a deep learning-based method is used, namely the Convolutional Neural Network (CNN). The CNN architecture used in this study is Resnet-50. The method proposed in this research is evaluated by measuring the performance values of accuracy, recall, precision, F1-score, and Cohen Kappa. The results of the study indicate that the PCA method has worked optimally in dimension reduction, without losing important features on CT-scan images of the lungs. Besides that, the proposed method has succeeded in classifying Covid-19 very well, as seen from the accuracy, Recall, Precision, F1-Score and Cohen Kappa values above 90%