Efficient Face Mask Detection Using Hybrid Deep Learning Algorithms

This study aims to evaluate the effectiveness of the Convolutional Neural Network ( CNN ) method combined with Support Vector Machine ( SVM ) in detecting URLs.Phishing.Phishing is one of the significant cyber threats, where attackers try to trick users into providing sensitive information through fake websites.With the increasing number of phishing attacks , there is a need for effective methods to detect and prevent this threat.In this study, a dataset containing URLs phishing and non-phishing data were used to train the CNN -SVM model .The training process involved feature extraction from URLs using CNN , which is capable of capturing complex patterns in the data, followed by classification using SVM , which is known for its ability to handle high-dimensional data.Testing was conducted across nine different scenarios to evaluate the performance of the model under various conditions.The test results showed that the hybrid CNN -SVM model achieved a precision of 95%, a recall of 92%, and an F1-Score of 93%, with an overall accuracy of 94%.These results indicate that the model is not only effective in detecting URLs phishing , but also has a good balance between precision and recall.This study indicates that the combination of CNN and SVM can be an effective solution for detecting URLs phishing , making a significant contribution to the development of better cyber security systems.

In the present study, gene expression profiles of acute myeloid leukemia (AML) samples were analyzed to identify feature genes with the capacity to predict the mutation status of FLT3/ITD. Two machine learning models, namely the support vector machine (SVM) and random forest (RF) methods, were used for classification. Four datasets were downloaded from the European Bioinformatics Institute, two of which (containing 371 samples, including 281 FLT3/ITD mutation-negative and 90 mutation-positive samples) were randomly defined as the training group, while the other two datasets (containing 488 samples, including 350 FLT3/ITD mutation-negative and 138 mutation-positive samples) were defined as the test group. Differentially expressed genes (DEGs) were identified by significance analysis of the micro-array data by using the training samples. The classification efficiency of the SCM and RF methods was evaluated using the following parameters: Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve. Functional enrichment analysis was performed for the feature genes with DAVID. A total of 585 DEGs were identified in the training group, of which 580 were upregulated and five were downregulated. The classification accuracy rates of the two methods for the training group, the test group and the combined group using the 585 feature genes were >90%. For the SVM and RF methods, the rates of correct determination, specificity and PPV were >90%, while the sensitivity and NPV were >80%. The SVM method produced a slightly better classification effect than the RF method. A total of 13 biological pathways were overrepresented by the feature genes, mainly involving energy metabolism, chromatin organization and translation. The feature genes identified in the present study may be used to predict the mutation status of FLT3/ITD in patients with AML.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. The main purpose of this proposed work is to develop a system that can determine whether a tweet is “spam” or “ham” and evaluate the emotion of the tweet. The extracted features after preprocessing the tweets are classified using various classifiers, namely, decision tree, logistic regression, multinomial naïve Bayes, support vector machine, random forest, and Bernoulli naïve Bayes for spam detection. The stochastic gradient descent, support vector machine, logistic regression, random forest, naïve Bayes, and deep learning methods, namely, simple recurrent neural network (RNN) model, long short-term memory (LSTM) model, bidirectional long short-term memory (BiLSTM) model, and 1D convolutional neural network (CNN) model are used for sentiment analysis. The performance of each classifier is analyzed. The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets with a particular sentiment.

The use of machine learning for the image classification process is growing all the time. Many methods can be used to classify an image with good accuracy. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are popular methods for this case. The two approaches have differences in the data training process to achieve classification objectives. Although there are some differences between these approaches, there are some advantages to both of them. This research explores the comparison of the two CNN and SVM methods by comparing the training process carried out and the accuracy results of the classification. The process stages are divided into pre-processing, training, and testing. The objects used are ten waste plastic bottles with different brands of medium size with a total data of 1100 images. Based on the observations, both methods have advantages and disadvantages in the data training and classification process. However, from the results, CNN's accuracy is better than SVM. The accuracy of both networks is 99% for CNN and 74% for SVM, respectively. So, from the results of experiments that have been carried out in the study, it was found that CNN was still better than SVM. Doi: 10.28991/ESJ-2022-06-05-011 Full Text: PDF

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Power system fault type classification and location prediction is critical in assessing the reliability of the power system, and later restoring it to a stable operating point followed by a fault. State of the art methods include sequence component, impedance measurement from the origin of the fault, and traveling wave based methods for fault type detection and classification problem. It is important to identify and classify the fault as quickly as possible for restoring power system stability to normal operation. Machine Learning and Deep Learning methods allow the analysis of large data of fault voltages and currents by using fast and efficient algorithms. These methods require large amount of data, however, with the recent advances in the field of power system, data acquisition using smart meters and Phasor Measurement Units (PMU), huge amount of system-wide data can be made available to analyze the problem of fault type classification and location prediction. This paper presents a comparative study of Stochastic Gradient Descent (SGD) based Deep Neural Network (DNN) and Machine Learning (ML) applied to power system fault type and location prediction problem. DNN architecture uses 10 hidden layers, each layer having 60 units with hyperbolic tangent as activation function, and a combination of Support Vector Machine (SVM) and Principal Component Analysis (PCA) method are considered. Comparative results in terms of time taken to run the algorithms, and accuracy of the results obtained are presented for a 3 machine 9 bus system. Results indicate that SVM method is an optimal choice for fault classification with high accuracy for location prediction and low computational requirements compared with DNN.

Background: Evaluating the risk of metastasis and recurrence of a cervical cancer patient is critical for appropriate adjuvant therapy. However, current risk assessment models usually involve the testing of tens to thousands of genes from patients’ tissue samples, which is expensive and timeconsuming. Therefore, computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images have received much attention recently. Objective: The prognosis of whether patients will have metastasis and recurrence can support accurate treatment for patients in advance and help reduce patient loss. It is also important for guiding treatment after surgery to be able to quickly and accurately predict the risk of metastasis and recurrence of a cervical cancer patient. Method: To address this problem, we propose a hybrid method. Transfer learning is used to extract features, and it is combined with traditional machine learning in order to analyze and determine whether patients have the risks of metastasis and recurrence. First, the proposed model retrieved relevant patches using a color-based method from H&E pathological images, which were then subjected to image preprocessing steps such as image normalization and color homogenization. Based on the labeled patched images, the Xception model with good classification performance was selected, and deep features of patched pathological images were automatically extracted with transfer learning. After that, the extracted features were combined to train a random forest model to predict the label of a new patched image. Finally, a majority voting method was developed to predict the metastasis and recurrence risk of a patient based on the predictions of patched images from the whole-slide H&E image. Results: In our experiment, the proposed model yielded an area under the receiver operating characteristic curve of 0.82 for the whole-slide image. The experimental results showed that the high-level features extracted by the deep convolutional neural network from the whole-slide image can be used to predict the risk of recurrence and metastasis after surgical resection and help identify patients who might receive additional benefit from adjuvant therapy. Conclusion: This paper explored the feasibility of predicting the risk of metastasis and recurrence from cervical cancer whole slide H&E images through deep learning and random forest methods.

Heart disease is one of the leading causes of mortality worldwide, emphasizing the need for early and accurate diagnosis. Traditional machine learning (ML) models such as Random Forest (RF) and Support Vector Machines (SVM) have been widely used for heart disease classification. However, these models often lack the ability to extract hierarchical patterns and long-range dependencies present in complex medical data. To address this limitation, we propose a hybrid deep learning framework that integrates XGBoost with Capsule Networks (XGBoost-CapsNet) and Convolutional Neural Networks (CNN) with Transformer Encoders (CNN-TE) to enhance classification performance.The study utilizes a structured dataset containing essential heart disease indicators. Feature selection is performed using XGBoost, which ranks attributes based on importance. The Capsule Network (CapsNet) is then used to preserve spatial relationships and hierarchical dependencies among features. Meanwhile, the CNN-TE model extracts spatial features through convolutional layers and captures long-range dependencies using a Transformer Encoder with multi-head self-attention mechanisms. The models are trained using optimized hyperparameters and evaluated against baseline ML models (Random Forest, SVM, and standalone CNN). Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are used for comparison.Experimental evaluations demonstrate that the proposed hybrid models significantly outperform traditional approaches. The XGBoost-CapsNet model achieves an accuracy of 98.2%, while CNN-TE reaches 97.4%, both surpassing standalone CNN (95.1%), Random Forest (94.2%), and SVM (91.8%). The AUC-ROC scores further validate the robustness of the models, with XGBoost-CapsNet scoring 0.99 and CNN-TE achieving 0.98. The use of feature selection with XGBoost improves interpretability and computational efficiency, while the hybrid deep learning models enable better feature extraction and classification accuracy.

Efforts towards an independent nation with high competitiveness can’t be separated from educational programs. Therefore, education must be able to produce quality graduates who have knowledge, master technology, and have technical skills, and adequate life skills. The timeliness of students in completing their studies is one of the supports for assessing the quality of higher education. Classification analysis can be used to predict whether a student is said to pass on time or not. Support Vector Machine (SVM) and Random Forest methods are part of the classification method. SVM and Random Forest classification analysis is done by using historical data alumni from FMIPA UNM of the graduation year 2019-2020 which come from the Administration, Academic and Student Affair Bureau of UNM. SVM accuracy level of RBF kernel with optimum value C = 1 and gamma = 1 is 68% and Random Forest accuracy with optimum value m = 2 and k = 500 is 72%. Therefore, the best method for determining the accuracy of the study duration of FMIPA UNM students is Random Forest..

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

The present study attempts to demarcate the areas susceptible to gully erosion in Phuentsholing, Bhutan, using Deep Learning CNN (convolution neural network) and artificial neuron network (ANN), Support Vector Machine (SVM) and maximum entropy, three basic machine learning techniques in the GIS setting. Application of deep learning technique is new in the field of gully erosion. Considering the 240 gully pixels and seventeen gully erosion conditioning factors (GECFs), the gully erosion susceptibility maps (GESMs) were prepared. Out of the 240 gully pixels, 70% were used as training datasets and 30% were used as validation datasets for modeling and judging the GESMs. The GECFs were selected based on the previous literatures and multi-collinearity test. The importance of the GECFs was assessed by the chi-square attribute evaluation (CSEA) and random forest (RF) methods. Finally, applying the receiver operating characteristics’ area under curve (AUC-ROC), RMSE, MAE and R-index, the robustness of the GESMs was evaluated and compared. The GESMs were classified using natural break classification method into very high, high, moderate, low and very low susceptible classes. Nearly, 20% of the study area has very high susceptibility to gully erosion. As per the results of CSEA and RF methods, sand concentration, land use\cover and altitudes have the largest contribution in making the area very susceptible to gully erosion. Results of the validation techniques recognized the entire selected model as accurate and robust. Among the selected models, the capability of CNN model (AUC = 0.910, MAE = 0.029, RMSE = 0.171 for training data and AUC = 0.929, MAE = 0.089, RMSE = 0.299 for testing data) in predicting the gully erosion susceptibility is higher than other models. The produced GESMs will be helpful to the researchers as well as decision makers in establishing gully erosion management strategies.

-Stroke diagnosis is a time-critical medical task that requires rapid and precise identification to initiate appropriate treatment and prevent severe neurological damage. This study introduces a machine learning–driven diagnostic framework designed to identify stroke using neuroimaging data. A comprehensive set of machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Network (CNN)—were trained and evaluated on a curated dataset of brain images to classify stroke and non-stroke cases. The proposed system leverages the strengths of both classical and deep learning algorithms. Logistic Regression enables examination of relationships between neuroimage features and stroke presence, while SVM effectively separates complex feature patterns. Random Forest and Decision Tree models provide structured rule-based classification derived from key imaging attributes. The inclusion of a CNN significantly enhances diagnostic accuracy by extracting high-level spatial and textural features from neuroimages without manual feature engineering. Experimental results indicate that the CNN model outperforms all other techniques, achieving 95.6% accuracy, 94.2% sensitivity, and 96.5% specificity. Random Forest and SVM also show strong performance with accuracies of 93.1% and 92.5%, respectively. The findings confirm the potential of machine learning, particularly deep learning, in improving stroke diagnosis and supporting clinicians with faster and more reliable decision-making. By harnessing modern computational intelligence, this framework contributes toward reducing diagnostic delays, enhancing patient outcomes, and lowering the overall healthcare burden associated with stroke. Key Words: Brain Imaging, Classification Algorithms, Convolutional Neural Network (CNN), CT Scan, Decision Tree, Deep Learning, Diagnostic Model, Logistic Regression, Machine Learning, MRI Scan, Neuroimages, Random Forest, Stroke Detection, Stroke Identification, Support Vector Machine (SVM).

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS