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Margesh, Keskar, and D. Maktedar Dhananjay. "Hybrid deep-spatio textural feature model for medicinal plant disease classification." Hybrid deep-spatio textural feature model for medicinal plant disease classification 30, no. 1 (2023): 356–65. https://doi.org/10.11591/ijeecs.v30.i1.pp356-365.
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The high-pace rise in the demands of medicinal plants towards pharmaceutical significances as well as the different ayurvedic or herbal remedials have forced agro-industries However, rising plant disease cases have limited the cumulative growth and hence both volumetric production as well as quality of medicine. In this paper a first of its kind evolutionary computing driven ROIspecific hybrid deep-spatio temporal textural feature learning model is developed for medicinal plant disease detection (HDST-MPD). To alleviate any possible class-imbalance problem, HDST-MPD model at first applied firefly heuristic driven fuzzy C-means clustering to retrieve ROI-specific RGB regions. Subsequently, to exploit maximum possible deep spatiotemporal textural features, it applied gray-level co-occurrence matrix (GLCM) and AlexNet transferable network. Here, the use of multiple GLCM features helped in exploiting textural feature distribution, while AlexNet deep model yielded high-dimensional features. These deep-spatio temporal textural feature (deep-STTF) features were fused together to yield a composite vector, which was trained over random forest ensemble to perform two-class classification to classify each sample medicinal image as normal or diseased. Depth performance assessment confirmed that the proposed model yields accuracy of 98.97%, precision 99.42%, recall 98.89%, F-measure 99.15%, and equal error rate of 1.03%, signifying its robustness towards real-time medicinal plant disease detection and classification.
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Keskar, Margesh, and Dhananjay D. Maktedar. "Hybrid deep-spatio textural feature model for medicinal plant disease classification." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (2023): 356. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp356-365.
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The high-pace rise in the demands of medicinal plants towards pharmaceutical significances as well as the different ayurvedic or herbal remedials have forced agro-industries However, rising plant disease cases have limited the cumulative growth and hence both volumetric production as well as quality of medicine. In this paper a first of its kind evolutionary computing driven ROI-specific hybrid deep-spatio temporal textural feature learning model is developed for medicinal plant disease detection (HDST-MPD). To alleviate any possible class-imbalance problem, HDST-MPD model at first applied firefly heuristic driven fuzzy C-means clustering to retrieve ROI-specific RGB regions. Subsequently, to exploit maximum possible deep spatiotemporal textural features, it applied gray-level co-occurrence matrix (GLCM) and AlexNet transferable network. Here, the use of multiple GLCM features helped in exploiting textural feature distribution, while AlexNet deep model yielded high-dimensional features. These deep-spatio temporal textural feature (deep-STTF) features were fused together to yield a composite vector, which was trained over random forest ensemble to perform two-class classification to classify each sample medicinal image as normal or diseased. Depth performance assessment confirmed that the proposed model yields accuracy of 98.97%, precision 99.42%, recall 98.89%, F-measure 99.15%, and equal error rate of 1.03%, signifying its robustness towards real-time medicinal plant disease detection and classification.
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Dr.Devaraj Verma C, Shruthishree S. H, Dr Harshvardhan Tiwari,. "AlexResNet+: A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 2420–38. http://dx.doi.org/10.17762/turcomat.v12i6.5686.
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The exponential rise in cancer diseases, primarily the breast cancer has alarmed academia-industry to achieve more efficient and reliable breast cancer tissue identification and classification. Unlike classical machine learning approaches which merely focus on enhancing classification efficiency, in this paper the emphasis was made on extracting multiple deep features towards breast cancer diagnosis. To achieve it, in this paper A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification named, AlexResNet+ was developed. We used two well known and most efficient deep learning models, AlexNet and shorted ResNet50 deep learning concepts for deep feature extraction. To retain high dimensional deep features while retaining optimal computational efficiency, we applied AlexNet with five convolutional layers, and three fully connected layers, while ResNet50 was applied with modified layered architectures. Retrieving the distinct deep features from AlexNet and ResNet deep learning models, we obtained the amalgamated feature set which were applied as input for support vector machine with radial basis function (SVM-RBF) for two-class classification. To assess efficacy of the different feature set, performances were obtained for AlexNet, shorted ResNet50 and hybrid features distinctly. The simulation results over DDMS mammogram breast cancer tissue images revealed that the proposed hybrid deep features (AlexResNet+) based model exhibits the highest classification accuracy of 95.87%, precision 0.9760, sensitivity 1.0, specificity 0.9621, F-Measure 0.9878 and AUC of 0.960.
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Setiadi, De Rosal Ignatius Moses, Ajib Susanto, Kristiawan Nugroho, Ahmad Rofiqul Muslikh, Arnold Adimabua Ojugo, and Hong-Seng Gan. "Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model." Computers 13, no. 8 (2024): 191. http://dx.doi.org/10.3390/computers13080191.
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In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination (R2), and mean average error (MAE), the results are 1.191621 × 10−5, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management.
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Yogesh, N., Purohit Shrinivasacharya, Nagaraj Naik, and B. M. Vikranth. "Chronic kidney Disease Classification through Hybrid Feature Selection and Ensemble Deep Learning." International Journal of Statistics in Medical Research 14 (March 3, 2025): 109–17. https://doi.org/10.6000/1929-6029.2025.14.11.
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Diagnosing and treating at-risk patients for chronic kidney disease (CKD) relies heavily on accurately classifying the disease. The use of deep learning models in healthcare research is receiving much interest due to recent developments in the field. CKD has many features; however, only some features contribute weightage for the classification task. Therefore, it is required to eliminate the irrelevant feature before applying the classification task. This paper proposed a hybrid feature selection method by combining the two feature selection techniques: the Boruta and the Recursive Feature Elimination (RFE) method. The features are ranked according to their importance for CKD classification using the Boruta algorithm and refined feature set using the RFE, which recursively eliminates the least important features. The hybrid feature selection method removes the feature with a low recursive score. Later, selected features are given input to the proposed ensemble deep learning method for classification. The experimental ensemble deep learning model with feature selection is compared to Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models with and without feature selection. When feature selection is used, the ensemble model improves accuracy by 2%. Experimental results found that these features, age, pus cell clumps, bacteria, and coronary artery disease, do not contribute much to accurate classification tasks. Accuracy, precision, and recall are used to evaluate the ensemble deep learning model.
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Sudeep K. Hase, Rashmi Soni. "Hybrid Feature Selection on Social Media Dataset for Sentiment Classification using Deep Learning Techniques." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 1899–918. https://doi.org/10.52783/cana.v32.4362.
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Sentiment classification involves determining the sentiment expressed in text, such as positive, negative, or neutral, but social media data presents challenges due to its high dimensionality, noise, and unstructured nature. This study proposes a novel sentiment classification approach by combining hybrid feature selection methods with deep learning techniques. Social media platforms generate vast amounts of data daily, which is often noisy, redundant, and irrelevant for sentiment analysis. Hybrid feature selection techniques, which integrate filter and wrapper-based methods, assist in reducing the feature space while retaining the most informative features. By applying deep learning models, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, classification performance can be substantially enhanced. The proposed framework uses hybrid feature selection to eliminate noisy and irrelevant features, thereby improving the model's generalization capabilities. Experimental results reveal that the combination of hybrid feature selection and deep learning techniques not only boosts sentiment classification accuracy but also decreases computational overhead. This study highlights the effectiveness of merging traditional feature selection methods with modern deep learning models to better address the complexities of social media datasets and deliver more precise sentiment analysis. The results achieved by proposed model is 98.50% on social media dataset which is higher than conventional approaches.
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Saproo, Dimple, Aparna N. Mahajan, Seema Narwal, and Niranjan Yadav. "Deep Feature Extraction and Classification of Diabetic Retinopathy Images using a Hybrid Approach." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21475–81. https://doi.org/10.48084/etasr.10188.
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Hybrid approaches have improved sensitivity, accuracy, and specificity in Diabetic Retinopathy (DR) classification. Deep feature sets provide a more holistic analysis of the retinal images, resulting in better detection of premature signs of DR. Hybrid strategies for classifying DR images combine the strengths of extracted deep features using pre-trained networks and Machine Learning (ML)-based classifiers to improve classification accuracy, robustness, and efficiency. Perfect pre-trained networks VGG19, ResNet101, and Shuffle Net were considered in this work. The networks were trained using a transfer learning approach, the pre-trained networks were chosen according to their classification accuracy in conjunction with the Softmax layer. Enhanced characteristics were extracted from the pre-trained networks' last layer and were fed to the machine learning-based classifier. The feature reduction and selection methods are essential for accomplishing the desired classification accuracy. ML-based kNN classifier was used to classify DR and a PCA-based feature reduction approach was utilized to obtain optimized deep feature sets. The extensive experiments revealed that ResNet101-based deep feature extraction and the kNN classifier delivered enhanced classification accuracy. It is concluded that combining deep features and the ML-based classifier employing a hybrid method enhances accuracy, robustness, and efficiency. The hybrid approach is a powerful tool for the premature identification of DR abnormalities. The PCA-kNN classification algorithm, which employs features obtained from the ResNet101, attained a peak classification accuracy of 98.9%.
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Chu, Yinghao, Chen Huang, Xiaodan Xie, Bohai Tan, Shyam Kamal, and Xiaogang Xiong. "Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling." Computational Intelligence and Neuroscience 2018 (November 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/5060857.
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This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs.
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Atteia, Ghada, Michael J. Collins, Abeer D. Algarni, and Nagwan Abdel Samee. "Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data." Remote Sensing 14, no. 21 (2022): 5569. http://dx.doi.org/10.3390/rs14215569.
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Predicting sea wave parameters such as significant wave height (SWH) has recently been identified as a critical requirement for maritime security and economy. Earth observation satellite missions have resulted in a massive rise in marine data volume and dimensionality. Deep learning technologies have proven their capabilities to process large amounts of data, draw useful insights, and assist in environmental decision making. In this study, a new deep-learning-based hybrid feature selection approach is proposed for SWH prediction using satellite Synthetic Aperture Radar (SAR) mode altimeter data. The introduced approach integrates the power of autoencoder deep neural networks in mapping input features into representative latent-space features with the feature selection power of the principal component analysis (PCA) algorithm to create significant features from altimeter observations. Several hybrid feature sets were generated using the proposed approach and utilized for modeling SWH using Gaussian Process Regression (GPR) and Neural Network Regression (NNR). SAR mode altimeter data from the Sentinel-3A mission calibrated by in situ buoy data was used for training and evaluating the SWH models. The significance of the autoencoder-based feature sets in improving the prediction performance of SWH models is investigated against original, traditionally selected, and hybrid features. The autoencoder–PCA hybrid feature set generated by the proposed approach recorded the lowest average RMSE values of 0.11069 for GPR models, which outperforms the state-of-the-art results. The findings of this study reveal the superiority of the autoencoder deep learning network in generating latent features that aid in improving the prediction performance of SWH models over traditional feature extraction methods.
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Kanyal, Hoshiyar Singh, Prakash Joshi, Jitendra Kumar Seth, Arnika, and Tarun Kumar Sharma. "A Hybrid Deep Learning Framework for MRI-Based Brain Tumor Classification Processing." International Journal of Experimental Research and Review 46 (December 30, 2024): 165–76. https://doi.org/10.52756/ijerr.2024.v46.013.
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Classifying tumors from MRI scans is a key medical imaging and diagnosis task. Conventional feature-based methods and traditional machine learning algorithms are used for tumor classification, which limits their performance and generalization. A hybrid framework is implemented for the classification of brain tumors using MRIs. The framework contains three basic components, i.e., Feature Extraction, Feature Fusion, and Classification. The feature extraction module uses a convolutional neural network (CNN) to automatically extract high-level features from MRI images. The high-level features are combined with clinical and demographic features through a feature fusion module for better discriminative power. The Support vector machine (SVM) was employed to classify the fused features as class label tumors by a classification module. The proposed model obtained 90.67% accuracy, 94.67% precision, 83.82% recall and 83.71% f1-score. Experimental results demonstrate the superiority of our framework over those existing solutions and obtain exceptional accuracy rates compared to all other frequently operated models. This hybrid deep learning framework has promising performance for efficient and reproducible tumor classification within brain MRI scans.
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Pan, Weijun, Yuhao Wang, Leilei Deng, Yanqiang Jiang, and Yuanfei Leng. "Aircraft Wake Vortex Recognition Method Based on Improved Inception-VGG16 Hybrid Network." Sensors 25, no. 9 (2025): 2909. https://doi.org/10.3390/s25092909.
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This paper proposes a hybrid deep learning network architecture (Inception-VGG16) to address the challenge of accurate aircraft wake vortex identification. The model first employs a Feature0 module for preliminary feature extraction of two-dimensional Doppler radar radial velocity data. This module comprises convolution, batch normalization, ReLU activation, and max pooling operations. Subsequently, improved InceptionB and InceptionC modules are utilized for parallel extraction of multi-scale features. The InceptionB former module adopts two parallel branches, combining 1 × 1 and 3 × 3 convolutions, and outputting 64-channel feature maps, while the InceptionC latter module expands the number of channels number to 128, enhancing the model’s feature representation capability. The backend employs the VGG16’s hierarchical structure, performing deep feature extraction through multiple convolution and pooling operations, and ultimately achieving wake vortex classification through fully connected layers. Experimental validation based on 3530 wind field samples collected at the Chengdu Shuangliu Airport demonstrates that compared to traditional methods (SVM, KNN, RF) and single deep networks (VGG16), the proposed hybrid model achieves a classification accuracy of 98.8%, significantly outperforming comparative traditional methods (SVM, KNN, RF) and single deep networks (VGG16). The model not only overcomes the limitations of single networks in processing multi-scale wake features but also enhances the model’s ability to identify wake vortices in complex backgrounds through deep feature hierarchies, providing a new technical solution for aviation safety monitoring systems based on deep learning.
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Tayyab, Muhammad, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, et al. "A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier." Sensors 25, no. 2 (2025): 441. https://doi.org/10.3390/s25020441.
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This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features. Feature fusion was employed to enhance the discriminative power of the extracted data and the physical parameters calculated by different feature extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. Compared to state-of-the-art methods, our approach achieved better performance in event recognition.
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Attallah, Omneya. "A Hybrid Trio-Deep Feature Fusion Model for Improved Skin Cancer Classification: Merging Dermoscopic and DCT Images." Technologies 12, no. 10 (2024): 190. http://dx.doi.org/10.3390/technologies12100190.
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The precise and prompt identification of skin cancer is essential for efficient treatment. Variations in colour within skin lesions are critical signs of malignancy; however, discrepancies in imaging conditions may inhibit the efficacy of deep learning models. Numerous previous investigations have neglected this problem, frequently depending on deep features from a singular layer of an individual deep learning model. This study presents a new hybrid deep learning model that integrates discrete cosine transform (DCT) with multi-convolutional neural network (CNN) structures to improve the classification of skin cancer. Initially, DCT is applied to dermoscopic images to enhance and correct colour distortions in these images. After that, several CNNs are trained separately with the dermoscopic images and the DCT images. Next, deep features are obtained from two deep layers of each CNN. The proposed hybrid model consists of triple deep feature fusion. The initial phase involves employing the discrete wavelet transform (DWT) to merge multidimensional attributes obtained from the first layer of each CNN, which lowers their dimension and provides time–frequency representation. In addition, for each CNN, the deep features of the second deep layer are concatenated. Afterward, in the subsequent deep feature fusion stage, for each CNN, the merged first-layer features are combined with the second-layer features to create an effective feature vector. Finally, in the third deep feature fusion stage, these bi-layer features of the various CNNs are integrated. Through the process of training multiple CNNs on both the original dermoscopic photos and the DCT-enhanced images, retrieving attributes from two separate layers, and incorporating attributes from the multiple CNNs, a comprehensive representation of attributes is generated. Experimental results showed 96.40% accuracy after trio-deep feature fusion. This shows that merging DCT-enhanced images and dermoscopic photos can improve diagnostic accuracy. The hybrid trio-deep feature fusion model outperforms individual CNN models and most recent studies, thus proving its superiority.
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Jiang, Xinhua, Heru Xue, Lina Zhang, Xiaojing Gao, Yanqing Zhou, and Jie Bai. "Hyperspectral Data Feature Extraction Using Deep Learning Hybrid Model." Wireless Personal Communications 102, no. 4 (2018): 3529–43. http://dx.doi.org/10.1007/s11277-018-5389-y.
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Zhang, Ran, Qianru Wu, and Yuwei Zhou. "Network Security Situation Element Extraction Based on Hybrid Deep Learning." Electronics 14, no. 3 (2025): 553. https://doi.org/10.3390/electronics14030553.
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Accurately extracting network security situation elements is an important basis for improving the situational awareness of industrial Internet security. This paper proposes an industrial internet security situation element extraction algorithm based on a hybrid neural network. Firstly, the powerful local feature extraction ability of convolutional neural networks (CNNs) was used to extract the features of key situation elements, and the obtained features were flattened and then input into long short-term memory networks (LSTMs) to solve the problem of the poor time feature extraction ability of CNNs. Then, the output features of the fully connected layer were input to the backpropagation (BP) network for classification, and LSTM was used to correct the prediction residual of the BP network to optimize the parameters of each module in the model and improve the classification effect and generalization ability. Comparative experimental results show that the accuracy of the model on the KDD Cup99 dataset and SCADA2014 dataset can reach 98.03% and 98.96%, respectively. Compared with other models, the model has higher classification accuracy and can provide more effective indicator data for security situation assessment.
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Biswas, Angona, and Md Saiful Islam. "A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification." Journal of Information Systems Engineering and Business Intelligence 9, no. 1 (2023): 1–15. http://dx.doi.org/10.20473/jisebi.9.1.1-15.
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Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement. Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming. Keywords: Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.
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Sukumaran, Asha, and Thomas Brindha. "Nature-inspired hybrid deep learning for race detection by face shape features." International Journal of Intelligent Computing and Cybernetics 13, no. 3 (2020): 365–88. http://dx.doi.org/10.1108/ijicc-03-2020-0020.
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PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.
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T V, Shruthi, Rachana K R, Bhuvana S, Neha Appaji Y, and Purnima R. "Face Forgery Detection Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39643.
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The rising occurrence of forged content poses a threat to the authenticity of multimedia. This research suggests a simplified hybrid architecture as an effective method for detecting face forgeries. The framework includes CNN for dependable classification, EfficientNet for robust feature extraction, and MTCNN for precise face detection. MTCNN ensures that high-quality input is produced for feature extraction by accurately localizing facial regions. The CNN classifier utilizes the extracted features in order to distinguish between authentic and manipulated content, and EfficientNet, which has become famous for its good performance and computational efficiency, is able to capture face patterns at a subtle level. Transfer learning enhances the adaptability of the model toward new manipulation techniques because it pre- trains on a large-scale dataset before fine-tuning on data specifically related to deepfakes. Key Words: Deep Learning, Deepfakes, Face Forgery, Multimedia forensics, CNN.
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Hafiza, Annisaa Alya, and Erwin Budi Setiawan. "Enhancing Cyberbullying Detection on Platform 'X' Using IndoBERT and Hybrid CNN-LSTM Model." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 655–72. https://doi.org/10.52436/1.jutif.2025.6.2.4321.
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Cyberbullying on social media platforms has become widespread in society. Cyberbullying can take many forms, including hate speech, trolling, adult content, racism, harassment, or rants. One social media platform that has many cyberbullies is Twitter, which has been renamed 'X'. The anonymous nature of this 'X' platform allows users from all over the world to commit cyberbullying as they can freely share their thoughts and expressions without having to account for their identity. This research aims to explore the influence of IndoBERT’s semantic features on hybrid deep learning models for cyberbullying detection while integrating TF-IDF feature extraction and FastText feature expansion to enhance text classification performance. Specifically, this study examines how IndoBERT’s semantic capabilities affect the hybrid deep learning model in detecting cyberbullying on platform 'X'. This study has 30,084 tweets with a hybrid deep learning approach that combines CNN and LSTM. In the IndoBERT scenario, IndoBERT features were first combined with TF-IDF, then expanded using FastText before being applied to the hybrid deep learning model. The test results produced the highest accuracy rate by: CNN (80.69%), LSTM (80.67%), CNN- LSTM (81.18%), CNN-LSTM-IndoBERT (82.05%). This research contributes to informatics by integrating hybrid deep learning (CNN-LSTM) with IndoBERT and TF-IDF, demonstrating its effectiveness in improving cyberbullying detection in Indonesian text. Future research can explore the use of other transformer-based models such as RoBERTa or ALBERT to enhance contextual understanding in cyberbullying classification.
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Wang, Jinghui, and Jun Wang. "MHDNet: A Multi-Scale Hybrid Deep Learning Model for Person Re-Identification." Electronics 13, no. 8 (2024): 1435. http://dx.doi.org/10.3390/electronics13081435.
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The primary objective of person re-identification is to identify individuals from surveillance videos across various scenarios. Conventional pedestrian recognition models typically employ convolutional neural network (CNN) and vision transformer (ViT) networks to extract features, and while CNNs are adept at extracting local features through convolution operations, capturing global information can be challenging, especially when dealing with high-resolution images. In contrast, ViT rely on cascaded self-attention modules to capture long-range feature dependencies, sacrificing local feature details. In light of these limitations, this paper presents the MHDNet, a hybrid network structure for pedestrian recognition that combines convolutional operations and self-attention mechanisms to enhance representation learning. The MHDNet is built around the Feature Fusion Module (FFM), which harmonizes global and local features at different resolutions. With a parallel structure, the MHDNet model maximizes the preservation of local features and global representations. Experiments on two person re-identification datasets demonstrate the superiority of the MHDNet over other state-of-the-art methods.
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Ali Al-Abyadh, Mohammed Hasan, Mohamed A. M. Iesa, Hani Abdel Hafeez Abdel Azeem, et al. "Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model." Computational Intelligence and Neuroscience 2022 (July 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/6595799.
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Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method’s accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.
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Ramadhanti, Windy, and Erwin Budi Setiawan. "Topic Detection on Twitter Using Deep Learning Method with Feature Expansion GloVe." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (2023): 780–92. https://doi.org/10.26555/jiteki.v9i3.26736.
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Twitter is a medium of communication, transmission of information, and exchange of opinions on a topic with an extensive reach. Twitter has a tweet with a text message of 280 characters. Because text messages can only be written briefly, tweets often use slang and may not follow structured grammar. The diverse vocabulary in tweets leads to word discrepancies, so tweets are difficult to understand. The problem often found in classifying topics in tweets is that they need higher accuracy due to these factors. Therefore, the authors used the GloVe feature expansion to reduce vocabulary discrepancies by building a corpus from Twitter and IndoNews. Research on the classification of topics in previous tweets has been done extensively with various Machine Learning or Deep Learning methods using feature expansion. However, To the best of our knowledge, Hybrid Deep Learning has not been previously used for topic classification on Twitter. Therefore, the study conducted experiments to analyze the impact of Hybrid Deep Learning and the expansion of GloVe features on classification topics. The total data used in this study was 55,411 datasets in Indonesian-language text. The methods used in this study are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hybrid CNN-RNN. The results show that the topic classification system with GloVe feature expansion using the CNN method achieved the highest accuracy of 92.80%, with an increase of 0.40% compared to the baseline. The RNN followed it with an accuracy of 93.72% and a 0.23% improvement. The CNN-RN Hybrid Deep Learning model achieved the highest accuracy of 94.56%, with a significant increase of 2.30%. The RNN-CNN model also achieved high accuracy, reaching 94.39% with a 0.95% increase. Based on the accuracy results, the Hybrid Deep Learning model, with the addition of feature expansion, significantly improved the system's performance, resulting in higher accuracy.
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Deepan, P., R. Vidya, M. Arsha Reddy, N. Arul, J. Ravichandran, and S. Dhiravidaselvi. "A Hybrid Gabor Filter-Convolutional Neural Networks Model for Facial Emotion Recognition System." Indian Journal Of Science And Technology 17, no. 35 (2024): 3696–703. http://dx.doi.org/10.17485/ijst/v17i35.1998.
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Objectives: This research aims to develop a hybrid facial emotion recognition system using integrated machine learning feature extraction techniques with convolutional neural networks model for improving the accuracy of facial emotion recognition (FER). Methods: This study introduces a novel approach that integrates various machine learning feature extraction techniques (HoG, LBP, SIFT and Gabor Filters) with Convolutional Neural Networks (CNNs). The study utilized a set of 10,500 facial emotion images of the CK+48 dataset with six different facial emotion recognition. Findings: The result from the experiments shows that the proposed Gabor Filter-CNN approach for estimating FER had higher accuracy as compared to other CNN, HoG-CNN, LBP-CNN, and SIFT-CNN. The specific approaches is based on the Gabor Filter – CNN paired results with an accuracy rate of 99.85% outstanding other models. Novelty: The proposed approach would ensure the combination of the ML extraction techniques for capturing the details of the textures/features and CNNs for other deep hierarchical features and further better prediction. Keywords: Hybrid Model, Facial expressions, Emotion recognition approach, Machine Learning, Deep Learning, Gabor filter approach, Feature extraction, Convolutional neural network
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Suneetha, Irala. "Hybrid Deep Learning Model for Skin Cancer Classification." E3S Web of Conferences 591 (2024): 09010. http://dx.doi.org/10.1051/e3sconf/202459109010.
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Skin cancer represents a significant public health concern worldwide, with melanoma accounting for its most lethal form. Timely identification and precise categorization of skin lesions play pivotal roles in enhancing treatment efficacy and fostering better patient outcomes. Deep learning approaches have showed promise in automatically classifying skin cancer from dermatoscopic images. In this paper, propose a hybrid deep learning model for skin cancer classification, combining the strengths of VGG16 and InceptionV3 architectures. VGG16 is known for its simplicity and effectiveness in feature extraction, while InceptionV3 excels in capturing fine-grained details and global context. The proposed hybrid model leverages the complementary features of these architectures to enhance classification performance. We train the model on a dataset of dermatoscopic images, consisting of cancer types, and evaluate its performance using conventional measures such as precision, accuracy, recall, and F1-score. Our experimental outcomes reveal that the hybrid model surpasses standalone VGG16 and InceptionV3 models, achieving superior accuracy in skin cancer classification. The proposed hybrid deep learning method holds promise for improving automated skin cancer diagnosis systems and enhancing patient care in dermatology clinics.
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Yogi, Aryan. "Hybrid Intrusion Detection System (IDS) Using Machine Learning and Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47975.
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Abstract - This work offers a Hybrid Intrusion Detection System (HIDS) that combines traditional machine learning and deep learning methods for efficient and scalable network attack identification. The system makes use of Principal Component Analysis (PCA) for reducing dimensionality and then utilizes a hybrid CNN-LSTM architecture for feature learning as well as classification. An ensemble method is also utilized to combine Random Forest with the CNN-LSTM to add robustness as well as generalization. The CICIDS2018 dataset, comprising modern real-world network traffic situations, is employed for testing. Our system detects with an accuracy of 99.1% on the test set, far better than conventional classifiers. This paper proves the efficacy of integrating statistical feature engineering with deep sequential models and ensemble techniques to counter cybersecurity attacks in real-time settings. Key Words: Intrusion Detection System, Machine Learning, Deep Learning, CNN-LSTM, PCA, Ensemble Learning, CICIDS2018
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Ahanin, Zahra, Maizatul Akmar Ismail, Narinderjit Singh Sawaran Singh, and Ammar AL-Ashmori. "Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages." Sustainability 15, no. 16 (2023): 12539. http://dx.doi.org/10.3390/su151612539.
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Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40% on SemEval-2018 and 53.45% on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models.
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Krishna, T. Shiva Rama. "Malware Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1847–53. http://dx.doi.org/10.22214/ijraset.2021.35426.
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Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.
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Gu, He, Tingwei Chen, Xiao Ma, Mengyuan Zhang, Yan Sun, and Jian Zhao. "CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification." Brain Sciences 15, no. 2 (2025): 124. https://doi.org/10.3390/brainsci15020124.
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Background: Brain–computer interface (BCI) technology opens up new avenues for human–machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application. Methods: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer’s self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer. Results: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods. Conclusions: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.
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Sharma, Amit, Dr V. K. Singh, and Dr Pushpendra Singh. "Deep CNN Based Hybrid Model for Image Retrieval." International Journal of Innovative Technology and Exploring Engineering 11, no. 9 (2022): 23–28. http://dx.doi.org/10.35940/ijitee.g9203.0811922.
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The popularity of deep features based image retrieval and classification task has grown a lot in the recent years. Feature representation based on Convolutional Neural Networks (CNNs) found to be very effective in terms of accuracy by various researchers in the field of visual content based image retrieval. The features which are neutral to their domain knowledge with automatic learning capability from their images are in demand in various image applications. For improving accuracy and expressive power, pre-trained CNN models with the use of transfer learning can be utilized by training them on huge volume of datasets. In this paper, a hybrid model for image retrieval is being proposed by using pre-trained values of hyper parameters as input learning parameters. The performance of the model is being compared with existing pre-trained models showing higher performance on precision and recall parameters
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Bhanja, Samit, and Abhishek Das. "A hybrid deep learning model for air quality time series prediction." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1611–18. https://doi.org/10.11591/ijeecs.v22.i3.pp1611-1618.
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Air quality (mainly PM2.5) forecasting plays an important role in the early detection and control of air pollution. In recent times, numerous deep learning-based models have been proposed to forecast air quality more accurately. The success of these deep learning models heavily depends on the two key factors viz. proper representation of the input data and preservation of temporal order of the input data during the feature’s extraction phase. Here we propose a hybrid deep neural network (HDNN) framework to forecast the PM2.5 by integrating two popular deep learning architectures, viz. Convolutional neural network (CNN) and bidirectional long short-term memory (BDLSTM) network. Here we build a 3D input tensor so that CNN can extract the trends and spatial features more accurately within the input window. Here we also introduce a linking layer between CNN and BDLSTM to maintain the temporal ordering of feature vectors. In the end, our proposed HDNN framework is compared with the state-of-the-art models, and we show that HDNN outruns other models in terms of prediction accuracy
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Kaur, Gagandeep, and Amit Sharma. "Automatic customer review summarization using deep learningbased hybrid sentiment analysis." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 2110–25. https://doi.org/10.11591/ijece.v14i2.pp2110-2125.
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Customer review summarization (CRS) offers business owners summarized customer feedback. The functionality of CRS mainly depends on the sentiment analysis (SA) model; hence it needs an efficient SA technique. The aim of this study is to construct an SA model employing deep learning for CRS (SADL-CRS) to present summarized data and assist businesses in understanding the behavior of their customers. The SA model employing deep learning (SADL) and CRS phases make up the proposed automatic SADL-CRS model. The SADL consists of review preprocessing, feature extraction, and sentiment classification. The preprocessing stage removes irrelevant text from the reviews using natural language processing (NLP) methods. The proposed hybrid approach combines review-related features and aspect-related features to efficiently extract the features and create a unique hybrid feature vector (HF) for each review. The classification of input reviews is performed using a deep learning (DL) classifier long shortterm memory (LSTM). The CRS phase performs the automatic summarization employing the outcome of SADL. The experimental evaluation of the proposed model is done using diverse research data sets. The SADL-CRS model attains the average recall, precision, and F1-score of 95.53%, 95.76%, and 95.06%, respectively. The review summarization efficiency of the suggested model is improved by 6.12% compared to underlying CRS methods.
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Rohith, R. Rohith R., S. Kavipriya S. Kavipriya, and Dayalan M. Joshua Dayalan M. Joshua. "Combining Feature Selection and Deep Learning for Precise COVID-19 Cases Forecasting: A Hybrid Approach." International Journal for Research Trends and Innovation 8, no. 11 (2023): 419–34. https://doi.org/10.5281/zenodo.10259171.
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Accurately predicting the number of new COVID-19 cases is crucial for informing public health policy and resource allocation decisions. In this study, we propose a hybrid approach for COVID-19 cases prediction using Light Gradient Boosting Machine (LightGBM) for feature selection with Optuna hyperparameter tuning and Long Short-Term Memory (LSTM). We use a multivariate time series dataset consisting of variables related to COVID-19 in India. We use LightGBM for feature selection and the selected features are used as input to the LSTM model. We use Optuna to tune the hyperparameters of LightGBM and evaluate the performance of our hybrid model using metrics like mean absolute percentage error (MAPE) and r-squared score(R2). Our experimental results show that our hybrid model outperforms both LSTM and LightGBM models individually and other similar combinations of feature selector algorithm and predictor models in terms of prediction accuracy. Specifically, our hybrid model achieves a MAPE of 0.87 on the test dataset. The selected features also provide insights into the most relevant factors for predicting COVID-19 cases in India.
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Rashmi Ashtagi. "Fusion of AI Techniques: A Hybrid Approach for Precise Plant Leaf Disease Classification." Journal of Electrical Systems 20, no. 1s (2024): 850–61. http://dx.doi.org/10.52783/jes.836.
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Classification of plant leaf diseases is an important step in protecting the world's food supply and agricultural yields. There has been encouraging progress in improving the efficiency and accuracy of plant leaf disease diagnosis via the combination of deep learning methods with artificial intelligence (AI) in recent years. This research introduces a new hybrid strategy, CNN+SVM and CNN+RF, that uses deep learning techniques like Convolutional Neural Networks (CNN) in conjunction with more traditional machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM). Moreover, two hybrid variants, CNN + RF and CNN + SVM, are proposed to exploit the strengths of both paradigms synergistically. To further improve classification accuracy, the study employs Particle Swarm Optimization (PSO) as a feature selection technique. PSO optimizes the feature subset for each classification model, facilitating the extraction of the most informative features, which leads to better discrimination between healthy and diseased plants. The dataset used for experimentation consists of a comprehensive collection of plant leaf images representing various diseases across multiple plant species. Experimental results demonstrate the efficacy of the proposed hybrid approach compared to individual classification methods. The hybrid models achieve higher accuracy rates and improved generalization performance, showcasing the synergistic benefits of combining AI and deep learning techniques. Furthermore, the feature selection process through PSO contributes significantly to enhancing the classification outcomes, providing insights into the discriminative power of selected features. This research contributes to the advancement of plant leaf disease classification methodologies by offering an innovative hybrid approach that leverages the complementary strengths of AI, deep learning, and feature selection techniques. The study's findings underscore the potential for improving plant leaf disease management strategies, ultimately leading to enhanced crop productivity and sustainable agriculture. The proposed hybrid framework can serve as a blueprint for similar classification tasks in other domains, demonstrating the broader impact of synergizing different AI techniques for improved accuracy and performance. CNN+RF gives 95% accuracy, 93% precision, 96% recall and 94% F1 score, whereas CNN+SVM gives 93% accuracy, 91% precision, 94% recall and 92% F1 score.
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Bharshankar, Gargi, and Meenakshi Thalor. "A Hybrid Acoustic And Deep Learning Approach For Enhanced Speech Emotion Recognition." A Hybrid Acoustic And Deep Learning Approach For Enhanced Speech Emotion Recognition 1, Vol. 1 No. 1 (2023): September 2023 (2024): 22. https://doi.org/10.59890/ijaamr.v1i1.291.
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Emotion recognition in speech is a key research topic in human-computer interaction. Understanding emotions in conversations can shed light on a person's well-being. This study introduces a hybrid architecture that combines acoustic and deep features for improved speech emotion recognition. Acoustic features like RMS energy and MFCC are extracted from voice records. Additionally, sound spectrogram images are processed using deep networks like VGG16 and ResNet to obtain deep features. These are merged into a hybrid feature vector, refined by the ReliefF algorithm. For classification, the Support Vector Machine is employed. Testing on datasets like RAVDESS and EMO-DB yielded accuracy rates up to 90.21%. Our method consistently outperformed existing techniques in accuracy.
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Habib, Md Ahasan, and M. J. Hossain. "Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering." Energies 17, no. 5 (2024): 1215. http://dx.doi.org/10.3390/en17051215.
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This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications.
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Gebeyehu, Seffi, and Zelalem Sintayehu Shibeshi. "Maize seed variety identification model using image processing and deep learning." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 990–98. https://doi.org/10.11591/ijeecs.v33.i2.pp990-998.
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Maize is Ethiopia’s dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (i.e., gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.
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Pandey, Avinash Chandra, and Dharmveer Singh Rajpoot. "Improving Sentiment Analysis using Hybrid Deep Learning Model." Recent Advances in Computer Science and Communications 13, no. 4 (2020): 627–40. http://dx.doi.org/10.2174/2213275912666190328200012.
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Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.
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Laaroussi, Houria, Fatima Guerouate, and Mohamed Sbihi. "A novel hybrid deep learning approachfor tourism demand forecasting." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1989. http://dx.doi.org/10.11591/ijece.v13i2.pp1989-1996.
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This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models.
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Almulihi, Ahmed, Hager Saleh, Ali Mohamed Hussien, et al. "Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction." Diagnostics 12, no. 12 (2022): 3215. http://dx.doi.org/10.3390/diagnostics12123215.
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Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the “silent killer,” is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper’s aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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Sulaiman, Adel, Swapandeep Kaur, Sheifali Gupta, et al. "ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images." Diagnostics 13, no. 12 (2023): 2121. http://dx.doi.org/10.3390/diagnostics13122121.
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Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.
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Yazdani, Muhammad Haris, Muhammad Muzammil Azad, Salman Khalid, and Heung Soo Kim. "A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates." Sensors 25, no. 3 (2025): 826. https://doi.org/10.3390/s25030826.
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Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures.
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Lu, Tianliang, Yanhui Du, Li Ouyang, Qiuyu Chen, and Xirui Wang. "Android Malware Detection Based on a Hybrid Deep Learning Model." Security and Communication Networks 2020 (August 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/8863617.
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In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection algorithms is low. Current state-of-the-art research shows that researchers started applying deep learning methods for malware detection. We proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU). First of all, analyze the Android malware; in addition to extracting static features, dynamic behavioral features with strong antiobfuscation ability are also extracted. Then, build a hybrid deep learning model for Android malware detection. Because the static features are relatively independent, the DBN is used to process the static features. Because the dynamic features have temporal correlation, the GRU is used to process the dynamic feature sequence. Finally, the training results of DBN and GRU are input into the BP neural network, and the final classification results are output. Experimental results show that, compared with the traditional machine learning algorithms, the Android malware detection model based on hybrid deep learning algorithms has a higher detection accuracy, and it also has a better detection effect on obfuscated malware.
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Borhade, Ratnaprabha Ravindra, Sheetal Sachin Barekar, Sharada N. Ohatkar, Piyush K. Mathurkar, Ravindra Honaji Borhade, and Pushpa Manoj Bangare. "ResneXt-Lenet: A hybrid deep learning for epileptic seizure prediction." Intelligent Decision Technologies 18, no. 3 (2024): 1675–93. http://dx.doi.org/10.3233/idt-240923.
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Encephalopathy is the result of epilepsy, which is defined as recurring seizures. Around the world, almost 65 million people suffer with epilepsy. Because an epileptic seizure involves a crucial clinical element and a clear contradiction with everyday activities, it can be difficult to predict it. The electroencephalogram (EEG) has been the established signal for clinical evaluation of brain activities. So far, several methodologies for the detection of epileptic seizures have been proposed but have not been effective. To bridge this gap, a powerful model for epileptic seizure prediction using ResneXt-LeNet is proposed. Here, a Kalman filter is used to preprocess the EEG signal to reduce noise levels in the signal. Then, feature extraction is performed to extract features, such as statistical and spectral. Feature selection is done using Fuzzy information gain that suggests appropriate choices for future processing, and finally, seizure prediction is done using hybrid ResneXt-LeNet, which is a combination of ResneXt and Lenet. The proposed ResneXt-LeNet achieved excellent performance with a maximum accuracy of 98.14%, a maximum sensitivity of 98.10%, and a specificity of 98.56%.
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E Loganathan, P Naveenkumar, C Santhosh, and P Shankareshwaran. "Cancer Disease Identification and Recommendation Using Hybrid Deep Learning Algorithms." International Research Journal on Advanced Science Hub 7, no. 01 (2025): 40–50. https://doi.org/10.47392/irjash.2025.006.
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It presents an automated system for cancer identification and classification using MATLAB, focusing on three types of cancer: brain tumour, skin cancer, and lung cancer. The system leverages advanced image processing techniques for cancer detection, feature extraction, and image segmentation to isolate cancerous regions in medical images. The core of the system is a Convolutional Neural Network (CNN), which is trained to predict the presence of cancer based on the extracted features. Feature selection methods are applied to reduce the complexity of the data, ensuring the CNN focuses on the most relevant characteristics of the suspected cancerous regions. The classification output not only confirms the presence of cancer but also distinguishes between different types of cancer, such as brain tumours, skin cancer, or lung cancer. Upon successful classification, the system provides medical recommendations, guiding clinicians toward appropriate next steps in diagnosis or treatment. This project aims to enhance cancer detection accuracy and efficiency, providing a non-invasive, automated solution to assist healthcare professionals in making informed decisions, potentially leading to earlier interventions and better patient care outcomes.
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Sonawale, Om. "Hybrid Deep Learning Framework for Personality Prediction in E-Recruitment." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49831.
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Abstract - The Personality prediction is a vital task in the domain of psychology, human-computer interaction, and user behavior analysis, with applications ranging from tailored advertisements to mental health assessments. Traditional methods rely heavily on self-report questionnaires or psychological assessments, which can be time-consuming and subjective. To overcome these limitations, researchers are exploring automated and objective methods using deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) algorithms, which excel at capturing complex patterns in multimodal data. In this work, we propose a hybrid framework that combines CNN-based feature extraction and NLP algorithms for predicting personality traits using data such as facial expressions, speech patterns, and text analysis. The CNN model is leveraged due to its robust feature extraction capabilities, enabling it to learn intricate patterns directly from raw data inputs, which correlate with the Big Five personality traits. Additionally, we integrate a Support Vector Classifier (SVC) to classify personality traits based on the extracted features, offering improved prediction accuracy across diverse data sources. Keywords—Personality Prediction, Big Five Personality Traits, CNN, NLP Algorithm, SVC Classifier
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Amit, Sharma, V.K. Singh Dr., and Pushpendra Singh Dr. "Deep CNN Based Hybrid Model for Image Retrieval." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 9 (2023): 23–28. https://doi.org/10.35940/ijitee.G9203.0811922.
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<strong>Abstract: </strong>The popularity of deep features based image retrieval and classification task has grown a lot in the recent years. Feature representation based on Convolutional Neural Networks (CNNs) found to be very effective in terms of accuracy by various researchers in the field of visual content based image retrieval. The features which are neutral to their domain knowledge with automatic learning capability from their images are in demand in various image applications. For improving accuracy and expressive power, pre-trained CNN models with the use of transfer learning can be utilized by training them on huge volume of datasets. In this paper, a hybrid model for image retrieval is being proposed by using pre-trained values of hyper parameters as input learning parameters. The performance of the model is being compared with existing pre-trained models showing higher performance on precision and recall parameters.
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Dilip Motwani, Et al. "Enhancing Intrusion Detection Systems with a Hybrid Deep Learning Model and Optimized Feature Composition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 369–80. http://dx.doi.org/10.17762/ijritcc.v11i10.8499.
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Systems for detecting intrusions (IDS) are essential for protecting network infrastructures from hostile activity. Advanced methods are required since traditional IDS techniques frequently fail to properly identify sophisticated and developing assaults. In this article, we suggest a novel method for improving IDS performance through the use of a hybrid deep learning model and feature composition optimization. RNN and CNN has strengths that the proposed hybrid deep learning model leverages to efficiently capture both spatial and temporal correlations in network traffic data. The model can extract useful features from unprocessed network packets using CNNs and RNNs, giving a thorough picture of network behaviour. To increase the IDS's ability to discriminate, we also offer feature optimization strategies. We uncover the most pertinent and instructive features that support precise intrusion detection through a methodical feature selection and engineering process. In order to reduce the computational load and improve the model's efficiency without compromising detection accuracy, we also use dimensionality reduction approaches. We carried out extensive experiments using a benchmark dataset that is frequently utilized in intrusion detection research to assess the suggested approach. The outcomes show that the hybrid deep learning model performs better than conventional IDS methods, obtaining noticeably greater detection rates and lower false positive rates. The performance of model is further improved by the optimized feature composition, which offers a more accurate depiction of network traffic patterns.
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Balakrishnan, Ms Bhavya. "Depression Risk Prediction using Hybrid Deep Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 2207–12. http://dx.doi.org/10.22214/ijraset.2023.57831.
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Abstract: This research endeavors to address the critical challenge of early prediction of depression, a pervasive mental health disorder that often eludes timely detection. Recognizing the substantial impact of late-stage diagnosis on treatment outcomes, this study introduces a robust machine learning model that leverages diverse data sources to predict the likelihood of an individual experiencing depression. The proposed model under- goes meticulous development, involving extensive data collection and pre-processing to curate a comprehensive dataset encompassing various aspects of an individual’s life. Machine learning algorithms are then applied to analyze the dataset, extracting patterns and features indicative of depressive tendencies. To enhance the model’s predictive performance and overall efficiency, the suggested system advocates the use of hybrid algorithms, specifically combining Convolutional Neural Network (ConvNet) and Recurrent Neural Network (RNN) variants. This hybrid approach brings forth several advantages, including spa- tial feature extraction and a hierarchy of features. The integration of RNN variants with ConvNet facilitates effective extraction of spatial features from diverse data types such as text, images, videos, and other spatially structured data. Additionally, the CNN layers in the hybrid model learn hierarchical representations of features, capturing both low-level and high-level spatial patterns. This unique capability enhances the model’s understanding of complex structures within the input data. The proposed model is meticulously trained and validated using a diverse set of metrics to ensure its reliability and generalizability. The anticipated outcome of this project holds significant potential to revolutionize early intervention strategies, facilitating timely support for individuals at risk of depression. By amalgamating advanced machine learning techniques with a holistic approach to data analysis, this study contributes to the ongoing efforts aimed at enhancing mental health outcomes and alleviating the societal burden associated with depression.
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Yousafzai, Bashir Khan, Sher Afzal Khan, Taj Rahman, et al. "Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network." Sustainability 13, no. 17 (2021): 9775. http://dx.doi.org/10.3390/su13179775.
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Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.
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Mucha, Swetha, and A. Ramesh Babu. "Classification of intracranial hemorrhage (CT) images using CNN-LSTM method and image-based GLCM features." MATEC Web of Conferences 392 (2024): 01075. http://dx.doi.org/10.1051/matecconf/202439201075.
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A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.