Indonesian Journal of Electrical Engineering and Computer Science
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Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review
Get PDFPostpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide
Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle
Get PDFElectric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%
Development of an IoT-based sleep pattern monitoring system for sleep disorder detection
Get PDFInadequate sleep can cause various health problems including heart disease and obesity. In this work, a sleep monitoring system that monitors human sleep patterns is developed using the internet of things (IoT) and Raspberry Pi. The system is designed to record any detected movements and process the data using machine learning to provide valuable insight into a person’s sleep patterns including sleep duration, the time taken to fall asleep, and the frequency of waking up. This information is very useful to provide the sleep disorder diagnostics of an individual including restless leg, parasomnia and insomnia syndrome besides giving recommendations to improve their sleep quality. Also, the system allows the processed data to be stored in the cloud database which can be accessed through a mobile application or web interface. The performance of the system is evaluated in terms of its accuracy and reliability in detecting sleep order diagnostics. Based on the confusion matrix, the results show the accuracy of the system is 90.32%, 91.80%, and 91.80% in detecting the restless leg, parasomnia and insomnia syndrome, respectively. Meanwhile, the system showed high reliability in monitoring 10 participants for 8 hours and updated the recorded data and its analysis in the cloud
Fractional-order PID controller tuned by particle swarm optimization algorithm for a planar CDPR control
Get PDFThe use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs
Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis
Get PDFTraditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL
Numerical modelling of the impact of coronavirus disease 2019 on fertility and population growth rate in Malaysia
Get PDFOver the past four years, the world has grappled with an unprecedented global pandemic caused by COVID-19, resulting in a significant surge in reported cases and fatalities across numerous countries. This health crisis has had far-reaching demographic repercussions, with a notable decline in global birth rates, plummeting by 1.12% in 2020 compared to the preceding year. This trend is exemplified in Malaysia, where the number of births dwindled from 123,751 in September 2019 to 116,434 during the corresponding period in 2020. The reluctance to have children stems from the heightened vulnerability to the virus, compounded by financial constraints due to widespread unemployment, further dampening population growth. This study is dedicated to comprehending the impact of COVID-19 on Malaysia's population growth rates and forecasting its trajectory for the subsequent five years following the outbreak. Researchers scrutinized the data using numerical methods and population modeling and employed the Euler method, aided by MATLAB software, to compare their findings against authentic population figures. Their analysis disclosed that the COVID-19 pandemic affected Malaysia's population growth rates, although it did not directly impact mortality rates, as recovery rates exceeded mortality rates. In essence, the pandemic has primarily influenced birth rates, contributing to a noteworthy demographic shift in the country
Fuzzy logic controller-based Luo converter for light electric vehicles
Get PDFA brushless DC motor (BLDC) drive fed by a Luo converter is presented in this paper for the use in electric vehicle (EV) applications. The proposed Luo converter provides stable and ripple free output for EV. An approach for getting the stable output voltage is by using a fuzzy logic controller to control the Luo converter. It helps to generate the appropriate pulse with respect to the feedback voltage. This proposed system has the advantages like voltage increase, high-gain output with low ripples and high efficiency. The performance of this proposed drive is tested through hardware prototype at varying line voltage levels and results are demonstrated. A comparative analysis is presented to justify the effectiveness of the proposed Luo converter fed EV motor
Multi-microgrids system’s resilience enhancement
Get PDFNowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs
Optimizing channel capacity for B5G with deep learning approaches in MISO-NOMA-HBF and BFNN
Get PDFThis study proposes the integration of a beamforming neural network (BFNN) and multiple-input single-output based non-orthogonal multiple access (MISO-NOMA) with hybrid beamforming (HBF) for cell edge users (CEU) in a millimeter wave (mmWave)-based beyond 5G cellular communication system. This system is referred to as MISO-NOMA-HBF-BFNN. The proposed scheme has been implemented to support multiple users simultaneously and also to considerably enhance and significantly improve the overall the sum channel capacity (SC) and user channel capacities. Additionally, the simulation results demonstrate the superiority of the proposed MISO-NOMA-HBF-BFNN scheme over the existing MISO-NOMA with HBF and MISO-OMA with HBFBFNN based schemes in terms of user capacities and SC
Malignant thyroid lump multi classification by TIRADS using DBA with transfer learning
Get PDFThyroid diseases have developed into significant illnesses in recent decades. These diseases affect the thyroid glands and are caused by elevated thyroid hormone levels or infections in the thyroid organs. It is challenging to resolve thyroid diagnosis using conventional parametric and nonparametric statistical techniques since it can be viewed as a classification problem. However, there are certain barriers in the manner of obtaining both efficacy and accuracy in thyroid nodule diagnosis. Deep learning (DL) and machine learning (ML) models have emerged as useful instruments for the diagnosis of sickness in the modern era. For the purpose of diagnosing and classifying thyroid diseases, this research introduces a novel deep belief network (DBF) with transfer learning, known as DBNTL. In this study, the pre-processed image was first pre-processed using a conventional multiresolution bilateral technique, and then it was subjected to a novel segmentation technique called fusion pooling integrated U-net segmentation. The DBN with transfer learning model is used to classify and grade malignant thyroid nodules in compliance with thyroid imaging-reporting-and-data-system (TIRADS) guidelines. In this model, the model's weights are obtained by transfer learning. A major metric for evaluating the efficacy of biological image processing applications, good sensitivity and specificity (97.28 and 97.22, respectively) were obtained for the recommended modes