Thematische Bibliographien / HYBRID CNN-RNN MODEL

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Auswahl der wissenschaftlichen Literatur zum Thema „HYBRID CNN-RNN MODEL“

Autor: Grafiati

Veröffentlicht am 11. September 2023

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "HYBRID CNN-RNN MODEL"

Dr. J. GLADSON MARIA BRITTO, Dr. NARENDHAR MULUGU, and Mrs. K SOWJANYA BHARATHI. "A HYBRID DEEP LEARNING APPROACH FOR BREAST CANCER DETECTION USING CNN AND RNN." Bioscan 19, Supplement 2 (2024): 272–86. https://doi.org/10.63001/tbs.2024.v19.i02.s2.pp272-286.

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Breast cancer remains one of the most prevalent cancers among women worldwide, making early detection essential for effective treatment. This paper presents a novel approach to breast cancer detection using a hybrid architecture that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). By harnessing the strengths of CNNs for feature extraction and RNNs for sequence analysis, this hybrid model aims to enhance the accuracy and efficiency of breast cancer detection from medical imaging data. In our approach, the CNN component extracts meaningful features from mammogra

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Zaheer, Shahzad, Nadeem Anjum, Saddam Hussain, et al. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model." Mathematics 11, no. 3 (2023): 590. http://dx.doi.org/10.3390/math11030590.

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Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and f

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Airlangga, Gregorius. "A Hybrid CNN-RNN Model for Enhanced Anemia Diagnosis: A Comparative Study of Machine Learning and Deep Learning Techniques." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 2 (2024): 366. http://dx.doi.org/10.24014/ijaidm.v7i2.29898.

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This study proposes a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model for the accurate diagnosis of anemia types, leveraging the strengths of both architectures in capturing spatial and temporal patterns in Complete Blood Count (CBC) data. The research involves the development and evaluation of various models of single-architecture deep learning (DL) models, specifically Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Convolutional Network (FCN). The models are trained and validated using stratified k-fold

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Krishnan, V. Gokula, M. V. Vijaya Saradhi, T. A. Mohana Prakash, K. Gokul Kannan, and AG Noorul Julaiha. "Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (2022): 133–39. http://dx.doi.org/10.17762/ijritcc.v10i12.5894.

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Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed

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Kiranpure, Ayush. "Cyclone Intensity Prediction Using Deep Learning on INSAT-3D IR Imagery: A Comparative Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45392.

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This study investigates the effectiveness of deep learning techniques in accurately estimating tropical cyclone intensity using infrared (IR) imagery from the INSAT-3D satellite. We assess the performance of three models—Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid CNN-RNN model—comparing them against traditional machine learning methods like Support Vector Machines (SVM) and Random Forests (RF). Results demonstrate that deep learning models significantly outperform traditional approaches, with the CNN-RNN model achieving the highest accuracy. These findings

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Ashraf, Mohsin, Fazeel Abid, Ikram Ud Din, et al. "A Hybrid CNN and RNN Variant Model for Music Classification." Applied Sciences 13, no. 3 (2023): 1476. http://dx.doi.org/10.3390/app13031476.

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Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine the original music characteristics. Comparatively, music classification using deep learning models has been shown to be dynamic and effective. Among the many neural networks, the combination of a convolutional neural network (CNN) and variants of a recurrent neural network (RNN) has not been signific

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Francis Densil Raj V. "A Novel CNN-RNN-LSTM Framework for Predictive Cardiovascular Diagnostics of Aortic Stenosis in a Large Scale 12-Lead ECG Dataset." Communications on Applied Nonlinear Analysis 32, no. 3 (2024): 685–700. https://doi.org/10.52783/cana.v32.2483.

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Aortic stenosis (AS) is a disease of the valve between the heart and aorta and may lead to heart failure if left untreated; it is one of the significant valvular heart diseases caused by the narrowing of this valve. Conventional diagnostic techniques are invasive and require resources. Machine learning and deep learning approaches for the non-invasive identification of AS were investigated using an extensive 12-lead ECG dataset of 10,646 patient records. A range of models was assessed for diagnostic performance, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural N

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Yu, Dian, and Shouqian Sun. "A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition." Information 11, no. 4 (2020): 212. http://dx.doi.org/10.3390/info11040212.

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Subject-independent emotion recognition based on physiological signals has become a research hotspot. Previous research has proved that electrodermal activity (EDA) signals are an effective data resource for emotion recognition. Benefiting from their great representation ability, an increasing number of deep neural networks have been applied for emotion recognition, and they can be classified as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination of these (CNN+RNN). However, there has been no systematic research on the predictive power and configurations of

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Behera, Bibhuti Bhusana, Binod Kumar Pattanayak, and Rajani Kanta Mohanty. "Deep Ensemble Model for Detecting Attacks in Industrial IoT." International Journal of Information Security and Privacy 16, no. 1 (2022): 1–29. http://dx.doi.org/10.4018/ijisp.311467.

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In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the

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Abdulkarim, Abdullahi, John K. Alhassan, and Sulaimon A. Bashir. "Document Classification in HEIs Using Deep Learning." Proceedings of the Faculty of Science Conferences 1 (March 1, 2025): 38–42. https://doi.org/10.62050/fscp2024.462.

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Higher Education Institutions (HEIs) are increasingly confronted with the complexities of evolving rules and requirements, necessitating innovative technology solutions to streamline document handling processes. Traditional paperwork methods are often inefficient and error-prone, leading to potential non-compliance. This research addresses these challenges by developing an AI-powered electronic document management system designed to automate compliance checks and simplify document handling as HEIs grow. The primary objective is to create a document classification model utilizing deep learning

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Dissertationen zum Thema "HYBRID CNN-RNN MODEL"

SONI, ANKIT. "DETECTING DEEPFAKES USING HYBRID CNN-RNN MODEL." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19168.

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We are living in the world of digital media and are connected to various types of digital media contents present in form of images and videos. Our lives are surrounded by digital contents and thus originality of content is very important. In the recent times, there is a huge emergence of deep learning-based tools that are used to create believable manipulated media known as Deepfakes. These are realistic fake media, that can cause threat to reputation, privacy and can even prove to be a serious threat to public security. These can even be used to create political distress, spread f

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Buchteile zum Thema "HYBRID CNN-RNN MODEL"

Ma, Zhiyuan, Wenge Rong, Yanmeng Wang, Libin Shi, and Zhang Xiong. "A Hybrid RNN-CNN Encoder for Neural Conversation Model." In Knowledge Science, Engineering and Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99247-1_14.

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Renjith, S., Rashmi Manazhy, and M. S. Sumi Suresh. "Recognition of Sign Language Using Hybrid CNN–RNN Model." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3591-4_2.

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Chopra, Sonali, Parul Agarwal, Jawed Ahmed, Siddhartha Sankar Biswas, and Ahmed J. Obaid. "RNN-CNN Based Hybrid Deep Learning Model for Mental Healthcare." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8074-7_30.

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Bhattacharya, Somnath, and Padmalini Singh. "CNN–RNN Hybrid Deep Learning Model for Monthly Rainfall Prediction." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7717-4_39.

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Guo, Long, Dongxiang Zhang, Lei Wang, Han Wang, and Bin Cui. "CRAN: A Hybrid CNN-RNN Attention-Based Model for Text Classification." In Conceptual Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00847-5_42.

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Kunndra, Chetanya, Arjun Choudhary, Jaspreet Kaur, Aryan Jogia, Prashant Mathur, and Varun Shukla. "NTPhish: A CNN-RNN Hybrid Deep Learning Model to Detect Phishing Websites." In Cryptology and Network Security with Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0641-9_40.

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Bensalah, Nouhaila, Habib Ayad, Abdellah Adib, and Abdelhamid Ibn El Farouk. "CRAN: An Hybrid CNN-RNN Attention-Based Model for Arabic Machine Translation." In Networking, Intelligent Systems and Security. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3637-0_7.

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Kumar, Abhishek, and Abhishek Kumar Mehto. "Efficient anomaly detection using GNN, CNN and RNN based hybrid models." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003530190-116.

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Asadi, Srinivasulu, A. V. Senthil Kumar, and Anupam Agrawal. "Enhancing Cardiovascular Disease Detection and Prediction." In Advances in Medical Diagnosis, Treatment, and Care. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7728-4.ch013.

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide, underscoring the need for highly accurate and scalable prediction models to enable early detection and timely intervention. However, current machine learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), often struggle with limitations such as low early detection accuracy, inefficient feature extraction, and scalability issues. To overcome these challenges, this study introduces an innovative hybrid CNN-RNN model. The model was tested on a dataset that included essential cl

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Srinivasulu, Asadi, Bhimsingh Bohara, A. V. Senthil Kumar, et al. "Enhancing Throat Cancer Prediction and Detection." In Advances in Medical Diagnosis, Treatment, and Care. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7728-4.ch011.

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Throat cancer continues to be a major global health challenge, highlighting the need for early detection and precise diagnosis to improve patient outcomes. While traditional deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have shown potential in analyzing medical images and predicting symptoms, they often encounter issues like overfitting, lack of interpretability, and difficulties handling imbalanced data. This research introduces a hybrid CNN-RNN model designed to enhance throat cancer prediction and detection by overcoming these limitat

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Konferenzberichte zum Thema "HYBRID CNN-RNN MODEL"

Raphael, Anisha, Abisri S, Anitha E, Ritika S, and Manju Venugopalan. "Attention Based CNN-RNN Hybrid Model for Image Captioning." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10923871.

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H C, Bhanujyothi, and I. Jeena Jacob. "Hybrid RNN-CNN model for predicting stock market trends." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895957.

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Momynkulov, Zeinel, Nurzhan Omarov, and Aigerim Altayeva. "CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban Area." In 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2024. http://dx.doi.org/10.1109/sist61555.2024.10629358.

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Surapally, Swathi, Siva Ramakrishna Jeevakala, and Sriya Pranathi Aluri. "Hybrid CNN and RNN Variant Model for Indian Music Genre Classification." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009650.

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Mohammad, Amanulla, G. Suryakala Eswari, Perla Ratna Kumari, A. Lakshmanarao, and D. Chandra Mouli. "A Hybrid CNN-RNN Model for Enhanced Pneumonia Detection using X-Ray Imaging." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796125.

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Preethi, R., Ritul Bharati, and S. Priya. "Predicting Antibiotic Resistance from Genomic Sequences Using a Hybrid CNN-RNN Model: A Comprehensive Approach." In 2024 Third International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS). IEEE, 2024. https://doi.org/10.1109/aicecs63354.2024.10957126.

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Sadek, Zayneb, Abir Hadriche, and Nawel Jmail. "An Efficient CNN and RNN Hybrid Model for the Detection of Epileptic Seizures in EEG Signals." In 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2024. https://doi.org/10.1109/bibe63649.2024.10820445.

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Salamkayala, Om V. P., Saeed Shiry Ghidary, Christopher Howard, Russell Campion, and Joideep Banerjee. "Detection of ICMPV6 DDOS Attacks Using Ensemble Stacking of Hybrid Model-1 (CNN-LSTM) and Model-2 (RNN-GRU)." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935151.

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Varshney, Rajat Kishor, Alok Katiyar, and Prashant Johri. "Hybrid CNN-RNN Models for Multimodal Analysis of Autism Spectrum Disorder Neuroimaging." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10956945.

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Simhadri, V., Kilari Jyothi, and Reddy Rakesh. "Quality-Aware Approach to Arrhythmia Detection Using CNN and Hybrid RNN Models." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035120.

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