Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest. This is because there are multiple stages in the IFS by considering non-membership functions © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.

Empowering the Magersaren community: technology dissemination for ground coffee added value improvement

This paper is aimed to share the community service experiences held at Boro Sumbersari hamlet which is located at 98A UB forest plot. Boro Sumbersari hamlet is inhabited by Magersaren community. The Magersaren community are farmers and forest workers who depend on forest for their livelihoods. Magersaren has been practicing agroforestry for a long time. They grow Robusta and Arabica coffee among other forest plants. Currently coffee is a favorite beverage, the number of its consumers continues to increase. Many people are interested in the ground coffee beans made by Magersaren traditionally, but it has not been widely marketed. The purpose of this community service program is to generate an alternative source of Magersaren household income, through the added value improvement of local flavored ground coffee beans they have. The added value of magersaren's ground coffee beans can be increased through product development technologies such as attractive packaging techniques and the creation of new variants ground coffe beans by adding brown sugar and powdered ginger. The execution of community service activities that have been carried out consists of: (1)program socialization;(2)focus group discussion;(3)production, packaging and management training;(4)small-business starting up;(5)program evaluation. These community service activities are held during the social distancing due to the COVID-19 pandemic. This condition becomes an obstacle to the effectiveness of program implementation. The start-up small business needs to be continuously supported in order to survive through a critical period of business development, especially under economic pressure during the pandemic.

3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation

The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient's lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.

Integrating religiosity into a technology acceptance model for the adoption of mobile payment technology

This research studies the effects of the religiosity on financial technology (fintech) adoption. The study examines religiosity as part of the Technology Acceptance Model (TAM) dimensions for the adoption of mobile payment technology. We explore the role of religiosity in TAM and recommend several policies for related organizations. The study uses professional sample calculation from 113 traditional markets under Perumda Pasar Jaya as a business entity whose capital is wholly or mostly owned by the regional government through regional assets of DKI Jakarta Province, Indonesia, which use mobile payment technology. We obtained 363 respondents from June 2020 to June 2021, coinciding with the Covid-19 pandemic. Hypothesis testing was done employing SmartPLS 3.2.9 software and questionnaires. The study also adapts previous studies to ensure the questionnaires are relevant to the research objects. The research result show that religiosity explained the formation of TAM by small businesses in traditional markets under Perumda Pasar Jaya Management. Religiosity and the adoption of mobile payment technology determined whether a user used fintech or not. As the research period was limited to June 2020-June 2021, including field research in the traditional markets, newer TAM mobile payment technology development and other TAM mobile payment-based research were not included. This research offers a new TAM development model using relig-iosity for mobile payment adoption in traditional markets. © 2023 by the authors;licensee Growing Science, Canada.

EFFECT OF IMAGE PRE-PROCESSING METHOD ON CONVOLUTIONAL NEURAL NETWORK CLASSIFICATION OF COVID-19 CT SCAN IMAGES

The presence of abnormalities on CT lung images of COVID-19 patients, such as ground-glass opacity and consolidation, can be used to aid in the detection of COVID-19. These abnormalities can be ambiguous and obscure, resulting in false detection by the doctor. This paper evaluates several image pre-processing methods for automatic detection of COVID-19 based on CT images. First, we used Watershed segmentation to separate the lung cavities. We retained the interior of the lung cavity, where the features of COVID-19 were located. Next, we evaluate the addition of a smooth-ing image method of Median and Gaussian filters to remove blood vessel spots. We also assess the contrast improvement-based methods using Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to highlight the features of COVID-19 further. Multi-dimensional extension of CLAHE (MCLAHE) is also used as an optimization of CLAHE method. In addition, the Inverted Threshold to Zero method is utilized to segment the COVID-19 features. We used transfer learning Convolution-al Neural Network (CNN) in VGG-19, ResNet50, Xception, and DenseNet201 for the classification process. The results show that classification accuracy can be improved by adding appropriate pre-processing techniques. CLAHE and MCLAHE have the highest accuracy with 91.20% and 91.60%, respectively. © 2022 ICIC International.

Lung Segmentation using MultiResUNet CNN based on Computed Tomography Image

Lung segmentation is the first step in medical image processing to determine various lung diseases. Currently, the image segmentation process will be more optimal by using deep learning through the convolution process. Various Convolution Neural Network (CNN) based architectures for image segmentation were created by many researchers, however U-Net is the current state of the art for medical image segmentation. Nevertheless, the modification of U-Net continues, and MultiResUNet is one of the new architectures claimed to be better. In this study, we use MultiResUNet for lung segmentation on Computed Tomography (CT) images as the first step to Covid-19 infection segmentation, and the results will be compared using the U-Net architecture. Based on the results of the segmentation experiment, we got satisfactory results. Using the Mean-IoU evaluation metric, it was concluded that the MultiResUNet score was slightly better than the U-Net score for patient lung segmentation, where there was an increase in the score of 1.33% (MultiResUNet=93.05%, U-Net=91.83%) in the dataset which we use. © 2022 IEEE.

Covid-19 Lung Segmentation using U-Net CNN based on Computed Tomography Image

In medical image analysis, lung segmentation is needed as an initial step in diagnosing various diseases in the lung area, including Covid-19 infection. Deep Learning has been used for image segmentation in recent years. One of the Deep Learning-based architectures widely used in medical image segmentation is U-Net CNN. U-Net employs a semantic segmentation approach, which has the benefit of being accurate in segmenting even though the model is trained on a limited quantity of data. Our work intends to assist radiologists in providing a more detailed visualization of COVID-19 infection on CT scans, including infection categories and lung conditions. We conduct preliminary work to segment lung regions using U-Net CNN. The dataset used is relatively small, consisting of 267 CT-scan images split into 240 (90%) images for training and 27 (10%) images for testing. The model is evaluated using the K-fold cross-validation (k=10) approach, which has been believed to be appropriate for models created with limited training data. The metric used for experiments is Mean-IoU. It is commonly used in evaluating the segmentation processes. The results achieved were satisfactory, with Mean-IoU scores ranging from 90.2% to 95.3% in each test phase (k1 – k10), with an average value of 93.3%. © 2022 IEEE.

An Interactive Dashboard to Support Policymaking of Reopening School on Covid-19 Pandemic

In mid-April 2020, UNESCO monitored 191 countries and stated that around 1.723 trillion students in the world were affected by the policy of school from home. It is feared that school closures could hamper the provision of education services and could disrupt the education process which will affect the level of quality of education. There is still no creation of a computational model for the spread of covid as the main framework for schools reopening safely during the pandemic situation. Although there is already a framework from WHO and the government, there is no measuring tool that can evaluate the effect of reopening schools while the Covid-19 pandemic. For this reason, this research seeks to produce a model for the spread of Covid-19 as a basis for determining policies for safely reopening schools during the pandemic. In this research, we produced a recommendation to reopen face-to-face learning in the form of a dashboard. Recommendations are given by predicting the number of cases in each subdistrict using a predictive model. The prediction results are also combined with the factors that have been determined by the government to give recommendations. The allotment of recommendations process involves a critical factor analysis process where we identify which factors are dominant as a basis of a controllable pandemic. © 2022 IEEE.

Semi-Supervised Segmentation of COVID-19 Infection on CT-Scan Lung Using Dynamic Mutual Training

Corona virus disease 2019 (COVID-19) 's global pandemic has caused the world to face a health crisis. Automated detection of COVID-19 infection from computed tomography (CT-scan) images has improved healthcare for treating COVID-19. However, segmentation of infected areas on CT-scan images of the lungs faces several challenges: detailed infection characteristics and low contrast differences between CT scans of infected lungs. It has a low data scale with a doctor's statement because it is still a new case, with a lot of data with pseudo labels, while pseudo labels have a low confidence level and a high error rate. Therefore, using the data of 1600 pseudo label images and 50 doctor label images, we apply pseudo supervision as the core idea, mutual training between two different models with a dynamic loss function called dynamic mutual training (DMT). DMT will do multi-training on pseudo labels with doctor's labels to be trusted in area segmentation. The results obtained are the most superior value of 91.32% with a loss value of 0.19 dice score 0.23, IOU 0.781, precision 0.843, sensitivity 0.753, and specificity 0.845. We also compare our method with other segmentation methods such as UNET, which is highly preferred in terms of medical images, and mask RCNN, which shows the best method in terms of segmentation. This comparison indicates that DMT provides the best experimental incentive with a dice score value of 2-30%, superior to cases segmentation areas affected by COVID-19 on CT scans of the lungs. © 2022. International Journal of Intelligent Engineering and Systems.All Rights Reserved

Empowering the Magersaren community: technology dissemination for ground coffee added value improvement

This paper is aimed to share the community service experiences held at Boro Sumbersari hamlet which is located at 98A UB forest plot. Boro Sumbersari hamlet is inhabited by Magersaren community. The Magersaren community are farmers and forest workers who depend on forest for their livelihoods. Magersaren has been practicing agroforestry for a long time. They grow Robusta and Arabica coffee among other forest plants. Currently coffee is a favorite beverage, the number of its consumers continues to increase. Many people are interested in the ground coffee beans made by Magersaren traditionally, but it has not been widely marketed. The purpose of this community service program is to generate an alternative source of Magersaren household income, through the added value improvement of local flavored ground coffee beans they have. The added value of magersaren's ground coffee beans can be increased through product development technologies such as attractive packaging techniques and the creation of new variants ground coffe beans by adding brown sugar and powdered ginger. The execution of community service activities that have been carried out consists of: (1) program socialization;(2) focus group discussion;(3) production, packaging and management training;(4) small-business starting up;(5) program evaluation. These community service activities are held during the social distancing due to the COVID-19 pandemic. This condition becomes an obstacle to the effectiveness of program implementation. The start-up small business needs to be continuously supported in order to survive through a critical period of business development, especially under economic pressure during the pandemic.

Fuzzy Unsupervised Approaches to Analyze Covid-19 Spread for School Reopening Decision Making

Virus SARS-Cov-2 causing Covid-19 spreads quickly and brings high risks to transmissions. The government to rule strictly to arrange strategies to minimize interactions through School-From-Home (SFH) policy. Unfortunately, the school closure is the potential to hamper deliveries of education services and may entail destructive impacts to quality education performance. There must be a consideration to school reopen safely during the pandemic. The objective of the research is to produce a model of Covid-19 spreads to analyze the readiness of school to reopen. This study adopts a SEIR model to predict the spread of Covid-19 using dataset from 23 March through 31 December 2020. The best model is selected from the one having the least error and adopted to predict the spread in the next 100 days starting from 01 January 2021 through 10 April 2021. Clustering was then implemented to acquire the character's proximity in each area using K-Means algorithm. While unsupervised fuzzy was picked out to seize the phenomenon of the dynamic as Covid-19 spread as a basis to decision making on school reopen safely during the pandemic. These whole concepts will serve the decision making effectively and intelligently by generating a better estimation. This study resulted in a Covid-19 spread model with an average error of 0.2% based on the RMSLE calculation.

Self-Attention Mechanism of RoBERTa to Improve QAS for e-health Education

This paper aims to discuss about the establishment of a health education system in the form of a Question Answer System (QAS) related to the current COVID-19 pandemic. QAS allows users to state information needs in the form of natural language questions, and then this system will return short text quotes or sentence phrases as answers. This is due to the tendency for recipients of information to more easily understand news/information when they can answer questions that may arise in their minds. The approach used was self-attention mechanism such as a Robustly Optimized BERT Pretraining Approach (RoBERTa), a method for question answering with span-based training that predicting the starting limit for answer start and the end limit for the answer index. The final results using 835 non-description questions, the best evaluation value on the training data showed the exact match of 91.7% and F1 value of 93.3%. RoBERTa tends to show the better results on non- description questions or questions with short answers compared to the description questions with complex answers. © 2021 IEEE.

Homecare and Hospital Stroke Therapy Comparison Using EEG Analysis

According to WHO data, stroke ranks second as a non-communicable disease that causes death, and ranks third as a cause of disability. Stroke causes serious neurological disorders, such as reduced motor skills of limbs and muscles, cognitive, visual and coordination significantly. The process of monitoring motor coordination function in stroke rehabilitation is generally in the form of observing movement abilities. This technique has lower quantifiable accuracy. Therefore, it is important to use objective approaches to obtain the appropriate diagnosis for effective rehabilitation process. Recently EEG has been used as a tool for monitoring stroke rehabilitation especially for motor coordination functions. Moreover, due to COVID-19 pandemic, it was reported by WHO that there was a significant decrease in visits of stroke patients to hospitals. Delay in the stroke therapy process could increase patient morbidity and mortality. Based on those reasons, home care stroke therapy then becomes an alternative solution. This research evaluates the effectiveness of home care stroke therapy compared to hospital care by using EEG. In order to determine the effectiveness of one therapy, a hypothesis T-test was calculated between the two methods. The EEG from C3 and C4 channels of 6 stroke patients when performing 8 times of rehabilitation program were compared. The motion that was tested is shoulder flexion extension on the affected upper limb from each patient. EEG preprocessing was done by applying Infinite Impulse Response for band pass filter and then Automatic Artifact Removal and Independent Component Analysis for artifact removal. Standard deviation (STD) values were calculated and analyzed for every patient in each rehabilitation program. The result shows that the STD value from home care therapy shows more increase compared to those who did the therapy at the hospital. The t-statistical value of the low alpha amplitude between homecare and hospital therapy also shows that there is a significant difference in the results of therapy carried out at home compared to in the hospital. From this result we conclude that stroke home care therapy in the new normal era is more effective compared to hospital care therapy for stroke patients. © 2021 IEEE.

Identifying Precautionary Measures for High-Risk Disease from Doctor's Answer Text Using LDA

The Online Health Consultation (OHC), which contains a QA collection of various diseases since 2014, has received an increasing number of visits due to the COVID-19. Based on the benefits and increasing health information need for people who seek information in OHC, health information related to precautionary measures to avoid diseases, especially high-risk diseases, become critical because not all seeker and readers of health information are diagnosed with certain diseases. However, It has currently unidentified whether the text of the doctor's answer corpus, especially in high-risk diseases, contains words that imply precautionary. This study aims to find the pattern of doctor's answer for high-risk diseases through the corpus of doctor's answer text on OHC by identifying whether the doctor's answer text contains words that imply precautionary against disease. Thus, it can help health information seekers and readers take precautionary against disease early on. This paper's contribution was to identify precautionary measures from doctor's answer text for high-risk disease in 2014-2021 using the best model of the two models, namely Single LDA (only LDA Method) and Hybrid LDA (a combination of LDA and Collapsed Gibbs Sampling). The results showed that the best model was Hybrid LDA, and medical experts identified groups of words with this model into four domains, namely symptoms/diagnosis, treatments, precautionary measurements, and general text. The pattern that emerges from the identification of precautionary measures shows (1) which precautionary measures are divided based on what disease, (2) Some words that mean precautionary measures also mean treatment or symptom/diagnosis. © 2021 IEEE.

Optimization Learning Approaches in Predicting Facebook Metrics from User Posts Behavior

The current situation of the Covid-19 pandemic has an impact on increasing the use of social media. In various aspects, social media has a role in human activities, especially in working-age groups. Breaking the stigma that social media interferes with someones' performance, we argue that using social media actually supports someones' work activities. In this preliminary study, we explore post behavior on Facebook social media networks for understanding user productivity. The dataset used in this study is gained from an online survey with the respondent of social media users over age 15 years old. Later on, based on surveys' responses, web scraping of Facebook post were set to complete the data needed. From the dataset, demographic features, metadata-based features, and behavior-based features are examined with some regression algorithms such Support Vector Regression (SVR) and Particle Swarm Optimization Extreme Learning Machine (PSO-ELM). The result from this study is only one feature that positively correlated to almost all other features during the pandemic. © 2021 IEEE.

Analysis the Opinion of School-from-Home during the COVID-19 Pandemic using LSTM Approach

The purpose of opinion analysis in this research is to perceive public responses concerning School-From-Home (SFH) policy during the pandemic in attempt to curb virus spread and worry about new cluster emergences. The policy entails diverse reactions from the societies, including the citizens in virtual world through their chirps in social media, such as Twitter. Analysis on the social media has proved that it has remarkable potentials to apprehend public opinions on various issues. The opinion analysis was performed to get insights about public perception towards SFH policy. As initially predicted, the result of our analysis would show that the public perceptions towards SFH would be mainly negative. The researcher adopted LSTM model as a deep learning approach. Moreover, implementing the N-Gram extraction technique was able to improve the model's performance. Model performance accuracy reached 83.30%. It is concluded that the increasing of model accuracy is about 0.018%. While the running time efficiency of LSTM has improved 19.4%. The results of the analysis of SFH's opinion were 77.90% negative and 22.10% positive. © 2021 IEEE.

Detection of COVID-19 on Chest X-Ray Images using Inverted Residuals Structure-Based Convolutional Neural Networks

China officially reported the COVID-19 coronavirus's existence to the World Health Organization (WHO) on December 31, 2019. Since then, it has spread and has infected millions of people around the world. COVID-19 is a highly contagious disease and it can cause severe respiratory distress. Insevere cases it can result in failure of the function of organs simultaneously. Recent studies haveshown that chest X-rays of patients suffering from COVID-19 show the specific characteristics of those infected with the virus. This paper presents a method to detect the presence of COVID-19 on chest X-ray images based on inverted residuals structure implemented in MobileNetV2 as a base model. We also explore the performance of using a Fully connected layer with dropout and using the Global Average Pooling layer as top layers of the base model to classify each image into COVID-19 or NonCOVID-19. Our proposed method was able to achieve COVID-19 detection with the best accuracy of 0.81, with precision, recall, and F1-score of 0.81, 0.75, and 0.77, respectively, using the Global AveragePooling layer with data augmentation version. © 2020 IEEE.

Monitoring stroke rehabilitation re-learning program using EEG parameter: A preliminary study for developing self-monitoring system for stroke rehabilitation during new normal

In the new normal, a period after Covid-19 outbreak, many things run in the new normal. Including stroke rehabilitation. During the Covid-19 and new normal era, stroke patients are not allowed to gather in a hospital in queue line for rehabilitation service. A new approach is needed to keep the rehabilitation running with a big caution to Covid-19. EEG is an alternative technology for supporting the self-monitoring stroke rehabilitation. In this study, EEG parameters such as mean, Standard deviation, mean absolute value were analyzed and tested to answer our hypotheses whether or not those parameters can be used for monitoring stroke rehabilitation progress. This study involved 3 stroke patients who underwent stroke rehabilitation using re-learning program. Each time stroke patient performed rehabilitation program EEG data was recorded. During two months measurement in total from 3 stroke patients, 12 set EEG data was obtained and analyzed. Two motions were recorded namely hand movements and elbow movements. C3 and C4 EEG channel are used to get the raw EEG data. Data processing such as filtering EEG band into alpha and beta band, noise artefact removal (ICA) and data calculation were done before obtaining the monitoring parameters. The result showed that during post stroke rehabilitation parameters such as Mean, Standard Deviation and Mean Absolute Value showed higher value in both EEG band, alpha and beta. In conclusion, EEG statistical parameters can be used as a monitoring parameter during stroke rehabilitation. In the era of new normal, this could be a solution for home care stroke rehabilitation program. © 2020 IEEE.