Bulletin of Electrical Engineering and Informatics
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Application of the outlier detection method for web-based blood glucose level monitoring system
Get PDFRecent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3-sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web-based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3-sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision-making by identifying and addressing extreme data points
Numerical simulation of one-dimensional perovskite solar cell model
Get PDFPerovskite solar cell (PSC) is one of the advanced third-generation solar cells that have rapid efficiencies but instability issues in terms of air, moisture, and UV light sensitivity becomes a barrier to commercialization. The instability issue is due to the charge accumulation at the interface of the PSC reducing its efficiency. This research focuses on the operation of PSCs through a one-dimensional (1D) drift-diffusion model for planar heterojunction PSCs. The model also accounts for the electric potential by Poisson’s equation, the ion generation, and the recombination rate. The method of lines technique is applied to solve the model for the perovskite layer numerically using the finite difference method which is then solved forward in time using the ‘ode15s’ solver in MATLAB. It is highlighted that the comparison with the experimental data from the reference shows good agreement. The effect of parameter thickness variation of the perovskite layer upon the efficiency of PSCs is analyzed. The result shows that the best efficiency obtained is 19.77% obtained at thickness 0.25 ðœ‡ð‘š. The results may prove useful for a guideline of the cell thickness that predicts the perovskites cell performance
Remote sensing in the analysis of the behavior of CO associated with confinement due to COVID-19, in the city of Manizales
Get PDFThis article analyzed the behavior of carbon monoxide (CO) levels in Manizales during pre-lockdown, lockdown, and post-lockdown, as a response to the coronavirus disease (COVID-19) pandemic. The analysis focuses on the data of CO levels obtained from the tropospheric monitoring instrument (TROPOMI), precipitation, and temperature (T) recorded by the network of stations of Caldas. The data allowed us to find that during the lockdown, the average value of CO was 9.92% lower than the value registered before the lockdown, and it was 11.75% lower after the lockdown. On the other hand, the correlation between CO levels and population density during the three periods was analyzed, obtaining an ð‘…2 = 0.816 after lockdown. Finally, considering other possible variables that can affect the CO levels, an analysis of the behavior of CO was carried out concerning the temperature and precipitation of the city registered before, during, and after the lockdown. Regarding CO and temperature, the correlation was inverse with Pearson’s ð‘Ÿ = −0.599 (Fisher’s 𑧠= −0.692), which also supports the decreasing trend of the value measured, and that the variation of CO levels does not depend only on lockdown but also on other factors. Regarding CO and precipitation, a positive correlation of Pearson’s ð‘Ÿ = 0.165 (Fisher’s 𑧠= 0.167) was obtained
Genetic programming in machine learning based on the evaluation of house affordability classification
Get PDFOne of the big challenges in machine learning is difficulty of achieving high accuracy in a short completion time. A more difficulties appeared when the algorithm needs to be used for solving real dataset from the survey-based data collection. Imbalance dataset, insufficient strength of correlations, and outliers are common problems in real dataset. To accelerate the modelling processes, automated machine learning based on meta-heuristics optimization such as genetic programming (GP) has started to emerge and is gaining popularity. However, identifying the best hyper-parameters of the meta-heuristics’ algorithm is the critical issue. This paper demonstrates the evaluation of GP hyper-parameters in modeling machine learning on house affordability dataset. The important hyper-parameters of GP are population size (PS), that has been observed with different setting in this research. The machine learning with GP was used to predict house affordability among employers with transport expenditure and job mobility as some of the attributes. The results from testing that run on hold-out samples show that GP machine learning can reach to 70% accuracy with split ratio 0.2 and GP PS 30. This research contributes to the advancement of automated machine learning techniques, offering potential for faster and more accurate real survey-based datasets
An attention-based channel estimation algorithm for next-generation point to point communication systems
Get PDFAccurate and robust estimation of channel parameters is essential in establishing reliable communication with characteristic optimal resource utilization in next-generation communication systems. Traditional techniques have limitations, such as the need for additional bandwidth and decreased spectral efficiency. Thus, there is a need for novel techniques that enhance the accuracy and robustness of channel parameter estimation in next-generation communication systems. To address this need, we propose in this paper a recurrent neural network (RNN)-based attention mechanism, to improve channel estimation accuracy and robustness in next-generation communication systems. The attention mechanism selectively focuses on the most relevant features while ignoring noise and interference. The attention network weights are initialized and are constantly updated in the course of network training. The weight values determine the significance of the features before passing them to the channel estimator. This allows the algorithm to adapt to varying channel conditions and improve its accuracy in challenging environments. The proposed attention-based algorithm performance is compared with three baseline techniques: learned denoising-based approximate message passing (LDAMP), Wasserstein generative adversarial networks (WGAN), and maximum likelihood (ML). The result evaluations indicate that the attention-based algorithm performs better than the existing artificial intelligence-based channel coding algorithms, in terms of robustness and accuracy
Covid-19 forecasting model based on machine learning approaches: a review
Get PDFAs coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting model
A clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network
Get PDFThe current paper presents a clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network (FANET). The technological advances of the last decade gave rise to emerging technologies. Unmanned aerial vehicles (UAVs) are small aircraft that proved their usefulness for different tasks nowadays. They can collaborate to achieve missions especially in areas where traditional networks cannot work or cannot accede. A FANET is composed by a number of these aircraft. For alike networks, the resources are limited. Indeed, an efficient energy management is required to extend the life of the network. This work is a clustering method for heterogeneous nodes of a FANET, each node is equipped with one sensor, and four different sensors are used. Clustering is grouping nodes with the aim of efficiency improvement. The clustering is done before the beginning of the rescue mission and depends on the types of sensors the nodes are equipped with and. The master election depends on the available energy of each one of the nodes. The simulation is done with a discrete event simulator (DES) and the results are compared to the algorithm of glowworm swarm optimization (GSO) to demonstrate the effectiveness of the suggested technique
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network
Get PDFPneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies
A novel modified mountain gazelle optimizer for tuning parameter proportional integral derivative of DC motor
Get PDFThis article presents a modified method of mountain gazelle optimizer (MMGO) as a direct current (DC) motor control. Mountain gazelle optimizer (MGO) is an algorithm inspired by the life of the mountain gazelle animal in nature. This animal concept has five essential steps that are duplicated in mathematical modeling. This article uses two tests to get the performance of the MMGO method. The first test uses a benchmark function test with a comparison method, namely the sine tree seed algorithm (STSA) and the original MGO. The second test is the application of MMGO as a DC motor control. The simulation results show that MMGO can reduce the overshoot of conventional proportional integral derivative (PID) control by 0.447% and has a better integral time square error (ITSE) value of 5.345 than conventional PID control. Thus, the MMGO method shows promising performance
Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset
Get PDFWireless capsule endoscopy is one of the diagnostic methods used to record the video of the gastrointestinal tract. The endoscopy capsule stays in the digestive system for at least eight hours. It is difficult for gastroenterologists to examine such a lengthy video and identify the ailment. Convolutional neural networks (CNN) are a powerful solution to several computer vision problems. CNN can speed up the reviewing time of the recorded video by classifying video frames into various categories. The primary emphasis of this research paper is to examine and evaluate the performance of three different CNN architectures-VGG, inception, and MobileNet-in classifying the disease. Experimental results demonstrate that MobileNetV2’s accuracy is 91%, whereas InceptionV3 and VGG16 have an accuracy of 94% which is better than the accuracy of MobileNetV3. However, MobileNeV2 performed relatively better than the other CNN models in terms of computational time and cost. The model’s F-score, precision, and recall values are computed and compared also