Advancing breast ultrasound diagnostics through hybrid deep learning models

Text classification is an essential component in a variety of applications of natural language processing. While the deep learning-based approach is becoming more popular, using vectors of word as an input for the models has proved to be a good way for the machine to learn the relation between words in a document. This paper proposes a solution for the text classification using hybrid deep learning approaches. Every existing deep learning approach has its own advantages and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed models CNN-LSTM, LSTM-CNN show enhanced accuracy over another approach.

Cyberbullying on social media platforms has become widespread in society. Cyberbullying can take many forms, including hate speech, trolling, adult content, racism, harassment, or rants. One social media platform that has many cyberbullies is Twitter, which has been renamed 'X'. The anonymous nature of this 'X' platform allows users from all over the world to commit cyberbullying as they can freely share their thoughts and expressions without having to account for their identity. This research aims to explore the influence of IndoBERT’s semantic features on hybrid deep learning models for cyberbullying detection while integrating TF-IDF feature extraction and FastText feature expansion to enhance text classification performance. Specifically, this study examines how IndoBERT’s semantic capabilities affect the hybrid deep learning model in detecting cyberbullying on platform 'X'. This study has 30,084 tweets with a hybrid deep learning approach that combines CNN and LSTM. In the IndoBERT scenario, IndoBERT features were first combined with TF-IDF, then expanded using FastText before being applied to the hybrid deep learning model. The test results produced the highest accuracy rate by: CNN (80.69%), LSTM (80.67%), CNN- LSTM (81.18%), CNN-LSTM-IndoBERT (82.05%). This research contributes to informatics by integrating hybrid deep learning (CNN-LSTM) with IndoBERT and TF-IDF, demonstrating its effectiveness in improving cyberbullying detection in Indonesian text. Future research can explore the use of other transformer-based models such as RoBERTa or ALBERT to enhance contextual understanding in cyberbullying classification.

Abstract- This research proposes a hybrid deep learning model for automated rice leaf disease detection, addressing the limitations of traditional methods. A pre-trained ResNet50V2 CNN is combined with a custom fully connected network, enhanced with batch normalization, dropout, and L2 regularization. Trained on 15,023 images from nine disease classes and healthy leaves, the model utilizes extensive data augmentation techniques such as rotation, shifts, zoom, and flipping to improve robustness. The model achieved 99.53% accuracy, surpassing existing benchmarks, and provides a reliable system for timely disease detection, enhancing rice crop management and yield. Keywords- Hybrid deep learning model, Rice leaf disease detection, Fully connected layer, Data augmentation, Dropout, Batch normalization, L2 regularization.

Introduction: Enterprise Human Resource Management (HRM), which tracks, evaluates, and improves employee performance, is essential to the expansion of a firm. However, conventional techniques like decision trees and linear regression frequently fall short in identifying intricate, non-linear relationships in employee data, which reduces their usefulness for making decisions in real time.Objective: The research aims to develop an intelligent, accurate, and scalable model for forecasting employee performance using a hybrid Deep Learning (DL) approach called the Intelligent Water Drops Driven Dynamic Long Short-Term Network (IntWD-DynLSTN).Methods: A real-world HR dataset that includes employee information like task completion rates, training hours, attendance, and performance ratings is used to train the algorithm. Preprocessing included encoding categorical data, using Z-score normalization, and addressing missing values by imputation. High-level characteristics were extracted from the structured HR data using Convolutional Neural Networks (CNN). These attributes were subsequently fed into a Dynamic Long Short-Term Network (DynLSTN) to identify sequential patterns in monthly employee performance. Finally, model hyperparameters were adjusted using the Intelligent Water Drops (IntWD) technique to enhance generality and accuracy.Results: Experimental results show that the proposed IntWD-DynLSTN model achieves higher prediction accuracy (0.98), precision (0.97), recall (0.97), and F1-score (0.98) compared to traditional and baseline methods.Conclusions: A scalable and dependable method for forecasting employee performance is provided by the suggested hybrid DL methodology. In dynamic organizational settings, it gives HR managers a strong tool for data-driven decision-making, facilitating quick interventions and efficient workforce management.

The tremendous development and rapid evolution in computing advancements has urged a lot of organizations to expand their data as well as computational needs. Such type of services offers security concepts like confidentiality, integrity, and availability. Thus, a highly secured domain is the fundamental need of cloud environments. In addition, security breaches are also growing equally in the cloud because of the sophisticated services of the cloud, which cannot be mitigated efficiently through firewall rules and packet filtering methods. In order to mitigate the malicious attacks and to detect the malicious behavior with high detection accuracy, an effective strategy named Multiverse Fractional Calculus (MFC) based hybrid deep learning approach is proposed. Here, two network classifiers namely Hierarchical Attention Network (HAN) and Random Multimodel Deep Learning (RMDL) are employed to detect the presence of malicious behavior. The network classifier is trained by exploiting proposed MFC, which is an integration of multi-verse optimizer and fractional calculus. The proposed MFC-based hybrid deep learning approach has attained superior results with utmost testing sensitivity, accuracy, and specificity of 0.949, 0.939, and 0.947.

Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks

Water quality prediction (WQP) plays an essential role in water quality management for aquaculture to make aquaculture production profitable and sustainable. In this work, we propose hybrid deep learning (DL) models, convolutional neural network (CNN) with the long short-term memory (LSTM) and gated recurrent unit (GRU) for aquaculture WQP. CNN can effectively fetch the aquaculture water quality characteristics, whereas GRU and LSTM can learn long-term dependencies in the time series data. We conduct experiments using the two different water quality datasets and present an extensive study on the impact of hyperparameters on the performance of the proposed hybrid DL models. Furthermore, the performance of hybrid CNN-LSTM and CNN-GRU models are compared with different baseline LSTM, GRU and CNN DL models and also with attention-based LSTM and attention-based GRU DL models. The results show that the hybrid CNN-LSTM outperformed all other models in terms of prediction accuracy and computation time.

Background: Classification of brain tumors is an integral aspect of medical imaging studies as different tumor features require multiclassification which aids in making the diagnosis. However, this task is of great difficulty owing to the nature of the brain MRI images. Recent deep learning (DL) models have made it possible to have a brain tumor classification with high accuracy, which is very useful to neurologists. Based on these new capabilities, this study intends to enhance brain tumor detection with the help of a hybrid transfer learning approach. Methods: This study developed a brain tumor diagnosis system using five advanced DL architectures: Xception, ResNet164, DenseNet121, DenseNet201, and Inception-ResNetV2. A hybrid DL model was created by integrating a deep-dense block and a softmax activation function in the final layers of these architectures. The proposed approach enhances classification accuracy and precision. Two experiments were conducted: Three-class classification, involving images from patients with gliomas, meningiomas, and pituitary tumors. Four-class classification, which included gliomas, meningiomas, pituitary tumors, and normal brain images. Results: The Xception architecture emerged as the most effective model in detecting brain tumors when implemented with the proposed hybrid DL modifications. The system achieved an accuracy of 99.71% on the three-class dataset and 96.17% on the four-class dataset, outperforming state-ofthe- art methods in brain tumor classification. Conclusion: This research shows that hybrid DL models can enhance the capabilities of brain tumor classification systems in the context of complex MRI datasets. The optimization of activation functions and the inclusion of deep-dense blocks increase both accuracy and precision, providing adequate solutions for neurologists. These results lead to the recognition of the Xception-based hybrid model as a robust and efficient model for detecting brain tumors.

Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects older adults, leading to memory loss, cognitive decline, and behavioural changes.It is marked by the accumulation of amyloid-beta plaques and tau tangles in the brain, which impair neuronal function and eventually cause cell death.Diagnosis typically involves clinical evaluations, cognitive assessments, and imaging techniques such as MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) scans.Early detection is crucial, as it allows for interventions that can potentially slow the disease's progression, enabling patients and their families to manage symptoms more effectively.The transition from Normal Control (NC) to Early Mild Cognitive Impairment (EMCI) may be reversible, whereas other stages are not.This makes it a particularly challenging classification problem, as the changes in biomarker values are typically minimal during the progression from NC to very mild stages.To address this issue, we created a hybrid deep learning model, GDP (combining GoogLeNet, DenseNet-121, and Principal Component Analysis (PCA)), to extract hybrid features using 26639 sMRI images from well-known MRI datasets, such as the Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI).These features fed into the SVM binary classification algorithm to distinguish very mild cases from healthy images.We created synthetic images using CycleGAN augmentation to balance the dataset across labels and expand its size.The proposed GDP model stands out from current state-of-the-art deep learning techniques, such as ResNet-50, DenseNet-169, VGG-16, EfficientNet, and Inceptionv3, by achieving an impressive accuracy of 98.98% and an AUC of 99.91%.These results significantly surpass the performance of existing methods, making it a promising tool for early Alzheimer's disease prediction.

RIFATA: Remora improved invasive feedback artificial tree algorithm-enabled hybrid deep learning approach for root disease classification

Background: Accurate subtype identification of lymphoma cancer is crucial for effective diagnosis and treatment planning. Although standard deep learning algorithms have demonstrated robustness, they are still prone to overfitting and limited generalization, necessitating more reliable and robust methods. Objectives: This study presents an autoencoder-augmented stacked ensemble learning (SEL) framework integrating deep feature extraction (DFE) and ensembles of machine learning classifiers to improve lymphoma subtype identification. Methods: Convolutional autoencoder (CAE) was utilized to obtain high-level feature representations of histopathological images, followed by dimensionality reduction via Principal Component Analysis (PCA). Various models were utilized for classifying extracted features, i.e., Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), AdaBoost, and Extra Trees classifiers. A Gradient Boosting Machine (GBM) meta-classifier was utilized in an SEL approach to further fine-tune final predictions. Results: All the models were tested using accuracy, area under the curve (AUC), and Average Precision (AP) metrics. The stacked ensemble classifier performed better than all the individual models with a 99.04% accuracy, 0.9998 AUC, and 0.9996 AP, far exceeding what regular deep learning (DL) methods would achieve. Of standalone classifiers, MLP (97.71% accuracy, 0.9986 AUC, 0.9973 AP) and Random Forest (96.71% accuracy, 0.9977 AUC, 0.9953 AP) provided the best prediction performance, while AdaBoost was the poorest performer (68.25% accuracy, 0.8194 AUC, 0.6424 AP). PCA and t-SNE plots confirmed that DFE effectively enhances class discrimination. Conclusion: This study demonstrates a highly accurate and reliable approach to lymphoma classification by using autoencoder-assisted ensemble learning, reducing the misclassification rate and significantly enhancing the accuracy of diagnosis. AI-based models are designed to assist pathologists by providing interpretable outputs such as class probabilities and visualizations (e.g., Grad-CAM), enabling them to understand and validate predictions in the diagnostic workflow. Future studies should enhance computational efficacy and conduct multi-centre validation studies to confirm the model’s generalizability on extensive collections of histopathological datasets.

Mammography and ultrasound based dual modality classification of breast cancer using a hybrid deep learning approach

Automatic identification of abnormal and irregular heart rhythms is necessary to reduce mortality. Tachyarrhythmia is a type of abnormally fast heartbeat that can be detected using electrocardiogram (ECG) signals. In the elderly, life-threatening tachyarrhythmia such as ventricular fibrillation (VFIB), atrial fibrillation (AFIB), and atrial flutter (AFL) can lead to sudden cardiac arrest. Here, we present a hybrid deep learning (HDL) model for automatic identification of tachyarrhythmia rhythms from heart rate variability (HRV) datasets based on a one-dimensional convolution neural network (1D CNN) and a long-term short-term memory (LSTM) model. In this study, we used the HRV database with five-second windows as input data for our HDL model. Four different statistical parameters have been used to determine the model efficiency: The average accuracy is 99.19%, the average precision is 91.75%, the recall is 93.63%, and the F1 score is 92.71%. The overall accuracy of the experiment was 98.4%. This model outperformed other state-of-the-art models. As a result, this method can be useful in clinical systems of cardiological care.KeywordsAFIBAFLVFIBHRVCNNLSTM

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.