Innovative solutions for aquaculture: detecting fish diseases with hybrid deep learning model and explainable artificial intelligence

Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound.

<p>Forecasting of energy consumption in Smart Buildings (SB) and using the extracted information to plan and operate power generation are crucial elements of the Smart Grid (SG) energy management. Prediction of electrical loads and scheduling the generation resources to match the demand enable the utility to mitigate the energy generation cost. Different methodologies have been employed to predict energy consumption at different levels of distribution and transmission systems. In this paper, a novel hybrid deep learning model is proposed to predict energy consumption in smart buildings. The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. In the model-building phase, the hybrid model is trained on the processed data. The hybrid deep learning (DL) model is based on the stacking of fully connected layers, and unidirectional Long Short Term Memory (LSTMs) on bi-directional LSTMs. The proposed model is designed to capture the temporal dependencies of energy consumption on dependent features and to be effective in terms of computational complexity, training time, and forecasting accuracy. The proposed model is evaluated on two benchmark energy consumption datasets yielding superior performance in terms of accuracy when compared with widely used hybrid models such as Convolutional (Conv) Neural Network-LSTM, ConvLSTM, LSTM encoder-decoder model, stacking models, etc. A mean absolute percentage error (MAPE) of 2.00% for case study 1 and a MAPE of 3.71% for case study 2 is obtained for the proposed forecasting DL model in comparison with LSTM-based models that yielded 7.80% MAPE and 5.099% MAPE for two datasets respectively. The proposed model has also been applied for multi-step week-ahead daily forecasting with an improvement of 8.368% and 20.99% in MAPE against the LSTM-based model for the utilized energy consumption datasets respectively.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3061370" target="_blank">https://dx.doi.org/10.1109/access.2021.3061370</a></p>

A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework

The Russia-Ukrainian War denotes an ongoing conflict between Russia and Ukraine. When Russia initiated it in February 2014, the primary focus was on whether or not Crimea and the Donbass were officially regarded as parts of Ukraine. After a military build-up on the Russian–Ukrainian border that began in late 2021, the conflict escalated dramatically when Russia initiated its assault of Ukraine on February 24, 2022. The objective of this piece is to investigate how the general population views the situation between Russia and Ukraine. Social media has become a major communication tool these days, and as a result, opinions can be found on sites like Facebook, Instagram, and Twitter. The study uses 11,250 of his tweets from his Twitter account on the conflict between Russia and Ukraine. Machine learning has demonstrated the applicability, strength, and potential of techniques such as object recognition, natural language processing, and image processing. Three feature extraction techniques, TF-IDF (term frequency-inverse document frequency), BoW (bag of words), and N-gram, have been used in the development and testing of our models. The Twitter API is used to build and test hybrid deep sentiment analysis learning models, which integrate support vector machines (SVM), recurrent neural networks (RNN), convolutional neural networks (CNN), and long short-term memory (LSTM) networks. When compared to single models on the Twitter API dataset, the hybrid models particularly the combination of deep learning models and SVM—improved sentiment analysis accuracy.

In order to enhance transparency and interpretability, the main goal of this project is to create a hybrid deep learning model for fake news detection by fusing Explainable AI (XAI) techniques like SHapley Additive exPlanations (SHAP) with XLNet, FastText, and CNN Algorithm. Introduction: Fake news rapid spread in the digital age has turned into a significant issue that influences social stability, public opinion, and political outcomes . False information has spread by virtue of social media platforms' inability to distinguish between authentic and fraudulent content . Despite their effectiveness, traditional fact-checking methods are time-consuming and unable to handle the volume of data generated daily . As a result, automated systems for detecting false news that utilize advanced artificial Intelligence demonstrated impressive performance in text classification tasks,such as identifying false news. It is challenging to comprehend how these models make decisions, though, because they function as black-box systems. In order to improve interpretability, explainable AI (XAI) techniques have been developed. The SHapley Additive exPlanations (SHAP) method is one that offers details on model predictions . Objectives: The objective of this project is to develop a sophisticated fake news detection system that combines advanced natural language processing and machine learning techniques. By integrating XLNet for superior language understanding, FastText for efficient word representation, and Convolutional Neural Networks (CNNs) for robust feature extraction, the system aims to enhance detection accuracy. Additionally, incorporating Explainable AI techniques, particularly SHAP, will provide clear and interpretable explanations of the model's predictions. This dual focus on performance and transparency seeks to create a reliable tool for identifying misinformation, ultimately fostering greater public trust in digital information sources. Methods: Convolutional Neural Networks (CNN), XL Net, and SHAP with Fast Text are examples of Explainable AI (XAI) techniques that were used in the study's hybrid deep learning methodology. Group 1: Robert and Bert Although methods are effective, they are not transparent enough for users to comprehend and have faith in their predictions. Group 2: Explainable AI and Fat text were used in combination with the Hybrid Model. Results: The hybrid model's accuracy of 92.3% represents a 5.6% improvement over the baseline accuracy of 87.4%. This shows that the hybrid approach is more effective at correctly distinguishing between real and fake news articles. Additionally, the hybrid model is more effective at reducing false positives, as evidenced by its 90.5% accuracy, which is 6.2% higher than the baseline model's 85.2% accuracy. Similarly, from 86.1% in the baseline model to 91.8% in the hybrid model, the hybrid model's recall increases by 6.6%, indicating that it is better at spotting fake news. Finally, the F1-score, which strikes a balance between recall and precision, increased from 85.6% to 91.1%, a 6.4% improvement. Conclusions: By combining XL Net, Fast Text, CNN, and Explainable AI techniques, the proposed hybrid deep learning model significantly increases the accuracy of fake news detection while maintaining interpretability. This tactic provides a robust and transparent framework for effectively combating misinformation.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

A hybrid deep learning framework with physical process description for simulation of evapotranspiration

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.

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

Accurate prediction of monthly runoff is critical for effective water resource management and flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) model, Fourier transform long short-term memory (FT-LSTM), to improve the prediction accuracy of monthly discharge time series in the Brahmani river basin at Jenapur station. We compare the performance of FT-LSTM with three popular DL models: LSTM, recurrent neutral network, and gated recurrent unit, considering different lag periods (1, 3, 6, and 12). The lag period, representing the interval between the observed data points and the predicted data points, is crucial for capturing the temporal relationships and identifying patterns within the hydrological data. The results of this study show that the FT-LSTM model consistently outperforms other models across all lag periods in terms of error metrics. Furthermore, the FT-LSTM model demonstrates higher Nash–Sutcliffe efficiency and R2 values, indicating a better fit between predicted and actual runoff values. This work contributes to the growing field of hybrid DL models for hydrological forecasting. The FT-LSTM model proves effective in improving the accuracy of monthly runoff forecasts and offers a promising solution for water resource management and river basin decision-making processes.

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

Attacks against computer system are viewed to be the most serious threat in the modern world. A zero-day vulnerability is an unknown vulnerability to the vendor of the system. Deep learning techniques are widely used for anomaly-based intrusion detection. The technique gives a satisfactory result for known attacks but for zero-day attacks the models give contradictory results. In this work, at first, two separate environments were setup to collect training and test data for zero-day attack. Zero-day attack data were generated by simulating real-time zero-day attacks. Ranking of the features from the train and test data was generated using explainable AI (XAI) interface. From the collected training data more attack data were generated by applying time series generative adversarial network (TGAN) for top 12 features. The train data was concatenated with the AWID dataset. A hybrid deep learning model using Long short-term memory (LSTM) and Convolutional neural network (CNN) was developed to test the zero-day data against the GAN generated concatenated dataset and the original AWID dataset. Finally, it was found that the result using the concatenated dataset gives better performance with 93.53% accuracy, where the result from only AWID dataset gives 84.29% accuracy.

BackgroundTumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients.Methods and methodsA total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA).ResultsThe areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674–0.788), 0.710 (95% CI, 0.649–0.766), 0.767 (95% CI, 0.710–0.819), and 0.857 (95% CI, 0.807–0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p < 0.001) in the development dataset, and the single-DL model (p = 0.028) in the testing dataset. Decision curve analysis demonstrated the hybrid-DL model had higher net benefit than other models across the majority range of threshold probabilities.ConclusionsThe proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.Critical relevance statementThe proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.Key points• Preoperative non-invasive identification of TDs is of great clinical significance.• The combined hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.• A preoperative nomogram provides gastroenterologist with an accurate and effective tool.Graphical

Using hybrid deep learning to predict spectral responses of quantum dot-embedded nanoporous thin-film solar cells

Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI