Traditional Machine Learning Models Research Articles

TL‐GNN: Android Malware Detection Using Transfer Learning

ABSTRACTMalware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state‐of‐the‐art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1‐measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.

“Image-Spectral” fusion monitoring of small cotton samples nitrogen content based on improved deep forest

Accurate monitoring of nitrogen nutrition is crucial for improving cotton yield and quality, as well as the ecological environment. The mainstream method for monitoring nutrition is to establish traditional machine learning (ML) models using a single data source. However, this approach has limitations such as limited access to feature information, model-fitting problems, and limited generalization. Deep learning (DL), on the other hand, has shown promise in complex nonlinear modeling tasks due to its flexible structure. However, it has its limitations, such as the fact that agronomic sample collection and testing are usually labor- and material-intensive, resulting in sample sizes that are too small to meet its training conditions. Therefore, there is an urgent need for DL models that can effectively integrate features from multiple data sources and accurately monitor crop nitrogen content, especially in scenarios with small samples. In this study, we conducted indoor pot experiments using the cotton variety Xinluzao 53 and subjected it to six nitrogen treatments. The data sources for our analysis included hyperspectral and digital images of the cotton leaves. To enhance representation learning capabilities, we enriched the multi-class base learners within each layer of the deep forest (DF) model and introduced skip connections. These enhancements improved the quality of inversion for both hyperspectral and digital image datasets. We then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning. Our focus was on three levels of fusion: feature-level fusion, decision-level fusion, and secondary decision-level fusion. This approach aimed to further enhance the accuracy and stability of nitrogen content inversion. The DF model satisfied the training condition for small samples. Compared to traditional ML algorithms and the original DF algorithm, the improved DF model achieved an increase in validation set R2 of 13.4–28.5% and 10.9–14.9%, respectively. These findings highlight the enhanced accuracy and stability of the improved DF model. Additionally, compared to the optimal inversion model using two single data sources, the “Image-Spectral” three-level fusion models exhibited improvements in validation set R2 of 8.6–9.3%, 10.5–11.2%, and 11.8–12.5% for feature-level, decision-level, and secondary decision-level fusion, respectively. The improved DF and three-level fusion model collectively contributed to the increased accuracy of cotton nitrogen content inversion. Among these models, the secondary decision-level fusion model demonstrated the most marked improvement. This methodology provides valuable insights into monitoring crop phenotypic parameters in situations with limited sample sizes.

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis.

To evaluate the performance of machine learning models in predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using computed tomography (CT) and magnetic resonance imaging (MRI). We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before January 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity. A total of 1690 patients from 24 studies were included. The meta-analysis calculated a pooled area under the curve (AUC) of 0.92 (95%CI-0.89-0.94), pooled sensitivity of 0.81 (95%CI-0.73-0.88), and pooled specificity of 0.88 (95%CI-0.82-0.92). We investigated 4 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 4 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep learning model was 0.95 and was 0.88 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90, and was 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.94, and was 0.83 in studies conducted in other countries. Machine learning has promising potential in predicting tumor response to nCRT in patients with locally advanced rectal cancer. Together with clinical information, machine-learning based models may bring us closer toward precision medicine. Compared to traditional machine learning models, deep learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine-learning based models may bring us closer toward precision medicine.

Attention-Oriented CNN Method for Type 2 Diabetes Prediction

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays

The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods’ labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews’ correlation coefficient (MCC), and area under the curve (AUC). The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances.

A hybrid framework for glaucoma detection through federated machine learning and deep learning models

BackgroundGlaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts.MethodDeveloping a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment.ResultThe hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis.ConclusionIn summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology’s potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare.

Integrating Pix2Pix and Computational Fluid Dynamics for Enhanced Indoor Airflow Prediction: A Case Study with Wing-Walls

Computational Fluid Dynamics (CFD) is often used for analyzing natural ventilation but is limited in early design stages due to its high computational demands and the need for detailed inputs. To overcome such limitations of CFD simulation, this paper proposes a method using a conditional Generative Adversarial Network (cGAN) based image-to-image translation (Pix2Pix) to predict the indoor airflow condition. Compared to other traditional machine learning models that predict averaged values like indoor air velocity, Pix2Pix can predict contour plot of indoor airflow for the given building floor plan image, which provides more intuitive feedback to designers. The proposed method was tested to understand indoor air movement caused by wing-walls attached to the windows. The model was trained using 1,153 pairs of floor plan images, incorporating variations in window widths, wing-wall depth, wing-wall angle, wind speed, and wind direction. It achieved a prediction accuracy of 94% and produced results in less than a second. Moreover, the model showed relatively better performance with the changes in windows and wing-walls properties rather than wind properties. This high performance indicates that Pix2Pix can serve as an efficient proxy model of conventional CFD simulations, helping designers optimize ventilation in building designs at an early stage without the need for complex inputs.

Comparison and analysis of prediction accuracy between traditional machine learning algorithms and XGBoost algorithm in music emotion classification

Music emotion classification refers to determining the emotional types of music, such as happiness, sadness, anger, and passion, based on aspects like rhythm, melody, and tone. The development of artificial intelligence can be applied to music recommendation by employing machine learning algorithms to ascertain emotional attributes of music, enabling precise music suggestions for users to enhance their musical experience. This paper compares and analyzes the classification performance of traditional Random Forest machine learning algorithms and the XGBoost model on a Turkish music emotion dataset. We selected a comprehensive dataset from Kaggle, containing a vast array of music samples and their corresponding emotional labels, making it an extensive music emotion classification dataset. From the experimental results, it's evident that the traditional Random Forest machine learning model outperforms the XGBoost model in terms of accuracy, precision, and recall. The accuracy of the traditional Random Forest machine learning model stands at 80.8%, whereas the XGBoost model's accuracy is 75%. The recall rate for the traditional Random Forest machine learning model is 80.8%, while for XGBoost, it's 77.2%. The F1 score for the traditional Random Forest machine learning model is 80.5%, whereas for XGBoost, it's 75.3%. These parameters indicate that the traditional Random Forest machine learning model exhibits superior predictive performance in music emotion classification. However, the XGBoost model possesses its own advantages, such as faster learning and prediction speeds, high accuracy, and strong generalization capabilities. In summary, the traditional Random Forest machine learning model demonstrates better robustness and interpretability, effectively handling samples with noise and missing data, thus finding widespread practical application. On the other hand, the XGBoost model excels in rapid training and prediction, coupled with higher accuracy and versatility, making it advantageous in dealing with large datasets. The research outcomes of this paper hold significant importance for the study and application of music emotion classification. The experimental results presented herein offer valuable insights for researchers and practitioners, aiding them in selecting appropriate machine learning models, optimizing, and adjusting them to achieve the best classification results.

Transformer-Based Approach to Pathology Diagnosis Using Audio Spectrogram

Early detection of infant pathologies by non-invasive means is a critical aspect of pediatric healthcare. Audio analysis of infant crying has emerged as a promising method to identify various health conditions without direct medical intervention. In this study, we present a cutting-edge machine learning model that employs audio spectrograms and transformer-based algorithms to classify infant crying into distinct pathological categories. Our innovative model bypasses the extensive preprocessing typically associated with audio data by exploiting the self-attention mechanisms of the transformer, thereby preserving the integrity of the audio’s diagnostic features. When benchmarked against established machine learning and deep learning models, our approach demonstrated a remarkable 98.69% accuracy, 98.73% precision, 98.71% recall, and an F1 score of 98.71%, surpassing the performance of both traditional machine learning and convolutional neural network models. This research not only provides a novel diagnostic tool that is scalable and efficient but also opens avenues for improving pediatric care through early and accurate detection of pathologies.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics. The results comprehensively understand each algorithm's effectiveness in identifying health misinformation and provide valuable insights for combating the pervasive spread of false health information in the digital landscape. GCN with TF-IDF gives the best result, as shown in the result section.•The research method involves a comparative analysis of classification algorithms to detect health misinformation, exploring traditional machine learning models and graph convolutional networks.•This research used algorithms such as Passive Aggressive Classifier, Random Forest, Decision Tree, Logistic Regression, Light GBM, GCN, GCN with BERT, GCN with TF-IDF, and GCN with Word2Vec were employed. Performance Metrics: Accuracy: for Passive Aggressive Classifier: 85.75 %, Random Forest: 86 %, Decision Tree: 81.30 %, Light BGM: 83.29 %, normal GCN: 84.53 %, GCN with BERT: 85.00 %, GCN with TR-IDF: 93.86 % and GCN with word2Vec: 81.00 %•Algorithmic performance metrics, including accuracy, precision, recall, and F1-score, were systematically evaluated to assess the efficacy of each model in detecting health misinformation, focusing on understanding the strengths and limitations of different approaches. The superior performance of Graph Convolutional Networks (GCNs) with TF-IDF embedding, achieving an accuracy of 93.86 %

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

Predicting stock price crash risk in China: A modified graph WaveNet model

The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2023. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.

Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches

The rapid growth of e-commerce has increased the need for retailers to understand and predict customer satisfaction to support data-driven managerial decisions. This study analyzes online consumer behavior through a comparative machine learning modeling approach to forecast future customer satisfaction based on review ratings. Using a large dataset of over 100 k online orders from a major retailer, traditional machine learning models including random forest and support vector machines are benchmarked against deep learning techniques like multi-layer perceptrons. The predictive models are assessed for their ability to accurately predict customer satisfaction scores for the next orders based on key e-commerce features including delivery time, order value, and location. The findings demonstrate that the random forest model can predict future satisfaction with 92% accuracy, outperforming deep learning. The analysis further identifies core drivers of satisfaction such as delivery time and order accuracy. These insights enable retail managers to make targeted improvements, like optimizing logistics, to increase customer loyalty and revenue. This study provides a framework for leveraging predictive analytics and machine learning to unlock data-driven insights into online consumer behavior and satisfaction for superior retail decision-making. The focus on generalizable insights across a major retailer enhances the practical applicability of the machine learning approach for the retail sector.

Predicting shear strength in UHPC beams through an innovative neural network with SHAP interpretation

Ultra-High performance concrete (UHPC) has garnered considerable attention in the construction industry due to its exceptional mechanical properties and durability. In the design of UHPC beams, accurately predicting shear strength is crucial. Inspired by the dense blocks in DenseNet, this paper proposes a novel neural network for predicting the shear strength of UHPC beams. A comprehensive database of UHPC beams was initially established through extensive literature data collection, amassing 619 experimental data samples. Preprocessing was subsequently conducted using mahalanobis distance (DM) to eliminate outliers. Subsequently, the database underwent detailed parameter analysis. In practical testing, the novel neural network model exhibited superior accuracy in predicting the shear strength of UHPC beams, surpassing traditional machine learning (ML) models and empirical formulas. Furthermore, it demonstrated excellent generalization capabilities, achieving an R2 value of 0.988 in the training set and 0.964 in the test set. The study introduced the shapley additive explanations (SHAP) method to conduct global and local interpretability analysis of the network model, demonstrating the contributions of each input parameter to the model predictions. Finally, based on this network model, a graphical user interface (GUI) was developed for researchers engaged in UHPC beam design, enabling users to easily input relevant parameters and obtain shear strength predictions. The findings of this study are expected to promote the application of ML models in predicting the complex nonlinear behavior of UHPC structures, providing strong support for engineering practice.

Fake News Detection Model

The proliferation of fake news across social media and various other platforms is a significant concern due to its potential to cause widespread social and national harm. Extensive research efforts are currently underway to identify and combat this issue. This study conducts an in-depth analysis of research pertaining to the detection of fake news, examining traditional machine learning models to determine the most effective approach. By leveraging supervised machine learning algorithms, a model will be developed to classify news articles as either true or false. This model will utilize tools such as Python's scikit-learn and NLP for textual analysis. The process will involve feature extraction and vectorization, with a focus on utilizing Python's scikit-learn library for text data tokenization and feature extraction, leveraging tools like Count Vectorizer and Tiff Vectorizer. Additionally, feature selection methods will be employed to identify the most relevant features for optimal precision, as determined by the results of the confusion matrix

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data

Predicting accurately the Normalized Difference Vegetation Index (NDVI) trends from RGB images are essential to monitor crops and identify issues related to plant diseases, and water shortages. The current NDVI prediction models are primarily based on traditional machine learning models which lack reliability due to the problem related to atmospheric conditions. To predict NDVI in Prince Edward Island using RGB drone imagery data, this paper proposed a novel framework integrating empirical curvelet transform and DenseNet models. Each channel of RGB drone imagery data was passed through empirical curvelet transform method where the curvelet coefficients were analysed which result in creating a new formula to design NDVI. The output of the new formula was sent to the deep DenseNet to predict the final NDVI. The proposed model was evaluated using quantitative metrics including, Q-Q plot, regression, correlation coefficients, structural similarity (SSIM), peak signal to noise ratio (PSNR) and mean square error (MSE) as well as accuracy (ACC), sensitivity (SEN), f1-score, specificity. The obtained results showed that the proposed model outperformed the previous models by scoring the highest values of SSIM = 0.98, and lowest MSE = 120. It is believed that the proposed model is helpful to support farmers in monitoring the growth and plant health as well as to identify crops problems.

Investigating emotional design of the intelligent cockpit based on visual sequence data and improved LSTM

To enhance affective experience and customer satisfaction in the intelligent cockpit of new energy vehicle (NEV-IC), this article proposes a novel method that combines the visual sequence data of eye movements with the sentiment prediction using improved Long Short-Term Memory (LSTM). Specifically, we used eye-tracking technology to capture users' visual sequence of design morphology for NEV-IC. We then adopted entropy-TOPSIS to compute the ranking of morphological components based on experts’ opinions, establishing the coupling between users' visual perception and experts' opinion to obtain the key morphological dataset of NEV-IC based on user visual sequence. To tackle the shortcomings of LSTM, meanwhile, we employed the sparrow search algorithm (SSA) to optimize the hyperparameters of the LSTM model. Moreover, an attention mechanism has been introduced to address LSTM's difficulty in preserving key information when processing the sequential data, enabling a stronger focus on critical sequential features within the user's visual path. To assess the efficacy of the proposed SSA-LSTM-Attention model, a dataset incorporating user emotional imagery was constructed, within the research framework of Kansei engineering (KE). This dataset, in conjunction with the morphological dataset of visual sequential features, was applied to our model. The study results indicated that compared to traditional machine learning models like BP neural network (BPNN), support vector regression (SVR), and LSTM, our model performed better in capturing the nonlinear relationship between user sentiment and design features. Additionally, it exhibited higher predictive accuracy, better generalization ability and stronger robustness.