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Tolba, Ahmed, Nihal N. Mostafa, and Karam M. Sallam. "Hybrid Deep Learning-Based Model for Intrusion Detection." Artificial Intelligence in Cybersecurity 1 (January 11, 2024): 1–11. http://dx.doi.org/10.61356/j.aics.2024.1198.
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There is an intensive need for intrusion detection systems (IDSs) due to incremental and frequent cyber-attacks. The first line of defense against online threats is an IDS. Researchers are using deep learning (DL) approaches to detect attackers and preserve user information. In this study, we introduce a hybrid DL-based model. The proposed model integrates LSTM and ResNet to eliminate the vanishing gradient problem and increase the accuracy of the classification model. The proposed model aims to classify between normal or an attack, with each attack either being a DoS, U2R, R2L, or a probe over the NSL-KDD dataset. The proposed model achieves 99.5% according to accuracy. The model was compared with other ML and DL models.
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T, S. Chandrakantha, N. Jagadale Basavaraj, and Abdullah Murshed Farhan Alnaggar Omar. "Lung Tumor Classification using Hybrid Deep Learning and Segmentation by Fuzzy C Means." Indian Journal of Science and Technology 17, no. 1 (2024): 70–79. https://doi.org/10.17485/IJST/v17i1.2124.
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Abstract <strong>Objectives:</strong> This study aims to employ a hybrid Deep Learning (DL) technique for automating tumor detection and classification in lung scans.<strong> Methods:</strong> The methodology involves three key stages: data preparation, segmentation using Fuzzy C Means (FCM), and classification using a hybrid DL model. The image dataset is sourced from the benchmark Lung Tumor (LT) data, and for segmentation, the FCM approach is applied. The hybrid DL model is created by combining a Pulse Coupled Neural Network (PCNN) and a Convolutional Neural Network (CNN). The study utilizes a dataset of 300 individuals from the NSCLC-Radiomics database. The validation process employs DICE and sensitivity for segmentation, while the hybrid model's confusion matrix elements contribute to performance validation. FCM and the hybrid model are employed for processing, segmenting, and classifying the images. Evaluation metrics such as Dice similarity and Sensitivity gauge the success of the segmentation method by measuring the intersection between ground truths and predictions. After segmentation evaluation, the classification process is executed, employing accuracy and loss in the training phase and metrics like accuracy and F1-score in the testing phase for model validation. <strong>Findings:</strong> The proposed approach achieves an accuracy of 97.43% and an F1-score of 98.28%. These results demonstrate the effectiveness of the suggested approach in accurately classifying and segmenting lung tumors. <strong>Novelty:</strong> The primary contribution of the research is a hybrid DL model based on PCCN+CCN. This ultimately raises the quality of the model, and these are carried out using real-time public medical images, demonstrating the model's originality. <strong>Keywords:</strong> Lung, Tumor, Segmentation, Classification, Hybrid model
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Choudhary, Laxmi, and Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence." Journal of Scientific Research and Reports 30, no. 11 (2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.
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The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.
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Madhuri Ghuge, Et al. "Deep Learning Driven QoS Anomaly Detection for Network Performance Optimization." Journal of Electrical Systems 19, no. 2 (2024): 97–104. http://dx.doi.org/10.52783/jes.695.
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In modern, ever-changing network environments, QoS must be high to provide reliable and efficient services. This study tests Deep Learning (DL), specifically CNN, LSTM, and a hybrid CNN-LSTM model, to identify abnormalities using QoS measurements like Availability, Bandwidth, Latency, Jitter, and Packet Loss. The study evaluates DL-based QoS management using UNSW-NB15 data. The hybrid CNN-LSTM model excels at QoS management, identifying anomalies in key metrics with few false detections. This method captures intricate network data patterns and interrelationships using deep learning, improving anomaly detection accuracy and efficiency. A hybrid model is used to quantify QoS parameters like Availability, Bandwidth, Latency, Jitter, and Packet Loss. The results show high values for Packet Delivery Ratio (PDR), Throughput, Round-Trip Time (RTT), Variation in RTT, and Packet Loss Rate (PLR), proving the proposed approach's effectiveness in maintaining QoS. The CNN, LSTM, and suggested hybrid model evaluation metrics include Accuracy, Precision, Recall, and F1-Score. The hybrid model outperforms the individual models with 98.67% accuracy, precision, recall, and F1-Score. This proves its anomaly detection resilience. False Positive Rate and True Positive Rate show that the hybrid model performs best, with a 0.01 false positive rate and 0.98 true positive rate. Graphical representations help visualize DL model parameter comparisons and False Positive/True Positive rates. DL-based methods, particularly the hybrid CNN-LSTM model, are crucial for QoS anomaly detection in this study. Measurable results show the model improves network dependability, resource allocation, and user satisfaction. The study also suggests researching advanced deep learning methods for real-time network processing and scalable solutions.
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Quan, Changqin, Zhiwei Luo, and Song Wang. "A Hybrid Deep Learning Model for Protein–Protein Interactions Extraction from Biomedical Literature." Applied Sciences 10, no. 8 (2020): 2690. http://dx.doi.org/10.3390/app10082690.
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The exponentially increasing size of biomedical literature and the limited ability of manual curators to discover protein–protein interactions (PPIs) in text has led to delays in keeping PPI databases updated with the current findings. The state-of-the-art text mining methods for PPI extraction are primarily based on deep learning (DL) models, and the performance of a DL-based method is mainly affected by the architecture of DL models and the feature embedding methods. In this study, we compared different architectures of DL models, including convolutional neural networks (CNN), long short-term memory (LSTM), and hybrid models, and proposed a hybrid architecture of a bidirectional LSTM+CNN model for PPI extraction. Pretrained word embedding and shortest dependency path (SDP) embedding are fed into a two-embedding channel model, such that the model is able to model long-distance contextual information and can capture the local features and structure information effectively. The experimental results showed that the proposed model is superior to the non-hybrid DL models, and the hybrid CNN+Bidirectional LSTM model works well for PPI extraction. The visualization and comparison of the hidden features learned by different DL models further confirmed the effectiveness of the proposed model.
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Wang, Xiaomei, Ijaz Ahmad, Danish Javeed, et al. "Intelligent Hybrid Deep Learning Model for Breast Cancer Detection." Electronics 11, no. 17 (2022): 2767. http://dx.doi.org/10.3390/electronics11172767.
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Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (accuracy 86.21%, precision 85.50%, sensitivity 85.60%, specificity 84.71%, F1-score 88%, while AUC 0.89 which overcomes the pathologist’s error and miss classification problem. Additionally, the efficiency of the proposed hybrid model was tested and compared with CNN-BiLSTM, CNN-LSTM, and current machine learning and deep learning (ML/DL) models, which indicated that the proposed hybrid model is more robust than recent ML/DL approaches.
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Li, Lu, Yongjiu Dai, Zhongwang Wei, et al. "Enforcing Water Balance in Multitask Deep Learning Models for Hydrological Forecasting." Journal of Hydrometeorology 25, no. 1 (2024): 89–103. http://dx.doi.org/10.1175/jhm-d-23-0073.1.
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Abstract Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges.
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Saleh, Hager, Sherif Mostafa, Lubna Abdelkareim Gabralla, Ahmad O. Aseeri, and Shaker El-Sappagh. "Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models." Applied Sciences 12, no. 18 (2022): 8967. http://dx.doi.org/10.3390/app12188967.
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Sentiment analysis (SA) is a machine learning application that drives people’s opinions from text using natural language processing (NLP) techniques. Implementing Arabic SA is challenging for many reasons, including equivocation, numerous dialects, lack of resources, morphological diversity, lack of contextual information, and hiding of sentiment terms in the implicit text. Deep learning models such as convolutional neural networks (CNN) and long short-term memory (LSTM) have significantly improved in the Arabic SA domain. Hybrid models based on CNN combined with long short-term memory (LSTM) or gated recurrent unit (GRU) have further improved the performance of single DL models. In addition, the ensemble of deep learning models, especially stacking ensembles, is expected to increase the robustness and accuracy of the previous DL models. In this paper, we proposed a stacking ensemble model that combined the prediction power of CNN and hybrid deep learning models to predict Arabic sentiment accurately. The stacking ensemble algorithm has two main phases. Three DL models were optimized in the first phase, including deep CNN, hybrid CNN-LSTM, and hybrid CNN-GRU. In the second phase, these three separate pre-trained models’ outputs were integrated with a support vector machine (SVM) meta-learner. To extract features for DL models, the continuous bag of words (CBOW) and the skip-gram models with 300 dimensions of the word embedding were used. Arabic health services datasets (Main-AHS and Sub-AHS) and the Arabic sentiment tweets dataset were used to train and test the models (ASTD). A number of well-known deep learning models, including DeepCNN, hybrid CNN-LSTM, hybrid CNN-GRU, and conventional ML algorithms, have been used to compare the performance of the proposed ensemble model. We discovered that the proposed deep stacking model achieved the best performance compared to the previous models. Based on the CBOW word embedding, the proposed model achieved the highest accuracy of 92.12%, 95.81%, and 81.4% for Main-AHS, Sub-AHS, and ASTD datasets, respectively.
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Sagu, Amit, Nasib Singh Gill, Preeti Gulia, Pradeep Kumar Singh, and Wei-Chiang Hong. "Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment." Sustainability 15, no. 3 (2023): 2204. http://dx.doi.org/10.3390/su15032204.
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Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.
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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 cross-validation to ensure robust performance. Key metrics such as test accuracy are utilized to provide a comprehensive assessment of each model's performance. The hybrid CNN-RNN model achieved the highest test accuracy of 90.27%, surpassing the CNN (89.88%), FCN (85.60%), MLP (79.77%), and RNN (73.54%) models. The hybrid model also demonstrated superior performance in cross-validation, with an accuracy of 87.31% ± 1.77%. Comparative analysis highlights the hybrid model's advantages over single-architecture DL models, particularly in handling imbalanced data and providing reliable classifications across all anemia types. The results underscore the potential of advanced DL architectures in medical diagnostics and suggest pathways for further refinements, such as incorporating attention mechanisms or additional feature engineering, to enhance model performance. This study contributes to the growing body of knowledge on AI-driven medical diagnostics and presents a viable tool for clinical decision support in anemia diagnosis
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Phyo, Pyae Pyae, and Yung-Cheol Byun. "Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction." Symmetry 13, no. 10 (2021): 1942. http://dx.doi.org/10.3390/sym13101942.
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The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.
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Ahmad, Shahab, Tahir Ullah, Ijaz Ahmad, et al. "A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection." Computational Intelligence and Neuroscience 2022 (June 24, 2022): 1–14. http://dx.doi.org/10.1155/2022/8141530.
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Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body’s normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model’s efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.
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Shruthi, Kikkeri Subramanya, and Bettahalli Naveen. "Enhanced detection of tomato leaf diseases using ensemble deep learning: INCVX-NET model." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4757–65. https://doi.org/10.11591/ijai.v13.i4.pp4757-4765.
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Automated leaf disease detection quickly identifies early symptoms, and saves time on large farms. Traditional methods like visual inspection and laboratory detection are prevalent despite being labor-intensive, time-consuming, and susceptible to human error. Recently, deep learning (DL) has emerged as a promising alternative for crop disease recognition. However, these models usually demand extensive training data and face problems in generalization due to the diverse features among different crop diseases. This complexity makes it difficult to achieve optimal recognition performance across all scenarios. To solve this issue, a novel ensemble approach INCVX-Net is proposed to integrate the three DL models, ‘Inception, visual geometry group (VGG)-16, and Xception’ using weighted averaging ensemble for tomato crop leaf disease detection. This approach utilizes the strengths of three DL models to recognize a wide range of disease patterns and captures even slight changes in leaf characteristics. INCVX-Net achieves an impressive 99.5% accuracy in disease detection, outperforming base models such as InceptionV2 (93.4%), VGG-16 Net (92.7%), and Xception (95.2%). This significant leap in accuracy demonstrates the growing power of ensemble DL models in disease detection compared to standalone DL models. The research paves the groundwork for future advancements in disease detection, enhancing precision agriculture through ensemble models.
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Yadav, Dhirendra Prasad, Ashish Sharma, Senthil Athithan, Abhishek Bhola, Bhisham Sharma, and Imed Ben Dhaou. "Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL." Sensors 22, no. 15 (2022): 5823. http://dx.doi.org/10.3390/s22155823.
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An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN.
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Alghazzawi, Daniyal, Omaimah Bamasag, Hayat Ullah, and Muhammad Zubair Asghar. "Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection." Applied Sciences 11, no. 24 (2021): 11634. http://dx.doi.org/10.3390/app112411634.
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DDoS (Distributed Denial of Service) attacks have now become a serious risk to the integrity and confidentiality of computer networks and systems, which are essential assets in today’s world. Detecting DDoS attacks is a difficult task that must be accomplished before any mitigation strategies can be used. The identification of DDoS attacks has already been successfully implemented using machine learning/deep learning (ML/DL). However, due to an inherent limitation of ML/DL frameworks—so-called optimal feature selection—complete accomplishment is likewise out of reach. This is a case in which a machine learning/deep learning-based system does not produce promising results for identifying DDoS attacks. At the moment, existing research on forecasting DDoS attacks has yielded a variety of unexpected predictions utilising machine learning (ML) classifiers and conventional approaches for feature encoding. These previous efforts also made use of deep neural networks to extract features without having to maintain the track of the sequence information. The current work suggests predicting DDoS attacks using a hybrid deep learning (DL) model, namely a CNN with BiLSTM (bidirectional long/short-term memory), in order to effectively anticipate DDoS attacks using benchmark data. By ranking and choosing features that scored the highest in the provided data set, only the most pertinent features were picked. Experiment findings demonstrate that the proposed CNN-BI-LSTM attained an accuracy of up to 94.52 percent using the data set CIC-DDoS2019 during training, testing, and validation.
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J. Kishore Kumar and Prof S. Ramakrishna. "A Hybrid Technique to Predict Brain Tumour using MRI Image." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 252–63. http://dx.doi.org/10.32628/cseit2410326.
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Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.
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Ghimire, Sujan, Ravinesh C. Deo, Hua Wang, Mohanad S. Al-Musaylh, David Casillas-Pérez, and Sancho Salcedo-Sanz. "Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results." Energies 15, no. 3 (2022): 1061. http://dx.doi.org/10.3390/en15031061.
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We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.
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Gupta, Ashmit. "Cross Model Sentiment Analysis in Stock Market: A Hybrid Approach Using Classical and Deep Learning Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50563.
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INTRODUCTION Background In the dynamic and often volatile world of financial markets, investor sentiment plays a pivotal role in driving short-term price fluctuations. The rise of social media platforms, online financial forums, and real-time news dissemination has transformed the traditional landscape of stock market analysis. These digital channels have become rich sources of unstructured, sentiment-laden text data that reflect the emotions, perceptions, and expectations of investors. Harnessing this data to forecast stock price movement has emerged as a key focus area in financial analytics. Sentiment analysis, a subfield of Natural Language Processing (NLP), is increasingly employed to extract emotional cues from financial texts. With the integration of Machine Learning (ML) and Deep Learning (DL) models, this process has evolved into a sophisticated tool for financial forecasting. However, while many models have been tested individually, there remains limited empirical research comparing classical ML models such as Support Vector Machines (SVM) and Naïve Bayes with DL architectures like LSTM (Long Short-Term Memory) and BERT (Bidirectional Encoder Representations from Transformers) in a hybrid ensemble framework. This study aims to fill this gap by conducting a comprehensive comparative analysis and proposing a hybrid sentiment analysis model that leverages the strengths of both ML and DL techniques for stock market prediction.
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Armaki, A. G., M. F. Fallah, M. Alborzi, and A. Mohammadzadeh. "A Hybrid Meta-Learner Technique for Credit Scoring of Banks' Customers." Engineering, Technology & Applied Science Research 7, no. 5 (2017): 2073–82. https://doi.org/10.5281/zenodo.1037248.
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Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.
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Kausar, Rehan, Farhat Iqbal, Abdul Raziq, Naveed Sheikh, and Abdul Rehman. "Enhanced Foreign Exchange Volatility Forecasting Using Ceemdan with Optuna-Optimized Ensemble Deep Learning Model." Sains Malaysiana 53, no. 9 (2024): 3229–39. http://dx.doi.org/10.17576/jsm-2024-5309-25.
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Foreign Exchange (FX) is the largest financial market in the world, with a daily trading volume that significantly exceeds that of stock and futures markets. The prediction of FX volatility is a critical financial concern that has garnered significant attention from researchers and practitioners due to its far-reaching implications in the financial markets. This paper presents a novel hybrid ensemble forecasting model integrating a decomposition strategy and three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination addresses individual models' limitations and further improves the accuracy and stability of FX volatility forecasting. The proposed approach utilizes the CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to form the input dataset for the DL models. In addition, we employed an attention mechanism to improve the effectiveness of the DL techniques. Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. The proposed approach is evaluated against various benchmark models using evaluation measures such as MSE, MAE, HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The results demonstrate that our proposed approach provides accurate and reliable forecasts of FX volatility under different forecasting regimes, making it a valuable tool for financial practitioners and researchers.
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Thilagavathy, C. "Leveraging Machine and Deep Learning Models for Load Balancing Strategies in Cloud Computing." Indian Journal Of Science And Technology 17, no. 45 (2024): 4722–31. https://doi.org/10.17485/ijst/v17i45.2728.
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Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in the cloud environment using the combined approach like random Forest(RF) for task prediction and Particle Swarm optimization for optimization and Convolutional Neural Networks (PSO-CNN) for resource prediction and allocation. Methods: The ensemble approach in the present study uses Random Forest (RF), a machine learning (ML) model for task prediction and Particle Swarm Optimization (PSO+CNN), a bio-inspired algorithm and Deep Learning (DL) model for optimization and resource allocation. The study employs PSO techniques to optimize CNN in order to address the investigation of algorithmic optimization in DL. The results show that the suggested model outperforms the other models like CNN-LSTM(Long Short-term memory), CNN-GRU(Gated Recurrent Unit), and PSO –SVM(Support Vector Machine) to increase the performance and efficacy of the cloud systems. The experiment is implemented using Python and assessed using Google Cluster dataset that is accessible to the public. Findings: The use of ML and DL techniques are found to be more efficient in cloud infrastructure than the conventional methods. The study examines the performance of the RF, PSO and CNN and the hybrid RF-PSO-CNN models. The accuracy, precision, and F1. Score metrics were used to assess the performance of the classification models. The recommended model RF-PSO-CNN outperforms them with an accuracy of 90% than the contrasted methods like CNN-LSTM, CNN- GRU and PSO-SVM. As a result, both the classification assessment metrics and resource consumption show that the proposed model performs effectively. Novelty: The novel ensemble approach suggests the combined RF-PSO-CNN for LB in cloud Computing. The task predicted by RF is assigned to the resource chosen by PSO and CNN, thereby improving the efficiency of task prediction and resource allocation. Most of the research uses any two ML or DL methods for either predicting the tasks to be scheduled or which resource to allocate. The study uses a combination of the ML (RF) method, bio-inspired algorithm (PSO) and a DL (CNN) model for both task and resource prediction concurrently and it examines the effectiveness of LB in the cloud context. Keywords: Load Balancing (LB), Task scheduling, Resource allocation, Random Forest (RF), Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO)
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Chandra Nath, Paresh, Mohammad Iftekhar Ayub, Ayan Nath, et al. "Enhancing Stock Price Prediction through Sentiment Analysis: A Comparative Study of Machine Learning and Deep Learning Models Using Financial News Data." Frontline Marketing, Management and Economics Journal 05, no. 02 (2025): 7–17. https://doi.org/10.37547/marketing-fmmej-05-02-02.
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This study explores the use of machine learning (ML) and deep learning (DL) models for predicting stock price movements through sentiment analysis of financial news articles. Four models were evaluated: Random Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results showed that deep learning models, particularly BERT, outperformed traditional ML models, achieving higher accuracy, precision, recall, and F1 scores. BERT’s ability to capture contextual relationships in text proved superior in handling the complexities of financial news. This research highlights the effectiveness of sentiment analysis in stock market prediction and suggests that advanced ML and DL techniques can enhance forecasting accuracy. Future work could focus on refining these models by integrating more data sources and exploring hybrid approaches.
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Raza, Asaf, Huma Ayub, Javed Ali Khan, et al. "A Hybrid Deep Learning-Based Approach for Brain Tumor Classification." Electronics 11, no. 7 (2022): 1146. http://dx.doi.org/10.3390/electronics11071146.
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Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.
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Wang, Yuhao. "Enhancing Diabetes Prediction Through Hybrid Deep Learning: Analysis of ML and DL Techniques." Applied and Computational Engineering 132, no. 1 (2025): 167–72. https://doi.org/10.54254/2755-2721/2024.20637.
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This interview investigates how machine learning (ML) and deep learning (DL) techniques are implemented in predicting diabetes, with the goal of identifying the most effective methods for early diagnosis. As diabetes prevalence continues to rise, developing accurate prediction models is essential for enabling timely interventions and reducing related health risks. The research compares traditional ML methods, including Support Vector Machines (SVM), Decision Trees, and Naive Bayes, against advanced DL models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN+LSTM model. The hybrid approach leverages the effectiveness of both CNNs and LSTMs, effectively analyzing data patterns in temporal and spatial respective. Extensive experimental results reveal that the CNN+LSTM model reaches to a dominant accuracy of 98%, significantly outperforming the other evaluated methods. This finding highlights the potential of hybrid deep learning approaches in improving the accuracy of diabetes prediction. The study concludes by discussing the implications of these results and suggests future research directions, including the exploration of more diverse datasets and the application of these models in clinical settings to enhance their generalizability and practical utility.
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Bhaskara Rao B. "Renaissance for Alzheimer’s Disease Detection using ML DL Techniques." Power System Technology 49, no. 1 (2025): 982–98. https://doi.org/10.52783/pst.1646.
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The early diagnosis of Alzheimer’s disease (AD) is very important in ensuring proper intervention in a timely manner so that patients can be managed better and have better outcomes. Earlier, cognitivedata and numerical patient information were used with conventional machine learning (ML) methods for early detection. One of the past ensemble-based approaches trained up to seven ML classifiers that included Decision Tree, Random Forest, SVM, ANN, and AdaBoost, gaining 93.92% accuracy. However, deep learning integration and optimal feature extraction was not present in that study. This study proposes a novel deep learning (DL) approach using advanced feature selection by Decision Tree and Random Forest, and Synthetic Minority Over-Sampling Technique (SMOTE) for class balancing. A hybrid deep learning approach based on Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) is presented. The model provides efficient, scalable, and adaptive, and a robust Alzheimer’s disease (AD) detection for the real-life patients. The models are trained successfully with patients' data from Kaggle.The model provides efficient early AD detection with better accuracy and stability.
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Akl, Ahmed A., Khalid M. Hosny, Mostafa M. Fouda, and Ahmad Salah. "A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans." PLOS ONE 18, no. 3 (2023): e0282608. http://dx.doi.org/10.1371/journal.pone.0282608.
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COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.
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Al-Jamimi, Hamdi A., Galal M. BinMakhashen, Muhammed Y. Worku, and Mohamed A. Hassan. "Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization." Electronics 12, no. 24 (2023): 4909. http://dx.doi.org/10.3390/electronics12244909.
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Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly in search of advanced machine learning solutions to improve load forecasting. Recently, deep learning (DL) has gained prominence as a significant area of interest in prediction efforts. This paper introduces an innovative approach to electric load forecasting, leveraging advanced DL techniques and making significant contributions to the field of energy management. The hybrid predictive model has been specifically designed to enhance the accuracy of multivariate time series forecasting for electricity consumption within the energy sector. In our comparative analysis, we evaluated the performance of our proposed model against ML-based and state-of-the-art DL models, using a dataset obtained from the Distribution Network Station located in Tetouan City, Morocco. Notably, the proposed model surpassed its counterparts, demonstrating the lowest error in terms of the Root-Mean-Square Error (RMSE). This outcome underscores its superior predictive capability and underscores its potential to advance the accuracy of electricity consumption forecasting.
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Iqbal, Farhat, Dimitrios Koutmos, Eman A. Ahmed, and Lulwah M. Al-Essa. "A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction." Risks 12, no. 9 (2024): 139. http://dx.doi.org/10.3390/risks12090139.
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The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.
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Johnson, Olanrewaju Victor, XinYing Chew, Khai Wah Khaw, and Zhi Lin Chong. "CRATSM: An Effective Hybridization of Deep Neural Models for Customer Retention Prediction in the Telecom Industry." Journal of Engineering Technology and Applied Physics 6, no. 2 (2024): 66–78. http://dx.doi.org/10.33093/jetap.2024.6.2.10.
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In the dynamic field of Customer Retention Prediction (CRP), strategic marketing and promotion efforts targeting specific customers are crucial. Understanding customer behavior and identifying churn indicators are vital for devising effective retention strategies. However, identifying customers likely to terminate services presents a challenge, leading to data imbalance issues. Existing CRP studies using Machine Learning (ML) techniques and data imbalance methods face problems such as overfitting and computational complexity. Similarly, recent CRP studies employing Deep Learning (DL) approaches rely on data sampling techniques, which can result in overfitting and a lack of cost sensitivity. Additionally, DL approaches struggle with slow convergence and get stuck in local minima. This paper introduces an effective hybrid of Deep Learning (DL) classifiers focusing on cost-metric integration to address data imbalance issues and period-shift Cosine Annealing Learning Rate (ps-CALR) to accelerate model training, ultimately enhancing performance. Three Telecom datasets, namely IBM, Iranian, and Orange, were used to assess the model performance. Empirical findings show that the hybrid DL classifiers significantly improved CRP over conventional ML. This paper contributes methodological advancements and practical insights for effective customer retention in the telecom industry.
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Wei, Nan, Changjun Li, Jiehao Duan, Jinyuan Liu, and Fanhua Zeng. "Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model." Energies 12, no. 2 (2019): 218. http://dx.doi.org/10.3390/en12020218.
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Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.
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Colombo, Daniele, Ersan Turkoglu, Weichang Li, Ernesto Sandoval-Curiel, and Diego Rovetta. "Physics-driven deep-learning inversion with application to transient electromagnetics." GEOPHYSICS 86, no. 3 (2021): E209—E224. http://dx.doi.org/10.1190/geo2020-0760.1.
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Machine learning, and specifically deep-learning (DL) techniques applied to geophysical inverse problems, is an attractive subject, which has promising potential and, at the same time, presents some challenges in practical implementation. Some obstacles relate to scarce knowledge of the searched geologic structures, a problem that can limit the interpretability and generalizability of the trained DL networks when applied to independent scenarios in real applications. Commonly used (physics-driven) least-squares optimization methods are very efficient local optimization techniques but require good starting models close to the correct solution to avoid local minima. We have developed a hybrid workflow that combines both approaches in a coupled physics-driven/DL inversion scheme. We exploit the benefits and characteristics of both inversion techniques to converge to solutions that typically outperform individual inversion results and bring the solution closer to the global minimum of a nonconvex inverse problem. The completely data-driven and self-feeding procedure relies on a coupling mechanism between the two inversion schemes taking the form of penalty functions applied to the model term. Predictions from the DL network are used to constrain the least-squares inversion, whereas the feedback loop from inversion to the DL scheme consists of the network retraining with partial results obtained from inversion. The self-feeding process tends to converge to a common agreeable solution, which is the result of two independent schemes with different mathematical formalisms and different objective functions on the data and model misfit. We determine that the hybrid procedure is converging to robust and high-resolution resistivity models when applied to the inversion of the synthetic and field transient electromagnetic data. Finally, we speculate that the procedure may be adopted to recast the way we solve inverse problems in several different disciplines.
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Yosra, Ali Hassan, and Sedqi Kareem Omar. "Credit Card Fraud Detection: A Comparative Study of Machine Learning and Deep Learning Methods." Engineering and Technology Journal 10, no. 05 (2025): 5165–76. https://doi.org/10.5281/zenodo.15496423.
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Credit card fraud has become a significant concern in the digital era, driven by the rise in online transactions and the sophistication of fraudulent activities. Traditional fraud detection systems are increasingly inadequate due to their static nature and limited adaptability to new attack patterns. In response, this study presents a comparative analysis of recent machine learning (ML) and deep learning (DL) techniques used for credit card fraud detection (CCFD). A total of 29 peer-reviewed studies published between 2019 and 2024 were reviewed, covering a range of ML models such as Decision Trees, Random Forest, XGBoost, and ensemble methods, alongside DL models including CNNs, LSTMs, AutoEncoders, and Graph Neural Networks. The analysis focuses on performance metrics, dataset characteristics, model limitations, and the effectiveness of imbalance handling strategies. Findings reveal that while DL models often achieve higher accuracy, they demand more computational resources, whereas ML models offer better efficiency and interpretability. The study concludes with a discussion on key challenges and suggests future research directions, including hybrid model development, improved imbalance handling, and real-time system deployment.
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A, Chandrasekar, and Yamuna K. "Using Deep Learning an Effiecient Bot Attack Detection Methods." South Asian Journal of Engineering and Technology 15, no. 2 (2025): 123–28. https://doi.org/10.26524/sajet.2025.15.13.
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Deep Learning (DL) is an effective way to detect botnet attacks. However, the amount of network traffic data and the required memory space are usually large. Therefore, it is almost impossible to use the DL method on memory-restricted IoT devices. In this paper, we reduce the size of the IoT network traffic data feature using the Long Short-Term Short-Term Memory Autoencoder (LAE) codec section. In order to classify network traffic samples correctly, we analyze long-term variables related to low-dimensional feature produced by LAE using Bi-directional Long Short-Term Memory (BLSTM). Comprehensive testing was performed with BoT-IoT databases to confirm the effectiveness of the proposed DL hybrid method. The results show that LAE significantly reduced the memory space required for data storage of large network traffic by 91.89%, and exceeded the standard features of reducing feature by 18.92 -27.03%. Despite the significant reduction in feature size, the deep BLSTM model shows strength against low model equity and over-equilibrium. It also acquires a good ability to adapt to the conditions of binary classification.
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Shankara Chari, Gowrishankar Shiva, and Jyothi Arcot Prashant. "Real-time driver drowsiness detection based on integrative approach of deep learning and machine learning model." Indonesian Journal of Electrical Engineering and Computer Science 39, no. 1 (2025): 592. https://doi.org/10.11591/ijeecs.v39.i1.pp592-602.
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Driver drowsiness is a major factor that contributing to road accidents. Several researches are ongoing to detect driver drowsiness, but they suffer from the complexity and cost of the models. This paper introduces a hybrid artificial intelligence (AI)-driven framework integrating deep learning (DL) and machine learning (ML) models for real-time drowsiness detection. The system utilizes a robust DL model to classify driver states based on facial images and support vector machine (SVM) model is trained to develop a cost-efficient yet robust facial landmark detector to extract key features such as eye aspect ratio (EAR) and mouth aspect ratio (MAR). We also introduce a multi-stage decision fusion mechanism that combines convolutional neural network (CNN) probability scores with EAR/MAR thresholds to enhance detection reliability and reduce false positives. Experimental results demonstrate that the proposed model achieves 98% accuracy and F1-score, significantly outperforming traditional DL approaches. Additionally, the SVM-based landmark predictor shows improved efficiency with lower mean squared error (MSE) without having higher computational requirements.
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Sella Veluswami, Jansi Rani, M. Ezhil Prasanth, K. Harini, and U. Ajaykumar. "Melanoma Skin Cancer Recognition and Classification Using Deep Hybrid Learning." Journal of Medical Imaging and Health Informatics 11, no. 12 (2021): 3110–16. http://dx.doi.org/10.1166/jmihi.2021.3898.
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Melanoma skin cancer is a common disease that develops in the melanocytes that produces melanin. In this work, a deep hybrid learning model is engaged to distinguish the skin cancer and classify them. The dataset used contains two classes of skin cancer–benign and malignant. Since the dataset is imbalanced between the number of images in malignant lesions and benign lesions, augmentation technique is used to balance it. To improve the clarity of the images, the images are then enhanced using Contrast Limited Adaptive Histogram Equalization Technique (CLAHE) technique. To detect only the affected lesion area, the lesions are segmented using the neural network based ensemble model which is the result of combining the segmentation algorithms of Fully Convolutional Network (FCN), SegNet and U-Net which produces a binary image of the skin and the lesion, where the lesion is represented with white and the skin is represented by black. These binary images are further classified using different pre-trained models like Inception ResNet V2, Inception V3, Resnet 50, Densenet and CNN. Following that fine tuning of the best performing pre-trained model is carried out to improve the performance of classification. To further improve the performance of the classification model, a method of combining deep learning (DL) and machine learning (ML) is carried out. Using this hybrid approach, the feature extraction is done using DL models and the classification is performed by Support Vector Machine (SVM). This computer aided tool will assist doctors in diagnosing the disease faster than the traditional method. There is a significant improvement of nearly 4% increase in the performance of the proposed method is presented.
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Alyahya, Mohammed, Husam Lahza, and Rayan Mosli. "Toward Reducing IDS Misclassification Using Hybrid DL and ML Approach." Advances in Artificial Intelligence and Machine Learning 04, no. 03 (2024): 2764–82. http://dx.doi.org/10.54364/aaiml.2024.43161.
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Operation centers often face challenges due to the high rate of misclassifications caused by the lower precision in Intrusion Detection System (IDS) models. Despite several research contributions ranging from machine learning and deep learning techniques aiming to reduce false positives and negatives, researchers and security experts consistently encounter a tradeoff between these two types of errors. This indicates a significant opportunity for further contributions in this field. We propose a hybrid model that combines Recurrent Neural Networks (RNN) feature extraction capabilities with Support Vector Machines (SVM) classification abilities. Our model achieves an impressive accuracy rate of 98.2% and significantly reduces misclassification errors compared to contemporary state-of-the-art models. This work shows the potential of hybrid approaches in improving accuracy and reducing false positive and negative errors.
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Reddy, Boyella Mala Konda, Dr A. Abdul Azeez Khan, Dr K. Javubar Sathick, and Dr L. Arun Raj. "Cyber Attack Recognition in an Internet of Things-Enabled Environment Using a Hybrid Optimised Deep Learning Approach." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 49–71. https://doi.org/10.58346/jowua.2025.i1.003.
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A cyber-attack is the malicious manipulation of computer networks and systems to compromise data or impede procedures and operations using malware. With the exponential growth in computational capacity, machine learning (ML) and deep learning (DL) approaches have emerged as promising countermeasures for advancing and identifying such threats. To address this challenge, a novel optimized deep hybrid attack detection model called SCEEHO-SPC-CNN-CD-DBN is proposed in this research article. Data is subjected to a preprocessing procedure before it is used for further processes. Here, the data undergoes a normalizing phase for pre-processing, during which the statistics and higher-order statistical features are retrieved. The cyber-attack detection process concludes with a hybrid DL model applied to the retrieved features. The proposed hybrid classifier integrates models such as the DBN (Deep Belief Network) with contrastive divergence (CD) and the split convolution module (SPC)-based CNN (Convolutional Neural Network). Training the CNN and DBN using the SCEEHO(Sea CrowEndorsed Elephant Herding optimization) model and fine tuning the ideal weights improves detection accuracy. Furthermore, have tested the developedSCEEHO-SPC-CNN-CD-DBN-based hybrid classifier on the CIC IoT Dataset 2023. The evaluated results, employing a wide range of statistical measures, demonstrate that the research model performs efficiently.
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Gomez-de la Peña, Eduardo, Giovanni Coco, Colin Whittaker, and Jennifer Montaño. "On the use of convolutional deep learning to predict shoreline change." Earth Surface Dynamics 11, no. 6 (2023): 1145–60. http://dx.doi.org/10.5194/esurf-11-1145-2023.
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Abstract. The process of shoreline change is inherently complex, and reliable predictions of shoreline position remain a key challenge in coastal research. Predicting shoreline evolution could potentially benefit from deep learning (DL), which is a recently developed and widely successful data-driven methodology. However, so far its implementation for shoreline time series data has been limited. The aim of this contribution is to investigate the potential of DL algorithms to predict interannual shoreline position derived from camera system observations at a New Zealand study site. We investigate the application of convolutional neural networks (CNNs) and hybrid CNN-LSTM (Long Short-Term Memory) networks. We compare our results with two established models: a shoreline equilibrium model and a model that addresses timescales in shoreline drivers. Using a systematic search and different measures of fitness, we found DL models that outperformed the reference models when simulating the variability and distribution of the observations. Overall, these results indicate that DL models have potential to improve accuracy and reliability over current models.
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Henry, Azriel, Sunil Gautam, Samrat Khanna, et al. "Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System." Sensors 23, no. 2 (2023): 890. http://dx.doi.org/10.3390/s23020890.
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Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.
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Inayathulla, Mohammed, and K. Rajasekhara Rao. "Enhancing Real-Time Violence Detection in Video Surveillance Using Hybrid Deep Learning Model." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 344–61. https://doi.org/10.58346/jowua.2025.i1.021.
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One of the crucial aspects of maintaining public safety and security in different environments is detecting violence in video surveillance (VS). Conventional systems are unable to accurately differentiate between violent and non-violent actions due to multi-factor nature and relative subtlety of violence, as well as environmental constraints. The use of advanced Deep Learning (DL) models, specifically Recurrent (NN) Neural Networks (RNN) and Convolutional NN (CNN), along with its types, such as ResNet, and bidirectional Long Short-Term Memory (Bi-LSTM) units, to address this problem. It serves as the focus of this research. To efficiently utilise both spatial (S) and temporal (T) data, the combination of ResNet50V2 architecture with bidirectional GRU and Bi-LSTM layers was employed by the suggested hybrid model. The model has a high success rate and much lower False Positives (FP) after being trained on a wide variety of real-world events. This model's computational efficiency and wide range of applications to various surveillance situations are also discussed, along with its potential for Real-Time (RT) operation. The DL architectures are an effective approach for creating VD systems that are reliable, adaptable, and scalable and it was demonstrated by the outcomes.
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Sedai, Ashish, Rabin Dhakal, Shishir Gautam, et al. "Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production." Forecasting 5, no. 1 (2023): 256–84. http://dx.doi.org/10.3390/forecast5010014.
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The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.
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Mohanty, Cheena, Sakuntala Mahapatra, Biswaranjan Acharya, et al. "Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy." Sensors 23, no. 12 (2023): 5726. http://dx.doi.org/10.3390/s23125726.
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Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.
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Almulihi, Ahmed, Hager Saleh, Ali Mohamed Hussien, et al. "Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction." Diagnostics 12, no. 12 (2022): 3215. http://dx.doi.org/10.3390/diagnostics12123215.
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Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the “silent killer,” is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper’s aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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Hidri, Adel, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri, and Minyar Sassi Hidri. "Opinion Mining and Analysis Using Hybrid Deep Neural Networks." Technologies 13, no. 5 (2025): 175. https://doi.org/10.3390/technologies13050175.
Full textAbstract:
Understanding customer attitudes has become a critical component of decision-making due to the growing influence of social media and e-commerce. Text-based opinions are the most structured, hence playing an important role in sentiment analysis. Most of the existing methods, which include lexicon-based approaches and traditional machine learning techniques, are insufficient for handling contextual nuances and scalability. While the latter has limitations in model performance and generalization, deep learning (DL) has achieved improvement, especially on semantic relationship capturing with recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The aim of the study is to enhance opinion mining by introducing a hybrid deep neural network model that combines a bidirectional gated recurrent unit (BGRU) and long short-term memory (LSTM) layers to improve sentiment analysis, particularly addressing challenges such as contextual nuance, scalability, and class imbalance. To substantiate the efficacy of the proposed model, we conducted comprehensive experiments utilizing benchmark datasets, encompassing IMDB movie critiques and Amazon product evaluations. The introduced hybrid BGRU-LSTM (HBGRU-LSTM) architecture attained a testing accuracy of 95%, exceeding the performance of traditional DL frameworks such as LSTM (93.06%), CNN+LSTM (93.31%), and GRU+LSTM (92.20%). Moreover, our model exhibited a noteworthy enhancement in recall for negative sentiments, escalating from 86% (unbalanced dataset) to 96% (balanced dataset), thereby ensuring a more equitable and just sentiment classification. Furthermore, the model diminished misclassification loss from 20.24% for unbalanced to 13.3% for balanced dataset, signifying enhanced generalization and resilience.
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Unlu, Altan, and Malaquias Peña. "Comparative Analysis of Hybrid Deep Learning Models for Electricity Load Forecasting During Extreme Weather." Energies 18, no. 12 (2025): 3068. https://doi.org/10.3390/en18123068.
Full textAbstract:
Extreme weather events present some of the most severe natural threats to the electric grid, and accurate load forecasting during those events is essential for grid management and disaster preparedness. In this study, we evaluate the effectiveness of hybrid deep learning (DL) models for electrical load forecasting in the IEEE 118-bus system. Our analysis focuses on the Connecticut region during extreme weather. In addition, we determine multivariate models capable of multi-input and multi-output forecasting while incorporating weather data to improve forecasting accuracy. This research is divided into two case studies that analyze different combined DL model architectures. Case Study 1 conducts CNN-Recurrent (RNN, LSTM, GRU, BiRNN, BiGRU, and BiLSTM) models with fully connected dense layers, which combine convolution and recurrent neural networks to capture both spatial and temporal dependencies in the data. Case Study 2 evaluates Hybrid CNN-Recurrent models with a fully connected dense layer model that incorporates a flattening step before the recurrent layers to increase the temporal learning process. Based on the results obtained from our simulations, the hybrid CNN-GRU-FC (using BiGRU) model in Case Study 2 obtained the best performance with an RMSE of 9.112 MW and MAPE of 11.68% during the hurricane period. The Hybrid CNN-GRU-FC model presents a better accuracy of bidirectional recurrent models for load forecasting under extreme weather conditions.
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T., Mathu, and Raimond Kumudha. "A novel deep learning architecture for drug named entity recognition." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1884–91. https://doi.org/10.12928/telkomnika.v19i6.21667.
Full textAbstract:
Drug named entity recognition (DNER) becomes the prerequisite of other medical relation extraction systems. Existing approaches to automatically recognize drug names includes rule-based, machine learning (ML) and deep learning (DL) techniques. DL techniques have been verified to be the state-of-the-art as it is independent of handcrafted features. The previous DL methods based on word embedding input representation uses the same vector representation for an entity irrespective of its context in different sentences and hence could not capture the context properly. Also, identification of the n-gram entity is a challenge. In this paper, a novel architecture is proposed that includes a sentence embedding layer that works on the entire sentence to efficiently capture the context of an entity. A hybrid model that comprises a stacked bidirectional long short-term memory (Bi-LSTM) with residual LSTM has been designed to overcome the limitations and to upgrade the performance of the model. We have contrasted the achievement of our proposed approach with other DNER models and the percentage of improvements of the proposed model over LSTM-conditional random field (CRF), LIU and WBI with respect to micro-average F1-score are 11.17, 8.8 and 17.64 respectively. The proposed model has also shown promising result in recognizing 2- and 3-gram entities.
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El-Shahat, Doaa, Ahmed Tolba, and Amit Krishan Kumar. "An Improved Deep Learning Model for Detecting Rice Diseases." Optimization in Agriculture 1 (January 10, 2024): 1–10. http://dx.doi.org/10.61356/j.oia.2024.1194.
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Early detection of rice plant diseases could help to quickly eradicate numerous diseases, such as fungi, viruses, and bacteria, consequently increasing rice yield. Traditional techniques for performing this task may not be the best because they take a long time, require experienced personnel, and are susceptible to a variety of infections. As a result, machine learning and deep learning approaches have recently been utilized to overcome these issues and present a more accurate model for detecting rice plant diseases. However, the current machine learning (ML) and deep learning (DL) models for this task produce unsatisfactory results due to many constraints, including high computational expenses and overfitting. To address these limitations and obtain more accurate disease detection for rice plants, we present a hybrid model of MobileNet and DNN (HMobileNetDNN). The small size of MobileNet minimizes computing costs, while the depth and complexity of DNN improve the model's capacity to capture complicated features, yielding satisfying results. Furthermore, the proposed HMobileNetDNN is also compared to four transfer learning-based DL models, namely ResNetV2, InceptionV3, MobilenetV2, and DensNet121, using the Paddy Doctor dataset. We employ several performance metrics to assess the effectiveness and efficiency of the models, like accuracy, precision, recall, F1 score, and area under the curve. The proposed model outperformed the comparing models, achieving values of 0.918, 0.918, 0.907, 0.912, and 0.949 for accuracy, precision, recall, F1 score, and area under the curve, respectively.
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Ullah, Ihtisham, Basit Raza, Sikandar Ali, Irshad Ahmed Abbasi, Samad Baseer, and Azeem Irshad. "Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System." Security and Communication Networks 2021 (December 3, 2021): 1–15. http://dx.doi.org/10.1155/2021/6136670.
Full textAbstract:
Software Defined Network (SDN) is a next-generation networking architecture and its power lies in centralized control intelligence. The control plane of SDN can be extended to many underlying networks such as fog to Internet of Things (IoT). The fog-to-IoT is currently a promising architecture to manage a real-time large amount of data. However, most of the fog-to-IoT devices are resource-constrained and devices are widespread that can be potentially targeted with cyber-attacks. The evolving cyber-attacks are still an arresting challenge in the fog-to-IoT environment such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Infiltration, malware, and botnets attacks. They can target varied fog-to-IoT agents and the whole network of organizations. The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other threats. The extensive simulations have been carried out using various DL algorithms and current state-of-the-art Coburg Intrusion Detection Data Set (CIDDS-001) flow-based dataset. For better analysis five DL models are compared including constructed hybrid DL models to distinguish the DL model with the best performance. The results show that proposed Long Short-Term Memory (LSTM) hybrid model outperforms other DL models in terms of detection accuracy and response time. To show unbiased results 10-fold cross-validation is performed. The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification.
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Yahya, Abdulsamad E., Atef Gharbi, Wael M. S. Yafooz, and Arafat Al-Dhaqm. "A Novel Hybrid Deep Learning Model for Detecting and Classifying Non-Functional Requirements of Mobile Apps Issues." Electronics 12, no. 5 (2023): 1258. http://dx.doi.org/10.3390/electronics12051258.
Full textAbstract:
As a result of the speed and availability of the Internet, mobile devices and apps are in widespread usage throughout the world. Thus, they can be seen in the hands of nearly every person, helping us in our daily activities to accomplish many tasks with less effort and without wasting time. However, many issues occur while using mobile apps, which can be considered as issues of functional or non-functional requirements (NFRs). Users can add their comments as a review on the mobile app stores that provide for technical feedback, which can be used to improve the software quality and features of the mobile apps. Minimum attention has been given to such comments by scholars in addressing, detecting, and classifying issues related to NFRs, which are still considered challenging. The purpose of this paper is to propose a hybrid deep learning model to detect and classify NFRs (according to usability, reliability, performance, and supportability) of mobile apps using natural language processing methods. The hybrid model combines three deep learning (DL) architectures: a recurrent neural network (RNN) and two long short-term memory (LSTM) models. It starts with a dataset construction extracted from the user textual reviews that contain significant information in the Arabic language. Several experiments were conducted using machine learning classifiers (MCLs) and DL, such as ANN, LSTM, and bidirectional LSTM architecture to measure the performance of the proposed hybrid deep learning model. The experimental results show that the performance of the proposed hybrid deep learning model outperforms all other models in terms of the F1 score measure, which reached 96%. This model helps mobile developers improve the quality of their apps to meet user satisfaction and expectations by detecting and classifying issues relating to NFRs.
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Nadeem, Ayesha, Muhammad Farhan Hanif, Muhammad Sabir Naveed, et al. "AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning." AIMS Geosciences 10, no. 4 (2024): 684–734. http://dx.doi.org/10.3934/geosci.2024035.
Full textAbstract:
<p>The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting.</p>