Classification result classifiers using TF-IDF with SMOTE.

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method.

Summary table: Oversampling techniques using SMOTE, ADASYN, and weighted rare classes.

Dataset

This dataset was created by Saumya Mohandas N

Contents

Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.

Additional file 2. The synthetic dataset generated through TVAE method.

Classification result of classifiers models using TF without SMOTE.

Background: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. Methods: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 5-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. Results: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by support vector machine with SMOTE-ENN (AUC = .942; balanced accuracy = .868, sensitivity = .898; specificity = .837). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. Conclusions: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Online searches through Web of Science and PubMed were conducted on 15 September, 2023 for articles published after 1950 using the following terms: TS = (ultra high dose rate OR ultra-high dose rate OR ultrahigh dose rate) AND TS = (in vivo OR animal model OR mice OR preclinical). The queries produced 980 results in total, with 564 results left after removing duplicate entries.The titles and abstracts were reviewed manually by two authors and the full-text of suitable manuscripts was further screened considering the factors such as topics, experiment condition and methods, research objects, endpoints, etc. The detailed record identification and screening flows based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) are summarized in Figure 1. Finally, forty articles were included in our analysis.The FLASH effect was confirmed if there were significant differences in experimental phenomena and data under the two radiation conditions. In the same article, the research items with different endpoints but otherwise identical conditions were regarded as one item. As summarized in Table 1, a total of 131 items were extracted from the 40 articles included in the analysis. For each item, the FLASH effect (1 represents significant sparing effect and 0 represents no sparing effect) and detailed parameters were recorded, including type and energy of the radiation, dose, dose rate, experimental object, pulse characteristics (if provided), etc.According to emulate the quantitative analyses of normal tissue effect in the clinic (QUANTEC), the probability of triggering the FLASH effect as a function of mean dose rate or dose was analyzed with the binary logistic regression model. The analysis was done using the SPSS software. For the statistical data items, there are large imbalances in the number of data entries with and without FLASH effect (people are more inclined to report the research with positive results). Therefore, a more balanced dataset was obtained by oversampling using the K-Means SMOTE algorithm (Figure S1), which was implemented using Python based on the imblearn library.The ROC curve (receiver operating characteristic curve) was plotted as FPR (False Positive Rate) against TPR (True Positive Rate) at different threshold values. The classification model was validated using the AUC (area under ROC curve) value, which was threshold and scale invariant.

1) Data Introduction • The Credit_Card_Transactions Dataset is a representative sample data for building fraud detection models, including anonymized real-world transaction data such as financial transaction type, amount, sender/receiver account balance, and fraud indicators.

2) Data Utilization (1) Credit_Card_Transactions Dataset has characteristics that: • This dataset provides individual transaction records on a row-by-row basis, reflecting the real-world class imbalance problem with the extremely low percentage of fraudulent transactions (isFraud=1). • It is an unprocessed raw data structure that allows you to directly utilize key variables such as transaction time, amount, and account change. (2) Credit_Card_Transactions Dataset can be used to: • Binary classification modeling: Fraud transaction detection models can be developed by applying imbalance processing techniques such as SMOTE and undersampling, and appropriate evaluation indicators such as F1-score and ROC-AUC. • Real-time anomaly detection: It can be used to build a real-time anomaly signal detection system through analysis of transaction patterns (amount, frequency, account change).

Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and effective species identification from training samples. However, limited infraspecific sampling remains a key challenge also in machine learning. In this study, we assessed whether a data augmentation approach may help to overcome the problem of scarce training data in automated visual species identification. The stepwise augmentation of data comprised image rotation as well as visual and virtual augmentation. The visual data augmentation applies classic approaches of data augmentation and generation of artificial images using a Generative Adversarial Networks (GAN) approach. Descriptive feature vectors are derived from bottleneck features of a VGG-16 convolutional neural network (CNN) that are then stepwise reduced in dimensionality using Global Average Pooling and PCA to prevent overfitting. Finally, data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space (SMOTE). Applied on four different image datasets, which include scarab beetle genitalia (Pleophylla, Schizonycha) as well as wing patterns of bees (Osmia) and cattleheart butterflies (Parides), our augmentation approach outperformed a deep learning baseline approach by means of resulting identification accuracy with non-augmented data as well as a traditional 2D morphometric approach (Procrustes analysis of scarab beetle genitalia).

Results of bioassay 439 dataset in experiment 1.

For each infection, the five rules with the highest lift values are chosen and sorted. The combinations of risk factors specified on the left leads to the given infection.

Dataset Card for Language Identification Dataset

  Sample Notebook:

https://www.kaggle.com/code/rishabhbhartiya/indian-language-classification-smote-resampled

  Kaggle Dataset link:

https://www.kaggle.com/datasets/processvenue/indian-language-identification

  Dataset Summary

A comprehensive dataset for Indian language identification and text classification. The dataset contains text samples across 18 major Indian languages, making it suitable for… See the full description on the dataset page: https://huggingface.co/datasets/Process-Venue/Language_Indentification_v2.

Results of bioassay 721 dataset in experiment 1.

Results of Bioassay 687 dataset in experiment 2.

Results of bioassay 362 dataset in experiment 1.

Results of Bioassay 373 dataset in experiment 2.

Average values of Accuracy, Kappa and imbalance ratio (min/maj) for the two methods in experiment 2.

Results of Bioassay 1608 dataset in experiment 2.