A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

Diabetes is a major chronic syndrome caused by a series of metabolic abnormalities in which blood glucose levels are abnormally high for an indeterminate amount of time. It influences various organs in the human body, resulting in a variety of complex diseases such as stroke, renal disease, pulmonary embolism, eyesight, and so on. Diabetes Disorders (DD) are presently one of the healthcare top causes of mortality. Predictive analytics in the health care system is a huge obstacle, but if accurate early prediction is achieved, the potential risk and degree of diabetes may be significantly decreased. Machine learning (ML) techniques are now used to analyze medical datasets at an earlier stage of life in keeping people safe. In this research, we utilized several ML approaches notably Logistic Regression, Decision Tree (DT), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) on PIMA Indian Diabetes Dataset in order to monitor and evaluate their performances in diabetes prediction. The performance of the various ML algorithms employed in this research suggests which algorithm is most suitable in diabetes prediction. It is observed that among all the models XGBoost had outperformed the other ML techniques with an accuracy of 80.73% while SVM was the second-best performing model with a classification accuracy of 80.21%. Thus, employing ML techniques, this study aims to assist doctors as well as clinicians in the early detection of diabetes.

Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.

Introduction Spine trauma represents a major cause of disability and substantial healthcare costs. Despite classification systems and risk scores, outcome prediction following spine trauma remains challenging. Machine learning (ML) approaches offer multidimensionality but vary widely in methodology, performance, and clinical application. This systematic review aims to characterize the use of ML for outcome prediction and prognostication in spine trauma. Methods A systematic search of PubMed, Embase, Scopus, Web of Science, and Cochrane Library was performed for publications from January 2015 to May 2025. Studies reporting ML for predicting clinical outcomes in spine trauma patients were identified using pre-defined criteria. Primary outcomes were types and performance metrics of ML models for spine trauma prognostication. Secondary outcomes included cost-effectiveness, clinical utility and implementation feasibility. Results Twenty-seven studies were included, of which 26 were published from 2020 onwards. Only one study was prospective, while the rest were retrospective. Commonly used ML algorithms for spine trauma prognosis were logistic regression (LR), extreme gradient boosting (XGBoost), Random Forest (RF), light gradient boosting machine (LightGBM), and support vector machine (SVM). Outcomes predicted include mortality, neurological function or recovery, ambulation and walking, self-care and independence, complications, treatment failure, length of stay, and discharge destination. Most studies employed internal testing and validation; only four reported external validation. Seven studies reported direct comparison with non-ML models or risk scores, demonstrating comparable or superior performance of ML models. Performance metrics include area under the curve (AUC), accuracy, sensitivity, specificity, precision, F1-score, Brier score, and others. The highest AUC was reported by a nomogram constructed using consensus clustering to predict postoperative pulmonary infection in patients with acute cervical spinal cord injury (internal validation; AUC 0.993; 95% CI 0.981–1.000). Quality of evidence varied between studies, but risk of bias was a concern. Conclusions Existing evidence on ML for spine trauma prognosis is characterized by retrospective study designs, homogeneous cohorts, and lack of external validation, limiting their generalizability and real-world application. While ML approaches demonstrate encouraging predictive performance for spine trauma outcomes, their clinical adoption is limited by methodological heterogeneity. More robust validation, standardized reporting, and comparison with traditional tools are needed before ML models can reliably inform clinical decision-making.

Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study

This research is based on a systematic review of machine learning (ML) approaches for the cognitive load (CL) assessment of applications for unmanned aerial system (UAS) operator training. The review synthesises evidence on how ML techniques have been applied to assess CL using diverse data sources, including physiological signals (e.g., EEG, HRV), behavioural measures (e.g., eye-tracking), and performance indicators. It highlights the effectiveness of models such as Support Vector Machines (SVMs), Random Forests (RFs), and advanced deep learning (DL) architectures such as Long Short-Term Memory (LSTM), as well as how the use of different methods affects the performance of ML models, with studies reporting accuracies of up to 98%. The findings also indicate that, compared with traditional UAS training approaches, ML approaches can enhance training by providing adaptive assessment, with methodological factors such as model selection, data preprocessing, and validation being central to ML assessment performance. These findings highlight the value of accurate CL assessment as a foundation for adaptive training systems, supporting enhanced UAS operator performance and operational safety. By consolidating the methodological insights and identifying research gaps, this review provides valuable background information for advancing ML-based CL assessment and its integration into adaptive UAS operator training systems to enhance UAS operator training.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Identification of damage in its early stage can have a great contribution in decreasing the maintenance costs and prolonging the life of valuable structures. Although conventional damage detection techniques have a mature background, their widespread application in industrial practice is still missing. In recent years the application of Machine Learning (ML) algorithms have been more and more exploited in structural health monitoring systems (SHM). Because of the superior capabilities of ML approaches in recognizing and classifying available patterns in a dataset, they have demonstrated a significant improvement in traditional damage identification algorithms. This review study focuses on the use of machine learning (ML) approaches in Ultrasonic Guided Wave (UGW)-based SHM, in which a structure is continually monitored using permanent sensors. Accordingly, multiple steps required for performing damage detection through UGWs are stated. Moreover, it is outlined that the employment of ML techniques for UGW-based damage detection can be subtended into two main phases: (1) extracting features from the data set, and reducing the dimension of the data space, (2) processing the patterns for revealing patterns, and classification of instances. With this regard, the most frequent techniques for the realization of those two phases are elaborated. This study shows the great potential of ML algorithms to assist and enhance UGW-based damage detection algorithms.

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to form protein coronas, which were analyzed using ESI-MS. Eight statistical and entropy-based features (mean, variance, skewness, kurtosis, Shannon entropy, approximate entropy, sample entropy, and permutation entropy) were extracted from spectral data. Additionally, Continuous Wavelet Transform (CWT) with Mexican hat wavelet was applied to convert mass spectrometry data into 128 × 128 RGB images for deep learning analysis. Classification was performed using traditional machine learning models (Random Forest, Support Vector Machine, Decision Tree, Deep Neural Network) and transfer learning with pre-trained CNNs (VGG16, ResNet50, Xception), evaluated through 5-fold cross-validation. Significant differences in spectral intensity parameters were observed between healthy individuals and choroidal melanoma patients (p < 0.001), with notably lower Mean_Intensity values in cancer patients (56.41 ± 46.06 vs. 111.02 ± 10.01, Cohen’s d = 1.64). While m/z parameters showed moderate differences that didn’t reach statistical significance (p = 0.082), entropy-based features demonstrated strong discriminative power. Among traditional machine learning models, Random Forest achieved the highest accuracy (0.959 ± 0.003) and ROC AUC (0.993 ± 0.000) with remarkable computational efficiency (3.90 s per fold). For deep learning approaches using CWT-generated images, VGG16 demonstrated superior performance (Accuracy: 0.976 ± 0.008, ROC AUC: 0.997 ± 0.002) despite requiring significantly higher computational resources (1349.52 s per fold). This study demonstrates that tear sample analysis using protein corona formation on gold nanoparticles with ESI-MS and advanced machine learning techniques offers a promising non-invasive approach for choroidal melanoma detection with performance metrics that compare favorably to existing methods. The significant differences in spectral intensity parameters between groups suggest distinctive proteomic signatures that can be leveraged for diagnostic purposes. While both traditional machine learning and deep learning approaches achieved exceptional performance, each offers distinct advantages in terms of computational efficiency and feature extraction capabilities.

Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

Optimized machine learning approaches for identifying vertical temperature gradient on ballastless track in natural environments

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

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

Significant advancements have been achieved recently in the use of artificial intelligence (AI) to solve healthcare-related problems. A potential method for identifying and treating many illnesses is machine learning (ML). The “Breast Cancer Dataset” was used in this work to perform classification tasks using ML techniques including Support Vector Machine (SVM), Random Forest (RF) and MultiLayer Perceptron (MLP) for identifying the forms of breast cancer, either Benign or Malignant. The SVM, RF, and MLP achieved impressive PR scores exceeding 0.98. However, MLP exhibited superior performance among these algorithms, achieving an exceptional AUC value of 99.71. Conversely, SVM slightly outperformed the other algorithms in terms of training accuracy, with a value of 97.90%. This work used shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) to shed light on how these categorization algorithms made judgments. These interpretability methodologies showed that the majority of characteristics consistently predicted the presence of malignant or benign cancer types. This study demonstrates how ML algorithms' decision-making procedures may be understood, possibly promoting the use of ML in clinical settings for medical diagnosis.