Synthetic Minority Oversampling Technique Research Articles

A novel generalised extreme value gradient boosting decision tree for the class imbalanced problem in credit scoring

The performance of credit scoring models can be compromised when dealing with imbalanced datasets, where the number of defaulted borrowers is significantly lower than that of non-defaulters. To address this challenge, we propose a gradient boosting decision tree with the generalised extreme value distribution model (GEV-GBDT). Our approach replaces the conventional symmetric logistic sigmoid function with the asymmetric cumulative distribution function of the GEV distribution as the activation function. We derive a novel loss function based on the maximum likelihood estimation of the GEV distribution within the boosting framework. This modification allows the model to focus more on the minority class by emphasising the tail of the response curve, and the shape parameter of the GEV distribution offers flexibility in controlling the model’s emphasis on minority samples. We examine the performance of this approach using four real-life loan datasets. The empirical results show that the GEV-GBDT model achieves superior classification performance compared to other commonly used imbalanced learning methods, including the synthetic minority oversampling technique and the cost-sensitive framework. Furthermore, we conduct performance tests on several datasets with varying imbalance ratios and find that GEV-GBDT performs better on extremely imbalanced datasets.

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

In the field of data mining and machine learning, dealing with imbalanced datasets is one of the most complex problems. The class imbalance issue significantly affects the classification of minority classes when using common classification algorithms. These algorithms often prioritize improving the performance of the majority class at the expense of the minority class, leading to misclassifying negative instances as positive ones. To address this problem, the Synthetic Minority Over-sampling Technique (SMOTE) has gained popularity to rebalance imbalanced data for classification. However, in this paper, we propose two algorithms to enhance the performance of imbalanced classification further. The first algorithm is PRO-SMOTE, an improvement over SMOTE. PRO-SMOTE relies on conditional probabilities to effectively rebalance imbalanced classes and improve the predictive performance metrics satisfactorily and reliably. By considering conditional probabilities, PRO-SMOTE can reduce the majority classes and optimally increase the minority class. Second, the PRO-SMOTEBoost algorithm, in turn, is based on the PRO-SMOTE to overcome classification anomalies and problems encountered by machine learning algorithms during classification, especially the weak ones. PRO-SMOTEBoost aims to maximize predictive precision to the greatest extent possible by combining the strengths of PRO-SMOTE with boosting techniques. Evaluating these algorithms using traditional machine learning algorithms such as Random Forests, C4.5, Naive Bayes, and Support Vector Machines has demonstrated excellent classification results. The performance metrics, encompassing F1-score, G-means, Precision, Accuracy, Recall, AUC-ROC, and Precision-Recall-curves, achieved by the proposed algorithm demonstrate a range that extends from over 90% to a flawless score of 100%. Compared to using these traditional algorithms individually, the utilization of PRO-SMOTEBoost has shown a significant improvement of 10% to 40% in performance metrics. Overall, the proposed algorithms, PRO-SMOTE and PRO-SMOTEBoost, offer effective solutions to address the challenges posed by imbalanced datasets. They provide improved predictive metrics and demonstrate their superiority when compared to traditional even modern machine learning algorithms.

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study

AbstractAn open research question lies in how machine learning (ML) can accelerate the design optimization of chemical processes which are at very early experimental development stage with limited data availability. As an example, this article investigates the design of an intensified microwave‐assisted ammonia production reactor with 46 experimental data. We present an integrated approach of neural networks and synthetic minority oversampling technique to quantify the nonlinear input‐output relationships of this process. For ammonia concentration predictions at discrete operating conditions, the approach demonstrates 96.1% average accuracy over other ML methods (e.g., support vector regression 84.2%). The approach has also been applied for continuous optimization, identifying the optimal synthesis conditions at 597.37 K, 0.55MPa with feed flow rate of 1.67 ×10−3 m3/s kg and hydrogen to nitrogen ratio of 1 which is consistent with experimental observations. The data‐driven model enables to integrate this reactor with existing ammonia production infrastructure and benchmark with conventional techniques.

Leveraging survival analysis in cost-aware deepnet for efficient hard drive failure prediction

AbstractMany real-world datasets, such as those used for failure and anomaly detection, are severely imbalanced, with a relatively small number of failed instances compared to the number of normal instances. To address these issues, this paper leverages the Backblaze hard disk drives (HDDs) data and makes several contributions to hard drive failure prediction. This research explores 1D convolutional neural networks (CNN) to utilize the sequential nature of hard drive sensor data. The performance of 1D CNN models is compared to traditional machine learning (ML) algorithms, such as the synthetic minority over-sampling technique (SMOTE) and weighted logistic regression (WLR), demonstrating superior results, suggesting the potential effectiveness of the proposed approaches. In addition to these efforts, this paper aims to provide a comprehensive understanding of hard drive longevity and the critical factors contributing to their eventual failure through survival analysis. The 1D CNN models employ weighted binary cross-entropy (WCE) loss and modified focal loss (MFL) functions to manage class imbalanced issues commonly observed in hard drive data. The findings suggest that 1D CNN models outperform traditional ML models, with regularization techniques like dropout and early stopping proving effective in controlling overfitting. Notably, the 1D CNN model with WCE loss demonstrated the best overall performance with a $$G_{mean}$$ G mean of 0.692, successfully maximizing the FDR without increasing the FAR. In parallel, the research also employs Cox regression to identify key SMART parameters influencing drive failure. The high concordance index (c-index) of the Cox model (0.958) adds confidence to the insights derived. The research thus sets a solid groundwork for data center management strategies, with a future focus on practical implementation and evaluation of these findings.

Enhancing SQL Injection Attack Detection Using Naïve Bayes and SMOTE Method on Imbalanced Datasets

SQL injection attack detection is a crucial aspect of cybersecurity, considering the potential damage that such attacks can cause. This study aims to evaluate the effectiveness of the Naive Bayes model in detecting SQL injection attacks on an imbalanced dataset. To address the data imbalance issue, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied. The study consists of two phases: first, training and testing the Naive Bayes model on the original dataset without SMOTE, and second, training and testing on the dataset with SMOTE applied. The results indicate that the Naive Bayes model on the dataset without SMOTE achieved an accuracy of 0.9948, F1 Score of 0.9885, Precision of 0.9906, and Recall of 0.9946. After applying SMOTE, the model's performance improved significantly, with an accuracy of 0.9950, F1 Score of 0.9950, Precision of 0.9950, and Recall of 0.9950. This improvement suggests that SMOTE effectively enhanced class balance in the dataset, improving the model's ability to detect both malicious and safe queries. The study recommends exploring other resampling methods, feature engineering analysis, and testing on more diverse datasets as well as implementation in real-world environments for future research.

Application of Dual-Tree Complex Wavelet Transform in Islanding Detection for a Hybrid AC/DC Microgrid with Multiple Distributed Generators

This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is aimed to detect and discriminate between the different fault/disturbance conditions that result in islanding and/or non-islanding conditions in a hybrid microgrid. For the islanding and non-islanding conditions detection by the AIDM, firstly, fault/disturbance signals are obtained from a test microgrid. Secondly, these signals are decomposed using Dual-Tree Complex Wavelet Transform. Thirdly, a Synthetic Minority Oversampling Technique (SMOTE) is applied for data preprocessing to increase the accuracy of the classifier. Finally, an artificial neural network (ANN) is used as the classifier for training and testing the proposed AIDM for different microgrid configurations and event scenarios. The proposed method is tested with different data categories from three different microgrid test systems with different scenarios. All modeling and simulations are executed in MATLAB Simulink Version 2023a. Results indicate that the proposed scheme could detect and discriminate between islanding and non-islanding conditions accurately in terms of dependability, precision, and accuracy. An average accuracy of 99–100% could be achieved when tested and validated with microgrid networks adapted from IEEE 13-bus systems.

Unravelling incipient accidents: a machine learning prediction of incident risks in highway operations

PurposeSafety research has focused on drivers, pedestrians and vehicles, with scarce attention given to highway traffic officers (HTOs). This paper develops a robust prediction model which enables highway safety authorities to predict exclusive incidents occurring on the highway such as incursions and environmental hazards, respond effectively to diverse safety risk incident scenarios and aid in timely safety precautions to minimise HTO incidents.Design/methodology/approachUsing data from a highway incident database, a supervised machine learning method that employs three algorithms [namely Support Vector Machine (SVM), Random Forests (RF) and Naïve Bayes (NB)] was applied, and their performances were comparatively analysed. Three data balancing algorithms were also applied to handle the class imbalance challenge. A five-phase sequential method, which includes (1) data collection, (2) data pre-processing, (3) model selection, (4) data balancing and (5) model evaluation, was implemented.FindingsThe findings indicate that SVM with a polynomial kernel combined with the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is the best model to predict the various incidents, and the Random Under-sampling (RU) algorithm was the most inefficient in improving model accuracy. Weather/visibility, age range and location were the most significant factors in predicting highway incidents.Originality/valueThis is the first study to develop a prediction model for HTOs and utilise an incident database solely dedicated to HTOs to forecast various incident outcomes in highway operations. The prediction model will provide evidence-based information to safety officers to train HTOs on impending risks predicted by the model thereby equipping workers with resilient shocks such as awareness, anticipation and flexibility.

Identifying Potential Natural Antibiotics from Unani Formulas through Machine Learning Approaches.

The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates. We utilized a machine learning approach to tackle these challenges, employing decision trees, kernels, neural networks, and probability-based methods. We used 12 machine learning algorithms and several techniques for preprocessing data, such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and Principal Component Analysis (PCA). To ensure that our model was optimal, we conducted grid-search tuning to tune all the hyperparameters of the machine learning models. The application of Multi-Layer Perceptron (MLP) with SMOTE pre-processing techniques resulted in an impressive accuracy precision and recall values. This analysis identified 20 important metabolites as essential components of the formula, which we predicted as natural antibiotics. In the final stage of our investigation, we verified our prediction by conducting a literature search for journal validation or by analyzing the structural similarity with known antibiotics using asymmetric similarity.

Optimizing the Learning Rate Hyperparameter for Hybrid BiLSTM-FFNN Model in a Tourism Recommendation System

Indonesia, with its abundant natural resources, is rich in captivating tourist attractions. Tourism, a vital economic sector, can be significantly influenced by digitalization through social media. However, the overwhelming amount of information available can confuse tourists when selecting suitable destinations. This research aims to develop a tourism recommendation system employing content-based filtering (CBF) and hybrid Bidirectional Long Short-Term Memory Feed-Forward Neural Network (BiLSTM-FFNN) model to assist tourists in making informed choices. The dataset comprises 9,504 rating matrices obtained from tweet data and reputable web sources. In various experiments, the hybrid BiLSTM-FFNN model demonstrated superior performance, achieving an accuracy of 93.36% following optimization with the Stochastic Gradient Descent (SGD) algorithm at a learning rate of about 0.193. The accuracy, after applying Synthetic Minority Over-sampling Technique (SMOTE) and fine-tuning the learning rate hyperparameter, showed a 14.3% improvement over the baseline model. This research contributes by developing a recommendation system method that integrates CBF and hybrid deep learning with high accuracy and provides a detailed analysis of optimization techniques and hyperparameter tuning.

Forecasting financial distress in listed Chinese construction firms: leveraging ensemble learning and non-financial variables

The construction industry, characterized by high business failure rates due to complex, capital-intensive projects, is crucial for China’s economy. Predicting financial distress and early warnings is crucial to prevent crises. While financial ratios are commonly used, non-financial information remains underexplored, with limited empirical evidence supporting its effectiveness in enhancing predictive accuracy. Additionally, most studies on financial distress prediction focus on constructing single classifiers without utilizing ensemble models that integrate multiple algorithms, resulting in suboptimal prediction accuracy. To address these problems, this study presents a model that uses accounting variables and firm characteristics, construction market dynamics, and macroeconomic indicators to predict the probability of failure one to two years in advance. The model uses a soft voting-based ensemble algorithm, financial ratios refined through the recursive feature elimination with cross-validation (RFECV) algorithm, and dataset balancing via Synthetic Minority Over-Sampling Technique + Tomek Link (SMOTETomek). The comparison results indicate that the predictive performance of the soft voting ensemble model outperforms all single classifiers across all combinations of input variables and prediction years. Additionally, incorporating non-financial variables, such as firm characteristics, construction market dynamics, and macroeconomic indicators, further enhances the model’s accuracy. The proposed model can be effectively employed to help stakeholders mitigate the risks associated with the financial distress of construction companies before the project implementation phase.

Super Learner Algorithm for Carotid Artery Disease Diagnosis: A Machine Learning Approach Leveraging Craniocervical CT Angiography.

This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of 90%. To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics.

An electrocardiogram signal classification using a hybrid machine learning and deep learning approach

An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.

A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method

One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions.

DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization

Traditional Partial Least Squares Regression (PLSR) models frequently underperform when handling data characterized by uneven categories. To address the issue, this paper proposes a Data Augmentation Partial Least Squares Regression (DAPLSR) model via manifold optimization. The DAPLSR model introduces the Synthetic Minority Over-sampling Technique (SMOTE) to increase the number of samples and utilizes the Value Difference Metric (VDM) to select the nearest neighbor samples that closely resemble the original samples for generating synthetic samples. In solving the model, in order to obtain a more accurate numerical solution for PLSR, this paper proposes a manifold optimization method that uses the geometric properties of the constraint space to improve model degradation and optimization. Comprehensive experiments show that the proposed DAPLSR model achieves superior classification performance and outstanding evaluation metrics on various datasets, significantly outperforming existing methods.

Unravelling sleep patterns: Supervised contrastive learning with self-attention for sleep stage classification

Sleep data scoring is a crucial and primary step for diagnosing sleep disorders to know the sleep stages from the PSG signals. This study uses supervised contrastive learning with a self-attention mechanism to classify sleep stages. We propose a deep learning framework for automatic sleep stage classification, which involves two training phases: (1) the feature representation learning phase, in which the feature representation network (encoder) learns to extract features from the electroencephalogram (EEG) signals, and (2) the classification network training phase, where a pre-trained encoder (trained during phase I) along with the classifier head is fine-tuned for the classification task. The PSG data shows a non-uniform distribution of sleep stages, with wake (W) (around 30% of total samples) and N2 stages (around 58% and 37% of total samples in Physionet EDF-Sleep 2013 and 2018 datasets, respectively) being more prevalent, leading to an imbalanced dataset. The imbalanced data issue is addressed using a weighted softmax cross-entropy loss function that assigns higher weights to minority sleep stages. Additionally, an oversampling technique (the synthetic minority oversampling technique (SMOTE) (Chawla et al., 2002)[1] ) is applied to generate synthetic samples for minority classes. The proposed model is evaluated on the Physionet EDF-Sleep 2013 and 2018 datasets using Fpz-Cz and Pz-Oz EEG channels. It achieved an overall accuracy of 94.1%, a macro F1 score of 92.64, and a Cohen’s Kappa coefficient of 0.92. Ablation studies demonstrated the importance of triplet loss-based pre-training and oversampling for enhancing performance. The proposed model requires minimal pre-processing, eliminating the need for extensive signal processing expertise, and thus is well-suited for clinicians diagnosing sleep disorders.

ISMOTE: A More Accurate Alternative for SMOTE

Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance.

Service priority classification using machine learning

This article details a procedure for classifying service cases with various priority levels based on machine learning (ML). It accurately defines the priority level of each service case. The presence of imbalanced datasets in service cases poses a challenge for achieving reliable classification accuracy. To address this, the use of the synthetic minority over-sampling technique (SMOTE) was proposed as the method for balancing the datasets prior to applying the ML method. From these experimental results, an improvement in the precision of the learning process was observed, which led to better outcomes in the test sets. This improvement was measured using the efficiency metrics from the confusion matrix. The experiment involved 6,182 service cases, categorized into four levels: critical, serious, moderate, and low. These were based on test comparisons with other ML methods. The accuracy achieved in the test data was 94.37%. By employing a hybrid technique to address the imbalance in SMOTE and the support vector machine model, it was found to be more effective than the comparative term frequency-inverse document frequency model that was used in conjunction with cosine similarity, which achieved an evaluation score of 70.14%.

B-111 Advancing Precision Medicine in Multiple Myeloma: Addressing Demographic Variabilities and Imbalanced Data in the NIH All of Us Research Program Cohort

Abstract Background The diagnosis of multiple myeloma (MM), a complex hematologic malignancy, presents unique challenges in prognostication and treatment planning, exacerbated by disparities across demographic groups and imbalanced datasets. The heterogeneity observed in MM's clinical manifestations and outcomes necessitates a nuanced approach to patient care, emphasizing the need for models that accurately predict long-term health outcomes across the diverse spectrum of MM participants. Given the variable response to treatment observed among different demographic cohorts, achieving precision in treatment strategies and ensuring equitable healthcare provision is paramount. Objective The objective of this study was to develop and assess predictive models tailored for various demographic groups of participants diagnosed with MM in the All of Us Research Program cohort, which is enrolling 1 million diverse participants who share multiple sources of data. Methods The Synthetic Minority Over-Sampling Technique (SMOTE) was used to rectify the prevalent imbalances within datasets, thereby significantly enhancing the predictive accuracy of patient outcomes over a 1-year post-diagnosis period. By addressing these imbalances, we provide a cornerstone for the development of more personalized and effective treatment paradigms. Employing a comprehensive dataset extracted from electronic health records (EHRs), we segmented participants based on key demographic variables such as age, gender, race, and socioeconomic status. This segmentation included detailed analyses of MM diagnosis, treatment regimens, and subsequent patient responses. To tackle the issue of dataset imbalance, two sets of predictive models were meticulously crafted for each demographic segment: one utilized the original dataset distributions and the other leveraged datasets balanced with SMOTE. A suite of performance metrics, including accuracy, precision, recall, Area Under the Curve (AUC), and F1 score, were deployed to evaluate and compare the efficacy of these models. Models trained on SMOTE-balanced datasets demonstrated marked improvements in prediction accuracy, from a baseline of 75% to over 90%. Precision rates climbed to 88%, and recall rates reached 90%, with an AUC of 0.95 and an F1 score of 0.89, highlighting the models' enhanced predictive capabilities. Results The application of SMOTE to balance datasets resulted in significant predictive accuracy improvements for minority groups within the MM patient population. This was especially evident in demographic cohorts historically underrepresented in clinical research, showcasing the potential of SMOTE in creating more equitable treatment outcomes. Conclusions Our findings underscore the potential of employing tailored predictive models in refining prognosis and treatment planning for multiple myeloma, particularly by addressing data imbalances. The integration of SMOTE into our methodology validates its efficacy in enhancing prediction accuracy, setting a new standard for oncological research. Implications Advocating for a more sophisticated and inclusive approach to data handling, our research contributes to a paradigm shift in oncological research, especially within hematological malignancies like MM. This study is poised to revolutionize predictive modeling in oncology, fostering more informed clinical decision-making and advancing the goals of precision medicine.

Spoofing countermeasure for fake speech detection using brute force features

Due to the progress in deep learning technology, techniques that generate spoofed speech have significantly emerged. Such synthetic speech can be exploited for harmful purposes, like impersonation or disseminating false information. Researchers in the area investigate the useful features for spoof detection. This paper extensively investigates three problems in spoof detection in speech, namely, the imbalanced sample per class, which may negatively affect the performance of any detection models, the effect of the feature early and late fusion, and the analysis of unseen attacks on the model. Regarding the imbalanced issue, we have proposed two approaches (a Synthetic Minority Over Sampling Technique (SMOTE)-based and a Bootstrap-based model). We have used the OpenSMILE toolkit, to extract different feature sets, their results and early and late fusion of them have been investigated. The experiments are evaluated using the ASVspoof 2019 datasets which encompass synthetic, voice-conversion, and replayed speech samples. Additionally, Support Vector Machine (SVM) and Deep Neural Network (DNN) have been adopted in the classification. The outcomes from various test scenarios indicated that neither the imbalanced nature of the dataset nor a specific feature or their fusions outperformed the brute force version of the model as the best Equal Error Rate (EER) achieved by the Imbalance model is 6.67 % and 1.80 % for both Logical Access (LA) and Physical Access (PA) respectively.