Combination Of Support Vector Machine Research Articles

Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database.

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

Enhancing Student Performance Prediction Using a Combined SVM-Radial Basis Function Approach

This research aims to improve student performance predictions using a combined SVM (Support Vector Machine) and radial basis function (RBF) approach. The developed model utilizes a combination of the strengths of SVM in handling class separation and the ability of RBF to capture complex patterns in data. Student assessment data, including math, reading, and writing scores, is used as a feature to predict student performance on tests. Preprocessing steps, including feature normalization and label encoding, are applied to prepare the data for model training. Next, the SVM model with the RBF kernel is initialized and optimized using GridSearchCV to find the best parameters. Model evaluation was carried out using the R2 metric to evaluate how well the model predicts student performance. Experimental results show that the combined SVM-RBF approach can improve student performance predictions with fairly accurate prediction results of 88%. The practical implication of this research is the development of a more accurate model for predicting student performance, which can be used as a tool to improve educational interventions and decision-making in educational institutions.

Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features

This study proposes a novel approach utilizing a voting-based ensemble technique to diagnose visible faults in photovoltaic (PV) modules from aerial images captured by unmanned aerial vehicles (UAVs), leveraging AlexNet features. The proposed method focuses on classifying commonly occurring visual faults such as glass breakage, snail trails, burn marks, delamination and discoloration. Two voting-based ensemble models, a two-class ensemble (combining support vector machines and k-nearest neighbor) and a three-class ensemble (integrating support vector machines, J48, and k-nearest neighbor) were developed and evaluated against individual machine learning classifiers. Results indicate that the two-class ensemble outperforms the three-class ensemble and other individual classifiers, achieving an accuracy of 98.30%. This approach not only enhances fault diagnosis accuracy but also reduces inspection costs and instrument monitoring efforts contributing to the sustainable and efficient operation of PV systems.

Abstract 5651: Personalized transcriptomic profiling for advanced prostate cancer: Guiding second-generation hormonal drug selection

Abstract Introduction: Second-generation hormonal drugs have demonstrated significant efficacy in extending the survival of patients with advanced prostate cancer. These drugs operate through two distinct molecular mechanisms: inhibition of androgen biosynthesis and blockade of the androgen receptor. However, due to concerns of side effects, concurrent usage of multiple drugs is not recommended. A notable challenge is the eventual development of drug resistance, rendering all second-generation hormonal therapies ineffective. Currently, there is a lack of guidelines for helping patients choose the most suitable drug. In the context of pharmacogenomics, genetic differences can influence drug susceptibility and efficacy, with variations in genomic sequences and regulatory mechanisms impacting drug response at the transcriptional and translational levels. Considered the rapid processing capabilities of Affymetrix transcriptomic microarrays, we hypothesized that distinct gene signatures in patients who respond well or poorly to second-generation hormonal drugs could be identified and could act as a companion diagnostic tool. Methods: We enrolled 180 advanced prostate cancer patients, who fulfilled Taiwan national health insurance criteria of using second-generation hormonal drugs, from seven medical centers or regional hospitals in Taiwan at 3 time points: prior to drug administration, 3 months after treatment started and drug resistance developed. The RNA expression data and clinical parameters were analyzed, and classification models were built using IPA and R. Good or poor responders were defined by 3-month prostate-specific antigen (PSA) lowered by 90 percent. Results:: Differential expression and enrichment analyses post-treatment highlighted the activation of NF-kappa B signaling and various immune cell pathways. Notably, pathways related to primary immunodeficiency and IL-17 became prominent following the development of drug resistance. At the initial stages, distinct alterations in cell cycle pathways, particularly G1/S and G2/M, were observed between good and poor responders. This was elucidated through principal component analysis and subsequent dbscan clustering. To further distinguish between these responder groups, we utilized an integrative approach for feature selection, combining support vector machine, random forest classifier, and lasso regression. Despite the innovative approach, the model's error rate stood at approximately 15%, attributed mainly to the limited number of cases. Conclusion: Our study highlights the potential of using transcriptomic analysis to predict responses to second-generation hormonal drugs in advanced prostate cancer, paving the way for personalized treatment strategies. However, the need for larger cohort studies is evident to refine these predictive models and enhance their accuracy. Citation Format: Li-Hsin Chang, Hao-Han Chang, Xin-Yi Lin, Shu-Chi Wang, Chih-Pin Chuu, Deng-Neng Chen, Shu-Pin Huang, Chia-Yang Li. Personalized transcriptomic profiling for advanced prostate cancer: Guiding second-generation hormonal drug selection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5651.

Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach

AbstractIn recent times, air quality prediction is turned out to be one of the important research topics among research communities to prevent lives from negative health impacts. Random fluctuations of PM2.5 level brought about by frequent variations in meteorological factors create difficulties air pollution management. Forecasting the quality of air using time series data serves as a defense mechanism against threatening hazards by providing immense support to take preventive measures. Besides, handling dynamic real time workloads, forecasted by the prediction model requires appropriate computing resources to distribute workloads based on demands. To achieve this goal, this paper proposes a new Air Quality Prediction‐enabled Resource Allocation scheme for cloud‐based software services, which offers dynamic adjustment of resources based on workload demands with high energy efficiency. The proposed system is a two phase system that executes both air quality prediction and resource allocation processes consecutively. A new weighted average ensemble classifier is designed by combining support vector machine (SVM), artificial neural network (ANN), and gradient boosting machine (GBM) techniques to measure PM2.5 level on time series information of Beijing PM2.5 dataset. The system then dynamically allocates appropriate computing resources using crossover particle swarm optimization (CPSO) algorithm based on the forecasted results of PM2.5 level. This system has the potential to contribute significantly to reducing energy consumption and improving air quality in cities worldwide. The experimental results conducted to determine the efficiency of the proposed system in terms of different metrics proves that it achieves greater performance with less error functions for PM2.5 level prediction as well as minimizes energy consumption for resource allocation when compared with existing methods.

Efficient Fragility Analysis of Cross-Fault Hydraulic Tunnels Combining Support Vector Machine and Improved Cloud Method

ABSTRACT One of the primary concerns in the field of seismic risk assessment for underground structures is establishing an accurate connection between seismic intensity and structural responses. The objective of this work is to conduct a rational support vector machine (SVM) model for generating mass data and improved fragility curves of cross-fault hydraulic tunnels (CFHTs). The results highlight that the 900 sets, multiple earthquake intensity measures, and cubic polynomial kernel function of the SVM model can improve reliability in evaluating structural performance. Additionally, the improved Cloud analysis method is more suitable for seismic performance than the typical Cloud analysis.

Deep learning based on 68Ga-PSMA-11 PET/CT for predicting pathological upgrading in patients with prostate cancer.

To explore the feasibility and importance of deep learning (DL) based on 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT in predicting pathological upgrading from biopsy to radical prostatectomy (RP) in patients with prostate cancer (PCa). In this retrospective study, all patients underwent 68Ga-PSMA-11 PET/CT, transrectal ultrasound (TRUS)-guided systematic biopsy, and RP for PCa sequentially between January 2017 and December 2022. Two DL models (three-dimensional [3D] ResNet-18 and 3D DenseNet-121) based on 68Ga-PSMA-11 PET and support vector machine (SVM) models integrating clinical data with DL signature were constructed. The model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Of 109 patients, 87 (44 upgrading, 43 non-upgrading) were included in the training set and 22 (11 upgrading, 11 non-upgrading) in the test set. The combined SVM model, incorporating clinical features and signature of 3D ResNet-18 model, demonstrated satisfactory prediction in the test set with an AUC value of 0.628 (95% confidence interval [CI]: 0.365, 0.891) and accuracy of 0.727 (95% CI: 0.498, 0.893). A DL method based on 68Ga-PSMA-11 PET may have a role in predicting pathological upgrading from biopsy to RP in patients with PCa.

A location-based model using GIS with machine learning, and a human-based approach for demining a post-war region

ABSTRACT Locating and removing landmines and other ERW (Explosive Remnants of War) is dangerous, hazardous, and time-consuming. It requires implementing multilevel on-site surveys: general non-technical surveys to mark the areas affected and technical surveys to determine the perimeter of related minefields. This paper introduces a landmine location-based prediction model, combining military experience with machine-learning techniques and spatiotemporal data, by introducing a new approach for area selection and adding military-based features for context modelling and model training. Besides predicting landmine’s location areas, this model classifies the affected regions by priority and difficulty of clearance, in such a way as to minimise the long time needed by surveys and reduce the danger related to that task, thus providing the clearance organisations with a good resource allocation for their operations. We applied several machine learning techniques that combine Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST), taking into consideration the imbalanced data problem and tweaking for the best performance and accuracy. The experimental results show that the model has the potential to provide reliable predictions and valuable services for demining operations on the field.

Neutrosophic Fuzzy Logic based SVM approach for Enhanced Skin Cancer Prediction

In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing.

Association of Pathological Features and Multiparametric MRI-Based Radiomics With TP53-Mutated Prostate Cancer.

TP53 mutations are associated with prostate cancer (PCa) prognosis and therapy. To develop TP53 mutation classification models for PCa using MRI radiomics and clinicopathological features. Retrospective. 388 patients with PCa from two centers (Center 1: 281 patients; Center 2: 107 patients). Cases from Center 1 were randomly divided into training and internal validation sets (7:3). Cases from Center 2 were used for external validation. 3.0T/T2-weighted imaging, dynamic contrast-enhanced imaging, diffusion-weighted imaging. Each patient's index tumor lesion was manually delineated on the above MRI images. Five clinicopathological and 428 radiomics features were obtained from each lesion. Radiomics features were selected by least absolute shrinkage and selection operator and binary logistic regression (LR) analysis, while clinicopathological features were selected using Mann-Whitney U test. Radiomics models were constructed using LR, support vector machine (SVM), and random forest (RF) classifiers. Clinicopathological-radiomics combined models were constructed using the selected radiomics and clinicopathological features with the aforementioned classifiers. Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC). P value <0.05 indicates statistically significant. In the internal validation set, the radiomics model had an AUC of 0.74 with the RF classifier, which was significantly higher than LR (AUC = 0.61), but similar to SVM (AUC = 0.69; P = 0.422). For the combined model, the AUC of RF model was 0.84, which was significantly higher than LR (0.64), but similar to SVM (0.80; P = 0.548). Both the combined RF and combined SVM models showed significantly higher AUCs than the radiomics models. In the external validation set, the combined RF and combined SVM models showed AUCs of 0.83 and 0.82. Pathological-radiomics combined models with RF, SVM show the association of TP53 mutations and pathological-radiomics features of PCa. 3 TECHNICAL EFFICACY: Stage 2.

Hybrid machine learning models for classifying failure modes of unstiffened steel plate girders subjected to patch loading

This paper, for the first time, develops novel hybrid machine learning models that combine Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting, with Moth-Flame Optimization algorithm, called MFO-SVM, MFO-NB, MFO-KNN, MFO-DT, MFO-RF, MFO-AB, MFO-GB, and MFO-XGB, respectively, to classify the failure modes of unstiffened steel plate girders subjected to patch loading. The experimental database is collected from the tests reported in the literature to develop the hybrid machine learning models. The Synthetic Minority Over-sampling Technique is adopted to address the imbalanced class problems of the database. Hyperparameters of machine learning models are optimized using the Moth-Flame Optimization algorithm. The performance of hybrid machine learning models is evaluated and compared through the confusion matrix using several metrics. The results show that the MFO-RF model proves more effective than other models in identifying the failure models of unstiffened steel plate girders. The Shapely Additive Explanations method is used to analyze the contribution of each variable to the failure modes of unstiffened steel plate girders both globally and locally. Web thickness is the most influencing feature for identifying the failure modes. The MFO-RF model is finally utilized to develop a web application for practical use at low cost and effort.

Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances

Marine debris is a significant environmental issue, necessitating the development of precise and efficient technologies for classifying and reducing its impact. This research tries to address the issues of image noise in ocean trash classification using four alternative filters the Gaussian, Bilateral, Mean, and Alpha-Trimmed Mean (ATM) filters for noise reduction and SVM for classification. The accuracy of categorization algorithms can be considerably impacted by the presence of noise in photos of maritime debris. In this study, we suggest a two-pronged strategy: To effectively decrease noise and improve the quality of the input photos, we first apply filters. These filters were chosen with care to protect significant details while eliminating extraneous noise. Second, we classify the debris into various categories based on its visual attributes using SVM, a powerful ML technique. The ATM filter significantly reduces noise and improves the clarity of photographs of ocean trash, according to the results of our experiments. This work proposes a novel approach for classifying marine debris using advanced machine learning algorithms. For improved classification accuracy, we suggest combining Support Vector Machines (SVM) with Adaptive Thresholding Mean (ATM) filtering and Histogram of Oriented Gradients (HOG) feature extraction. According to this research, the ATM filter is a promising option for noise reduction in ocean trash imaging, potentially increasing the precision of subsequent classification algorithms and assisting in efficient environmental monitoring and marine ecosystem conservation efforts. The language used for execution is Python.

An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology.

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.

Revolutionizing Education: Advanced Machine Learning Techniques for Precision Recommendation of Top-Quality Instructional Materials

AbstractThe integration of educational technology in the modern classroom has transformed the way students learn yet challenges in providing high-quality materials persist. To address this, we propose a novel support vector-based long short-term memory (LSTM) recommendation model. Our model combines support vector machines (SVM) and LSTM networks to enhance accuracy. The SVM analyzes material content, identifying key features for topic relevance. Meanwhile, the LSTM assesses word sequences to predict material relevance to the topic. We conducted experiments on a diverse instructional dataset, demonstrating superior performance in accuracy and relevance compared to existing models. Our model adapts to new data and continuously improves based on user feedback. Therefore, our Support Vector-based LSTM recommendation model can revolutionize instructional material recommendations. Its accuracy and relevance enhance student engagement and learning outcomes, optimizing the educational experience.

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer

The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC). In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers-linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)-were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic-radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs). Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set. ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy.

Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM

With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.

Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems

In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system’s vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for generating training data. The initial states are randomly selected and the CS model is deployed for each initial state, where power system generation and loads are adjusted dynamically and power flows are redistributed to quantify the vulnerability metric. Furthermore, the proposed hybrid machine learning model deploys a combined Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve the learning precision. The classification model is trained by SVM, which divides the data into two categories with and without load shedding. Then, GBR is adopted only for the data with load shedding to determine the relationship between input power outage states and the vulnerability metric. The proposed vulnerability analysis approach is applied to several test systems and the results are analyzed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The power system vulnerability can be quantified by cascading outage simulations. However, there are two challenges: i) there are a huge number of possible initial states and we cannot enumerate all these initial states for the cascading outage simulation. Neither can we precisely quantify the bus vulnerability. ii) The cascading outage simulation may be time-consuming for large-scale power systems, which is challenging for the online application. To address the above challenges, we expect to design a machine learning technique to predict the power system vulnerability, which can train the model in an offline way and then use it for the online application. Firstly, since there is not enough operation data from practical power systems, we develop a cascading outage simulator, using off-line AC power flows, for generating synthetic training data. Secondly, we observe that the training precision by directly applying the regression model may be very poor because the output of the machine learning model may take on an uneven distribution concerning input parameters. Thus, we propose a hybrid machine learning model with a combined classification and regression method, where the classification model is employed to remove the data without the load shedding, and the regression model then determines the relationship between input power outage states and the vulnerability metric. The proposed model and method have been tested on several systems including a practical large-scale Polish power system to show the effectiveness.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

Fracture porosity is crucial for storage and production efficiency in fractured tight reservoirs. Geophysical image logs using resistivity measurements have traditionally been used for fracture characterization. This study aims to develop a novel, hybrid machine-learning method to predict fracture porosity using conventional well logs in the Ahnet field, Algeria. Initially, we explored an Artificial Neural Network (ANN) model for regression analysis. To overcome the limitations of ANN, we proposed a hybrid model combining Support Vector Machine (SVM) classification and ANN regression, resulting in improved fracture porosity predictions. The models were tested against logging data by combining the Machine Learning approach with advanced logging tools recorded in two wells. In this context, we used electrical image logs and the dipole acoustic tool, which allowed us to identify 404 open fractures and 231 closed fractures and, consequently, to assess the fracture porosity. The results were then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models were used to obtain comprehensive results, which were subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms into fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well log analysis made it possible to reliably estimate fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data are absent or expensive.

Smart screening system for breast cancer: the use of thermographic images, evolutionary algorithms, and oversampling.

Purpose.This study aimed to develop a computer system for automatic detection of thermographic changes indicating breast malignancy risk.Materials and methods. The database contained 233 thermograms of women, including 43 with malignant lesions and 190 with no malignant lesions. Five classifiers were evaluated (k-Nearest Neighbor, Support Vector Machine, Decision Tree, Discriminant Analysis, and Naive Bayes) in combination with oversampling techniques. An attribute selection approach using genetic algorithms was considered. Performance was assessed using accuracy, sensitivity, specificity, AUC, and Kappa statistics.Results. Support vector machines combined with attribute selection by genetic algorithm and ASUWO oversampling obtained the best performance. Attributes were reduced by 41.38%, and accuracy was 95.23%, sensitivity was 93.65%, and specificity was 96.81%. The Kappa index was 0.90, and AUC was 0.99.Conclusion. The feature selection process lowered computational costs and improved diagnostic accuracy. A high-performance system using a new breast imaging modality could positively aid breast cancer screening.