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Artykuły w czasopismach na temat „HYBRID CNN-RNN MODEL”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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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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Le, An Thanh, Masoud Shakiba, Iman Ardekani, and Waleed H. Abdulla. "Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network." Applied Sciences 14, no. 19 (2024): 9118. http://dx.doi.org/10.3390/app14199118.

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This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing

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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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Cheng, Yepeng, Zuren Liu, and Yasuhiko Morimoto. "Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting." Information 11, no. 6 (2020): 305. http://dx.doi.org/10.3390/info11060305.

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Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn’t consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not

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Alagarsundaram, Poovendran, Surendar Rama Sitaraman, Kalyan Gattupalli, Harikumar Nagarajan, Venkata Surya Bhavana Harish Gollavilli, and Jayanth S. Jayanth.S. "Enhancing Healthcare Delivery with Cloud Computing Using CNN-RNN Models for Kidney Disease Diagnosis and Management." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 275–82. https://doi.org/10.35629/5252-0703275282.

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Chronic Kidney Disease (CKD) is a chronic disease that must be diagnosed early to avoid kidney failure. This research proposes a hybrid model using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to predict CKD from different patient medical information. The proposed model is instantiated on cloud resources for elastic storage and secure management of data with HIPAA-standard encryption for privacy. Comparison in terms of performance of the current cloud-based approach and the proposed CNN-RNN model reveals colossal improvement. The proposed approach optimizes critical me

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Nasser, Ahmed Raoof, and Omar Younis Alani. "Investigation of Multiple Hybrid Deep Learning Models for Accurate and Optimized Network Slicing." Computers 14, no. 5 (2025): 174. https://doi.org/10.3390/computers14050174.

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In 5G wireless communication, network slicing is considered one of the key network elements, which aims to provide services with high availability, low latency, maximizing data throughput, and ultra-reliability and save network resources. Due to the exponential expansion of cellular networking in the number of users along with the new applications, delivering the desired Quality of Service (QoS) requires an accurate and fast network slicing mechanism. In this paper, hybrid deep learning (DL) approaches are investigated using convolutional neural networks (CNNs), Long Short-Term Memory (LSTM),

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Lambamo, Wondimu, Ramasamy Srinivasagan, Worku Jifara, and Ali Alzahrani. "Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1050–62. https://doi.org/10.11591/ijai.v13.i1.pp1050-1062.

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Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover,

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Park, Kyoungjong. "Performance comparison of machine learning and deep learning models for supply chain tier order quantity prediction: Emphasis on tree-based and CNN-BILSTM approaches." Journal of Infrastructure, Policy and Development 8, no. 14 (2024): 9683. http://dx.doi.org/10.24294/jipd9683.

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This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive pe

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Bilal, Hazrat, Yibin Tian, Ahmad Ali, et al. "An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques." Bioengineering 11, no. 12 (2024): 1290. https://doi.org/10.3390/bioengineering11121290.

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This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output. The output of the ETC model was then added to the original feature space by producing an enriched feature matrix, which is then us

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Muhammad Kamran Abid, Rabia Sajjad, Muhammad Fuzail, Ahmad Naeem, Naeem Aslam, and Kiran Shahzadi. "INTEGRATING TEMPORAL DYNAMICS IN FACIAL EMOTION RECOGNITION USING HYBRID CNN-RNN MODELS FOR ENHANCED HUMAN-COMPUTER INTERACTION." Kashf Journal of Multidisciplinary Research 2, no. 06 (2025): 1–15. https://doi.org/10.71146/kjmr463.

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Facial Emotion Recognition (FER) is still an important branch in computer vision and artificial intelligence, mainly benefiting Human-Computer Interaction (HCI). Existing FER systems, which are mainlybased on Convolutional Neural Networks (CNNs) for analysis of static images, do not support the dynamic evolution of human emotions over time. To address these issues, this work presents a novel model that incorporates temporal information in FER using a hybrid CNN-RNN (Recurrent Neural Network). The proposed method uses CNNs for spatial emotion feature extraction, and RNNs to model the sequential

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Sreekala, Keshetti, Srilatha Yalamati, Annemneedi Lakshmanarao, Gubbala Kumari, Tanapaneni Muni Kumari, and Venkata Subbaiah Desanamukula. "A hybrid convolutional neural network-recurrent neural network approach for breast cancer detection through Mask R-CNN and ARI-TFMOA optimization." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 3 (2025): 3084. https://doi.org/10.11591/ijece.v15i3.pp3084-3094.

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This paper presents a novel hybrid deep learning-based approach for breast cancer detection, addressing critical challenges such as overfitting and performance degradation in varying data conditions. Unlike traditional methods that struggle with detection accuracy, this work integrates a unique combination of advanced segmentation and classification techniques. The segmentation phase leverages Mask region-based convolutional neural network (R-CNN), enhanced by the adaptive random increment-based tomtit flock metaheuristic optimization algorithm (ARI-TFMOA), a novel algorithm inspired by natura

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Lambamo, Wondimu, Ramasamy Srinivasagan, Worku Jifara, and Ali Alzahrani. "Bi-Directional Hybrid Deep Learning model for Speaker Iden-tification." International Journal of Advanced Science and Computer Applications 3, no. 1 (2023): 43–54. http://dx.doi.org/10.47679/ijasca.v3i1.43.

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Speaker identification is the process of automatically determining who is speaking from the known speakers by the model. It is crucial in voice-based authentication, forensic investigations, security and surveillance. In recent studies, the combination of convolutional neural network (CNN) and recurrent neural network (RNN) variants performed better than separate models of both. However, only limited studies are conducted in speaker identification using a combination of CNN and RNN variants. In this study, we proposed speaker identification using hybrid two-dimensional CNN (2DCNN) and bidirect

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Pawar, Mahendra Eknath, Rais Allauddin Mulla, Sanjivani H. Kulkarni, Sajeeda Shikalgar, Harikrishna B. Jethva, and Gunvant A. Patel. "A Novel Hybrid AI Federated ML/DL Models for Classification of Soil Components." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (2022): 190–99. http://dx.doi.org/10.17762/ijritcc.v10i1s.5823.

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The soil is the most fundamental component for the survival of any living thing that can be found on this planet. A little less than 41 percent of Indians are employed in agriculture, which accounts for approximately 19 percent of the country's gross domestic product. As is the case in every other industry, researchers and scientists in this one are exerting a lot of effort to enhance agricultural practices by utilising cutting-edge methods such as machine learning, artificial intelligence, big data, and so on. The findings of the study described in this paper are predicated on the assumption

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UTKU, Anıl. "Kentsel Trafik Tahminine Yönelik Derin Öğrenme Tabanlı Verimli Bir Hibrit Model." Bilişim Teknolojileri Dergisi 16, no. 2 (2023): 107–17. http://dx.doi.org/10.17671/gazibtd.1167140.

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The traffic density problem has become one of the most important problems of urban life. The time and fuel spent due to traffic density is a significant loss for vehicle users and countries. Applications developed to reduce the time spent in traffic cannot make successful predictions about long-term traffic density. Traffic data obtained from cameras, sensors and mobile devices highlights the use of artificial intelligence technologies in order to solve the traffic management problem. In this study, a hybrid prediction model has been proposed by using CNN and RNN models for traffic density pre

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Alshattnawi, Sawsan, and Hadeel Rida Alshboul. "Combined Deep Learning Approaches for Intrusion Detection Systems." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 19 (2024): 144–55. http://dx.doi.org/10.3991/ijim.v18i19.49907.

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Cybersecurity has become increasingly important because of the widespread use of data and its enormous global storage. Hackers and other invaders always want to breach data security by interfering with network traffic. The breaches must be stopped by several tools, such as firewalls. Other solutions, such as intrusion detection systems (IDSs), may detect network intrusions effectively. In this paper, we introduce a hybrid technique (CNN-LSTM) that combines the convolutional neural network (CNN) with long short-term memory (LSTM), a modified version of the recurrent neural network (RNN). The mo

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Dr. Bairysetti Prasad Babu and Dr. Kusuma Sundara Kumar. "CNN-RNN-Bayesian Hybrid Method for Predicting Neonatal ICU Cardiac Arrests." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 2934–42. https://doi.org/10.47392/irjaeh.2025.0434.

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Infant cardiac arrest is a serious medical emergency that needs to be identified quickly in order to be effectively treated. The goal of this study is to apply sophisticated statistical techniques to create a Cardiac Machine Learning Model (CMLM) that can predict neonatal cardiac arrest in the Cardiac Intensive Care Unit (CICU). The model makes use of physiological markers and makes use of prediction methods like logistic regression and support vector machines. The diagnostic procedure is enhanced by imaging techniques such as computed tomography and echocardiography. With a delta-p value of 0

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Anito, Wondimu Lambamo, Ramasamy Srinivasagan, Worku Jifara, and Ali Alzahrani. "Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1050. http://dx.doi.org/10.11591/ijai.v13.i1.pp1050-1062.

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<p><span>Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched c

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Eang, Chanthol, and Seungjae Lee. "Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers." Sensors 25, no. 1 (2024): 25. https://doi.org/10.3390/s25010025.

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This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. T

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Ram Kumar, R. P., Racha Varun, Jageer Sreeshwan, Kondroju Arun Kumar, Upasana Rana, and A. Rajyalakshmi. "Feasible Skin Lesion Detection using CNN and RNN." E3S Web of Conferences 430 (2023): 01050. http://dx.doi.org/10.1051/e3sconf/202343001050.

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A prevalent form of cancer that affects millions of individuals globally is skin cancer. The visual examination of skin lesions, however, is a challenging and time-consuming procedure that calls for the knowledge of dermatologists. The proposed effort intends to create an accurate, feasible and effective system for detecting skin lesions that can help dermatologists identify and treat a variety of skin conditions. To extract features from skin lesion photos, the method uses a pre-trained Convolutional Neural Network (CNN). These characteristics are then fed into a Recurrent Neural Network (RNN

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Hafsa, Qazi, and Nath Kaushik Baij. "A Hybrid Technique using CNN+LSTM for Speech Emotion Recognition." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1126–30. https://doi.org/10.35940/ijeat.E1027.069520.

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Automatic speech emotion recognition is a very necessary activity for effective human-computer interaction. This paper is motivated by using spectrograms as inputs to the hybrid deep convolutional LSTM for speech emotion recognition. In this study, we trained our proposed model using four convolutional layers for high-level feature extraction from input spectrograms, LSTM layer for accumulating long-term dependencies and finally two dense layers. Experimental results on the SAVEE database shows promising performance. Our proposed model is highly capable as it obtained an accuracy of 94.26%.

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Liang, Youzhi, Wen Liang, and Jianguo Jia. "Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN." Advances in Artificial Intelligence and Machine Learning 03, no. 02 (2023): 1110–22. http://dx.doi.org/10.54364/aaiml.2023.1165.

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Vibration signals have been increasingly utilized in various engineering fields for analysis and monitoring purposes, including structural health monitoring, fault diagnosis and damage detection, where vibration signals can provide valuable information about the condition and integrity of structures. In recent years, there has been a growing trend towards the use of vibration signals in the field of bioengineering. Activity-induced structural vibrations, particularly footstep-induced signals, are useful for analyzing the movement of biological systems such as the human body and animals, provid

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AZEEZ, AMMAR. "Automated Emotion Recognition Using Hybrid CNN-RNN Models on Multimodal Physiological Signals." AlKadhim Journal for Computer Science 3, no. 2 (2025): 20–29. https://doi.org/10.61710/kjcs.v3i2.100.

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Emotion recognition has emerged as one of the cornerstones of human-computer interaction, thus opening new frontiers in healthcare, education, and entertainment. The ability to automate emotion recognition processes using hybrid Convolutional Neural Network-Recurrent Neural Network models offers a promising avenue for decoding complex emotional states. The proposed study develops an approach for the integration of electrocardiogram, galvanic skin response, and facial expressions for performing emotion recognition in an accurate and efficient manner. This hybrid architecture combines the streng

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Vinod, Cinana. "A Hybrid Deep Neural Network for Multimodal Deepfake Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45707.

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Abstract—Detecting AI-manipulated media, especially deep- fake images, videos, and audio, is critical in combating privacy and security threats. This project introduces a hybrid deep learning model that integrates content and trace feature extrac- tion to enhance detection accuracy and robustness. By utilizing CNN and RNN architectures, the model processes datasets from DeepFake and Face2Face algorithms, effectively identifying subtle manipulations even under challenging conditions like video compression. Experimental results highlight its superior perfor- mance over baseline methods, supporte

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Guo, Qingchun, Zhenfang He, Zhaosheng Wang, Shuaisen Qiao, Jingshu Zhu, and Jiaxin Chen. "A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models." Water 16, no. 19 (2024): 2870. http://dx.doi.org/10.3390/w16192870.

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Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term

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Chaphekar, Minal, and Omprakash Chandrakar. "An Improved Deep Learning Models with Hybrid Architectures Thyroid Disease Classification Diagnosis." Journal of Neonatal Surgery 14, no. 4S (2025): 1151–62. https://doi.org/10.52783/jns.v14.1925.

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Diagnosing thyroid disease is challenging because the disease presents itself through a spectrum of subtle and diverse symptoms. The study presents a refined deep learning method that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to improve both diagnostic accuracy and efficiency. The CNN module extracts spatial features from thyroid ultrasound images, and the RNN module analyzes these features in sequences to identify temporal patterns that can reveal the progression or type of thyroid conditions. The performance of our model against a dat

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Odeh, Ammar, and Anas Abu Taleb. "XSSer: hybrid deep learning for enhanced cross-site scripting detection." Bulletin of Electrical Engineering and Informatics 13, no. 5 (2024): 3317–25. http://dx.doi.org/10.11591/eei.v13i5.7905.

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The importance of an effective cross-site scripting (XSS) detection system cannot be overstated in web security. XSS attacks continue to be a prevalent and severe threat to web applications, making the need for robust detection systems more crucial than ever. This paper introduced a hybrid model that leverages deep learning algorithms, combining recurrent neural network (RNN) and convolutional neural network (CNN) architectures. Our hybrid RNN-CNN model emerged as the top performer in our evaluation, demonstrating outstanding performance across key metrics. It achieved an impressive accuracy o

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Lei, Yuxiang. "Research on ink color matching method based on CNN-Transformer model." Advances in Engineering Innovation 16, no. 4 (2025): None. https://doi.org/10.54254/2977-3903/2025.22683.

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This paper proposes a CNN-Transformer hybrid model for ink formulation prediction, named CTNet. The model leverages Convolutional Neural Networks (CNN) to extract local features from the spectral reflectance of sample surfaces and incorporates the self-attention mechanism of the Transformer to achieve efficient mapping between color and formulation. In addition, Bayesian optimization is introduced for hyperparameter tuning, further enhancing model performance. Experimental results demonstrate that CTNet outperforms CNN, RNN, LSTM, and the standard Transformer model in terms of Mean Absolute Er

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Sandeep, Biradar Veeresh, and Nase Gururaj. "Creating Synthetic Pictures from Text utilizing RNN and CNN." Advancement in Image Processing and Pattern Recognition 8, no. 1 (2024): 1–4. https://doi.org/10.5281/zenodo.13772404.

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<em>A fresh machine learning task is to synthesize textual descriptions into visual representations. Using a mix of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), one such method creates artificial images from text. RNNs, particularly those with Long Short-Term Memory (LSTM) units, absorb and comprehend the sequential structure of textual input. These networks collect contextual information and provide descriptive embedding’s. These embedding’s are used by a convolutional neural network (CNN) picture generating model to produce coherent and detailed pict

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Goonathilake, M. D. P. P., and P. P. N. V. Kumara. "Stance-Based Fake News Identification on Social Media with Hybrid CNN and RNN-LSTM Models." International Journal on Advances in ICT for Emerging Regions (ICTer) 16, no. 3 (2023): 1–12. http://dx.doi.org/10.4038/icter.v16i3.7234.

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Today, fake news can be readily generated and disseminated via social media platforms. Misinformation and hoaxes propagated via online social media or traditional news media are commonly referred to as fake news. Stance-based fake news is based on the opinions of an audience rather than providing correct facts. This study presents a hybrid model focusing on the CNN model and RNN-LSTM model to identify fake news. A balanced dataset of 216k news items called ‘SherLock-FakeNewsNet’ is explored throughout the study resulting in the proposed hybrid model. The NLTK toolkit is utilized to perform som

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Arshad, Muhammad Zeeshan, Ankhzaya Jamsrandorj, Jinwook Kim, and Kyung-Ryoul Mun. "Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor." Sensors 22, no. 21 (2022): 8226. http://dx.doi.org/10.3390/s22218226.

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Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to improve the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data were gathered from elderly subjects equipped with three IMU sensors while they walked. The input taken only from the waist sensor was used to train 16 deep-learning models including a CNN, RN

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Zhang, Langlang, Jun Xie, Xinxiu Liu, Wenbo Zhang, and Pan Geng. "Research on water quality prediction based on PE-CNN-GRU hybrid model." E3S Web of Conferences 393 (2023): 02014. http://dx.doi.org/10.1051/e3sconf/202339302014.

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Sewage treatment is a complex and nonlinear process. In this paper, a prediction method based on convolutional neural network (CNN) and gated recurrent unit (GRU) hybrid neural network is proposed for the prediction of dissolved oxygen concentration in sewage treatment. Firstly, akima 's method is used to complete the filling preprocessing of missing data, and then the integrated empirical mode decomposition (EEMD) algorithm is used to denoise the key factors of water quality data. Pearson correlation analysis is used to select better water quality parameters as the input of the model. Then, C

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Kang, Taehyung, Dae Yeong Lim, Hilal Tayara, and Kil To Chong. "Forecasting of Power Demands Using Deep Learning." Applied Sciences 10, no. 20 (2020): 7241. http://dx.doi.org/10.3390/app10207241.

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The forecasting of electricity demands is important for planning for power generator sector improvement and preparing for periodical operations. The prediction of future electricity demand is a challenging task due to the complexity of the available demand patterns. In this paper, we studied the performance of the basic deep learning models for electrical power forecasting such as the facility capacity, supply capacity, and power consumption. We designed different deep learning models such as convolution neural network (CNN), recurrent neural network (RNN), and a hybrid model that combines bot

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Gong, Liyun, Miao Yu, Vassilis Cutsuridis, Stefanos Kollias, and Simon Pearson. "A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction." Horticulturae 9, no. 1 (2022): 5. http://dx.doi.org/10.3390/horticulturae9010005.

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In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on a hybrid of an explanatory biophysical model—the Tomgro model, and a machine learning model called CNN-RNN. The Tomgro and CNN-RNN models are calibrated/trained for predicting tomato yields while different fusion approaches (linear, Bayesian, neural network, random forest and gradient boosting) are exploited for fusing the prediction result of individual models for obtaining the final prediction results. The experimental results have shown that the model fusion approach achieves more ac

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Khamparia, Aditya, Babita Pandey, Shrasti Tiwari, Deepak Gupta, Ashish Khanna, and Joel J. P. C. Rodrigues. "An Integrated Hybrid CNN–RNN Model for Visual Description and Generation of Captions." Circuits, Systems, and Signal Processing 39, no. 2 (2019): 776–88. http://dx.doi.org/10.1007/s00034-019-01306-8.

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Uly, Novem, Hendry Hendry, and Ade Iriani. "CNN-RNN Hybrid Model for Diagnosis of COVID-19 on X-Ray Imagery." Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 14, no. 1 (2023): 57–67. http://dx.doi.org/10.31849/digitalzone.v14i1.13668.

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Abstract This research aims to implement deep learning in determining Covid-19 or normal cases using X-Ray imagery. The method used is CNN (ResNet50) and RNN (LSTM). The research phase begins with data collection, data preprocessing, method modeling, method testing and method evaluation. The data was taken from the kagle.com site with the amount of data used 1.000 images where 500 covid data and 500 normal data, the data is divided into 80% training data, 10% validation data and 10% test data. The results of the evaluation by calculating the ResNet50-LSTM confusion matrix have a value of

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Angamuthu, T., and A. S. Arunachalam. "A novel hybrid cnn-rnn model for sugarcane disease identification in agricultural fields." Journal of Energy Engineering and Thermodynamics, no. 51 (January 20, 2025): 1–11. https://doi.org/10.55529/jeet.51.1.11.

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The world's most important crop is sugarcane, which is the main source of both sugar and ethanol. The existence of sugarcane diseases, which result in the removal of afflicted crops, is a persistent problem in the sugar business. Small-scale farmers risk suffering large financial losses if these diseases are not identified and treated early. The growing incidence of illnesses and farmers' inadequate understanding of disease diagnosis and identification were the focus of this investigation. The application of deep learning methods, including machine learning and computer vision, showed promise.

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D. Mohanapriya. "Federated Learning and Biometric Identification for Continuous User Authentication Using Hybrid Neural Models." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 681–89. https://doi.org/10.52783/jisem.v10i26s.4275.

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Continuous user authentication is one way to improve security, especially in environments where traditional password-based systems have outlived their effectiveness. A promising solution for user identification will be behavioral biometrics, which map internal patterns – such as keystrokes and mouse movements. Centralized storage of biometric data presents privacy issues. Toward this goal, we propose a federated learning approach for user identification and authentication that integrates a hybrid CNN-RNN model to efficiently capture spatio-temporal patterns of user behavior. While RNN captures

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Qi, Yijun. "CNNs and RNNs in aspect-level sentiment analysis and comparison." Applied and Computational Engineering 6, no. 1 (2023): 1118–26. http://dx.doi.org/10.54254/2755-2721/6/20230417.

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Aspect-based sentiment analysis (ABSA), also known as fine-grained sentiment analysis, may offer a precise polarity for each aspect in statement aspect. CNN and RNN neural network models, the most fundamental and widely used deep neural network models, have been created by researchers using a variety of methodologies to offer reliable findings in ABSA. This paper will sort out the development process of CNN and RNN models in completing various tasks in aspect-level sentiment analysis and find out the current SOTA model. In addition, by comparing the representatives of the two models in the sam

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Aslam, Naeem, Ahsan Nadeem, Muhammad Kamran Abid, and Muhammad Fuzail. "Text-Based Sentiment Analysis Using CNN-GRU Deep Learning Model." Journal of Information Communication Technologies and Robotic Applications 14, no. 1 (2023): 16–28. http://dx.doi.org/10.51239/jictra.v14i1.318.

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Sentiment analysis identifies both positive and negative viewpoints from sources like social media, surveys, and reviews by automating text analysis with artificial intelligence (AI). Using data to inform decisions is made easier by this. Deep Learning (DL) has gained a lot of interest in recent years from academia and industry because of its outstanding performance. Convolutional neural networks (CNN) and recurrent neural networks (RNN) are the two deep learning designs that are most frequently utilized. Because they can examine enormous amounts of data, neural networks have the potential to

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Airlangga, Gregorius. "A Hybrid Model for Human DNA Sequence Classification Using Convolutional Neural Networks and Random Forests." Jurnal Informatika Universitas Pamulang 9, no. 2 (2024): 71–78. https://doi.org/10.32493/informatika.v9i2.39355.

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Human DNA sequence classification is a fundamental task in genomics, essential for understanding genetic variations and its implications in disease susceptibility, personalized medicine, and evolutionary biology. This study proposes a novel hybrid model combining Convolutional Neural Networks (CNN) for feature extraction and Random Forest classifiers for final classification. The model was evaluated on a dataset of human DNA sequences, with achieving an accuracy of 75.34%. The results showed that performance metrics, including precision, recall, and F1-scores across multiple classes, showed si

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Hariguna, Taqwa, and Athapol Ruangkanjanases. "Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT." HighTech and Innovation Journal 4, no. 2 (2023): 315–26. http://dx.doi.org/10.28991/hij-2023-04-02-06.

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This study aimed to address the limitations of sentiment analysis by developing a more accurate and flexible sentiment scoring model using ChatGPT in combination with KNN, RNN, and CNN algorithms. To achieve this, primary data from ChatGPT and secondary data from Kaggle were utilized for training. The model's performance was evaluated, yielding an impressive accuracy rate of 88.17%. This research underscores ChatGPT's pivotal role in offering theoretical insights and precise data for diverse applications. The novelty of this study lies in its innovative approach of combining KNN, RNN, and CNN

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