Classical Learning Methods Research Articles

Improved Patch-Mix Transformer and Contrastive Learning Method for Sound Classification in Noisy Environments

Improved Patch-Mix Transformer and Contrastive Learning Method for Sound Classification in Noisy Environments

A smart recommender model based on learning method for sentiment classification

A smart recommender model based on learning method for sentiment classification

Determining the Identity of Nucleotides and the Energy of Binding of tRNAs to Their Aminoacyl-tRNA Synthetases Using a Simple Logistic Model

This study showed that the predictor in logistic regression can be applied to estimating the Gibbs free energy of tRNAs’ recognition of and binding to their aminoacyl-tRNA synthetases. Then, 24 linear logistic regression models predicting different classes of tRNAs loaded with a corresponding amino acid were trained in a machine learning classification method, reducing the misclassification error to zero. The models were based on minimal subsets of Boolean explanatory variables describing the favorite presence of nucleotides or nucleosides localized in the different parts of the tRNA. In 90% of cases, they agree with the components of the consensus strand in a class of tRNAs loaded by a given amino acid. According to the proposed theoretical model, the values of the free energy for the entry of the recognition state in the process of tRNA charging were obtained, and the inputs from identity nucleotides and the tRNA strand backbone were distinguished. Almost all the resulting models indicated leading anticodon tandems defining the first and second positions of the anticodon (positions 35 and 36 of the tRNA strand) and the small sets (up to six positions) of the other nucleotides as the natural identity nucleotides most influential in the free energy balance. The magnitude of their input to this energy depends on the position in the strand, favoring positions −1, 35, and 36. The role of position 34 is relatively smaller. These identity attributes may not always be fully arranged in a real single adaptor molecule but were comprehensively present in a given tRNA class. A detailed analysis of the resulting models showed that the absolute value of the energy of binding the tandem 35–36 decreases with the number of identity positions, as well as with the decreasing number of possible hydrogen bonds. On the other hand, in these conditions, the absolute value of the energy of binding of other identity nucleotides increases. All the models indicate that the nucleotide-independent energy of the repulsion tRNA backbone decreases with the number of identity nucleotides. It was also shown that the total free energy change in entering the recognition state increases with the amino acid mass, making this process less spontaneous, which may have an evolutionary reference.

DDoS Attack Detection with Machine Learning

Nowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to assess the accuracy of the model in training and testing processes. RF classification provides robustness and interpretability, MLP offers deep learning capabilities for complex patterns, and K-NN delivers simplicity and adaptability for instance-based learning. Together, these methods can contribute to a comprehensive DDoS attack detection system using machine learning. There are two types of classification setups: binary and multi-class classification. Binary classification involves identifying traffic as either a DDoS attack or normal using the NSL-KDD dataset. Multi-class classification, on the other hand, distinguishes between various types of DDoS attacks (such as DoS, Probe, U2R, and Sybil) and normal traffic using the NSL-KDD dataset. Feature engineering is also involved in this experiment to convert the categorical features into numerical values for detecting DDoS attack. Our model's performance was effective compared to other machine learning methods. RF achieved the highest accuracy rates: 99.35% in binary classification and 97.71% in multi-class classification. K-NN followed with 99.15% in binary and 97.35% in multi-class classification, while MLP achieved 90.63% in binary and 84.33% in multi-class classification.

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data

PurposeSpontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortality rate of 40 ​%, accurate diagnosis and prompt initiation of appropriate antibiotic therapy are crucial for optimizing patient outcomes and preventing life-threatening complications. This study aimed to expand the use of computational models to improve the diagnostic accuracy of SBP in cirrhotic patients by incorporating a broader range of data, including clinical variables and laboratory values. Patients and methodsWe employed 5 machine learning classification methods - Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Random Forest, utilizing a variety of demographic, clinical, and laboratory features and biomarkers. ResultsAscitic fluid markers, including white blood cell (WBC) count, lactate dehydrogenase (LDH), total protein, and polymorphonuclear cells (PMN), significantly differentiated between SBP and non-SBP patients. The Random Forest model demonstrated the highest overall accuracy at 86 ​%, while the Naive Bayes model achieved the highest sensitivity at 72 ​%. Utilizing 10 key features instead of the full feature set improved model performance, notably enhancing specificity and accuracy. ConclusionOur analysis highlights the potential of machine learning to enhance the accuracy of SBP diagnosis in cirrhotic patients. Integrating these models into clinical workflows could substantially improve patient outcomes. To achieve this, ongoing multidisciplinary research is crucial. Ensuring model interpretability, continuous monitoring, and rigorous validation will be essential for the successful implementation of real-time clinical decision support systems.

A lightweight machine learning methods for malware classification

A lightweight machine learning methods for malware classification

Efficient federated learning for pediatric pneumonia on chest X-ray classification

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

Peningkatan Hasil Belajar Siswa pada Pembelajaran Agama Katolik Melalui Metode Problem Based Learning Kelas IX SMP Negeri 2 Tujuh Belas Tahun 2024

This research aims to improve student learning outcomes in Catholic Religion learning through the application of the Problem Based Learning Method in Class IX students of SMP Negeri 2 Seventeen in 2024. The approach taken in this research is quantitative with Classroom Action Research (PTK) consisting of Planning, Carrying out learning, observations, group discussions, individual assignments/tests, group awards and determining individual and group grades. Research Subject: Class IX students of SMP Negeri 2 Seventeen. Data collection techniques Direct Observation Techniques, Indirect Observation Techniques, Direct Communication Techniques, Indirect Communication Techniques, Measurement/Test Techniques, Documentation Techniques which are expected to be able to show changes in the actions given. The data obtained was analyzed using descriptive statistics. This research consists of three cycles. In the first cycle the average student score was 6.79 or 68.42%, and the student score in the second cycle was 8.83 or 75.17%. Meanwhile, the average value in the third cycle was 9.05 or 93.75%. From the results of this research, it can be concluded that learning using the Problem Based Learning Method for Class IX students at SMP Negeri 2 Seventeen is able to improve the remaining learning outcomes so that they meet the Minimum Completeness Criteria (KKM).

Acoustic Emission Energy Release Rate Model for Classification of Damage Development in Large Fiber Reinforced Plastic Composite Structures

Acoustic emission identification of fracture mechanisms in composite materials is of great importance for the practical assessment of the serviceability of aerospace structures, such as unmanned aerial vehicles, rocket motors, and others. Many scientific research studies conducted in recent decades have demonstrated the unique capabilities of the Acoustic Emission (AE) method to detect and identify different failure mechanisms, including fiber breakage, matrix cracking, and delaminations in fracture tests of composite specimens. However, testing large composite structures, in many cases, can be accompanied by multiple time-overlapped fracture events activated simultaneously, resulting in considerable difficulties in the classification and assessment of combined prolonged AE signals. In this work, we propose the Energy Release Rate (ERR) model for the classification of AE activity related to fiber-reinforced plastic (FRP) composite fracture. The model identifies accurately both micro-scopic and macro-scopic fracture mechanisms in large aerospace structures having complex composition of materials. The model is practically applied using several machine learning methods and provides a simple means to identify and discriminate typical fracture mechanisms and their combinations. Specifically, we describe ERR model, the choice of most informative AE parameters, the choice of suitable machine learning classification methods, their training and testing on FRP specimens, and validating the model in practical tests of large full-size FRP composite structures.

A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.

Recent advancements in deep learning have significantly improved the intelligent classification of gastrointestinal (GI) diseases, particularly in aiding clinical diagnosis. This paper seeks to review a computer-aided diagnosis (CAD) system for GI diseases, aligning with the actual clinical diagnostic process. It offers a comprehensive survey of deep learning (DL) techniques tailored for classifying GI diseases, addressing challenges inherent in complex scenes, clinical constraints, and technical obstacles encountered in GI imaging. Firstly, the esophagus, stomach, small intestine, and large intestine were located to determine the organs where the lesions were located. Secondly, location detection and classification of a single disease are performed on the premise that the organ's location corresponding to the image is known. Finally, comprehensive classification for multiple diseases is carried out. The results of single and multi-classification are compared to achieve more accurate classification outcomes, and a more effective computer-aided diagnosis system for gastrointestinal diseases was further constructed.

Robust Face Recognition System Based on Deep Facial Feature Extraction and Machine Learning

Facial Recognition is a common challenge in research for security applications. Wide-ranging applications of this technology include biometrics, security data, access to controlled locations, smart cards, and surveillance systems. A practical approach to identifying individuals depends on factors such as partial face occlusion, illumination, age, expression, makeup, and poses. These complex face recognition variables affect most face recognition systems. In this paper, we propose a deep transfer learning for facial features based on the pose and illumination variations of celebrities’ faces in a free environment for face recognition. The new approach that combines deep learning methods for feature extraction with machine learning classification methods. Also, studies the performance of the pre-trained model (VGGFace2 with ResNet-50 model weights) for feature extraction with a Multilayer Perceptron Classifier (MLP), Decision Tree, and Bootstrap Aggregation (Bagging) to perform classification. The convolution neural network has lately achieved excellent advancement in facial recognition. The outcome indicated that VGGFace2 with MLP obtained more precision and provided the best results (Accuracy, F-measure, Recall, and Precision). The results for all metric for the proposed model is 99%. The benchmark for this model is a dataset of 105 people’s faces.

Application of machine learning classification algorithms to predict probability of channel presence from seismic data

The article analyzes the application of various classification algorithms (Random Forest, Extra Trees, Gradient Boosting, Multi-layer Perceptron, Gaussian Naive Bayes, Voting Classifier) based on supervised machine learning, both for individual seismic attributes (amplitude, phase, frequency) and for a set of seismic attributes calculated in the interval of channel system development for probabilistic assessment of presence of channels. The object of the study is the deposits of the Tyumen suite of a group of deposits located in the Khanty-Mansiysk Autonomous Okrug. The purpose of this work is to find the most effective seismic attributes and machine learning classification methods to optimize the methodology and improve the accuracy of forecasting the probability of channel system presence.

Extraction of Maize Distribution Information Based on Critical Fertility Periods and Active–Passive Remote Sensing

This study proposes a new method for integrating active and passive remote sensing data during critical reproductive periods in order to extract maize areas early and to address the problem of low accuracy in the classification of maize-growing areas affected by climate change. Focusing on Jiaozuo City, this study utilized active–passive remote sensing images to determine the optimal time for maize identification. The relative importance of features was assessed using a feature selection method combined with a machine learning algorithm, the impact of both single-source and multi-source features on accuracy was analyzed to generate the optimal feature subset, and the classification accuracies of different machine learning classification methods for maize at the tasseling stage were compared. Ultimately, this study identified the most effective remote sensing features and methods for maize detection during the optimal fertility period. The experimental results show that the feature set optimized for the tasseling stage significantly enhanced maize recognition accuracy. Specifically, the random forest (RF) method, when applied to the multi-source data fusion feature set, yielded the highest accuracy, improving classification accuracy by 24.6% and 4.86% over single-source features, and achieving an overall accuracy of 93.38% with a Kappa coefficient of 0.91. Data on the study area’s maize area were also extracted for the years 2018–2022, with accuracy values of 93.83%, 98.77%, 97%, and 98.05%, respectively.

Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods

Background & Aim Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples. Methods & Materials Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy. Results The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively. Conclusion The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.

Using ensemble learning method and binary decision tree algorithm for drought intensity level classification

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Hyperparameter Optimization for Convolutional Neural Network-Based Sentiment Analysis

The rapid development of digital technology brings about the importance of product or service opinions as a data source in business intelligence. Sentiment analysis is a business intelligence tool that can help gauge market trends by analysing the available opinions. Various studies have been developed to solve sentiment analysis problems, from selecting feature engineering techniques to selecting sentiment classification models based on classical or deep learning methods. However, these studies still select hyperparameters on a trial basis, which poses problems in terms of time and not optimal performance. Therefore, this research proposes the implementation of Bayesian Optimization to automate the selection of hyperparameters in sentiment analysis based on a Convolutional Neural Network (CNN). The results show that implementing the Bayesian Optimization method has the lowest computation time compared to Random and Grid Searches, which is only 175.746765 seconds, in the fifth trial. In addition, it made a reasonably large cut in terms of times compared to manual trial-based hyperparameter tuning that needs 540 trials. In the model assessment process, implementing Bayesian Optimization gives the highest values on Precision and Specificity, which are 0.8168 and 0.7176, respectively. Thus, implementing Bayesian Optimization is the right choice for sentiment analysis tasks since the precision of predicting negative sentiment classes is vital in hospitality business intelligence, especially the use of related information for product improvement or hotel service improvement. The implementation of Bayesian Optimization not only applied as a reliable hyperparameter selection technique for classification tasks but also regression prediction tasks.

Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery

Inflammatory diseases such as asthma, rheumatoid arthritis, and cardiovascular conditions are driven by overproduction of leukotriene B4 (LTB4), a potent inflammatory mediator. Leukotriene A4 hydrolase (LTA4H) plays a critical role in converting leukotriene A4 into LTB4, making it a prime target for drug discovery. Despite ongoing efforts, developing effective LTA4H inhibitors has been challenging due to the complex binding properties of the enzyme and the structural diversity of potential inhibitors. Traditional drug discovery methods, like high-throughput screening (HTS), are often time-consuming and inefficient, prompting the need for more advanced approaches. Quantitative Structure-Activity Relationship (QSAR) modeling, enhanced by ensemble machine learning techniques, provides a promising solution by enabling accurate prediction of compound bioactivity based on molecular descriptors. In this study, six ensemble machine learning methods—AdaBoost, Extra Trees, Gradient Boosting, LightGBM, Random Forest, and XGBoost—were employed to classify LTA4H inhibitors. The dataset, comprising 636 compounds labeled as active or inactive based on pIC50 values, was processed to extract 450 molecular descriptors after feature engineering. The results show that the LightGBM model achieved the highest classification accuracy (83.59%) and Area Under the Curve (AUC) value (0.901), outperforming other models. XGBoost and Random Forest also demonstrated strong performance, with AUC values of 0.890 and 0.895, respectively. The high sensitivity (95.24%) of the XGBoost model highlights its ability to accurately identify active compounds, though it exhibited slightly lower specificity (61.36%), indicating a higher false-positive rate. These findings suggest that ensemble machine learning models, particularly LightGBM, are highly effective in predicting bioactivity, offering valuable tools for early-stage drug discovery. The results indicate that ensemble methods significantly enhance QSAR model accuracy, making them viable for identifying promising LTA4H inhibitors, potentially accelerating the development of anti-inflammatory therapies.

Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning.

Although it is well known that the morphology of Gram-negative rods changes on exposure to antibiotics, the morphology of antibiotic-resistant bacteria in the absence of antibiotics has not been widely investigated. Here, we studied the morphologies of 10 antibiotic-resistant strains of Escherichia coli and used bioinformatics tools to classify the resistant cells under light microscopy in the absence of antibiotics. The antibiotic-resistant strains showed differences in morphology from the sensitive parental strain, and the differences were most prominent in the quinolone-and β-lactam-resistant bacteria. A cluster analysis revealed increased proportions of fatter or shorter cells in the antibiotic-resistant strains. A correlation analysis of morphological features and gene expression suggested that genes related to energy metabolism and antibiotic resistance were highly correlated with the morphological characteristics of the resistant strains. Our newly proposed deep learning method for single-cell classification achieved a high level of performance in classifying quinolone-and β-lactam-resistant strains.