Relevant bibliographies by topics / Hybrid deep-STTF feature learning

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  2. Dissertations / Theses
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Academic literature on the topic 'Hybrid deep-STTF feature learning'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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

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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 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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Dissertations / Theses on the topic "Hybrid deep-STTF feature learning"

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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal
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Pan, Hsiang-Hua, and 潘香樺. "A Deep Learning Framework with Region Features and Hybrid Regression for Age Estimation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dazdp6.

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碩士<br>國立臺灣科技大學<br>機械工程系<br>106<br>We propose the Region-based Hybrid Framework (RHF) with moving segmentation and soft-boundary regression for age estimation. The RHF is an ensemble of VGG networks, and each VGG net considers a specific facial region as input. The VGG is selected from a comparison of pretrained facial models originally designed for face recognition, but trained again for age estimation by transfer learning. To improve the accuracy of RHF, we implement two schemes, the moving segmentation and soft boundary regression. The moving segmentation better determines the boundary ages
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Book chapters on the topic "Hybrid deep-STTF feature learning"

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Jena, Pradeep Kumar, Bonomali Khuntia, Sarbajit Mohanty, and Charulata Palai. "Multimodal Face Recognition System Using Hybrid Deep Learning Feature." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-4359-9_19.

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Kadhar, S. A. Abdul, and S. Brintha Rajakumari. "Fake News Classification Using Feature Based Hybrid Deep Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86293-9_21.

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Khaire, Utkarsh Mahadeo, R. Dhanalakshmi, and K. Balakrishnan. "Hybrid Marine Predator Algorithm with Simulated Annealing for Feature Selection." In Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003226147-7.

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Verma, Jyoti, Isha Kansal, Renu Popli, et al. "A Hybrid Images Deep Trained Feature Extraction and Ensemble Learning Models for Classification of Multi Disease in Fundus Images." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_14.

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AbstractRetinal disorders, including diabetic retinopathy and macular degeneration due to aging, can lead to preventable blindness in diabetics. Vision loss caused by diseases that affect the retinal fundus cannot be reversed if not diagnosed and treated on time. This paper employs deep-learned feature extraction with ensemble learning models to improve the multi-disease classification of fundus images. This research presents a novel approach to the multi-classification of fundus images, utilizing deep-learned feature extraction techniques and ensemble learning to diagnose retinal disorders an
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Liu, Siqi, Daguang Xu, S. Kevin Zhou, Sasa Grbic, Weidong Cai, and Dorin Comaniciu. "Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Images." In Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13969-8_10.

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Muduli, Debendra, Santosh Kumar Sharma, Debasish Pradhan, Madhusmita Das, Suryakanta Mahapatra, and Saroj Kumar sahoo. "A Hybrid Deep Learning Approach for COVID-19 Detection: Feature Fusion and SVM Classification." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8093-8_8.

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Krishnachalitha, K. C., and C. Priya. "Wireless Sensor Network-Based Hybrid Intrusion Detection System on Feature Extraction Deep Learning and Reinforcement Learning Techniques." In Intelligent Computing and Innovation on Data Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3284-9_38.

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Akanksha, Akanksha. "Tamil Language Automatic Speech Recognition Based on Integrated Feature Extraction and Hybrid Deep Learning Model." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9719-8_23.

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Kaur, Jasleen, and Jatinderkumar Saini. "Deep Learning and Super-Hybrid Textual Feature Based Multi-category Thematic Classifier for Punjabi Poetry." In Applied Computational Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2719-5_5.

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Wei, Xiongfei, Jing Wang, Yi Ruan, and Yuanjie Fang. "Wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-0232-2_8.

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Conference papers on the topic "Hybrid deep-STTF feature learning"

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Sreepadh, A. S., P. Sheshu, R. G. Reshiha, T. Vinay Kumar, and Gurusamy Jeyakumar. "A Hybrid Feature Selection Model for Early Prediction of Metabolic Syndrome." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933207.

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Yuvalatha, S., S. Nithyapriya, R. Tamizh Kuzhali, S. Savitha, S. K. Muthusundar, and Antonidoss A. "Capsule-Infused Vision Transformer: A Hybrid Deep Learning for Hierarchical Feature Learning." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026478.

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Yue, Yuan, Jeremiah D. Deng, Tapabrata Chakraborti, Dirk De Ridder, and Patrick Manning. "Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781741.

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Zaman, Wasim, Muhammad Farooq Siddique, and Jong-Myon Kim. "Centrifugal Pump Fault Detection with Hybrid Feature Pool and Deep Learning." In 2023 20th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2023. http://dx.doi.org/10.1109/ibcast59916.2023.10712967.

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Subbarayudu, B., Y. Giri Vardhan, B. Jahnavi, and P. Karuna Kumari. "A Hybrid Feature Optimization and Deep Learning Model for Brain Tumor Detection." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11042081.

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Mondal, Sourav, Renusree Bonthagorla, Harshitha Kotapati, Harika Tummala, and Debnarayan Khatua. "Deep Learning for Skin Cancer: Hybrid Feature Extraction with GoogleNet and MobileNet." In 2025 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE, 2025. https://doi.org/10.1109/amathe65477.2025.11080891.

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Gan, Li. "Research on optimization of face recognition algorithm in smart campus driven by multimodal feature fusion: construction of hybrid model based on dynamic weight allocation." In International Conference on Machine Vision and Deep Learning (MVDL 2025), edited by Chengzhong Xu and Dickson K. W. Chiu. SPIE, 2025. https://doi.org/10.1117/12.3071111.

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Shyamala, N., and S. Mahaboob Basha. "Hybrid Deep Learning Model for Robust Brain Tumor Detection: Integrating Filtering and Feature Extraction." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013737.

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Dongre, Aditya Kumar, and G. Kalaiarasi. "A Survey on Fake News Detection Using Multivariate Feature Selection and Hybrid Deep Learning Approach." In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET). IEEE, 2025. https://doi.org/10.1109/icaet63349.2025.10932142.

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Gowthamy, J., C. Suleka, W. Kenitawin Helena Pereira, and P. Monish Kumar. "Towards Smart Hybrid Feature Analysis and Deep neuroevolutionary Brain Tumour Prediction based on Transfer Learning." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774740.

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Reports on the topic "Hybrid deep-STTF feature learning"

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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