A Hybrid Deep Learning Approach with Interpretability on BiLSTM-Dense SHAP Enhanced Model (BDSEM) for Depression Prediction

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.

Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either "Normal" or "Anomalous" based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.

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.

Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

Technological advancements have enabled users to digitize and store an unlimited number of multimedia documents, including images and videos. However, the heterogeneous nature of multimedia content poses significant challenges in efficient indexing and retrieval. Traditional approaches primarily focus on visual features, often neglecting the semantic context, which limits retrieval efficiency. This paper proposes a hybrid deep learning and knowledge graph approach for intelligent image indexing and retrieval. By integrating deep learning models such as EfficientNet and Vision Transformer (ViT) with structured knowledge graphs, the proposed framework enhances semantic understanding and retrieval performance. The methodology incorporates feature extraction, concept classification, and hierarchical knowledge graph structuring to facilitate effective multimedia retrieval. Experimental results on benchmark datasets, including TRECVID, Corel, and MSCOCO, demonstrate significant improvements in precision, robustness, and query expansion techniques. The findings highlight the potential of combining deep learning with knowledge graphs to bridge the semantic gap and optimize multimedia indexing and retrieval.

Objectives: This study aims to enhance software quality prediction by addressing the limitations of traditional machine learning models—namely, feature redundancy and high computational costs—through a hybrid deep learning approach integrated with Genetic Algorithms (GA) for efficient and accurate defect prediction. Methods:The proposed method combines Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for deep feature learning, alongside GA for optimal feature selection. Model pruning and quantization techniques were employed to improve computational efficiency. The hybrid model was evaluated using publicly available software defect datasets. Findings:The hybrid approach achieved an accuracy of 89.2%, surpassing traditional classifiers like Random Forest and Support Vector Machines (SVMs). Furthermore, computational efficiency was improved by 35%, confirming the effectiveness of the model in balancing accuracy and performance. Novelty:Unlike conventional models, this approach integrates deep learning with evolutionary feature selection and computational optimization strategies, resulting in a robust, scalable, and efficient solution for software defect prediction. This combination ensures high predictive performance while minimizing resource consumption, making it suitable for large-scale and real-time applications.

Predicting light-matter interaction in semi-transparent elliptical packed beds using hybrid deep learning (HDL) approach

A hybrid deep reinforcement learning approach for a proactive transshipment of fresh food in the online–offline channel system

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline’s acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time–frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

Smart grids, pivotal in modern energy distribution, confront a mounting cybersecurity threat landscape due to their increased connectivity. This study introduces a novel hybrid deep learning approach designed for robust intrusion detection, addressing the imperative to fortify the security of these critical infrastructures. Renamed as Intrusion Detection for Smart Grid Using a Hybrid Deep Learning Approach, the study amalgamates Conv1D for spatial feature extraction, MaxPooling1D for dimensionality reduction, and GRU for modeling temporal dependencies. The research leverages the Edge-IIoTset Cyber Security Dataset, encompassing diverse layers of emerging technologies within smart grids and facilitating a nuanced understanding of intrusion patterns. Over 10 types of IoT devices and 14 attack categories contribute to the dataset's richness, enhancing the model's training and evaluation. The proposed hybrid model's architecture is detailed, emphasizing the synergy of convolutional and recurrent neural networks in addressing complex intrusion scenarios. This research not only contributes to the evolving field of intrusion detection in smart grids but also sets the stage for creating adaptive security systems. The convergence of a hybrid deep learning approach with a comprehensive cyber security dataset marks a significant stride towards fortifying smart grids against evolving cybersecurity threats. The proposed model achieves 98.20 percentage.

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices.

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning‐based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.

Infectious diseases emerge unprecedentedly, posing serious challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce and cause damage to host cells, and antibiotic resistance genes (ARGs) allow pathogens to evade otherwise curable treatments. Simultaneous identification of VFs and ARGs can save pathogen surveillance time, especially in situ epidemic pathogen detection. However, most tools can only predict either VFs or ARGs. Few tools that predict VFs and ARGs simultaneously usually have high false-negative rates, are sensitive to the cutoff thresholds and can only identify conserved genes. For better simultaneousprediction of VFs and ARGs, we propose a hybrid deep ensemble learning approach called HyperVR. By considering both best hit scores and statistical gene sequence patterns, HyperVR combines classical machine learning and deep learning to simultaneously and accurately predict VFs, ARGs and negative genes (neither VFs nor ARGs). For the prediction of individual VFs and ARGs, insilico spike-in experiment (the VFs and ARGs in real metagenomic data), and pseudo-VFs and -ARGs (gene fragments), HyperVR outperforms the current state-of-the-art prediction tools. HyperVR uses only gene sequence information without strict cutoff thresholds, hence making prediction straightforward and reliable.

This paper develops a data-driven approach to dynamically integrate tactical production and predictive maintenance planning for a multi-state system composed of several series-parallel machines. The objective is to determine an integrated lot-sizing and preventive maintenance strategy that will minimize the sum of maintenance and production costs, while satisfying the demand for all products over the entire horizon. A rolling horizon planning strategy is adopted to continuously update the production and maintenance plans based on new data obtained through sensors. Unlike the existing integrated models, we develop a hybrid deep learning (DL) approach to coordinate maintenance and production decisions for a multi-state system composed of multiple machines. To accurately predict the health condition of each machine, the developed hybrid DL method combines the powers of convolutional neural network (CNN), long-short-term memory (LSTM), and attention technique. We use multi-state reliability theory to estimate the production capacity. Furthermore, a genetic algorithm is developed to solve large-scale problems. Benchmarking data are used to compare the results of our data-driven approach with a model-based approach, a pure LSTM, and a CNN-LSTM approach. This comparison is based on prediction accuracy, solution quality, and computational time. The obtained results show the superiority of the suggested CNN-LSTM-attention data-driven framework integrating maintenance and production.

ObjectivesTo evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa).MethodsThis retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS ≥ 2 lesions across prior and current studies. The heatmaps for each patient’s prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores.ResultsThe model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81).ConclusionsOur proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy.Key Points• Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach.• The diagnostic accuracy of our csPCa detection AI model improved by including clinical parameters.