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Journal articles on the topic 'Hybrid deep-STTF feature learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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 fire

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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 fir

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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

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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 pro

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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 Eli

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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 reducin

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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

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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 highe

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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 dat

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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 convolu

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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

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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 use

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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 classi

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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 o

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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 f

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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 mo

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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 b

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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 cybe

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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 det

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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 h

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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 discrepanc

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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 fro

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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

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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 f

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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 pres

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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 malw

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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,

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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. Co

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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 o

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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.

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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

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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). Moreov

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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 Suppor

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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

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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

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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 l

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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 an

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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

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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

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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 h

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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 be

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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

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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 mos

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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 cap

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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

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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 extra

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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 individ

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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

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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 classify

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