Relevant bibliographies by topics / Hybrid deep learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hybrid deep learning'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Hybrid deep learning"

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Yang, Mu, Zheng-Hao Liu, Ze-Di Cheng, Jin-Shi Xu, Chuan-Feng Li, and Guang-Can Guo. "Deep hybrid scattering image learning." Journal of Physics D: Applied Physics 52, no. 11 (2019): 115105. http://dx.doi.org/10.1088/1361-6463/aafa3c.

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Zhihua Chen, Zhihua Chen, Xiaolin Ju Zhihua Chen, Haochen Wang Xiaolin Ju, and Xiang Chen Haochen Wang. "Hybrid Multiple Deep Learning Models to Boost Blocking Bug Prediction." 網際網路技術學刊 23, no. 5 (2022): 1099–107. http://dx.doi.org/10.53106/160792642022092305018.

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<p>A blocking bug (BB) is a severe bug that could prevent other bugs from being fixed in time and cost more effort to repair itself in software maintenance. Hence, early detection of BBs is essential to save time and labor costs. However, BBs only occupy a small proportion of all bugs during software life cycle, making it difficult for developers to identify these blocking relationships. This study proposes a novel blocking bug prediction approach based on the hybrid deep learning model, a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CN
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Pravallika, V., V. Uday Kiran, B. Rahul, N. Neelima, G. Rishi Patnaik, and DR Sreejyothshna Ankam. "Deep Learning-Based Image Captioning: A Hybrid CNN-LSTM Approach." International Journal of Research Publication and Reviews 6, no. 4 (2025): 2459–63. https://doi.org/10.55248/gengpi.6.0425.1392.

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Wu, Chong. "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology." International Journal of Machine Learning and Computing 11, no. 3 (2021): 182–87. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1033.

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K, Mr Pragash, Deepak R, Gopinath R, Shasteeswaran Shasteeswaran, Ravianand Tharmiya, and Rohit S.K. "Emotion Detection In Facial Expressions And Speech Using Deep Hybrid Learning." International Journal of Research Publication and Reviews 5, no. 12 (2024): 1711–19. https://doi.org/10.55248/gengpi.5.1224.3508.

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Nehal, Mohamed Ali, Mostafa Abd El Hamid Marwa, and Youssif Aliaa. "Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models." International Journal of Data Mining & Knowledge Management Process (IJDKP) 9, no. 2/3 (2019): 19–27. https://doi.org/10.5281/zenodo.3340668.

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Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN we
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Anagnostara, Ioanna Marina, Emmanouil Tsardoulias, and Andreas L. Symeonidis. "Deep Reinforcement Learning and Imitation Learning for Autonomous Parking Simulation." Electronics 14, no. 10 (2025): 1992. https://doi.org/10.3390/electronics14101992.

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In recent years, system intelligence has revolutionized various domains, including the automotive industry, which has fully incorporated intelligence through the emergence of Advanced Driver Assistance Systems (ADAS). Within this transformative context, Autonomous Parking Systems (APS) have emerged as a foundational component, revolutionizing the way vehicles navigate and park with precision and efficiency. This paper presents a comprehensive approach to autonomous parallel parking, leveraging advancements in Artificial Intelligence (AI). Three state-of-the-practice approaches—Imitation Learni
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Gao, Fengjuan, Yu Wang, Lingyun Situ, and Linzhang Wang. "Deep Learning-Based Hybrid Fuzz Testing." International Journal of Software and Informatics 11, no. 3 (2021): 335–55. http://dx.doi.org/10.21655/ijsi.1673-7288.00261.

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Sun, Yi, Xiaogang Wang, and Xiaoou Tang. "Hybrid Deep Learning for Face Verification." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 10 (2016): 1997–2009. http://dx.doi.org/10.1109/tpami.2015.2505293.

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Lee, Sungjun, and Kisung Seo. "Hybrid Pruning of Deep Learning System." Transactions of The Korean Institute of Electrical Engineers 69, no. 11 (2020): 1750–54. http://dx.doi.org/10.5370/kiee.2020.69.11.1750.

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Dissertations / Theses on the topic "Hybrid deep learning"

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Singh, Amarjot. "ScatterNet hybrid frameworks for deep learning." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/285997.

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Image understanding is the task of interpreting images by effectively solving the individual tasks of object recognition and semantic image segmentation. An image understanding system must have the capacity to distinguish between similar looking image regions while being invariant in its response to regions that have been altered by the appearance-altering transformation. The fundamental challenge for any such system lies within this simultaneous requirement for both invariance and specificity. Many image understanding systems have been proposed that capture geometric properties such as shapes
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Yin, Yuan. "Physics-Aware Deep Learning and Dynamical Systems : Hybrid Modeling and Generalization." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS161.

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L'apprentissage profond a fait des progrès dans divers domaines et est devenu un outil prometteur pour modéliser les phénomènes dynamiques physiques présentant des relations hautement non linéaires. Cependant, les approches existantes sont limitées dans leur capacité à faire des prédictions physiquement fiables en raison du manque de connaissances préalables et à gérer les scénarios du monde réel où les données proviennent de dynamiques multiples ou sont irrégulièrement distribuées dans le temps et l'espace. Cette thèse vise à surmonter ces limitations dans les directions suivantes: améliorer
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Kabore, Raogo. "Hybrid deep neural network anomaly detection system for SCADA networks." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0190.

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Les systèmes SCADA sont de plus en plus ciblés par les cyberattaques en raison de nombreuses vulnérabilités dans le matériel, les logiciels, les protocoles et la pile de communication. Ces systèmes utilisent aujourd'hui du matériel, des logiciels, des systèmes d'exploitation et des protocoles standard. De plus, les systèmes SCADA qui étaient auparavant isolés sont désormais interconnectés aux réseaux d'entreprise et à Internet, élargissant ainsi la surface d'attaque. Dans cette thèse, nous utilisons une approche deep learning pour proposer un réseau de neurones profonds hybride efficace pour l
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ISAKSSON, LARS JOHANNES. "HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER." Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.

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Precision medicine holds the potential to revolutionize healthcare by providing every patient with personalized treatments and decisions tailored to his or her individual needs. This might be enabled by the large influx of potentially diagnostic information from new sources such as genetics and modern imaging techniques, provided the relevant information can be extracted. One such framework that has started to demonstrate promise in radiology, especially in the assessment of cancer, is radiomics; the practice of characterizing images by extracting a substantial amount of quantitative mathemati
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Déchelle-Marquet, Marie. "Deep learning based physical-statistics modeling of ocean dynamics." Electronic Thesis or Diss., Sorbonne université, 2023. https://theses.hal.science/tel-04166816.

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La modélisation des phénomènes dynamiques en géophysique repose sur une compréhension de la physique sous-jacente, décrite sous la forme d'EDP, et sur leur résolution par des modèles numériques. Le nombre croissant d'observations de systèmes physiques, l'essor récent de l'apprentissage profond et l'énorme puissance de calcul requise par les solveurs numériques, qui entrave la résolution des modèles existants, suggèrent que l'avenir des modèles physiques pourrait être orienté données. Mais pour cela, l'apprentissage profond doit relever plusieurs défis, tels que l'interprétabilité et la cohéren
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Theobald, Claire. "Bayesian Deep Learning for Mining and Analyzing Astronomical Data." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0081.

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Dans cette thèse, nous abordons le problème de la confiance que nous pouvons avoir en des systèmes prédictifs de type réseaux profonds selon deux directions de recherche complémentaires. Le premier axe s'intéresse à la capacité d'une IA à estimer de la façon la plus juste possible son degré d'incertitude liée à sa prise de décision. Le second axe quant à lui se concentre sur l'explicabilité de ces systèmes, c'est-à-dire leur capacité à convaincre l'utilisateur humain du bien fondé de ses prédictions. Le problème de l'estimation des incertitudes est traité à l'aide de l'apprentissage profond ba
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Buvari, Sebastian, and Kalle Pettersson. "A Comparison on Image, Numerical and Hybrid based Deep Learning for Computer-aided AD Diagnostics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279977.

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Alzheimer’s disease (AD) is the most common form of dementia making up 60- 70% of the 50 million active cases worldwide and is a degenerative disease which causes irreversible damage to the parts of the brain associated with the ability of thinking and memorizing. A lot of time and effort has been put towards diagnosing and detecting AD in its early stages and a field showing great promise in aiding with early stage detection is deep learning. The main issues with deep learning in the field of AD detection is the lack of relatively big datasets that are typically needed in order to train an ac
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Benkirane, Fatima Ezzahra. "Integration of contextual knowledge in deep Learning modeling for vision-based scene analysis." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCA002.

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La vision par ordinateur a connu une évolution importante, passant des méthodes traditionnelles aux modèles d'apprentissage profond. L’un des principaux objectifs des tâches de vision par ordinateur est d’émuler la perception humaine. En effet, le processus classique effectué par les modèles d’apprentissage profond dépend entièrement des caractéristiques visuelles, reflétant simplement la manière dont les humains perçoivent visuellement leur environnement. Cependant, pour que les humains comprennent l’environnement qui les entoure, leur raisonnement dépend non seulement de leurs capacités visu
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Chaulagain, Dewan. "Hybrid Analysis of Android Applications for Security Vetting." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1555608766287613.

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Duong, Nam duong. "Hybrid Machine Learning and Geometric Approaches for Single RGB Camera Relocalization." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0008.

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Au cours des dernières années, la relocalisation de la caméra à base d'images est devenue un enjeu important de la vision par ordinateur appliquée à la réalité augmentée, à la robotique ainsi qu'aux véhicules autonomes. La relocalisation de la caméra fait référence à la problématique de l'estimation de la pose de la caméra incluant à la fois la translation 3D et la rotation 3D. Dans les systèmes de localisation, le composant de relocalisation de la caméra est nécessaire pour récupérer la pose de la caméra après le suivi perdu, plutôt que de redémarrer la localisation à partir de zéro.Cette thè
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Books on the topic "Hybrid deep learning"

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Li, Yuecheng, and Hongwen He. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-79206-9.

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He, Hongwen, and Li Yeuching. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Morgan & Claypool Publishers, 2022.

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Li, Yeuching, and Hongwen He. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Morgan & Claypool Publishers, 2022.

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Yeuching, Li, and He Hongwen. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Springer International Publishing AG, 2022.

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Li, Yeuching, and Hongwen He. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Morgan & Claypool Publishers, 2022.

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Boden, Margaret A. 4. Artificial neural networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0004.

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Artificial neural networks (ANNs) are made up of many interconnected units, each one capable of computing only one thing. ANNs have myriad applications, from playing the stock market and monitoring currency fluctuations to recognizing speech or faces. ANNs are parallel-processing virtual machines implemented on classical computers. They are intriguing partly because they are very different from the virtual machines of symbolic AI. Sequential instructions are replaced by massive parallelism, top-down control by bottom-up processing, and logic by probability. ‘Artificial neural networks’ conside
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Oswald, Laura R. Doing Semiotics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198822028.001.0001.

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Structural semiotics is a hybrid of communication science and anthropology that accounts for the deep cultural codes that structure communication and sociality, endow things with value, move us through constructed space, and moderate our encounters with change. Doing Semiotics: A Research Guide for Marketers at the Edge of Culture, shows readers how to leverage these codes to solve business problems, foster innovation, and create meaningful experiences for consumers. In addition to the basic principles and methods of applied semiotics, the book introduces the reader to branding basics, strateg
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Book chapters on the topic "Hybrid deep learning"

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Dubisetty, Vidyanadha Babu, T. Muralikrishna, Lakshmi Prasad Koya, D. Saradha Mani, and S. D. Ruhi Parveen. "Diabetic retinopathy using deep learning." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559139-17.

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Arora, Rashmi, Jayant Dhingra, and Abhinav Sharma. "Face Mask Detection Using Deep Learning." In Hybrid Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_36.

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Madhavi, K. R., G. Madhavi, C. V. Krishnaveni, and Padmavathi Kora. "COVID-19 Detection Using Deep Learning." In Hybrid Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_26.

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Verma, Deepali, Akarsh Verma, Aman Verma, and Hariome Sharan Gupta. "Applications of Deep Learning for Composites Materials." In Hybrid Composite Materials. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2104-7_7.

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Wali, Wafa, and Bilel Gargouri. "Sentence Similarity Computation Based on Deep Learning." In Hybrid Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_40.

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Tripathy, B. K., Sudershan Sridhar, and Sharmila Banu K. "Voice recognition system using deep learning." In Hybrid Computational Intelligent Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003381167-19.

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Raju, Penmetsa Kanaka, Lakkoju Devika, Kondepati Trishaswi, Koyya Poojitha, and Lanka Mohana Krishna. "Skin cancer classification using deep learning." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559139-36.

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Thakare, Anuradha D., Shilpa Laddha, and Ambika Pawar. "Deep Learning for Information Retrieval." In Hybrid Intelligent Systems for Information Retrieval. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003187974-8.

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Tamilselvi, Mani, Subra Sundra Selvamony Kalaivani, Venkat Sunderasan, Kotapati Sailaja, Dhaarani Gopal, and R. Karthick. "Deep learning for object detection and identification." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559139-29.

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Elleuch, Mohamed, and Monji Kherallah. "Convolutional Deep Learning Network for Handwritten Arabic Script Recognition." In Hybrid Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_11.

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Conference papers on the topic "Hybrid deep learning"

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Chaturvedi, Rajnish Kumar, and Nitin Arvind Shelke. "Music generation using hybrid deep neural model." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007294.

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Kusi, Narayan Prasad, Sung Hwan Ahn, and Dong Ho Kim. "Hybrid ARQ for URLLC Using Deep Learning." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10826805.

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Devi, S. Vijaya Amala, K. Vijayalakshmi, R. Santhana Krishnan, J. Relin Francis Raj, R. Umesh, and N. Soundiraraj. "Hybrid Deep Learning Methods for Enhancing Parkinson's Disease Early Detection." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933259.

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Jihane, Benbrik, Rattal Salma, Ghoumid Kamal, and Ar-Reyouchi El Miloud. "Advancing Healthcare Diagnostics with a Hybrid AI Model." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933484.

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Karthikeyan, P., M. Karthik, R. Sowndarya, S. Sanjeev Gandhi, E. S. Sundaresh, and P. Gowtham. "Design and Development of Hybrid Quadratic Boost Converter." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933245.

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Sundar, Koushik, Bhavani M, Jaeyalakshmi M, and Vijayakumar R. "Document Analysis Using Adaptive Hybrid Deep Learning Techniques." In 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). IEEE, 2024. https://doi.org/10.1109/icesic61777.2024.10846280.

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Kumar, B. Ramana, Farhana Bano, M. Sirisha, Mrutyunjaya S. Yalawar, Fathima S.K, and Polu Srinivasa Reddy. "Brain Tumor Detection Using Hybrid Deep Learning Approaches." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910697.

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Chandira Prabha, S., S. Kaviyadharshini, and A. Annie Micheal. "A Hybrid Deep Learning Model for Violence Detection." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914952.

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Sharma, Rishabh, and Abhinav Mishra. "Radish Classification by using Hybrid Deep Learning Approach." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810964.

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Win, Aye Mya Mya, and Ah Nge Htwe. "Abnormal Fall Detection by Hybrid Deep Learning Model." In 2025 IEEE Conference on Computer Applications (ICCA). IEEE, 2025. https://doi.org/10.1109/icca65395.2025.11011186.

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Reports on the topic "Hybrid deep 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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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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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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