ROC-AUC Score Research Articles

Predicting and Explaining Customer Response to Upselling in Telecommunications: A Malaysian Case Study

This research explores the predictive capabilities of XGBoost (XGB) and Random Forest (RF) models for customer upsell responses, emphasizing the use of Explainable Artificial Intelligence (XAI) techniques to gain insights. Initially trained without hyperparameter tuning, both models were later optimized using 5-fold cross-validation. While RF consistently achieved high accuracy (0.99), XGB exhibited lower accuracy (0.85) yet demonstrated superior precision and recall. Post-tuning, XGB maintained its competitive edge despite a slight decrease in ROC-AUC scores (0.76 and 0.75 versus RF's 0.67 and 0.72), indicating proficiency in classifying positive cases. XAI techniques complemented XGB’s prediction, revealing significant predictors such as inactive duration in days, race (Chinese), total communication count, age, and active period in days. Lesser predictive value was attributed to factors such as race (Indian), gender (female), and region (northern). While the feature importance plot provided a broad overview, it did not detail specific attribute relationships to predictions. To address this, a summary violin plot was employed to illustrate how feature importance varies with actual values, enhancing the understanding of each feature's impact. Results indicated that longer inactivity periods negatively influenced predictions, while non-Chinese ethnicity, higher communication frequency, and younger age were associated with positive outcomes. Dependence plots further elucidated these relationships, highlighting how older non-Chinese customers and those with shorter inactive periods and frequent communication were more likely to accept offers. Local explanations using Shapley's force plot and LIME offered deeper insights into specific instances. Overall, the study underscores the complementary use of XAI techniques to understand a model’s predictions.

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study

This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with mood and anxiety disorders. A cohort of 43 patients was monitored over a two-year period, with data collected from smartphone applications and wearable devices. This research aimed to differentiate between the day before panic (DBP) and stable days without symptoms. With RandomForest, GradientBoost, and XGBoost classifiers, the study analyzed 3,969 data points, including 254 DBP events. The XGBoost model demonstrated performance with a ROC-AUC score of 0.905, while a simplified model using only the top 10 variables maintained an ROC-AUC of 0.903. Key predictors of panic events included evaluated Childhood Trauma Questionnaire scores, increased step counts, and higher anxiety levels. These findings indicate the potential of machine learning algorithms leveraging digital phenotypes to predict panic symptoms, thereby supporting the development of proactive and personalized digital therapies and providing insights into real-life indicators that may exacerbate panic symptoms in this population.

Knowledge distillation with resampling for imbalanced data classification: Enhancing predictive performance and explainability stability

Machine learning classification models often struggle with imbalanced datasets, leading to poor performance in minority classes. While preprocessing approaches like resampling can improve minority class detection, they may introduce sampling bias and reduce model explainability. This study proposes a novel method combining random undersampling (RUS) with knowledge distillation (KD) to enhance both predictive performance and explainability stability for imbalanced data classification. Our approach employs a two-step learning process: (1) training a balanced teacher model using RUS and (2) training an imbalanced student model through response-based KD, utilizing both soft and hard targets. We hypothesize that this method mitigates class imbalance while preserving important information from the original dataset. We evaluated our proposed model against baseline and RUS-only models using five diverse imbalanced datasets from various domains. Performance was assessed using stratified 10-fold cross-validation with ROC-AUC and PR-AUC scores. Explainability stability was measured by the cosine similarity of SHAP values across cross-validation folds. Results demonstrate that our proposed model consistently outperforms both baseline and RUS-only models regarding ROC-AUC and PR-AUC scores across all datasets. Moreover, it exhibits superior explainability stability in the majority of cases, addressing the sampling bias issue associated with traditional resampling methods. This research contributes to the field of machine learning by offering a novel approach that simultaneously improves predictive performance and maintains explainability for imbalanced data classification.

Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics framework

Three AI developments, classified as forms of human enhancements, center around progress at the intersection of AI, nanotechnology and biotechnology. Our research advances the understanding of AI and human enhancement by data-driven analytics and offers practical tools for future research and societal applications. It is based on a survey that was launched by PRC in February 2021 to more than 5,000 respondents from the U.S. It consists of about 100 questions that are grouped using a prefix code by the 3 above-mentioned human enhancement, science role, concerns and excitements, perceived algorithm fairness and demographics. To investigate this survey and extract insights regarding the general attitude, a data analytics framework is proposed that consists of clustering using DBSCAN and K-means, ANOVA for clusters, PCA, t-SNE and UMAP for graphical visualization, prediction and advanced customers’ profiles analyses. Both clustering methods indicate distinct profiles for AI customers. Most of them are moderate, but two smaller groups define the tech-ethics advocates and tech-forward visionaries. For prediction, in the multi-class classification task, the ROC-AUC score is 0.852 and average F1 Score is 0.987. Following the results, both technology creators and legislators must work collaboratively to ensure that technological advancements are ethically grounded, widely accepted and aligned with societal values.

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid–based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student’s t test and Cohen’s D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values (<0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 0.05$$\\end{document}) and the majority of Cohen’s D values (>0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$> 0.5$$\\end{document}) showed that MEF-AlloSite’s 1–6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery.

Vestibular schwannomas (VSs) represent the most common cerebellopontine angle tumors, posing a challenge in preserving facial nerve (FN) function during surgery. We employed the Extreme Gradient Boosting machine learning classifier to predict long-term FN outcomes (classified as House-Brackmann grades 1-2 for good outcomes and 3-6 for bad outcomes) after VS surgery. In a retrospective analysis of 256 patients, comprehensive pre-, intra-, and post-operative factors were examined. We applied the machine learning (ML) classifier Extreme Gradient Boosting (XGBoost) for the following binary classification: long-term good and bad FN outcome after VS surgery To enhance the interpretability of our model, we utilized an explainable artificial intelligence approach. Short-term FN function (tau = 0.6) correlated with long-term FN function. The model exhibited an average accuracy of 0.83, a ROC AUC score of 0.91, and Matthew's correlation coefficient score of 0.62. The most influential feature, identified through SHapley Additive exPlanations (SHAP), was short-term FN function. Conversely, large tumor volume and absence of preoperative auditory brainstem responses were associated with unfavorable outcomes. We introduce an effective ML model for classifying long-term FN outcomes following VS surgery. Short-term FN function was identified as the key predictor of long-term function. This model's excellent ability to differentiate bad and good outcomes makes it useful for evaluating patients and providing recommendations regarding FN dysfunction management.

A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.

The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resources and therapies. Computational drug repositioning is an effective strategy that redirects authorized drugs to new therapeutic purposes. This strategy holds significant promise for newly emerging diseases, as drug discovery is a lengthy and expensive process. Through this study, we present an ensemble method based on the convolutional neural network integrated with genetic algorithm and deep forest classifier for virus-drug association prediction (CGDVDA). We generated feature vectors by combining drug chemical structure and virus genomic sequence-based similarities, and extracted prominent deep features by applying the convolutional neural network. The convoluted features are optimized using the genetic algorithm and classified using the ensemble deep forest classifier to predict novel virus-drug associations. The proposed method predicts drugs for COVID-19 and other viral diseases in the dataset. The model could achieve ROC-AUC scores of 0.9159 on fivefold cross-validation. We compared the performance of the model with state-of-the-art approaches and classifiers. The experimental results and case studies illustrate the efficacy of CGDVDA in predicting drugs against viral infectious diseases.

Bank Direct Marketing Campaign Success Prediction

Nowadays, there are more new and innovative bank marketing strategies available that contribute to customer engagement and acquisition. Banks must launch marketing campaigns that can successfully attract customers from other competitors. Therefore, the purpose of the research is to apply Machine Learning techniques in predicting the success of a bank’s direct marketing campaign using supervised classification algorithms. Six algorithms are implemented which are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB) and Extreme Gradient Boosting (XGBoost). In this paper, feature selection is performed, and 8 selected features are used to make the prediction. The experimental result shows that the Random Forest model has the highest accuracy and ROC AUC score which makes it the champion model in this paper. Still, these results may vary according to the nature of data preprocessing and algorithms implemented.

An Ensemble Learning Algorithm with Hyperparameter Optimization (ELAHO) Model for Early Detection of Maternal Health Risk (MHR) Level Using Machine Learning

Despite the advancement in the healthcare system, maternal health risk remains high, which is the most challenging aspect nowadays. There is a need to develop an effective model for early detection, monitoring, and prediction of maternal health risk levels during pregnancy. The machine learning intelligence model has proven its effectiveness and robustness in providing accurate and reliable prediction, analysis, and interpretation of medical data, reducing several risk factors for early diagnosis in healthcare. In this research work, we proposed an ensemble learning algorithm with a hyperparameter optimization (ELAHO) model using machine learning algorithms to improve its robustness, effectiveness, and model performance. The proposed method uses a hybrid model of logistic regression and support vector machines (LG-SVM) to predict maternal health risk levels during pregnancy. The method utilized Python software for training, testing, and validation. We evaluated the performance of our proposed model using accuracy, precision, sensitivity, f1-score, and ROC-AUC score. The proposed models outperformed the conventional models and achieved 100% predictive accuracy. The proposed approach has the potential to be adapted as an intelligence-monitoring system for early medical diagnosis during pregnancy. The proposed techniques will help medical professionals make quick decisions accurately and enhance monitoring to improve the level of care offered to pregnant mothers and their unborn children.

Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans

Brain tumors, characterized by abnormal cell growth, pose a significant challenge in clinical imaging due to their complex and diverse structures. Early and accurate identification, classification, localization, and segmentation of these tumors are critical to reducing mortality. However, the extensive data generated by MRI scans makes manual segmentation time-consuming and impractical for clinical use. To address these challenges, we propose, a hybrid deep learning model that precisely segmented tumor regions with U-Net to enable YOLOv8s to efficiently detect, classify and localize tumors. The model was trained and validated using The Cancer Imaging Archive (TCIA) dataset, which includes MRI images of brain tumors, and the Cancer Genome Atlas (CGA) low-grade glioma dataset, which includes data from 110 patients with FLAIR aberrant segmentation masks. The proposed hybrid model was evaluated using several performance metrics, including F1 score, specificity, recall, precision, accuracy, and ROC-AUC score. Hybrid proposed performed highly, achieving a precision of 97.8 %, accuracy of 98.6 %, recall of 95.2 %, F-1 score of 96.3 %, specificity of 89.1 %, and ROC-AUC score of 98.5 %. The integration of YOLOv8s and U-Net in Enhanced TumorNet offers a powerful solution for the automated analysis of brain tumors in MRI scans, significantly improving detection and segmentation accuracy. This hybrid approach holds great potential for clinical applications, enhancing the efficiency and effectiveness of brain tumor diagnosis.

Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms.

Background: This study investigates the application of Machine Learning techniques to predict clinical outcomes in microsurgical clipping treatments of cerebral aneurysms, aiming to enhance healthcare processes through informed clinical decision making. Methods: Relying on a dataset of 344 patients' preoperative characteristics, various ML classifiers were trained to predict outcomes measured by the Glasgow Outcome Scale (GOS). The study's results were reported through the means of ROC-AUC scores for outcome prediction and the identification of key predictors using SHAP analysis. Results: The trained models achieved ROC-AUC scores of 0.72 ± 0.03 for specific GOS outcome prediction and 0.78 ± 0.02 for binary classification of outcomes. The SHAP explanation analysis identified intubation as the most impactful factor influencing treatment outcomes' predictions for the trained models. Conclusions: The study demonstrates the potential of ML for predicting surgical outcomes of ruptured cerebral aneurysm treatments. It acknowledged the need for high-quality datasets and external validation to enhance model accuracy and generalizability.

Machine learning-driven insights into phase prediction for high entropy alloys

The unique properties of high-entropy alloys (HEAs) have attracted considerable attention, largely due to their dependence on the choice among three distinct phases: solid solution (SS), intermetallic compound (IM), or a blend of both (SS + IM). For this reason, precise phase prediction is key to identifying the optimal element combinations needed to develop HEAs with the required characteristics. Due to large compositional domain of HEAs is opportune to design new HEAs with desired output. A machine learning tool is exploited to discover and characterize high entropy alloys with satisfying targets. Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. The ML models such as multi layer precreptron MLP, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), KNN, XGB nad SVM Classifier algorithm were used for the identifying the phase of HEAs. After assessing the accuracy and tuning of each model, an random forest classifier (accuracy = 0.914. precision = 0.916, ROC-AUC score = 0.97) model showed the best predictive capabilities for phase prediction. The new HEA was designed based on prediction and successfully validated with thermodynamic simulation. Data AvailabilityData will be made available on request

Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis

In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.

Struggling Models: An Analysis of Logistic Regression and Random Forest in Predicting Repeat Buyers with Imbalanced Performance Metrics

Predicting repeat buyers is essential for businesses seeking to improve customer retention and maximize profitability. This study examines the effectiveness of logistic regression and random forest algorithms in forecasting repeat buyers, utilizing an e-commerce dataset from Kaggle. Despite the theoretical strengths of these models, our results indicate significant performance challenges. Both models were evaluated on key metrics: accuracy, precision, recall, F1 score, and ROC-AUC. The findings revealed that the models logistic regression and random forest performed poorly, with accuracy hovering around 50%, precision and recall demonstrating imbalanced performance, and ROC-AUC scores barely exceeding random guessing levels. Such metrics highlight the limited discriminative power of these models in identifying repeat buyers. The analysis suggests that issues such as data quality, feature relevance, and class imbalance contribute to these shortcomings. Specifically, the models struggled to effectively learn from the data, leading to suboptimal predictions. These results underscore the need for enhanced feature engineering, better handling of class imbalance, and possibly exploring more advanced algorithms. This study provides a critical assessment of the limitations inherent in using Logistic Regression and Random Forest for predicting repeat buyers, hence implements feature engineering, SMOTE and hyperparameter tuning using RandomSearchCV to get better result.

CMShark: A NetFlow and machine-learning based crypto-jacking intrusion-detection method

Crypto-jacking attack is a novel type of cyber-attack on the internet that has emerged because of the popularity of digital currencies. These attacks are the most common type of attacks in the cryptocurrency field because of their specific features such as easy scenario, un-traceability, and ease of secrecy. In crypto-jacking attacks, it is common to embed malicious code inside website scripts. Different techniques have been provided to deal with Crypto-jacking attacks, but crypto-jacking attackers bypass them by limiting resources. The crypto-mining services provided on the internet are legal, and due to the anonymous nature of cryptocurrencies, client identification is a challenging task. Improving the accuracy and performance of the Crypto-jacking attack detection methods are the main objectives of this study. In this paper, a hybrid network-based method to identify these attacks to achieve better and more accurate results. The proposed solution (CMShark) is a combination of machine learning (ML) models, IP blacklisting and payload inspection methods. In the ML model, the packets are classified using size patterns; in IP blacklisting, attacks are detected based on known infected addresses and infected scripts. In payload inspection, the provided information on the packet payload is searched for any suspicious keywords. The proposed method relies solely on the network and is deployed on the edge of the network, making it infrastructureindependent. The proposed detection model reaches an accuracy score of 97.02%, an F1-score of 96.90% a ROC AUC score of 97.20% in input NetFlow classification; and a 93.98% accuracy score, 94.30% F1-score and 97.30% ROC AUC score in output NetFlow classification.

Predictive analytics in customer behavior: Anticipating trends and preferences

In order to effectively manage their customers, businesses need to thoroughly analyze the costs and advantages associated with various alternative expenditures and investments and determine the most effective way to allocate resources to marketing and sales activities over time. Those in charge of making decisions will reap the benefits of decision support models that estimate the value of the customer portfolio and tie expenses to customers' purchasing behavior. In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. The evaluation criteria considered in the work include precision, recall, F1-Score, and ROC-AUC. The accuracy values obtained for DT, RT, LR, SVM, and gradient boosting are 0.787, 0.806, 0.826, 0.826, and 0.823, respectively. The results emphasize RT and LR's good performance, while the values of 0.620, 1, 0.766, and 0.878 for the precision, recall, F1-score, and ROC-AUC score outperform the rest. The novelty of this work lies in employing a comprehensive set of machine learning algorithms to predict customer behavior, with a particular emphasis on the superior performance of RF and LR models, as demonstrated by their high precision, recall, F1-score, and ROC-AUC values.

Application of deep ensemble learning for palm disease detection in smart agriculture

Agriculture has notably become one of the fields experiencing intensive digital transformation. Leveraging state-of-the-art techniques in this domain has provided numerous advantages for agricultural activities. Deep learning (DL) algorithms have proven beneficial in addressing various agricultural challenges. This study presents a comprehensive investigation into applying DL models for palm disease detection and classification in the context of smart agriculture. The research aims to address the limitations observed in previous studies and improve the robustness and generalizability of the results. To achieve this, a two-stage optimization methodology is employed. First, transfer learning and fine-tuning techniques are applied using various pre-trained deep neural network models. The experiments show promising results, with all models achieving high accuracy rates during training and validation. Furthermore, their performance on unseen test data is also assessed to ensure practical applicability. The top-performing models are MobileNetV2 (92.48 %), ResNet (92.42 %), ResNetRS50 (92.30 %), and DenseNet121 (92.01 %). Second, a deep ensemble learning approach is applied to enhance the models' generalization capability further. The best-performing models with different criteria are combined using the ensemble technique, resulting in remarkable improvements in disease detection tasks. DELM1 emerges as the most successful ensemble model, achieving an ROC AUC Score of 99 %. This study demonstrates the effectiveness of deep ensemble learning models in palm disease detection and classification for smart agriculture applications. The findings contribute to advancing disease detection systems and emphasize the potential of ensemble learning. The study provides valuable insights for future research, guiding the application of DL techniques to address critical agricultural challenges and improve crop health monitoring systems. Another contribution is combining various plant diseases and insect pest classes using diverse datasets. A comprehensive classification system is achieved by considering different disease classes and stages within the white scale category, improving the model's robustness.

Optimizing Breast Cancer Diagnosis with Machine Learning Algorithms

Breast cancer is the most common disease among women worldwide, and it continues to pose a serious threat to global health [2]. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods, while effective, have limitations in consistency and speed. Medical diagnostics have undergone a revolution with the introduction of machine learning (ML), which provides tools to analyze complex data and increase diagnosis accuracy. Using the Breast Cancer Wisconsin (Diagnostic) Dataset[1], this investigation dives into the application of several machine learning (ML) algorithms to improve the effectiveness of the diagnosis of breast cancer in terms of accuracy. Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Support Vector Classifier (SVC), XGBoost Classifier (XGBC), and Convolutional Neural Network (CNN) are used. Improved performance metrics were obtained across models by applying optimization approaches, particularly genetic algorithm for the selection of features, following preprocessing and initial training. RandomForestClassifier, XGBClassifier, and CNN notably showed significant enhancements in accuracy, ROC AUC score, recall, precision, and F1 score post-optimization. Ensemble methods and deep learning architectures proved effective in handling complex data and reducing overfitting, with Random Forest Classifier standing out as consistently superior. Conversely, Decision Tree Classifier and Support Vector Classifier exhibited mixed results, showcasing the importance of optimization strategies. This research shows the capability of ML in augmenting breast cancer diagnostics, advocating for further exploration of ensemble methods and advanced neural networks to refine diagnostic tools and improve patient outcomes.

Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning.

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human-computer interaction technologies.

X Bot Detection Using One-Class Classification Methods with Isolation Forest Algorithm

X bots pose a significant issue in the social media landscape, with many shared links originating from bot-like accounts. This study introduces the application of the Isolation Forest algorithm, aimed explicitly at identifying anomalies such as bots by analyzing X account details. This study utilizes a dataset that merges data from Botometer with supplementary metrics like ‘average tweets per day’ and ‘account age in days’, contributed by David Martín Gutiérrez. This approach was adopted due to the increasing difficulties accessing the X API. The dataset comprises 37,438 instances, with 25,013 labeled human accounts and 12,425 labeled bot accounts. Pre-processing is performed to remove irrelevant features, and the dataset is split into Training, Validation, and Test sets in a 70:15:15 ratio. The training set undergoes hyperparameter and threshold tuning to identify the best configuration for this specific dataset (n_estimators: 50, contamination: 0.5, bootstrap: True), achieving a training set F1-score of 0.211001. Despite these optimization efforts, the Isolation Forest model's performance remains relatively low. The Test set evaluation yields modest precision, recall, and F1-score values (0.1801, 0.2795, and 0.2190, respectively), with a ROC AUC score of 0.3272. While the Isolation Forest algorithm shows promise in detecting X bots, its performance on this specific dataset is limited. Isolation Forest may not be the most suitable algorithm for this particular bot detection task on this dataset. Future work will explore techniques to enhance the performance of bot detection for a more comprehensive analysis.