Traditional Learning Research Articles

StudyNotion - An Edtech Website

In an era defined by rapid technological advancement, education stands as a crucial arena ripe for transformation. Our Edtech website emerges as a beacon of innovation, aiming to revolutionize traditional learning paradigms through the seamless integration of cutting-edge technology. With a multifaceted approach, our platform offers a comprehensive suite of tools and resources tailored to meet the diverse needs of students, educators, and institutions alike. At the heart of our platform lies a commitment to accessibility and inclusivity, ensuring that learning opportunities transcend geographical boundaries and socioeconomic barriers. Through interactive modules, engaging content, and personalized learning pathways, we empower learners to cultivate their intellectual curiosity and unlock their full potential. For educators, our platform serves as a dynamic hub for professional development, fostering collaboration, and knowledge sharing within a vibrant community of practice. From virtual classrooms to collaborative project management tools, we provide the scaffolding necessary to facilitate effective teaching in the digital age.Furthermore, our platform harnesses the power of data analytics and artificial intelligence to deliver actionable insights and personalized recommendations, facilitating data-driven decision-making and enhancing learning outcomes. In essence, our Edtech website represents more than just a digital learning platform; it embodies a vision for the future of education—one where technology serves as a catalyst for empowerment, innovation, and lifelong learning. Join us on this transformative journey as we redefine the possibilities of education in the 21st century and beyond. This abstract succinctly captures the essence of the Edtech website, highlighting its goals, features, and the transformative potential it holds for education.The abstract for the Edtech Website project encapsulates the endeavor to create an innovative educational platform tailored for diverse learners. This project aims to develop a user-friendly website offering interactive learning resources, personalized study tools, and collaborative features. Key objectives include providing seamless navigation, curating diverse educational content, implementing adaptive learning algorithms, and fostering real-time collaboration among students and educators. Emphasizing scalability, compatibility, and security, the website seeks to empower learners globally with accessible, engaging, and personalized learning experiences, ultimately fostering academic excellence and lifelong learning opportunities.

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.

ERIS: A WORDPLAY IN CATULLUS 40

Abstract In poem 40, through a series of rhetorical questions, Catullus confronts Ravidus about what made him commit such a foolish action as to fall in love with Catullus’ own lover. The poem ends with the lines: eris, quandoquidem meos amores | cum longa uoluisti amare poena, ‘You will be, since you have chosen to love my lover at the risk of receiving a long punishment’. There is a long-standing tradition of scholarship which testifies to the frequency with which Catullus incorporates wordplay in his poems, including bilingual puns. This essay proposes another such pun by arguing that Catullus is making a play on words through the homophony of the Latin verb eris and the Greek noun ἔρις.

The scientific knowledge of three large language models in cardiology: multiple choice questions examination-based performance

Background: The integration of artificial intelligence (AI) chatbots like Google’s Bard, OpenAI’s ChatGPT, and Microsoft’s Bing Chatbot into academic and professional domains, including cardiology, has been rapidly evolving. Their application in educational and research frameworks, however, raises questions about their efficacy, particularly in specialized fields like cardiology. This study aims to evaluate the knowledge depth and accuracy of these AI chatbots in cardiology using a multiple-choice question (MCQ) format. Methods: The study was conducted as an exploratory, cross-sectional study in November 2023 on a bank of 100 MCQs covering various cardiology topics that was created from authoritative textbooks and question banks. These MCQs were then used to assess the knowledge level of Google’s Bard, Microsoft Bing, and ChatGPT 4.0. Each question was entered manually into the chatbots, ensuring no memory retention bias. Results: The study found that ChatGPT 4.0 demonstrated the highest knowledge score in cardiology, with 87% accuracy, followed by Bing at 60% and Bard at 46%. The performance varied across different cardiology subtopics, with ChatGPT consistently outperforming the others. Notably, the study revealed significant differences in the proficiency of these chatbots in specific cardiology domains. Conclusion: This study highlights a spectrum of efficacy among AI chatbots in disseminating cardiology knowledge. ChatGPT 4.0 emerged as a potential auxiliary educational resource in cardiology, surpassing traditional learning methods in some aspects. However, the variability in performance among these AI systems underscores the need for cautious evaluation and continuous improvement, especially for chatbots like Bard, to ensure reliability and accuracy in medical knowledge dissemination.

Abstract PO5-07-04: Machine learning models can help physicians reduce unnecessary ultrasound guided breast biopsies

Abstract Background Breast ultrasound is widely used as a diagnostic tool, the weakness of the method though is a high false positive rate, leading to unnecessary biopsies. In recent years, machine learning (ML) is gaining attention for its excellent performance in image-recognition tasks. The objective of this study was to train and validate ML models to predict malignancy of breast masses identified by ultrasound using clinical features of the patients, image information from the ultrasound reports and attributes extracted from the images. Methods We prospectively collected clinical and ultrasonographic attributes as well as the images from 927 lesions classified as BI-RADS 2, 3, 4a, 4b, 4c, 5 and 6 submitted to percutaneous biopsy in four institutions. Images in PNG format were loaded with OpenCV library and converted to gray scale. A total of nine attributes were extracted from the annotated area. We trained and validated five ML algorithms (histogram gradient boosting, logistic regression, a pipeline of these two classifiers, stacking ensemble of several classifiers and lightgbm), randomly sampling 80% of the dataset for training and 20% for validation using a cross fold strategy. We trained and tested the models with five different combinations of the data collected and compared the performances. The model with the lowest mean errors was selected and the threshold tuned to minimize the cases that the model classified as benign and were truly cancer (“false not cancer”). Image preprocessing, attribute extraction, and traditional machine learning was performed in Python. Results The clinical characteristics of the patients and ultrasonographic attributes of the lesions withdrawn from the exam reports are described in Table 1. The biopsy results distribution across the BI-RADS classification of the ultrasound report is shown in Table 2 and are in accordance with the literature. Among the five tested models, the logistic regression was the one with the lowest mean errors and was selected to tune the thresholds minimizing “false not cancer” cases. Our target was up to 2% of this error to stay within the BI-RADS 3 classification and thus dismiss biopsy. Table 3 describes five different combinations of data used to construct the models. All these combinations had < 0.5% of "false not cancer”, way below the 2% threshold from the BI-RADS 3 category. The mean error of the models classifying benign lesions as cancer and inducing unnecessary biopsies varied from 22.7% to 23.3% depending on the combination of data used. Conclusions Machine learning models can potentially help physicians avoid unnecessary ultrasound guided breast biopsies using unsophisticated clinical features and attributes from the images missing < 0.5% of cancer cases. Table 1. Clinical characteristics of the included patients and ultrasound features of the breast lesions. Table 2. Histology (malignant or benign) distribution across BI-RADSⓇ classification. *We had five lesions missing this data. Table 3. Mean performances of the five models and parameters used to build them. Citation Format: Isabela Buzatto, Danilo Carlotti, Nilton Onari, Ana Luiza Faim, Sarah Recife, Licerio Miguel, Ruth Bonini, Liliane Silvestre, Lucas Figueira, Daniel Guimarães Tiezzi. Machine learning models can help physicians reduce unnecessary ultrasound guided breast biopsies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-04.

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Emotion Detection System for Malayalam Text using Deep Learning and Transformers

Emotion Detection System for Malayalam Text using Deep Learning and Transformers

Effectiveness of spaced repetition learning using a mobile flashcard application among dental students: A randomized controlled trial.

Dental education in India predominantly relies on traditional lecture-based learning (LBL), which may hinder student engagement and learning outcomes. To address these limitations, innovative learning methodologies, such as spaced repetition learning (SRL), are imperative. SRL prioritizes active recall and can enhance long-term knowledge retention. This study aims to assess the effectiveness of SRL delivered through a mobile flashcard application, in enhancing knowledge retention among dental undergraduates. This single-blind randomized controlled trial (CTRI/2023/10/059347), conducted in Belagavi, India, involved 90 dental students who were equally distributed into control (LBL) and test (lecture followed by SRL demonstration) groups after randomization. Rigorous expert review ensured the quality of PowerPoint presentation and mobile flashcard contents. Knowledge assessments were conducted at baseline, first, and third months using a validated and reliable questionnaire. A perception survey on learning techniques was administered after the first month. Analysis methods included descriptive analysis, Pearson's chi-square test, independent t-test, and repeated measures ANOVA with Bonferroni's post hoc test. The pre- and post-intervention knowledge showed no significant differences, but the SRL group exhibited significantly higher retention at both first month (p ≤ 0.001) and third months (p ≤ 0.001) than the LBL group. Repeated measures ANOVA revealed significant pairwise differences in mean knowledge scores in SRL group. Students had significantly favorable perception toward SRL than LBL group. SRL delivered through mobile flashcards significantly enhances knowledge retention compared to LBL among dental students. Positive student perceptions support SRL's integration into dental curricula, with implications for improving knowledge retention among them.

Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets.

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate GNN inference on large-scale graphs by jointly pruning the graph structure and the model weights. Though promising, GLT encounters robustness and generalization issues when deployed in real-world scenarios, which are also long-standing and critical problems in deep learning ideology. In real-world scenarios, the distribution of unseen test data is typically diverse. We attribute the failures on out-of-distribution (OOD) data to the incapability of discerning causal patterns, which remain stable amidst distribution shifts. In traditional spase graph learning, the model performance deteriorates dramatically as the graph/network sparsity exceeds a certain high level. Worse still, the pruned GNNs are hard to generalize to unseen graph data due to limited training set at hand. To tackle these issues, we propose the Resilient Graph Lottery Ticket (RGLT) to find more robust and generalizable GLT in GNNs. Concretely, we reactivate a fraction of weights/edges by instantaneous gradient information at each pruning point. After sufficient pruning, we conduct environmental interventions to extrapolate potential test distribution. Finally, we perform last several rounds of model averages to further improve generalization. We provide multiple examples and theoretical analyses that underpin the universality and reliability of our proposal. Further, RGLT has been experimentally verified across various independent identically distributed (IID) and out-of-distribution (OOD) graph benchmarks.

Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.

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.

Tamás Turán. Ignaz Goldziher as a Jewish Orientalist: Traditional Learning, Critical Scholarship, and Personal Piety. Berlin: De Gruyter Oldenbourg, 2023

Tamás Turán. Ignaz Goldziher as a Jewish Orientalist: Traditional Learning, Critical Scholarship, and Personal Piety. Berlin: De Gruyter Oldenbourg, 2023

Detection of Cardiovascular Diseases from ECG Images Using Machine Learning and Deep Learning Methods

Abstract: Cardiovascular diseases (CVDs) are the leading cause of death globally, emphasizing the importance of early prediction and classification for saving lives. Electrocardiogram (ECG) is a widely used, cost-effective, and noninvasive tool for measuring heart electrical activity, crucial for detecting CVDs. This study leverages deep learning techniques to predict four major cardiac abnormalities—abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal individuals—using a public dataset of ECG images from cardiac patients. The research explores transfer learning with pretrained deep neural networks such as SqueezeNet and AlexNet, as well as introduces a novel convolutional neural network (CNN) architecture for cardiac abnormality prediction. Additionally, pretrained models and the new CNN architecture are utilized as feature extraction tools for traditional machine learning algorithms including support vector machine, K-nearest neighbors, decision tree, random forest, and Naïve Bayes. Experimental results showcase the superiority of the proposed CNN model over existing works, achieving 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Furthermore, when the proposed CNN model is utilized for feature extraction, it achieves the highest score of 99.79% using the Naïve Bayes algorithm.

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.

Comparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Images

The spine is composed of pieces of bone called vertebrae that lie between the skull and the tailbone. Various medical conditions can affect the spine. In this study, two types of degenerative diseases, scoliosis, and spondylolisthesis, were studied. Deep AI architectures have recently enabled further disease diagnosis innovation using medical images. Various traditional and deep learning studies use medical images for disease diagnosis in the literature. This study aims to classify spine X-ray images according to three possible conditions (Normal, Scoliosis, and Spondylolisthesis) and to exploit the potential of these X-ray images to detect possible diseases occurring in the spine. The performance of deep learning models and optimization algorithms used in this process was evaluated. The study uses a data set created and/or analyzed during an existing study. This data set consists of images that belong to three different classes: scoliosis, spondylolisthesis, or x-ray images of normal (i.e. healthy) individuals. A total of 338 spine X-ray images, 188 scoliosis images, 79 spondylolisthesis images, and 71 normal images. Six different deep-learning architectures have been used in the study. These architectures are Alexnet, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, and EfficientNet-bo. While working on these deep architectures, each model has been evaluated using different optimization algorithms. These optimization algorithms are RmsProp, SGDM, and Adam. According to the classification processes, the deep learning model with the highest accuracy value was Alexnet, and the optimization algorithm used with it, Sgdm (99.01%), and the training time lasted 38 seconds. According to the classification processes, the deep learning model with the fastest completion time (30 seconds) was Alexnet and the optimization algorithm used with it was RmsProp. An accuracy rate of 98.02% has been obtained in the training of this model.

Investigating the Impact of Social Media on Secondary School Students’ Motivation and Engagement in English Language Learning

Abstract: This study explores the potential of social media platforms, particularly Instagram and Twitter, to supplement traditional English language learning for secondary school students. It acknowledges the limitations of traditional methods in fostering real-world language use and seeks to investigate how social media can enhance student motivation and engagement with English outside the classroom. Employing a quantitative approach, the study surveyed 100 randomly selected male students in grades eleven and twelve from Al-Hassan Bin Hashim Post Basic School in Muscat, Oman. Participants, aged 16-17, were active users of either Instagram or Twitter. Data was collected through a questionnaire consisting of 10 closed-ended and 5 open-ended questions. The study found a positive student perception towards the utility of Instagram and Twitter for English language learning. However, students also acknowledged that social media, despite its potential, cannot replace traditional classroom instruction entirely

Improving Cardiovascular Stroke Risk Prediction with Machine Learning

Improving Cardiovascular Stroke Risk Prediction with Machine Learning

Blended Learning for Aviation School

Blended learning has been identified as a highly promising instructional approach within vocational education institutions. The objective of this study is to examine the disparities in speaking skills learning strategies between traditional (face-to-face) instruction and blended learning. Additionally, the study seeks to ascertain the impact of these learning strategies on students' speaking abilities in both traditional and blended learning environments. The present study employs a quantitative research approach, utilizing an experimental design. The sample for this study comprised 120 cadets, with 24 cadets representing the Medan Aviation Polytechnic and 96 cadets representing the Surabaya Aviation Polytechnic. The results of this study suggest that the mere adoption of technology and the removal of the traditional classroom setting do not suffice in establishing an optimal learning environment for individuals learning a second or foreign language. Nevertheless, there exists a considerable opportunity to amalgamate conventional educational settings with contemporary technological advancements in order to enhance students' academic achievements across various abilities and sub-skills, with a particular emphasis on oral communication proficiency. Based on the results obtained from the study, it is possible to draw the following conclusions: 1) A statistically significant disparity exists in the proficiency of English speaking skills when employing the blended learning approach. 2) A statistically significant discrepancy is observed in the proficiency of English speaking skills for the experimental group, thereby corroborating the efficacy of post-implementation learning strategies.

Email spam detection by deep learning models using novel feature selection technique and BERT

Due to the influx of advancements in technology and the increased simplicity of communication through emails, there has been a severe threat to the global economy and security due to upsurge in volume of unsolicited During the training of models, high-dimensional and redundant datasets may reduce the classification results of the model due to high memory costs and high computation. An important data processing technique is feature selection which helps in selecting relevant features and subsets of information from the dataset. Therefore, choosing efficient feature selection techniques is very important for the best performance of classification of a model. Moreover, most of the research has been performed using traditional machine learning techniques, which are not enough to deal with the huge amount of data and its variations. Also,spammers are becoming smarter with technological advancement. Therefore, there is a need for hybrid techniques consisting of deep learning and conventional algorithms to cope with these problems. We have proposed a novel scheme in this paper for email spam detection, which will result in an improved feature selection approach from the original dataset and increase the accuracy of the classifier as well. The literature has been studied to explore the efficient machine learning models that have been applied by different researchers for email spam detection and feature selection to acquire the best results. Our method, GWO-BERT, has given remarkable results with deep learning techniques such as CNN, biLSTM and LSTM. We have compared our models with RF and LSTM and used dataset: “Lingspam,” which is a publicly available dataset. With different experiments, our technique, GWO-BERT, obtained 99.14% accuracy, which is almost equal to 100 percent.