Positive Class Research Articles

Perceptions of Student Misbehavior and Restitution Practices in Addressing Primary School Misconduct at Madania Makassar

Fostering a conducive classroom atmosphere is challenging for teachers aiming for ready, attentive, and productive student behavior. While various teaching strategies are employed to achieve learning objectives, not all effectively address the issue of student misbehavior. Classroom management, particularly addressing misbehavior, is a common challenge for teachers. Madania School emphasizes teacher training to manage students both during and outside lesson periods. This study explores teachers' perceptions of student misbehavior, their handling strategies, and the implementation of restitution through qualitative research involving interviews and surveys with SD Madania School teachers. The findings reveal a thorough analysis of teachers' perceptions through collaborative discussions, seeking solutions for enhanced student performance. Teachers employ diverse methods, including rewards, punishment, and restitution, for effective student management. Notably, experienced teachers favor restitution, which fosters heightened student awareness of mistakes and promotes improved performance and a positive class environment. The study emphasizes the importance of effective teacher practices, particularly restitution, in addressing misbehavior and cultivating a conducive learning atmosphere.

Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models.

This research compares traditional machine learning classification methods to innovative techniques that use deep learning models. Established usual classification models such as k-Nearest Neighbors (kNN), Gradient Boosting, Support Vector Machine (SVM), Neural Network, CN2 rule inducer, Naive Bayes, Stochastic Gradient Descent (SGD), and Tree, and deep learning models such as Neural Decision Forest and Multilayer Perceptron used. The investigation, which was carried out using the Orange and Python tools, evaluates their diagnostic effectiveness in breast cancer detection. The evaluation uses UCI's publicly accessible Wisconsin Diagnostic Data Set, enabling transparency and accessibility in the study approach. The mean radius ranges from 6.981 to 28.110, while the mean texture runs from 9.71 to 39.28 in malignant and benign cases. Gradient boosting and CN2 rule inducer classifiers outperform SVM in accuracy and sensitivity, whereas SVM has the lowest accuracy and sensitivity at 88%. The CN2 rule inducer classifier achieves the greatest ROC curve score for benign and malignant breast cancer datasets, with an AUC score of 0.98%. MLP displays distinguish positive and negative classes, with a higher AUC-ROC of 0.9959. with accuracy of 96.49%, precision of 96.57%, recall of 96.49%, and an F1-Score of 96.50%. Among the most commonly used classifier models, CN2 rule and GB performed better than other models. However, MLP from deep learning produced the greatest overall performance.

클래스넷을 활용한 또래생태 및 학급규범 개선: 학생의 학업 열의와 사회적 행동

The objective of this study was to evaluate the impact of teacher-led interventions in the classroom on students’ academic and social behavior. The intervention focused on improving positive peer relationships and classroom norms. To fulfill this purpose, 115 classes (N = 2,858 students) across elementary, middle, and high school levels were divided into an experimental group (n = 66 classes) and a control group (n = 49 classes). Students’ academic engagement (behavior, emotion), social behavior (overt and relational aggression, prosocial behavior), teacher―student relationships (support, conflict), teacher alignment, and peer network attributes were collected at the beginning and end of the semester through student and teacher reports as well as peer nominations. Multilevel analyses and longitudinal social network analyses were utilized to evaluate the impact of the teacher-led intervention. The results of the multilevel analysis revealed that teacher alignment, teacher―student conflict, and an initial level of academic engagement were significant factors to improve academic engagement. In terms of overt and relational aggression, teacher support played a crucial role in the experimental effect, with varying impacts observed in elementary and middle school groups. Longitudinal social network analysis revealed significant classroom intervention effects on prosocial behavior. Both peer choice and socialization (influence) effects were significant in the experimental group, while only the peer influence effect was observed in the control group. This suggests that the class-based intervention contributed to the development of prosocial behaviors as class norms. This study has educational significance in that it highlights the importance of creating positive class norms and peer ecology to enhance students’ academic engagement and social behavior.

ASSESSMENT OF THE EFFICIENCY OF THE SUCCESS PREDICTION MODEL USING MACHINE LEARNING METHODS

The work evaluated the created model for predicting the success of users of the Moodle educational platform in order to determine the effectiveness and expediency of its use. The determination of the efficiency and quality of the model was performed according to the following indicators: sensitivity, specificity and balanced accuracy. Also, a ROC curve was constructed to reflect the classifier's ability to correctly recognize positive classes and reject negative classes when the threshold value changes, and the AUC (Area Under Curve) was determined. Methods of assessing success risks and requirements for creating models based on machine learning methods are analyzed. On the basis of user data from the database of the Moodle educational platform, indicators affecting the success of students were formed. A logistic regression model was built for predicting success, which was then tested in practice. The created model makes it possible to predict the success of students with an accuracy of 84%. The overall efficiency of the model is 89%. It was established that the Scikit-learn library provides an opportunity to create an effective model for solving classification problems in machine learning. The use of a logistic regression model to classify the success of users of the Moodle platform will allow creating a model that allows you to predict the success of students based on the collected data. Using machine learning methods and Python libraries, the quality and efficiency of the model was determined. A solution to the problem of predicting students' success is proposed by creating a model based on machine learning methods and the Moodle platform. A Python program was developed to analyze the data of users of the Moodle platform. The presented information shows that choosing the Scikit-Learn library will allow creating an effective model for processing data and predicting results. The use of the created model for forecasting will allow to quickly analyze the success of users and form, if necessary, appropriate ratings. The forecast obtained with the help of the model will be useful both for teachers and educational institutions. Because it will allow planning changes in educational programs and materials, as well as the educational process as a whole.

Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine

This research aims to classify public sentiment regarding the issue of climate change and tourism. The research problem addressed in this study pertains to the classification of public sentiment concerning climate change within the tourism sector. Specifically, the study aims to explore and classify the public's sentiments regarding the impact of climate change on tourism activities.The methodology employed is CRISP-DM, which encompasses stages of business understanding, data understanding, modeling, evaluation, and deployment. Specifically, the SVM and SMOTE algorithms are utilized in the modeling stage to achieve optimal results. By leveraging this systematic approach and advanced algorithms, the study seeks to comprehensively analyze public sentiment towards climate change within the context of tourism, thus contributing valuable insights to academia and industry practitioners. Applying CRISP-DM methodology coupled with SVM and SMOTE algorithms enhances the rigor and effectiveness of sentiment analysis in addressing the complexities of climate change discourse in the tourism sector. The findings of this research demonstrate that the SVM and SMOTE algorithms yield promising results in sentiment classification, with an accuracy of 86.15% +/- 1.68% (micro average: 86.15%), precision of 85.17% +/- 2.16% (micro average: 85.11%) (positive class: Positive), recall of 87.64% +/- 3.39% (micro average: 87.64%) (positive class: Positive), f_measure of 86.34% +/- 1.79% (micro average: 86.35%) (positive class: Positive), and AUC of 0.923 +/- 0.012 (micro average: 0.923) (positive class: Positive). These metrics indicate the effectiveness and reliability of the SVM and SMOTE algorithms in accurately classifying sentiment toward climate change in the context of tourism. The high accuracy, precision, recall, f_measure, and AUC scores suggest that the models produced by these algorithms are robust and capable of capturing nuanced sentiment patterns, thereby contributing to the advancement of sentiment analysis techniques in climate change research within the tourism domain.

Contribution of Social Bonds to the Development of Scientific Research Identity in Adolescent Participants of a Science Enrichment Program

An abundance of research on self-determination theory has shown that satisfaction of basic needs for autonomy, competence, and relatedness among adolescents promotes the experience of optimal career-related outcomes such as academic motivation and engagement. However, few studies have explored how satisfaction of these needs promotes identification with particular careers, and less attention has been paid to the role of relatedness satisfaction in these developmental processes compared to autonomy and competence. We addressed these issues in the current study by examining the relationship between relatedness and identification as a research scientist. Five latent classes of scientific research identity development were identified using growth mixture modeling: (a) strong positive growth, (b) moderate positive growth, (c) weak positive growth, (d) no growth, and (e) strong negative growth. Results of logistic regression analyses indicated that need for relatedness was a significant positive predictor of membership in the strong positive growth class relative to the no growth class. Implications for identity development as a research scientist and the social and motivational influences undergirding this process are discussed.

ENHANCING SENTIMENT ANALYSIS ACCURACY IN DELIVERY SERVICES THROUGH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE

Paxel is one of the delivery services that use the application. On Google Play, there are more than 10 thousand users leaving reviews. From this review data, a sentiment analysis was then carried out to determine the level of user satisfaction with Paxel's services. The methods used in this study are Random Forest (RF) and Support Vector Machine (SVM), as well as applying Synthetic Minority Oversampling Technique (SMOTE) to overcome data imbalance. The results showed that the method testing by dividing the data into two, namely training data and testing data by 80:20, stated that by applying the SMOTE, a higher accuracy value was obtained, where the accuracy of the RF method reached 91%, and the SVM method reached 87%. The level of user satisfaction with Paxel services tends to be neutral. This can be seen in the classification of the RF method with F1-Score values for the Positive class 89%, Neutral class 93%, and Negative class 92%.

Analysis Sentiment to Religious Based Issues Netizen Comments on President Jokowi 's Instagram using the Naive Bayes Classifier Method


 
 
 
 The presence of Instagram is one of the most popular social media platforms. Make people make a habit of posting photo and video content. In this way, Instagram can become a source of public opinion and sentiment data, which data can be used for social studies. Social studies is still a crucial issue for countries today, including Indonesia, is a matter of religion. Apart from problems regarding religion, public sentiment can be researched from Instagram. The sentiment dataset related to religion is taken from public comments from President Joko Widodo's content regarding Merry Christmas Greetings and Visits to Churches. The analysis system will use sentiment analysis using a method from machine learning, namely the naive Bayes classifier to determine positive & negative sentiments. The comment dataset is 2,600 in which the positive and negative sentiment class datasets are determined manually by experts in the field of linguistics or communication totaling 1,309 to be used as training data. the results of testing the Naïve Bayes classifier training data with a total of 1309 obtained an accuracy prediction of 97.63% and test data where positive & negative sentiment classes had not been determined before hand amounted to 1291 in sentiment analysis obtained positive sentiment 70.56% & negative sentiment 29.44%.
 
 
 

Application of Naive Bayes Classifier Method to Analyze Social Media User Sentiment Towards the Presidential Election Phase

This research aims to analyze the sentiment of social media users towards the election. The author collected data in this research through a literature study and observation. The author uses a classification method with the Naïve Bayes Classifier Algorithm and Support Vector Machine to analyze sentiment results. Next, this research extracts word assessment features using TextBlob, which changes text into positive or negative classes. Based on the research results, after going through the text preprocessing stage of more than 15,000 tweets, 11,000 clean tweets were obtained, which were then labelled using the text blob library in Python. The labelling results show that 4,000 tweets are positive, and the rest are harmful, indicating that most social media users' sentiment towards the election is positive. Words that often appear in the positive class express support and confidence in implementing elections that are considered honest and fair. On the other hand, words in the negative class reflect negative sentiment towards implementing elections, which are considered unsuccessful and time-consuming. The Naïve Bayes method provides accuracy, precision, and recall values of 85%, 80%, and 75%. In the Support Vector Machine method, testing is carried out with three kernels (linear, RBF, and poly), where the poly kernel with the best parameter values C is ten and degree is 1 produces the highest accuracy, precision, and recall of 90%, 90%, and 85%, respectively.

High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm

In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as intensity, texture and shape. The feature selection task is carried out on a high-dimensional feature bank, where the search space is denoted by O(2n) and n=473. The proposed evolutionary search strategy was compared in terms of the Jaccard coefficient and accuracy classification with different state-of-the-art methods. The highest feature selection rate, along with the best classification performance, was obtained with a subset of four features, representing a 99% discrimination rate. In the last stage, the feature subset was used as input to train a support vector machine using an independent testing set. The classification of coronary stenosis cases involves a binary classification type by considering positive and negative classes. The highest classification performance was obtained with the four-feature subset in terms of accuracy (0.86) and Jaccard coefficient (0.75) metrics. In addition, a second dataset containing 2788 instances was formed from a public image database, obtaining an accuracy of 0.89 and a Jaccard Coefficient of 0.80. Finally, based on the performance achieved with the four-feature subset, they can be suitable for use in a clinical decision support system.

Public Sentiment towards the Medan-Binjai Water Pipeline Excavation using the Naïve Bayes Method

The work on this project received sharp scrutiny from Non-Governmental Organizations (NGOs) because it was suspected that it was not in accordance with the Work Operation Standards (SOP) and not in accordance with the Technical Instructions (Guidelines for Implementation) and Juknis (Technical Instructions) .Naive Bayes is based on a simplified estimate that attribute values are conditionally independent of each other when given output values. Obtain an accuracy value of 0.75, with a negative precision classification of 0.75, negative recall1.00 f1-score negative0.86 and a negative3 support value. Precision classification Positive 0.00 positive recall 0.00 f1-score positive 0.00 and positive support value 1. This means that the performance of the system's success in retrieving information that has a positive value in the document is very low. -Binjai using the Naive Bayes algorithm method, it can be concluded as follows: 1) The accuracy level produced by the naïve Bayes classifier is 75%. 2) The advantages of this study are that it has good accuracy, precision and recall values, so that it is enough to be used in a system. 3) The deficiency in this research is in the performance of the system in finding the success of the system to find back information in the positive class, which is equal to 0.00%. This is because the amount of training data in the positive class is less than the negative class or it can be said that the training data used in this study is not balanced.

Epistemic consequences of unfair tools

Abstract This article examines the epistemic consequences of unfair technologies used in digital humanities (DH). We connect bias analysis informed by the field of algorithmic fairness with perspectives on knowledge production in DH. We examine the fairness of Danish Named Entity Recognition tools through an innovative experimental method involving data augmentation and evaluate the performance disparities based on two metrics of algorithmic fairness: calibration within groups; and balance for the positive class. Our results show that only two of the ten tested models comply with the fairness criteria. From an intersectional perspective, we shed light on how unequal performance across groups can lead to the exclusion and marginalization of certain social groups, leading to voices and experiences being disregarded and silenced. We propose incorporating algorithmic fairness in the selection of tools in DH to help alleviate the risk of perpetuating silence and move towards fairer and more inclusive research.

Machine Learning on Opinion Mining of Netizen's Hate Speech


 
 
 
 
 
 
 
 Netizen comments written in an online news portal through social media platforms, one of which is Instagram, can be used as material in the sentiment analysis process, which can be classified into positive, negative, or neutral sentiments. Sentiment analysis is part of the study of text mining, the science of discovering unknown knowledge by automatically extracting information from large volumes of unstructured text into useful information. The resulting information is in the form of sentiment towards a topic, whether it tends to be positive, negative, or neutral. The classification method used in this research is Support Vector Machine (SVM) and TF-IDF data weighting to classify text. Stages to perform data analysis are pre-processing to clean data, word weighting, labeling data into positive, negative, or neutral classes, and classifying and visualizing data with graphs. Accuracy tests using 70:30 split data showed that the accuracy reached 98%. Tests with 80:20 and 90:10 split data also showed high accuracy of 98% and 99%.
 
 
 

Dataset meta-level and statistical features affect machine learning performance

What dataset features affect machine learning (ML) performance has primarily been unknown in the current literature. This study examines the impact of tabular datasets' different meta-level and statistical features on the performance of various ML algorithms. The three meta-level features this study considered are the dataset size, the number of attributes and the ratio between the positive (class 1) and negative (class 0) class instances. It considered four statistical features for each dataset: mean, standard deviation, skewness and kurtosis. After applying the required scaling, this study averaged (uniform and weighted) each dataset’s different attributes to quantify its four statistical features. We analysed 200 open-access tabular datasets from the Kaggle (147) and UCI Machine Learning Repository (53) and developed ML classification models (through classification implementation and hyperparameter tuning) for each dataset. Then, this study developed multiple regression models to explore the impact of dataset features on ML performance. We found that kurtosis has a statistically significant negative effect on the accuracy of the three non-tree-based ML algorithms of the Support vector machine (SVM), Logistic regression (LR) and K-nearest neighbour (KNN) for their classical implementation with both uniform and weighted aggregations. This study observed similar findings in most cases for ML implementations through hyperparameter tuning, except for SVM with weighted aggregation. Meta-level and statistical features barely show any statistically significant impact on the accuracy of the two tree-based ML algorithms (Decision tree and Random forest), except for implementation through hyperparameter tuning for the weighted aggregation. When we excluded some datasets based on the imbalanced statistics and a significantly higher contribution of one attribute compared to others to the classification performance, we found a significant effect of the meta-level ratio feature and statistical mean and standard deviation features on SVM, LR and KNN accuracy in many cases. Our findings open a new research direction in understanding how dataset characteristics affect ML performance and will help researchers select appropriate ML algorithms for a possible optimal accuracy outcome.

A Unique Variance in Future Motivation for Observed Active Behaviors on Positive Class Participation Beyond the Effect of Current Motivation

AbstractBased on self‐determination theory, this study investigates a unique variance in the effect of aspirations (future motivation) on the observed active behaviors on positive class participation, while controlling for academic motivation (current motivation). In Study 1, 364 fifth‐ and sixth‐grade elementary school students participated in a survey to confirm the validity of the Aspirations Index for Children. Confirmatory factor analysis on the theoretically hypothesized model showed an acceptable fit for the data. In Study 2, 297 fifth‐ and sixth‐grade elementary school students participated in this survey, assessing their aspirations and academic motivation. Furthermore, their homeroom teachers evaluated the students’ academic behaviors on positive class participation. The results of multilevel structural equation modeling, which controls for the effect of current motivation, indicated the unique variance in aspirations (future motivation): intrinsic aspirations were positively associated with active behaviors on positive class participation, whereas extrinsic aspirations showed a negative relation. This study concludes that future motivation, specifically intrinsic aspirations, facilitates active learning behaviors beyond the effect of current motivation.

Identifying latent subgroups in the older population seeking primary health care for a new episode of back pain – findings from the BACE-N cohort

BackgroundBack pain is the number one condition contributing to years lived with disability worldwide, and one of the most common reasons for seeking primary care. Research on this condition in the ageing population is sparse. Further, the heterogeneity of patients with back pain complicates the management in clinical care. It is possible that subgrouping people with similar characteristics would improve management. This paper aimed to identify latent classes based on demographics, pain characteristics, psychosocial behavior, and beliefs and attitudes about back pain, among older patients seeking primary care with a new episode of back pain, and to examine if there were differences regarding the classes’ first point-of-contact.MethodsThe study was part of the international BACE (Back complaints in elders) consortium and included 435 patients aged ≥ 55 years seeking primary care (general practitioners, physiotherapists, and chiropractors) in Norway from April 2015 to March 2020. A latent class analysis was performed to identify latent classes. The classes were described in terms of baseline characteristics and first point-of-contact in primary care.ResultsFour latent classes were identified. The mean age was similar across groups, as were high expectations towards improvement. Class 1 (n = 169, 39%), the “positive” class, had more positive attitudes and beliefs, less pain catastrophizing and shorter duration of current pain episode. Class 2 (n = 31, 7%), the “fearful” class, exhibited the most fear avoidance behavior, and had higher mean pain intensity. Class 3 (n = 33, 8%), the “distressed” class, had the highest scores on depression, disability, and catastrophizing. Finally, class 4 (n = 202, 46%), the “hopeful” class, showed the highest expectations for recovery, although having high pain intensity. The identified four classes showed high internal homogeneity, sufficient between-group heterogeneity and were considered clinically meaningful. The distribution of first point-of-contact was similar across classes, except for the positive class where significantly more patients visited chiropractors compared to general practitioners and physiotherapists.ConclusionsThe identified classes may contribute to targeting clinical management of these patients. Longitudinal research on these latent classes is needed to explore whether the latent classes have prognostic value. Validation studies are needed to evaluate external validity.Trial registrationClinicaltrials.gov NCT04261309.

Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach

This study addresses the challenge of class imbalance in sentiment analysis by utilizing synthetic data to balance training datasets. We introduce an innovative approach using the GPT-3 model’s sentence-by-sentence generation technique to generate synthetic data, specifically targeting underrepresented negative and neutral sentiments. Our method aims to align these minority classes with the predominantly positive sentiment class in a Coursera course review dataset, with the goal of enhancing the performance of sentiment classification. This research demonstrates that our proposed method successfully enhances sentiment classification performance, as evidenced by improved accuracy and F1-score metrics across five deep-learning models. However, when compared to our previous research utilizing fine-tuning techniques, the current method shows a relative shortfall. The fine-tuning approach yields better results in all models tested, indicating the importance of data novelty and diversity in synthetic data generation. In terms of the deep-learning model used for classification, the notable finding is the significant performance improvement of the Recurrent Neural Network (RNN) model compared to other models like CNN, LSTM, BiLSTM, and GRU, highlighting the impact of the model choice and architecture depth. This study emphasizes the critical role of synthetic data quality and strategic deep-learning model implementation in sentiment analysis. The results suggest that the careful consideration of training data and model attributes is vital for optimal sentiment classification.

Misclassification in Weakly Supervised Object Detection.

Weakly supervised object detection (WSOD) aims to train detectors using only image-category labels. Current methods typically first generate dense class-agnostic proposals and then select objects based on the classification scores of these proposals. These methods mainly focus on selecting the proposal having high Intersection-over-Union with the true object location, while ignoring the problem of misclassification, which occurs when some proposals exhibit semantic similarities with objects from other categories due to viewing perspective and background interference. We observe that the positive class that is misclassified typically has the following two characteristics: 1) It is usually misclassified as one or a few specific negative classes, and the scores of these negative classes are high; 2) Compared to other negative classes, the score of the positive class is relatively high. Based on these two characteristics, we propose misclassification correction (MCC) and misclassification tolerance (MCT) respectively. In MCC, we establish a misclassification memory bank to record and summarize the class-pairs with high frequencies of potential misclassifications in the early stage of training, that is, cases where the score of a negative class is significantly higher than that of the positive class. In the later stage of training, when such cases occur and correspond to the summarized class-pairs, we select the top-scoring negative class proposal as the positive training example. In MCT, we decrease the loss weights of misclassified classes in the later stage of training to avoid them dominating training and causing misclassification of objects from other classes that are semantically similar to them during inference. Extensive experiments on the PASCAL VOC and MS COCO demonstrate our method can alleviate the problem of misclassification and achieve the state-of-the-art results.

Interpretable machine learning for predicting chronic kidney disease progression risk.

Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnostic and decision-making. A cohort of 491 patients with clinical data was collected for this study. The dataset was randomly split into an 80% training set and a 20% testing set. To achieve the first objective, we developed four ML algorithms (logistic regression, random forests, neural networks, and eXtreme Gradient Boosting (XGBoost)) to classify patients into two classes-those who progressed to CKD stages 3-5 during follow-up (positive class) and those who did not (negative class). For the classification task, the area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance in discriminating between the two classes. For survival analysis, Cox proportional hazards regression (COX) and random survival forests (RSFs) were employed to predict CKD progression, and the concordance index (C-index) and integrated Brier score were used for model evaluation. Furthermore, variable importance, partial dependence plots, and restrict cubic splines were used to interpret the models' results. XGBOOST demonstrated the best predictive performance for CKD progression in the classification task, with an AUC-ROC of 0.867 (95% confidence interval (CI): 0.728-0.100), outperforming the other ML algorithms. In survival analysis, RSF showed slightly better discrimination and calibration on the test set compared to COX, indicating better generalization to new data. Variable importance analysis identified estimated glomerular filtration rate, age, and creatinine as the most important predictors for CKD survival analysis. Further analysis revealed non-linear associations between age and CKD progression, suggesting higher risks in patients aged 52-55 and 65-66 years. The association between cholesterol levels and CKD progression was also non-linear, with lower risks observed when cholesterol levels were in the range of 5.8-6.4 mmol/L. Our study demonstrated the effectiveness of interpretable ML models for predicting CKD progression. The comparison between COX and RSF highlighted the advantages of ML in survival analysis, particularly in handling non-linearity and high-dimensional data. By leveraging interpretable ML for unraveling risk factor relationships, contrasting predictive techniques, and exposing non-linear associations, this study significantly advances CKD risk prediction to enable enhanced clinical decision-making.

Edge‐Preserved Tversky Indexive Hellinger with Deep Perceptive Czekanowski‐Based Image Classification

The texture is identifiable in optical and easy ways. Texture classification is an imperative region in texture analysis, where it gives descriptors for classifying the images. The categorization of normal and abnormal matter by magnetic resonance (MR), computed tomography (CT), and texture images has made noteworthy evolution in modern years. Recently, different novel robust classification techniques have been introduced to classify the different kinds of images for prediction. However, the accuracy of classification was not improved with lesser time. To address these issues, the edge‐preserved Tversky indexive Hellinger and deep perceptive Czekanowski classifier (ETIH‐DPCC) technique is introduced to segment and classify the images with more accuracy. The ETIH‐DPCC technique includes diverse processes namely preprocessing, segmentation, feature extraction, as well as classification. At first, different types of images, such as magnetic resonance imaging, CT, and texture, are used as input. With the acquired input, edge‐preserving normalized adaptive bilateral filtering is employed to carry the image preprocessing. In this stage, the noisy pixels are removed and edges are preserved. Then, the Tversky‐indexed quantile regression is applied to segment the images into diverse texture regions. After that, the feature extraction is performed on the segmented region using Hellinger kernel feature extraction, where a more informative feature for image prediction is extracted. During this process, the irrelevant features are avoided to decrease the dimensionality and feature extraction time. These extracted features are finally classified into positive and negative classes for disease prediction using DPCC. DPCC comprises multiple layers to deeply analyze the association between training and testing features. With this, the prediction accuracy is improved. Experimental outcomes show that the ETIH‐DPCC technique efficiently enhances prediction accuracy and less time compared to conventional methods.