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Benedecta Indah Nugraheni ◽  
Herman Dwi Surjono ◽  
Gregorius Punto Aji ◽  

This review aimed at providing a comprehensive overview of how the flipped classroom can give the positive effects on developing students’ critical thinking skills. Data were collected from four databases, which included Google Scholar, ResearchGate, EBSCO, and Emerald. This study synthesized the findings of 16 studies published from 2015 to 2020. The results revealed that there were many learning activities that could be designed in a flipped classroom both outside and inside the classroom so that it allowed students to be actively involved in learning, flipped classrooms could also be integrated with other learning methods and utilize various technologies to increase their effectiveness in developing students’ critical thinking skills.

2021 ◽  
Vol 11 (23) ◽  
pp. 11400
Andra-Maria Mircea-Vicoveanu ◽  
Elena Rezuș ◽  
Florin Leon ◽  
Silvia Curteanu

This study is based on the consideration that the patients with rheumatoid arthritis and ankylosing spondylitis undergoing biological therapy have a higher risk of developing tuberculosis. The QuantiFERON-TB Gold test result was the output of the models and a series of features related to the patients and their treatments were chosen as inputs. A distribution of patients by gender and biological therapy, followed at the time of inclusion in the study, and at the end of the study, is made for both rheumatoid arthritis and ankylosing spondylitis. A series of classification algorithms (random forest, nearest neighbor, k-nearest neighbors, C4.5 decision trees, non-nested generalized exemplars, and support vector machines) and attribute selection algorithms (ReliefF, InfoGain, and correlation-based feature selection) were successfully applied. Useful information was obtained regarding the influence of biological and classical treatments on tuberculosis risk, and most of them agreed with medical studies.

2021 ◽  
Vol 2021 ◽  
pp. 1-30
Wei Cui ◽  
Guoying Meng ◽  
Aiming Wang ◽  
Xinge Zhang ◽  
Jun Ding

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

2021 ◽  
Vol 12 (6) ◽  
pp. 1-21
Jayant Gupta ◽  
Carl Molnar ◽  
Yiqun Xie ◽  
Joe Knight ◽  
Shashi Shekhar

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN ) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.

2021 ◽  
Vol 1 (2) ◽  
pp. 117-129
Franty Faldy Palempung ◽  
Ferry J N Sumual

­Abstrak: Tulisan ini secara spesifik memaparkan dampak metode pembelajaran daring bagi ketuntasan belajar siswa. Peristiwa Covid-19 yang terjadi awal tahun 2020 hingga sampai sekarang, masih menyebakan kesulitan bagi semua element. Imbas dari pandemi ini di sektor Pendidikan mengharuskan pembelajaran online dilaksanakan. Akibat dari penerapan pembelajaran daring ini, masih menyebabkan kesulitan bagi sebagai indvidu bahkan institusi karena masih belum lengkapnya pra-sarana dalam kegiatan pembelajara daring. Topik ini ditulis dengan menggunakan metode kualitatif deskriftif dengan pendekatan studi literatur. Hasil pada uraian ini mengemukakan bahwa pengertian ketuntasan belajar menurut Permendikbud adalah pencapaian kompetensi sikap, pengetahuan, dan keterampilan meliputi ketuntasan penguasaan substansi dan ketuntasan belajar dalam konteks kurun waktu belajar. Ketuntasan belajar peserta didik merupakan komponen keluaran yang diperoleh dari hasil suatu proses pembelajaran yang didukung oleh orang tua, guru dan lingkungan. Berhasil tidaknya pembelajaran daring bagi ketuntasan pembelajaran, diperlukan kerja sama semua komponen Pendidikan itu sendiri.Abstract: This paper specifically describes the impact of online learning methods on the completion of student learning. The Covid-19 event that occurred in early 2020 until now, still makes it difficult for all elements. The impact of this pandemic in the Education sector requires that online learning be implemented. As a result of the application of online learning in, still causes difficulties for as an individual even an institution because it is still incomplete pre-facilities in the activities of online learners. This topic is written using qualitative methods with a literature study approach. The results in this description suggest that the understanding of the completion of learning according to Permendikbud is the achievement of attitude competence. Knowledge, and skills include the completion of the mastery of substance and the completion of learning in the context of the study period. The completion of learning of learners is a component of the output obtained from the results of a learning process supported by parents, teachers and the environment. The success of online learning for the completion of learning, requires the cooperation of all components of Education itself.

2021 ◽  
Vol 11 (1) ◽  
Borim Ryu ◽  
Sooyoung Yoo ◽  
Seok Kim ◽  
Jinwook Choi

AbstractAlthough several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.

2021 ◽  
Jian Zhao ◽  
ZhiWei Zhang ◽  
Jinping Qiu ◽  
Lijuan Shi ◽  
Zhejun KUANG ◽  

Abstract With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

2021 ◽  
Vol 11 (1) ◽  
Farideh Jalali-najafabadi ◽  
Michael Stadler ◽  
Nick Dand ◽  
Deepak Jadon ◽  
Mehreen Soomro ◽  

AbstractIn view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

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