Detection and classification of threats and vulnerabilities on hacker forums based on machine learning

Machine learning methods are widely used in automated technologies. Classification prediction is a machine learning based on data mining. Today, many technological devices can make new predictions by gaining experience from past data with machine learning methods. Machine learning is widely studied in two types, supervised and unsupervised. The limits of the objectives in supervised learning are predetermined. In unsupervised learning, there are no predetermined targets. In this learning, the machines are required to determine the targets automatically. Prediction process is one of the basic components of machine learning. Machines need to use some algorithms in order to perform the prediction process on the basis of data mining. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. These algorithms can be applied with the help of some tools on data sets. In this study, kNN algorithm was used to estimate Iris data set classification using Orange tool. The success of the KNN algorithm depends on using the correct attribute and changing the optimum k value. As a result of the tests, it was determined that when the k neighbor value was selected as 15, it was the most suitable k neighbor value, providing 98.67% classification prediction success in the Iris dataset.

Obesity is one of the fastest-growing health risks today, especially in countries like India, where lifestyle changes and poor physical activity are becoming common. Obesity causes diseases such as diabetes, heart disease, and high blood pressure. Regular health check-ups sometimes miss early symptoms. There is a need for smarter, continuous monitoring systems. This work proposes a health monitoring solution that uses Wireless Body Area Networks (WBANs) with machine learning (ML) techniques to identify obesity levels and detect irregular health patterns. A Real-world obesity dataset was used, containing personal and habit features like age, weight, height, food habits, physical activity, and family history. These were used to imitate data that wearable sensors may collect. Several ML models were trained and tested-including Support Vector Machine (SVM), K- Nearest Neighbours (KNN). Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and XGBoost - as well as a hybrid SVM+KNN model. The models were evaluated using various performance metrics. Among them, XGBoost gave the best overall accuracy of around 96%, followed by Random Forest and SVM. The hybrid model offered stable and reliable results in borderline cases. By integrating wearable sensors along with ML algorithms, the proposed system supports the detection of obesity-related risks beforehand and provides continuous health status, especially useful in remote and mobile healthcare settings.

Several billion people worldwide are afflicted by chronic kidney disease (CKD), a common and possibly fatal ailment. For efficient management and treatment of CKD, an early and precise diagnosis is essential. Machine learning (ML) algorithms have recently demonstrated promising results in a number of medical fields. This work uses a big dataset of clinical, demographic, and laboratory data gathered from a large cohort of CKD patients to provide a machine learning method to CKD diagnosis. To ensure the dependability and robustness of the ML models, the dataset handle values that are missing, outliers, and class imbalances during preprocessing. Numerous machine learning techniques encompass k -nearest neighbors (KNN), Random Forest, logistic regression, support vector machines (SVM), Naive Bayes classifiers, and feed -forward neural networks. Cross -validation technique is applied to train and test feed forward neural networks. Model performance is evaluated by estimating the accuracy of the model, with the Random Forest machine learning model achieving the highest accuracy. With the use of perceptron, we suggested a combined model that combines random forest with logistic regression which has the best accuracy; as a result, we hypothesized that more complicated clinical data may be used with this technology to diagnose disorders.

Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data . Method : Data collected in this applied developmental research included 996 records with 130 features obtained by interviewing and completing questionnaires in a mental hospital in the city of Sari, Iran in 2021. After preprocessing, the number of features was reduced to 91, and then through Principal Component Analysis (PCA) reduced to 35 factors. Modeling was done in Python software with K -Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms. The models were evaluated to select algorithms with higher accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to determine the optimal parameters of the selected algorithms . Results: Among the machine learning algorithms, random forest with 91% accuracy and support vector machine with 90% accuracy showed better performance. The genetic algorithms did not make any notable increase in prediction accuracy. Whereas considering N=30, T=150, W=0.9, c1=2, and c2=2 in the particle swarm optimization algorithm increased the prediction accuracy up to 3.3 %. Conclusion : With less classification error compared to similar studies, the PSO -SVM model designed in this study can be used in patient data monitoring with acceptable accuracy and can be used in intelligent systems in psychiatric centers.

Cardiotocograms (CTGs) is a simple and inexpensive way for healthcare providers to monitor fetal health, allowing them to take step to lessen infant as well as mother died. The technology operates by emitting ultrasound pulses and monitoring the response, revealing information such as fetal heart rate (FHR), fetal movements, uterine contractions, and more. Knowing the state of fetal, doctors and patients can take necessary steps in a time. Machine learning can play a vital role in this field. In this paper, we classified the state of fetal including normal state, suspect state, pathological state on the fetal disease dataset using seven machine learning model named AdaBoost (AdB), Random Forest (RF), K- nearest Neighbors (K-NN), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Decision Tree Classifier (DTC), and Logistic Regression (LR). To validate the experimental task, we used several performance metrics containing accuracy, precision, recall, and F1-score. We also used a scaling technique named standard scalar for doing an unbiased dataset. Among the classification models, GCB outperforms the best by achieving the accuracy $$95\%$$ , precision (for normal $$96\%$$ , suspect $$85\%$$ , pathological $$97\%$$ ), recall (for normal $$98\%$$ , suspect $$78\%$$ , pathological $$94\%$$ ), and F1-score (for normal $$97\%$$ , suspect $$81\%$$ , pathological $$96\%$$ ). Although, RF, SVM, and K-NN perform better precision ( $$100\%$$ ) in the class of pathological state only.

This study introduces a machine learning (ML) adaptive interface for classifying transparent conductive oxide (TCO) materials by examining their optical characteristics, such as their wavelength, optical density, absorption, and transmission. Six supervised ML models were considered in this study, namely decision tree, random forest, extreme gradient boosting (XGBoost), support vector classifier (SVC), K -nearest neighbors (KNN), and multilayer perceptron (MLP). These models were evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and learning curves. The comparison findings revealed that XGBoost delivered the best performance, achieving an accuracy of $$96.21\%$$ 96.21 % and a perfect area under the curve (AUC) score of 1.00, effectively distinguishing all five sampled TCO classes: aluminum-doped zinc oxide (AZO), fluorine-doped tin oxide (FTO), indium tin oxide (ITO), magnesium-doped zinc oxide (MZO), and zinc oxide (ZnO). These findings reveal ML’s potential for both scientific and industrial integration in material science research. XGBoost, as a high-precision model, may significantly reduce process time and equipment-intensive laboratory characterizations by allowing for the rapid and precise classification of data from optical measurements. This expedites the search for materials suitable for use in photovoltaics, optoelectronics, and display technologies, all of which require high transparency and conductivity. Overall, this work expands the potential for using ML to categorize materials, particularly XGBoost. It provides both scientific understanding and practical applications in innovative substance informatics and industrial quality control.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

Background: Cardiovascular diseases belong to the top three leading causes of mortality in the Philippines with 17.8 % of the total deaths. Lifestyle-related habits such as alcohol consumption, smoking, poor diet and nutrition, high sedentary behavior, overweight, and obesity have been increasingly implicated in the high rates of heart disease among Filipinos leading to a significant burden to the country's healthcare system. The objective of this study was to predict the presence of heart disease using various machine learning algorithms (support vector machine, naïve Bayes, random forest, logistic regression, decision tree, and adaptive boosting) evaluated on an anonymized publicly available cardiovascular disease dataset. Methodology: Various machine learning algorithms were applied on an anonymized publicly available cardiovascular dataset from a machine learning data repository (IEEE Dataport). A web-based application system named Heart Alert was developed based on the best machine learning model that would predict the risk of developing heart disease. An assessment of the effects of different optimization techniques as to the imputation methods (mean, median, mode, and multiple imputation by chained equations) and as to the feature selection method (recursive feature elimination) on the classification performance of the machine learning algorithms was made. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). Results: The support vector machine without imputation and feature selection obtained the highest performance metrics (90.2% accuracy, 87.7% sensitivity, 93.6% specificity, 94.9% precision, 91.2% F1-score and an area under the receiver operating characteristic curve of 0.902) and was used to implement the heart disease prediction system (Heart Alert). Following very closely were random forest with mean or median imputation and logistic regression with mode imputation, all having no feature selection which also performed well. Conclusion: The performance of the best four machine learning models suggests that for this dataset, imputation technique for missing values may or may not be done. Likewise, recursive feature elimination for feature selection may not apply as all variables seem to be important in heart disease prediction. An early accurate diagnosis leading to prompt intervention efforts is very crucial as it improves the patient's quality of life and diminishes the risk of developing cardiac events.

The purpose of this paper is to explore the role of Machine Learning (ML) in fortifying the security of cloudbased Internet of Things (IoT) systems, using a comprehensive security management approach. The methodological approach involved comparing different ML techniques such as Decision Trees, Random Forest, Support Vector Machines, and Convolutional Neural Networks. Their effectiveness was evaluated based on the accuracy of threat detection in cloud-based IoT systems. The findings revealed that Convolutional Neural Networks demonstrated the highest accuracy rate (98%) in threat detection, thereby significantly enhancing the security of IoT systems. It also identified improvements in threat detection, prevention, response, and system recovery across all ML techniques. Research limitations were primarily the rapidly evolving nature of both ML and IoT technologies, necessitating continual reassessments. The scope was also limited to cloud-based IoT systems, leaving room for further research on other types of IoT systems. The practical implications included improved system security, which could lead to increased trust and wider adoption of IoT technology in various sectors, from healthcare to home security. The social implications entail a safer digital environment, contributing to data privacy and reducing the risk of cyber threats for individuals and communities. The originality of this paper lies in its comprehensive approach to IoT security management using ML, providing valuable insights into the effectiveness of different ML techniques in enhancing threat detection accuracy.

Diabetic Retinopathy (DR) is a highly threatening microvascular complication of diabetes mellitus. Diabetic patients must be screened annually for DR; however, it is practically not viable due to the high volume of patients, lack of resources, economic burden, and cost of the screening procedure. The use of machine learning (ML) classifiers in medical science is an emerging frontier and can help in assisted diagnosis. The few available proposed models perform best when used in similar population cohorts and their external validation has been questioned. Therefore, the purpose of our research is to classify the DR using different ML methods on Saudi diabetic data, propose the best method based on accuracy and identify the most discriminative interpretable features using the socio-demographic and clinical information. This cross-sectional study was conducted among 327 diabetic patients in Almajmaah, Saudi Arabia. Socio-demographic and clinical data were collected using a systematic random sampling technique. For DR classification, ML algorithm including, linear discriminant analysis, support vector machine, K nearest neighbor, random forest and its variate ranger random forest classifiers were used through cross-validation resampling procedure. In classifying DR, ranger random forest outperforms the other methods by accurately classifying 86% of the DR patients on the test data. HbA1c (p<0.001) and duration of diabetes (p<0.001) were the most influential risk factor that best discriminated the DR patients. Other influential risk factors were the body mass index (p<0.001), age-onset (p<0.001), age (p<0.001), systolic blood pressure (p<0.05), and the use of medication (p<0.05) that significantly discriminated the DR patients. Based on the present study findings, integrating ophthalmology and ML can transform diagnosing the disease pattern that can help generate a compelling clinical effect. ML can be used as an added tool for clinical decision-making and must not be the sole substitute for a clinician. We will work to examine the classification performance of multi-class data using more sophisticated ML methods.

The problem persists despite the heavy consequences of jail and the death penalty imposed on those found guilty of selling drugs. This situation creates an alarm among academics and managers who work in practice. Hotbeds for drug dealers are on the rise, and their financial impact on the global economy is moving forward. Drug peddlers distract society by warping peace, justice, and order and threaten the Sustainable Development Goals (SDGs). This study evaluated secondary data containing qualities and suspect drug groups to determine whether machine learning techniques could predict the suspect drug group. We developed a prediction model using nine machine learning algorithms: AdaBoost (AB), naïve Bayes (NB), logistic regression (LR), K nearest neighbour (KNN), random forest (RF), decision tree (DT), neural network (NN), CN2 and support vector machine (SVM). The study utilized Orange data mining software to obtain a more accurate perspective on the data. Predictive accuracy was determined using 5-fold stratified cross-validation. Friedman’s test was conducted, and the results showed that the performance of each algorithm was significantly different. Also, the study compared the models and compiled the results. The results reveal that the random forest has the highest accuracy compared to the others. This prediction model implies that high accuracy can help the government make informed decisions by accurately identifying and classifying suspected individuals and offenders. This prediction will help the government refer suspected individuals and offenders who meet the qualifications for specific drug law sections, like prosecution or rehabilitation, to those sections with a faster and more accurate rate. The outcome of this study should be helpful to law enforcement agents, analysts, and other drug practitioners who may find machine learning tools dependable to detect and classify drug offenders.KeywordsDrug groupClassificationMachine learningOffendersDrug peddling

Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms

Pneumonia is one of the common diseases amongst children in Bangladesh. Many children die from pneumonia in Bangladesh. Pneumonia is an infection that infects the air sacs in one or both lungs. In Bangladesh, nearly 50,000 children die of pneumonia every year. For diseases forecasting, Machine learning algorithms are popular and used extensively. Machine Learning allows us to fulfill such a task with much consequence. We established our dataset from the particular obtainable from our survey. For prognosticating pneumonia, we employed six traditional Machine Learning algorithms. They are K- Nearest Neighbor (KNN), Naive Bayes classifier, Decision Tree, Support Vector Machine (SVM), Neural Network algorithm, and Random Forest. For implementing these algorithms, we applied Scikit-leam, Pandas, NumPy, and for visualizing our data, we have used Matplotlib and seaborn. By proper interpretation, we considered the best performing algorithm for the prediction of pneumonia. We have measured to classify whether pneumonia declines under pneumonia (Positive) and pneumonia (Negative) class. Among all the algorithms, we have chosen the best algorithm which is provided us best accuracy and F1-score. By the best accomplishing algorithm, our model can predict pneumonia quite well.

Learning disabilities (LD) affect a significant number of school-age children and often go undiagnosed due to the limitations of traditional evaluation methods, which are time-consuming, resource-intensive, and reliant on subjective interpretation. The early and accurate identification of LD is essential for implementing timely educational interventions that can improve the academic and social development of affected students. This research aims to develop an automated system that leverages machine learning (ML) techniques to predict learning disabilities based on a variety of input features derived from behavioral, cognitive, academic, and psychological assessments. A dataset comprising relevant indicators—such as reading and writing abilities, attention span, memory retention, logical reasoning, language skills, and classroom behavior—was collected and preprocessed for analysis. Feature selection techniques were employed to identify the most predictive attributes. Several machine learning models were implemented and compared, including Decision Trees, Support Vector Machines (SVM), Random Forest, k- Nearest Neighbors (k-NN), and Logistic Regression. Each model was trained and validated using standard cross-validation techniques to ensure generalization and robustness. Among the evaluated algorithms, the Random Forest classifier yielded the highest accuracy, outperforming others in terms of precision, recall, F1-score, and overall predictive performance. The results demonstrate that machine learning models, when trained on appropriate features, can effectively predict the likelihood of a learning disability in children with high accuracy and reliability. The proposed system not only aids educators, parents, and healthcare professionals in early detection but also reduces dependence on extensive manual testing. By integrating such intelligent diagnostic tools into educational institutions, it becomes possible to facilitate early intervention strategies, personalize learning plans, and ultimately enhance the learning experience and developmental trajectory of students at risk.

Soil texture identification using LIBS data combined with machine learning algorithm