Enhanced Rice Leaf Disease Detection: A Hybrid Deep Learning 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.

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.

Advancing breast ultrasound diagnostics through hybrid deep learning models

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

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.

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

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.

Highlights MobileNetV3+U-Net lightweight network improves sugarcane segmentation accuracy and speed. The image processing algorithm removes interference information from the predicted sugarcane segmentation images. Improved YOLOX network enhances the accuracy of sugarcane stem node detection and provides better visualization. Hybrid deep learning models are utilized to extract complete feature information required for sugarcane harvesting. Abstract. The efficiency of intelligent sugarcane harvesters in harvesting depends on the effectiveness of identifying and locating the sugarcane during the harvesting process. In the actual harvesting process, accurately extracting valid features of sugarcane amidst the dense and interwoven sugarcane becomes a challenging task. To address this issue, we propose a hybrid deep learning approach to extract sugarcane stem contours and internal stem node feature information from sugarcane efficiently in the context of a complex harvest. Firstly, this study combined the MobileNetV3 and U-Net networks to segment overall images that contain information about the external contours of the sugarcane stem. Then, the extracted overall profile images were optimized using a variety of image processing techniques to meet the requirements of harvesting. Lastly, the improved YOLOX model was utilized to identify the internal stem node features of sugarcane from the optimized overall images. The experimental results on a real sugarcane dataset show that the proposed external sugarcane stem segmentation model achieves a high mean intersection over union (MIoU) of 91.68% with an average segmentation time of just 0.025 seconds. Moreover, the proposed model for internal stem node recognition in sugarcane achieves an average precision (AP) of 96.19% with an average detection time of 0.026 seconds. Additionally, this study compares image segmentation models such as PSPNet and DeepLabv3+ with target detection models such as YoloV5 and YoloV7. The experimental results show that the sugarcane feature extraction models proposed in this article all exhibit high accuracy and robustness.

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

This research paper introduces a novel hybrid deep learning model that combines the strengths of EfficientNet B3 and VGG16 to address the challenge of accurately classifying orange leaf diseases into three distinct categories: healthy, sooty mold sick, and citrus canker sick. The modern farming industry is clouded by higher risks of plant disease infestation that can both greatly reduce yields and impair quality. This has given rise to the growing need for advanced technologies that could quickly and precisely detect any outbreak of disease. Utilizing the refined functionalities of convolutional neural networks (CNNs), our model, in particular, will make the process of disease classification automated and with this level of automation both the speed and accuracy of crop management practices will be enhanced. The methodology is based on the taking of a large dataset of orange leaf images and then data preprocessing, training, and evaluation of possibly a hybrid model. We gathered evidence that the proposed model provides a 97.87% overall accuracy level, which is significantly better than the individual rates of EfficientNet B3 and VGG16. The EfficientNet B3 is considered to be a highly efficient model; and, the VGG16 is popular for its robust characteristic in the extraction of features. The hybrid model fitted the bills by providing high accuracy, recall, and F1 scores across the disease groups thereby followed to augment the diagnosis rates to a great extent.

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.

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.

Identify suitable artificial groundwater recharge zones using hybrid deep learning models

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

N6-methyladenine (6mA) is an important DNA methylation modification that serves a crucial function in various biological activities. Accurate prediction of 6mA sites is essential for elucidating its biological function and underlying mechanism. Although existing methods have achieved great success, there remains a pressing need for improved prediction accuracy and generalization cap ability across diverse species. This study aimed to develop a robust method to address these challenges. We proposed HD-6mAPred, a hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN) and attention mechanism, along with various DNA sequence coding schemes. Firstly, DNA sequences were encoded using four different ways: one-hot encoding, electron-ion interaction pseudo-potential (EIIP), enhanced nucleic acid composition (ENAC) and nucleotide chemical properties (NCP). Secondly, a hold-out search strategy was employed to identify the optimal features or feature combinations for both BiGRU and CNN. Finally, the attention mechanism was introduced to weigh the importance of features derived from the BiGRU and CNN. A series of experiments on the Rosaceae, rice and Arabidopsis datasets were conducted to demonstrate the superiority of HD-6mAPred. In Rosaceae, the HD-6mAPred model achieved excellent performance: accuracy (ACC) of 0.996, Matthew correlation coefficient (MCC) of 0.993, sensitivity (SN) and specificity (SP) of 0.995 and 0.998, respectively. In rice, the evaluation metrics are 0.952 (ACC), 0.905 (MCC), 0.955 (SN), and 0.949 (SP). In Arabidopsis, the corresponding metrics are 0.937 (ACC), 0.875 (MCC), 0.927 (SN), and 0.948 (SP). Compared to existing methods, these results demonstrate that HD-6mAPred achieves state-of-the-art performance in predicting 6mA sites across three plant species. Furthermore, HD-6mAPred not only improves the accuracy of 6mA site prediction, but also shows excellent generalization capability across species. The source code utilized in this study is publicly accessible at https://doi.org/10.5281/zenodo.15355131.