Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers.

Graduation rates indicate school success. Predicting student graduation helps schools identify students in danger of dropping out and intervene early to enhance academic performance. It can also assist policymakers create graduation and dropout prevention initiatives. However, based on a literature search, predicting student graduation rates from admission test scores is difficult. School grades are a better predictor of timely tertiary graduation than acceptance test scores because college success requires cognitive abilities and self-regulation competencies, which are better indexed by school grades. Self-efficacy, school academic culture, and future expectations can also affect student graduation rates. Finally, the selective admissions modality needs to be refined. This study aims to (1) predict private high school graduation with eight algorithms: Random tree, Naïve Bayes Multinomial, Support Vector Machine (SVM), Random forest (RF), K-Nearest Neighbor, Ada Boost, Multilayer perceptron, Logistic regression, and (2) compare the performance of the eight algorithms. According to research, the Random tree, Naïve Bayes Multinomial, Random forest (RF), and Ada boost algorithms all perform at 99.49% for the first aim. For the second objective, the Random Tree approach outperforms other algorithms in Accuracy (99.49%), Precision (100%), F-Measure (99.74%), and consumption time (0 seconds). Therefore, the Random tree algorithm outperforms others. This research contributes in two ways: scientifically by testing eight algorithms—Random tree, Naïve Bayes Multinomial, Support Vector Machine (SVM), Random forest (RF), K-Nearest Neighbor, Ada Boost, Multilayer perceptron, and Logistic regression—to predict private high school graduation, and secondly by recommending school administrators to develop a selective enrollment model.

Landslides represent one of the most widespread natural hazards affecting mountainous regions globally. Their occurrence is primarily attributed to natural factors such as high-intensity precipitation, steeper topography, and seismic activity. These factors are further exacerbated by anthropogenic activities, including unregulated infrastructural developments, mining operations, and the expansion of transportation infrastructure such as roads and railways. Understanding landslide susceptibility is essential for sustainable socio-economic development in mountainous regions. This study aims to conduct a systematic comparison of empirical, numerical, and machine learning approaches for slope stability assessment, with the objective of identifying their respective strengths, limitations, and real-time applications. The critical parameters involved in slope stability analyses have been collected through extensive field visits. The slope stability analysis encompasses empirical approaches, including rock mass classification methods and kinematic analysis; numerical procedures such as Limit Equilibrium Analysis (LEA) and Finite Element Analysis (FEA); and machine learning techniques, specifically Random Forest (RF), Gradient Boosting Machine (GBM), and XGBoost. These methods are applied to seven road-cut slopes situated along a proposed tunnel path in a geologically complex mountainous region. The objective is to evaluate the effectiveness of each approach and to assess the potential of advanced machine learning models as viable validation tools for conventional slope stability assessment outcomes. The study reveals that the surveyed locations exhibit varying stability grades as determined by Rock Mass Classification systems and corresponding Factor of Safety (FOS) values. The outcomes of the machine learning algorithms are compared against those obtained from Finite Element Analysis (FEA), with observed discrepancies ranging from 2 to 10% across the different ML models.

Gravitational waves are theorized to be gravitationally lensed when they propagate near massive objects. Such lensing effects cause potentially detectable repeated gravitational wave patterns in ground- and space-based gravitational wave detectors. These effects are difficult to discriminate when the lens is small and the repeated patterns superpose. Traditionally, matched filtering techniques are used to identify gravitational-wave signals, but we instead aim to utilize machine learning techniques to achieve this. In this work, we implement supervised machine learning classifiers (support vector machine, random forest, multi-layer perceptron) to discriminate such lensing patterns in gravitational wave data. We train classifiers with spectrograms of both lensed and unlensed waves using both point-mass and singular isothermal sphere lens models. As the result, classifiers return F1 scores ranging from 0:852 to 0:996, with precisions from 0:917 to 0:992 and recalls ranging from 0:796 to 1:000 depending on the type of classifier and lensing model used. This supports the idea that machine learning classifiers are able to correctly determine lensed gravitational wave signals. This also suggests that in the future, machine learning classifiers may be used as a possible alternative to identify lensed gravitational wave events and to allow us to study gravitational wave sources and massive astronomical objects through further analysis. KEYWORDS: Gravitational Waves; Gravitational Lensing; Geometrical Optics; Machine Learning; Classification; Support Vector Machine; Random Tree Forest; Multi-layer Perceptron

Dengue, Zika, and chikungunya viruses pose a serious threat globally and circulate widely in America. These diseases share similar symptoms in their early stages, which can make early diagnosis difficult. In this study, two predictive models based on Decision Trees and Random Forests were developed to classify dengue, Zika, and chikungunya, with the aim of being supportive and easily interpretable for the medical community. To achieve this, a dataset was collected from a clinic in Sincelejo, Colombia, including the signs, symptoms, and laboratory results of these diseases. The Pan American Health Organization (PAHO) Diagnostic Guide 2022 methodology for the differential classification of dengue and chikungunya was applied by assigning evaluative weights to symptoms in the dataset. In addition, a bootstrapping resampling technique based on the central limit theorem was used to balance the target variable, and cross-validation was used to train the models. The main results were obtained with the Random Forest technique, achieving an accuracy of 99.7% for classifying chikungunya, 99.1% for dengue, and 98.8% for Zika. This study represents a significant advance in the differential prediction of these diseases through the use of automatic learning techniques and the integration of clinical and laboratory information.

The paper deals with the assessment of slope stability on the road II/595 near the village Zlatno before and after the landslide caused by floods in 2010. The calculation of factor of safety was made using GEO 5 software. For proposal comprehensive assessment and possible remedial work is necessary to know the geological conditions and choose the appropriate method for assessing slope stability. The critical factors of safety have been determined by Petterson, Bishop and Sarma Methods. After finding the most unfavorable slip surface a proposal of remediation measures has been made. The paper presents various solutions to ensure slope stability of the road. The most appropriate remedial action is Variant III - reinforced slope due to its greater factor of safety.

Electrodialysis (ED) based desalination is widely recognized as an effective water purification technique that conserves water by selective removal of dissolved salts and impurities. Herein, machine learning (ML) models such as Multiple Linear Regression (MLR), Support Vector Regression (SVR) Multi-Layer perceptron (MLP), and Random Forest (RF) have been developed to accurately forecast salt removal ratio, energy consumption, and current efficiency of electrodialysis toward the desalination. An electrodialysis system having 65 membrane cell pairs of 30 × 60 cm2 is used for the present study. The 144 datasets of selected independent variables such as time, feed concertation, treated flow rate, concentrated flow rate, and applied potential were used for the ML study. The models underwent validation through the assessment of the determination coefficient (R2) and Mean Squared Error (MSE). The MLP and RF algorithm prediction accuracy was higher than MLR and SVR. The MLP model prediction was higher due to R2 of 0.9730, and 0.8292 than the R2 of 0.9496, and 0.8053 for salt removal and current efficiency, respectively. However, RF predicts energy consumption more effectively than MLP (R2: 0.8875 vs. 0.8157). Additionally, RF achieved the fastest computational time among all models. Additionally, physicochemical and electrochemical properties of membranes ensured the stability of membranes.

Electromyography and dynamometry in the prediction of risk of falls in the elderly using machine learning tools

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess the efficiency of nine different models: Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, Gated Recurrent Unit (GRU), Multi-layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Network (CNN). Concluded comprehensive investigation and performance evaluation, the study validates that ensemble methods such as Random Forest and Decision Tree, together with deep learning models like LSTM and GRU, significantly exceed conventional models in terms of accuracy and detection abilities. The best-performing models attained up to 99.99% accuracy in recognizing malicious network activity. This exceptional performance validates that the strong potential of participating intelligent learning-based methods into ZTA to create scalable and dynamic security solutions with high accuracy. These findings illustrate the value of ML/DL in enhancing the threat detection layer of ZTA, eventually providing a stronger resistance to advanced attacks cyber threats.

Prediction of the conditions under which landslides may occur is essential for designing sustainable risk-mitigation measures and cost-effective geotechnical structures. Slope stability analyses typically account for slope geometry, soil mechanical properties and groundwater conditions to determine the performance of a slope with respect to a specified factor of safety. These properties vary both spatially and over time; geotechnical design codes require the use of factored design parameter values to account for possible worst-case conditions. Furthermore, standard geotechnical analyses typically exclude the dynamic hydrological processes of rainfall infiltration and loss of matric suction that often trigger landslides. Slope stability assessment is particularly challenging in developing countries with limited resources for acquiring slope data, meeting conservative design standards and mitigating landslide risk. This study applied a combined slope hydrology and stability model to address these issues for a residual soil slope in the tropics. This paper presents a method for maximising stability information from limited data, disaggregating the effects of three different design parameter sets and factor of safety threshold choices, as well as diagnosing the dominant geotechnical and dynamic landslide-triggering factors. This modelling approach provides a more transparent basis for sustainable slope-management decisions.

Research efforts in the prediction of stroke and diabetes prioritize early detection in order to enhance patient outcomes. To achieve this, a variety of methodologies are integrated. Existing studies, on the other hand, are marred by imbalanced datasets, lack of diversity in their datasets, potential bias, and inadequate model comparisons; these flaws underscore the necessity for more comprehensive and inclusive research methodologies. This paper provides a thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes. The research employed widely used algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost Classifier, to examine medical data and derive significant findings. The XGBoost Classifier demonstrated superior performance, with an outstanding accuracy, precision, recall, and F1-score of 87.5%. The comparative examination of the algorithms indicated that the Decision Tree, Random Forest, and XGBoost classifiers consistently exhibited strong performance across all measures. The models demonstrated impressive discrimination capabilities, with the XGBoost Classifier and Random Forest reaching accuracy rates of roughly 87.5% and 86.5% respectively. The Decision Tree Classifier exhibited notable performance, with an accuracy rate of 83%. The overall accuracy of the models was evident in the F1-score, a metric that incorporates recall and precision, where the XGBoost model exhibited a marginal improvement of 2% over the Random Forest and Decision Tree models, and 4.25 percent over the last two. The aforementioned results underscore the effectiveness of the XGBoost Classifier, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes. Furthermore, combining datasets improves model performance by utilizing relative features. This integrated dataset improves the model's efficiency and creates a resilient and comprehensive prediction model, improving healthcare outcomes. The findings of this research make a valuable contribution to the advancement of AI-driven diagnostic systems, hence enhancing the quality of healthcare decision-making.

Enhancing safety protocols in train stations requires proactive approaches to foresee and reduce any mishaps. In order to maximize safety in railroad environments, this study presents a novel machine learning framework that combines neural networks with random forest techniques. This hybrid model departs from traditional single-algorithm approaches by utilizing a variety of methods to enhance accident investigation and prediction. The chapter focuses on using this hybridized method, showcasing the functions of random forests and neural networks in predicting future events and assessing accident data from the past. The strategy of incorporating neural networks (multi-layer perceptron (MLP)) and random forests into the accident analysis framework is covered in the study. It also investigates if this model can be implemented in real-time and at scale within railway station safety systems. The results of the research reveal that the hybridized machine learning approach is a promising tool for improving safety standards in railroad stations.

The COVID-19 pandemic, starting in Wuhan, China in December 2019, led to widespread health and economic challenges, causing millions of deaths globally. Beyond physical health, it triggered a mental health crisis, especially during lockdowns. To understand and address this, a study collected data using 90 features during the lockdown period. Machine learning (ML) was employed to detect key features impacting mental health crises. Three ML algorithms—random forest, random tree, and multilayer perceptron—were chosen. Random forest, known for robustness, achieved 97.58% accuracy. Random tree, a supervised algorithm with decision trees, yielded 93.24% accuracy. Multilayer perceptron (MLP), an artificial neural network, achieved 94.20% accuracy by learning nonlinear relationships. A 10-fold cross-validation method was used to evaluate these ML models, enhancing performance by reducing bias and overfitting. It involves dividing data into ten subsets, training on nine, and evaluating the remaining, repeating this ten times to estimate true performance on unseen data.

Using Ensemble StackingC Method and Base Classifiers to Ameliorate Prediction Accuracy of Pedagogical Data

The Yunnan–Tibet traffic corridor runs through the Three Rivers Region, southeastern Tibetan Plateau, which is characterized by high-relief topography and active tectonics, with favourable conditions for landslides. It is of great significance to identify the key predisposing factors of landslides and to reveal the landslide susceptibility in this area. A total of 2,308 landslides were identified as learning samples through remote sensing interpretation and detailed field surveys, and four machine learning algorithms involving logistic regression (LR), random forest (RF), naïve Bayes (NB) and multilayer perceptron (MLP) were compared to model the landslide susceptibility. Through the multicollinearity test, 13 influential factors were selected as conditioning factors. The area under the curve (AUC) values of LR, RF, NB and MLP models are .788, .918, .785 and .836 respectively, indicating that the four models have good or very good prediction accuracy in landslide susceptibility assessment along the Yunnan–Tibet traffic corridor. In addition, the elevation, slope, rainfall, distance to rivers, and aspect play a major role in landslide development in the study area. The susceptibility zoning map based on the best RF model shows that the areas with high susceptibility and very high susceptibility account for 12.24% and 6.72%, respectively, and are mainly distributed along the Jinsha River, the Lancang River and the G214 highway.