Conventional Machine Learning Research Articles

Classification and detection of dental images using meta-learning

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input. Instead of just memorizing a task, this is accomplished through teaching a model how to learn. Algorithms for meta-learning are typically trained on a collection of training problems, each of which has a limited number of labelled instances. Multiple X-ray classification tasks, including the detection of pneumonia, coronavirus disease 2019, and other disorders, have demonstrated the effectiveness of meta-learning. Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods. Due to the high cost and lengthy collection process associated with dental imaging datasets, this is significant for dental X-ray classification jobs. The ability to train models that are more resistant to fresh input is another benefit of meta-learning.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

Comparative Evaluation of Sentiment Analysis Methods: From Traditional Techniques to Advanced Deep Learning Models

Abstract. Sentiment evaluation plays a crucial role in deciphering public perception and consumer responses in today's digital landscape. This investigation offers a thorough assessment of diverse sentiment evaluation techniques, contrasting conventional machine learning methodologies with cutting-edge deep learning frameworks. In particular, the research scrutinizes the efficacy of Bidirectional Encoder Representations from Transformers (BERT)-derived architectures (BERT-Base and Robustly Optimized BERT Pretraining Approach (RoBERTa)), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Support Vector Machines (SVM), and Naive Bayes classifiers. The study gauges these approaches based on their precision, recall, F1-metric, overall accuracy, and computational efficiency using an extensive sentiment evaluation dataset. The results reveal that BERT-based models, particularly RoBERTa, achieve the highest accuracy (87.44%) and F1-score (0.8746), though they also require the longest training time (approximately 3 hours). CNN and LSTM models strike a balance between performance and efficiency, while traditional methods like SVM and Naive Bayes offer faster training and deployment with moderate accuracy. The insights gained from this study are valuable for both researchers and practitioners, highlighting the trade-offs between model performance, computational demands, and practical deployment considerations in sentiment analysis applications.

Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models.

A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning model that is robust to overfitting. This method can be applied to predict various properties of a liquid system, including viscosity or surface tension, based on molecular features drawn from the ab initio calculated free energy of solvation. Furthermore, we propose a modified kernel model that includes Arrhenius temperature dependence to incorporate theoretical principles and diminish extreme nonlinearity in the model. The modified kernel model demonstrated significant improvements in certain scenarios and possible extensions to various theoretical concepts of molecular systems.

Employing Xception convolutional neural network through high-precision MRI analysis for brain tumor diagnosis

The classification of brain tumors from medical imaging is pivotal for accurate medical diagnosis but remains challenging due to the intricate morphologies of tumors and the precision required. Existing methodologies, including manual MRI evaluations and computer-assisted systems, primarily utilize conventional machine learning and pre-trained deep learning models. These systems often suffer from overfitting due to modest medical imaging datasets and exhibit limited generalizability on unseen data, alongside substantial computational demands that hinder real-time application. To enhance diagnostic accuracy and reliability, this research introduces an advanced model utilizing the Xception architecture, enriched with additional batch normalization and dropout layers to mitigate overfitting. This model is further refined by leveraging large-scale data through transfer learning and employing a customized dense layer setup tailored to effectively distinguish between meningioma, glioma, and pituitary tumor categories. This hybrid method not only capitalizes on the strengths of pre-trained network features but also adapts specific training to a targeted dataset, thereby improving the generalization capacity of the model across different imaging conditions. Demonstrating an important improvement in diagnostic performance, the proposed model achieves a classification accuracy of 98.039% on the test dataset, with precision and recall rates above 96% for all categories. These results underscore the possibility of the model as a reliable diagnostic tool in clinical settings, significantly surpassing existing diagnostic protocols for brain tumors.

Generative adversarial network model to classify human induced pluripotent stem cell-cardiomyocytes based on maturation level

Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification by incorporating synthetic data. This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability.

Data-driven multivariate time series prediction of in-vehicle equipment failure rates

Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance.

Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning

Backgrounds. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive experience. Previous research has shown that electroencephalogram (EEG) profiles induced by 3D VR differ from those of 2D VR in various aspects, including brain rhythm power, activation, and functional connectivity. However, studies focused on classifying EEG in 2D and 3D VR contexts remain limited.Methods. A 56-channel EEG was recorded while visual stimuli were presented in 2D and 3D VR. The recorded EEG signals were classified using two machine learning approaches: traditional machine learning and deep learning. In the traditional approach, features such as power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classifiers-support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF)-were used. For the deep learning approach, a specialized convolutional neural network, EEGNet, was employed. The classification performance of these methods was then compared.Results. In terms of accuracy, precision, recall, and F1-score, the deep learning method outperformed traditional machine learning approaches. Specifically, the classification accuracy using the EEGNet deep learning model reached up to 97.86%.Conclusions. EEGNet-based deep learning significantly outperforms conventional machine learning methods in classifying EEG signals induced by 2D and 3D VR. Given EEGNet's design for EEG-based brain-computer interfaces (BCI), this superior classification performance suggests that it can enhance the application of 3D VR in BCI systems.

Optimized Machine Learning Algorithms for Predicting the Punching Shear Resistance of Flat Slabs with Transverse Reinforcement

In the calculation of reinforced concrete (RC) flat slabs with transverse reinforcement, punching shear resistance is one of the most critical factors. It is true that design provisions may be implemented, but they often result in significant biases and deviations from expectations. This study aims to present an optimized machine learning (ML) algorithm for estimating the punching shear resistance. Four machine learning (ML) algorithms (SVR, DT, RF, and XGBoost) with Bayesian optimization (BO) are presented in this paper to provide accurate predictions for flat slabs. The adoptability and optimization of the models are achieved through the analysis of a database of 337 test specimens with nine design parameters. Machine learning (ML) techniques are used to estimate punching shear resistance, which is compared with design provisions and equations relating to critical shear crack theory (CSCT). According to this study, Bayesian optimization is still capable of improving the performance of conventional machine learning algorithms, while the XGBoost-based model offers advanced capabilities. Predictions based on BO-XGBoost are in good agreement with actual values (MAE, RMSE, and R2 are 0.09 MN, 0.14 MN, and 0.92, respectively) in test set. Following a detailed explanation using Shapley additive explanation (SHAP), a high-performance ML approach is used to investigate the predictive results. With the proposed optimized algorithms, it is possible to determine the punching shear resistance of flat slabs with transverse reinforcement during the preliminary stages of the construction.

Feature Transfer Learning for Fatigue Life Prediction of Additive Manufactured Metals With Small Samples

ABSTRACTA feature transfer learning (FTL)‐based model is proposed to address small‐sample problems in fatigue life prediction of additively manufactured (AM) metals. Transfer component analysis (TCA) is studied for data alignment before model training. Correspondingly, two TCA improvement strategies are further considered to aggregate training data from distinct AM processing conditions. An experimental database consisting of 103 fatigue data is built for model evaluation. The results demonstrate that the proposed model outperforms conventional machine learning models and other transfer learning‐based models in terms of accuracy and data demand, showing good applicability for AM fatigue life assessment.

Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches

Surface Electromyography (sEMG) has become an essential tool in various fields, including prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent years, the application of machine learning and deep learning techniques to sEMG signal classification has gained significant interest. This survey provides a detailed exploration of feature extraction methods for sEMG classification, from traditional handcrafted features to learned features. ObjectivesThis review aims to provide a comprehensive overview of feature extraction techniques for sEMG signal classification, focusing on both handcrafted and learned features. It seeks to advance research by offering a deeper understanding of fundamental concepts in sEMG signal analysis, along with comparisons and summaries of state-of-the-art approaches. Materials and MethodsThe survey covers various feature extraction techniques used for sEMG classification, including signal acquisition, preprocessing, and the application of conventional machine learning and deep learning classifiers. It offers taxonomies, definitions, and performance comparisons, equipping researchers with a broad understanding of current methodologies. ResultsHandcrafted features combined with traditional machine learning classifiers have demonstrated strong performance, especially with smaller datasets. However, deep learning techniques have shown superior results in many applications, despite challenges related to data availability and model interpretability. The survey highlights key findings regarding the performance of both approaches. ConclusionThis study bridges the gap between traditional and learned feature extraction techniques for sEMG signal classification. It provides a valuable resource for researchers and practitioners, offering insights that can guide future advancements. Key areas for future research include addressing data scarcity in deep learning and improving model interpretability for clinical applications.

MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts

N6-methyladenosine (m6A) modification, a prevalent epigenetic mark in eukaryotic cells, is crucial in regulating gene expression and RNA metabolism. Accurately identifying m6A modification sites is essential for understanding their functions within biological processes and the intricate mechanisms that regulate them. Recent advances in high-throughput sequencing technologies have enabled the generation of extensive datasets characterizing m6A modification sites at single-nucleotide resolution, leading to the development of computational methods for identifying m6A RNA modification sites. However, most current methods focus on specific cell lines, limiting their generalizability and practical application across diverse biological contexts. To address the limitation, we propose MST-m6A, a novel approach for identifying m6A modification sites with higher accuracy across various cell lines and tissues. MST-m6A utilizes a multi-scale transformer-based architecture, employing dual k-mer tokenization to capture rich feature representations and global contextual information from RNA sequences at multiple levels of granularity. These representations are then effectively combined using a channel fusion mechanism and further processed by a convolutional neural network to enhance prediction accuracy. Rigorous validation demonstrates that MST-m6A significantly outperforms conventional machine learning models, deep learning models, and state-of-the-art predictors. We anticipate that the high precision and cross-cell-type adaptability of MST-m6A will provide valuable insights into m6A biology and facilitate advancements in related fields. The proposed approach is available at https://github.com/cbbl-skku-org/MST-m6A/ for prediction and reproducibility purposes.

Quantum-assisted federated intelligent diagnosis algorithm with variational training supported by 5G networks

In the realm of intelligent healthcare, there is a growing ambition to reshape medical services through the integration of artificial intelligence (AI). However, conventional machine learning faces inherent challenges such as privacy issues, delayed updates, and protracted training times, particularly due to the hesitance of medical institutions to directly share sensitive data, with possible noises. In response to these concerns, a Quantum-Assisted Federated Intelligent Diagnosis Algorithm (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-QuAFIDA) is proposed, applied into real medical data. Leveraging the capabilities of the 5G mobile network, this approach works the connection between Internet of Medical Things (IoMT) devices through the 5G, synchronizing training and updating the server model without disrupting their real-world applications. In our quest to safeguard patient data and enhance training efficiency, our study employs an innovative heuristic approach marked by a nested loop structure. Specifically, the inner loop is dedicated to training the beta-variational quantum eigensolver (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-VQE) to approximate the expectation values of the proposed algorithm; the outer loop trains the β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-QuAFIDA to reduce the relative entropy towards the target. This approach involves a balance between privacy considerations and the urgency of training. Results demonstrate that representations with low-rank attained through β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-QuAFIDA offer an effective approach for acquiring low-rank states. This research signifies a step forward in the synergy between AI and 5G technologies, presenting a novel avenue for the advancement of intelligent healthcare.

Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning

AbstractFusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.

Fake News Detection Using Machine Learning

Abstract: The identification of fake news has grown in importance as false information circulates quickly via digital channels. The goal of this project is to combine deep learning and machine learning to create an automated system that can identify news stories as legitimate or fraudulent. The preprocessed text data in the dataset, which comprises of tagged news items, was done using methods such tokenization, stop word removal, and padding. A Convolutional Neural Network (Conv1D), which is based on deep learning, was combined with more conventional machine learning models, such as Naive Bayes, in a hybrid approach. After converting the text input into a series of tokens, semantic meanings were captured by using word embeddings. Important patterns from the word sequences were intended to be captured by the Conv1D model with global max pooling, whereas the Naive Bayes also used in the project.

Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.

Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital. We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks. Our work focuses on addressing two key challenges in the medical fields, data imbalance and the variety of data types, in order to boost the performance of machine learning algorithms. Furthermore, we also employ SHapley Additive Explanations (SHAP) value visualization to interpret the model predictions and identify both the key data features and disease codes associated with readmission risks, identifying a specific set of diagnosis codes that are significant predictors of readmission within 30 days. Through extensive benchmarking and the application of a variety of feature engineering techniques, we successfully improved the area under the curve (AUROC) score from 0.628 to 0.7 across our models on the test dataset. We also revealed that specific diagnoses, including cancer, COPD, and certain social factors, are significant predictors of 30-day readmission risk. Conversely, bacterial carrier status appeared to have minimal impact due to lower case frequencies. Our study demonstrates how we effectively utilize routinely collected hospital data to forecast patient readmission through the use of conventional machine learning while applying explainable AI techniques to explore the correlation between data features and patient readmission rate.

Enhancing Efficacy in Breast Cancer Screening with Nesterov Momentum Optimization Techniques

In the contemporary landscape of healthcare, machine learning models are pivotal in facilitating precise predictions, particularly in the nuanced diagnosis of complex ailments such as breast cancer. Traditional diagnostic methodologies grapple with inherent challenges, including excessive complexity, elevated costs, and reliance on subjective interpretation, which frequently culminate in inaccuracies. The urgency of early detection cannot be overstated, as it markedly broadens treatment modalities and significantly enhances survival rates. This paper delineates an innovative optimization framework designed to augment diagnostic accuracy by amalgamating momentum-based optimization techniques within a neural network paradigm. Conventional machine learning approaches are often encumbered by issues of overfitting, data imbalance, and the inadequacy of capturing intricate patterns in high-dimensional datasets. To counter these limitations, we propose a sophisticated framework that integrates an adaptive threshold mechanism across an array of gradient-based optimizers, including SGD, RMSprop, adam, adagrad, adamax, adadelta, nadam and Nesterov momentum. This novel approach effectively mitigates oscillatory behavior, refines parameter updates, and accelerates convergence. A salient feature of our methodology is the incorporation of a momentum threshold for early stopping, which ceases training upon the stabilization of momentum below a pre-defined threshold, thereby pre-emptively preventing overfitting. Leveraging the Wisconsin Breast Cancer Dataset, our model achieved a remarkable 99.72% accuracy and 100% sensitivity, significantly curtailing misclassification rates compared to traditional methodologies. This framework stands as a robust solution for early breast cancer diagnosis, thereby enhancing clinical decision making and improving patient outcomes.

Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

Unveiling diagnostic information for type 2 diabetes through interpretable machine learning

The interpretability of disease prediction models is often crucial for their trustworthiness and usability among medical practitioners. Existing methods in interpretable artificial intelligence improve model transparency but fall short in identifying precise, disease-specific primal information. In this work, an interpretable deep learning-based algorithm called the data space landmark refiner was developed, which not only enhances both global interpretability and local interpretability but also reveals the intrinsic information of the data distribution. Using the proposed method, a type 2 diabetes mellitus diagnostic model with high interpretability was constructed on the basis of the electronic health records from two hospitals. Moreover, effective diagnostic information was directly derived from the model’s internal parameters, demonstrating strong alignment with current clinical knowledge. Compared with conventional interpretable machine learning approaches, the proposed method offered more precise and specific interpretability, increasing clinical practitioners’ trust in machine learning-supported diagnostic models.

MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information

Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14–15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP.