Dense Tissue Pattern Characterization Using Deep Neural Network

Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function with AlexNet, ResNet-18 model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘sigmoid’, ‘tanh’, ‘ReLu’, and ‘leakyReLu’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ReLu’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset using ResNet-18 model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 using ResNet-18 model. Finally, it has been concluded that the ResNet-18 model and ReLu activation function yield outstanding performance for the task.

Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies.

Assessing the applicability of Fusion Landsat-MODIS data for mapping agricultural land use - A case study in An Giang Province

This study aimed to evaluate the applicability of using time-series data of spatiotemporal fusion Landsat-MODIS imagery for mapping agricultural land use in An Giang province, Vietnam. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was adopted for fusion techniques to integrate the relatively high spatial resolution of Landsat (30 meters) and frequently revisit time of MODIS (MOD09Q1, 8-days). The Maximum Likelihood Classifier (MLC) was then used to classify the land cover categories based on variations of NDVI (Normalized Difference Vegetation Index) time-series over the observation period. The overall accuracy is about 84.9%, and a kappa coefficient of K=0.7, which revealed the effectiveness of using Fusion Landsat-MODIS NDVI data in land cover classification at the provincial scale. The current of the agricultural land use was finally mapped, including seven categories, namely built-up areas (10.49%), double rice crops (4.8%), triple rice crops (68.24%), perennial tree/orchards (4.08%), annual crops (7%), water surfaces (3.07%), and forest (2.32%). The results indicate that the agricultural land use cover can be detected in detail using Fusion Landsat-MODIS imagery. The classification is dramatically higher compared to the map classified by a conventional method of solely Landsat 8 image analysis (overall accuracy of 67.3% and Kappa coefficient K=0.35). The research outcomes will support the detailed information for managers in evaluating the impact of climate change on the rice cropping system toward sustainable agriculture development.

Frailty in the elderly: screening possibilities in Primary Health Care

ABSTRACT Objectives: to evaluate two instruments for screening frailty in the elderly in Primary Health Care. Methods: this is an observational, cross-sectional study, with a quantitative approach, with 396 elderly people. SPSS software helped to perform the statistical analyses. The study used the kappa coefficient and Spearman’s correlation. Results: the kappa coefficient between the Clinical-Functional Vulnerability Index 20 and the Edmonton Frailty Scale was 0.496, considered moderate. There was a positive and significant correlation (r = 0.77; p < 0.001) between the frailty conditions and the total score of the two instruments. Conclusions: when this article assessed fragility through the kappa coefficient, both instruments presented positive correlation and agreement. However, the identification of frailty was higher when it used the Edmonton Frailty Scale.

The Effectiveness of Comparison of the Use of the Kuesioner Pra Skrining Perkembangan (KPSP) with Denver II on Development Children aged 0 – 72 months in the Dadok Primary Health Center Work Padang

Background: The issues of toddler boom and improvement that want for use as a reference withinside the detection encompass 10% of youngsters reaching early age abilities, 50% of youngsters will attain their abilities, 75% of youngsters will gain extra abilities, 90% of youngsters could have on the way to attain the age restriction on the present-day still. However, some time ago, they encountered several growth and development problems which were quite worrying for preschool children. Objectives: This study was to determine the accuracy of developmental outcomes for children aged 0 – 72 months between the Guide to the Kuesioner Pra Skrining Perkembangan (KPSP) and the Denver Developmental Screening Test II (Denver II). Questioner Pra Skrining Perkembangan (KPSP) and the Denver Developmental Screening Test II (Denver II) have a good result, valid and reliable to view developmental outcomes for children aged 0 – 72 months in Dadok Primary Health Center. Methods: The research method used is analytic with the design used being cross-sectional, then the Cohen's Kappa coefficient statistical test is carried out. The research sampling technique used purposive sampling as many as 56 children aged 0-72 months with exclusion criteria, namely children who were sick, had physical disabilities, and experienced other developmental disorders that could not be measured with the KPSP and Denver II instruments. The research into finished from December 2020 to December 2021. Results: The effects of this examination discovered that the improvement of youngsters elderly 0-72 months turned into nevertheless in the precise or everyday category. This may be visible from the effects of developmental tests on the usage of the KPSP and Denver II instruments, each of that has equal effectiveness for use in assessing improvement in youngsters. This is evidenced by the results of the Cohen's Kappa coefficient statistical test with a Kappa value of 0.638 which means it is good (0.61-0.80). Conclusion: It is expected for parents to implement an early detection program for child development by the child's ability at home. Parents are expected to attend seminars or training on growth and development according to the child's age level. invite the child to be more diligent in moving, provide stimulation to the leg muscles, or take the child to therapy so that it can be handled properly.

Automatic Short Answer Grading System in Indonesian Language Using BERT Machine Learning

A system capable of automatically grading short answers is a very useful tool. The system can be created using machine learning algorithms. In this study, a machine system using BERT is proposed. BERT is an open-source system that is set to English by default. The use of languages other than English Language is a challenge to be implemented in BERT. This study proposes a novel system to implement Indonesian Language in the BERT system for automatic grading of short answers. The experimental results were measured using two measuring instruments: Cohen's Kappa coefficient and the Confusion Matrix. The result of measuring the BERT output of the implemented system has a Cohen Kappa coefficient of 0.75, a precision of 0.94, a recall of 0.96, a Specificity of 0.76 and an F1 Score of 0.95. Based on the measurement results, it can be seen that the implementation of the automatic short answer grading system in Indonesian Language using BERT machine learning has been successful.

Application of Advanced Land Observing Satellite 3 (ALOS-3) Data to Land Cover and Vegetation Mapping

Advanced Land Observing Satellite 3 (ALOS-3) is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of Land Cover and Vegetation (LCV) information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of LCV types. We simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the ALOS-3 simulated (ALOS-3S) images were utilized for the classification and mapping of LCV types in two cool temperate ecosystems. This research dealt with classification and mapping of 17 classes in the Hakkoda site and 25 classes in the Zao site. We employed a Gradient Boosted Decision Tree (GBDT) classifier with 10-fold cross-validation method for assessing the potential of ALOS-3S images. In the Hakkoda site, we obtained overall accuracy, 0.811 and kappa coefficient, 0.798. In the Zao site, overall accuracy and kappa coefficient were 0.725 and 0.711 respectively. Regardless of limited temporal scenes available in the research, ALOS-3S images showed high potential (at least 0.711 kappa-coefficient) for the LCV classification. The availability of more temporal scenes from ALOS-3 satellite is expected for improved classification and mapping of LCV types in the future.

Relationship of Selected Anthropometric and Linear Kinematical Variables with the Performance of Toe-Touch Skill in Kabaddi

The purpose of this study was to find out the relationship of selected anthropometric and linear kinematical variables with the performance of toe-touch skill among male kabaddi players (raiders). Materials and Methods. One hundred male raiders were selected for this study. The age of the subjects ranged between 18 to 25 years. Selected anthropometric variables: foot length, upper leg length, lower leg length, thigh girth and calf girth were measured by standardized equipment. Selected linear kinematical variables were measured by digital software ‘Kinovea version-0.9.3’. The toe-touch skill performed by raiders was assessed by three experts rating. The inter-rater reliability of the scores awarded by the experts to the subjects was tested by Cohen’s Kappa test and Kappa coefficient was found significant. Results. Spearman’s rank correlation revealed that there was significant correlation in case of thigh girth (rs = 0.230, p = 0.022), distance (rs = 0.245, p = 0.014) and center of gravity (rs = -0.270, p = 0.007) variables, and there was not significant correlation in case of upper leg length (rs = 0.048, p = 0.634), lower leg length (rs = -0.90, p = 0.373), calf girth (rs = 0.093, p = 0.355), foot length (rs = -0.17, p = 0.863) and time (rs = -0.006, p = 0.952) variables with the performance of toe-touch skill in kabaddi. Conclusion. The study concludes that thigh girth and distance positively and center of gravity negatively contributes to the performance of toe-touch skill in male kabaddi players.

Comparison of Acquired Activated Protein C Resistance, Using the CAT and ST-Genesia® Analysers and Three Thrombin Generation Methods, in APS and SLE Patients

Background: Acquired activated protein C resistance (APCr) has been identified in antiphospholipid syndrome (APS) and systemic lupus erythematosus (SLE). Objective: To assess agreement between the ST-Genesia® and CAT analysers in identifying APCr prevalence in APS/SLE patients, using three thrombin generation (TG) methods. Methods: APCr was assessed with the ST-Genesia using STG-ThromboScreen and with the CAT using recombinant human activated protein C and Protac® in 105 APS, 53 SLE patients and 36 thrombotic controls. Agreement was expressed in % and by Cohen's kappa coefficient. Results: APCr values were consistently lower with the ST-Genesia® compared to the CAT, using either method, in both APS and SLE patients. Agreement between the two analysers in identifying APS and SLE patients with APCr was poor (≤65.9%, ≤0.20) or fair (≤68.5%, ≥0.29), regardless of TG method, respectively; no agreement was observed in thrombotic controls. APCr with both the ST Genesia and the CAT using Protac®, but not the CAT using rhAPC, was significantly greater in triple antiphospholipid antibody (aPL) APS patients compared to double/single aPL patients (p < 0.04) and in thrombotic SLE patients compared to non-thrombotic SLE patients (p < 0.05). Notably, the ST-Genesia®, unlike the CAT, with either method, identified significantly greater APCr in pregnancy morbidity (median, confidence intervals; 36.9%, 21.9–49.0%) compared to thrombotic (45.7%, 39.6–55.5%) APS patients (p = 0.03). Conclusion: Despite the broadly similar methodology used by CAT and ST-Genesia®, agreement in APCr was poor/fair, with results not being interchangeable. This may reflect differences in the TG method, use of different reagents, and analyser data handling.

Comparison of GEOBIA classification algorithms based on Worldview-3 imagery in the extraction of coastal coniferous forest

Šume primorskih četinjača, sa svojom ekološkom, ekonomskom, estetskom i društvenom funkcijom, predstavljaju važan dio europskih šumskih zajednica. Osnovni cilj ovoga rada je usporediti najkorištenije GEOBIA (engl. Geographic Object-Based Image Analysis) klasifikacijske algoritme (engl. Random Trees – RT, Maximum Likelihood – ML, Support Vector Machine – SVM) s ciljem izdvajanja šuma primorskih četinjača na visoko-rezolucijskom WorldView-3 snimku unutar topografskog slijevnog područja naselja Split. Metodološki okvir istraživanja uključuje (1) izvođenje izoštrenog multispektralnog snimka (WV-3_MS-a); (2) testiranje segmentacijskih korisničko-definiranih parametara; (3) dodavanje testnih uzoraka; (4) klasifikaciju segmentiranog modela; (5) procjenu točnosti klasifikacijskih algoritama, te (6) procjenu točnosti završnog modela. RT se prema korištenim pokazateljima (correctness – COR, completeness – COM i overall quality – OQ) pokazao kao najbolji algoritam. Iterativno postavljanje segmentacijskih parametara omogućilo je detekciju najprikladnijih vrijednosti za generiranje segmentacijskog modela. Utvrđeno je da sjene mogu uzrokovati značajne probleme ako se klasificiranje vrši na visoko-rezolucijskim snimkama. Modificiranim Cohen’s kappa coefficient (K) pokazateljem izračunata je točnost konačnog modela od 87,38%. WV-3_MS se može smatrati kvalitetnim podatkom za detekciju šuma primorskih četinjača primjenom GEOBIA metode.