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Background: Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be accessible. To overcome these challenges, deep learning (DL) methods have emerged. However, DL-based NER methods may need help identifying long-distance relationships within text and require significant annotated datasets. Results: This research has proposed a novel model to address the challenges in natural language processing. The Improved Green anaconda-assisted Bi-GRU based Hierarchical ResNet BNER model (IGa-BiHR BNERM) is the model. IGa-BiHR BNERM model has shown promising results in accurately identifying named entities. The MACCROBAT dataset was obtained from Kaggle and underwent several pre-processing steps such as Stop Word Filtering, WordNet processing, Removal of non-alphanumeric characters, stemming Segmentation, and Tokenization, which is standardized and improves its quality. The pre-processed text was fed into a feature extraction model like the Robustly Optimized BERT –Whole Word Masking model. This model provides word embeddings with semantic information. Then, the BNER process utilized an Improved Green Anaconda-assisted Bi-GRU-based Hierarchical ResNet BNER model (IGa-BiHR BNERM). Conclusion: To improve the training phase of the IGa-BiHR BNERM, the Improved Green Anaconda Optimization technique was used to select optimal weight parameter coefficients for training the model parameters. After the model was tested using the MACCROBAT dataset, it outperformed previous models with a tremendous accuracy rate of 99.11%. This model effectively and accurately identifies biomedical names within the text, significantly advancing this field. [ABSTRACT FROM AUTHOR]

Online discussion forums are widely used by students to ask and answer questions related to their learning topics. However, not all questions posted by students receive timely and appropriate feedback from instructors, which can affect the quality and effectiveness of the online learning experience. Therefore, it is important to automatically identify and prioritize student questions from online discussion forums, so that instructors can provide better support and guidance to the students. In this paper, we propose a novel hybrid convolutional neural network (CNN) + bidirectional gated recurrent unit (Bi-GRU) multi-output classification model, which can perform this task with high accuracy and efficiency. Our model consists of two outputs: the first one classifies whether the post is a question or not, and the second one classifies whether the classified question is urgent or not urgent. Our model leverages the advantages of both CNN and Bi-GRU layers to capture both local and global features of the input data, as well as the Bidirectional Encoder Representations from Transformers (BERT) model to provide rich and contextualized word embeddings. The model achieves an F1-weighted score of 94.8% when classifying whether the posts are questions or not, and obtains an 88.5% F1-weighted score while classifying the question into urgent and non-urgent. Distinguishing and classifying urgent student questions with high accuracy and coverage can help instructors provide timely and appropriate feedback, a key factor in reducing dropout rates and improving completion rates. [ABSTRACT FROM AUTHOR]

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain–computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain–computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods. [ABSTRACT FROM AUTHOR]

Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), aims to extract subjective information from textual data. With the proliferation of social media platforms like Twitter, accurately determining public sentiment has become increasingly important for businesses, policymakers, and researchers. This study introduces the Dual Joint Classifier (DJC), which integrates the strengths of RoBERTa and BERT architectures. The DJC model leverages Bidirectional Gated Recurrent Units (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) layers to capture complex sequential dependencies and nuanced sentiment expressions. Advanced training techniques such as Focal Loss and Hard Sample Mining address class imbalance and improve model robustness. To further validate the DJC model's robustness, the larger TweetEval Sentiment dataset was also included, on which DJC outperformed conventional models despite increased training time. Evaluations were conducted on the Twitter US Airlines and Apple Twitter Sentiment datasets to verify experiments. The DJC model achieved 87.22% and 93.87% accuracies, respectively, and demonstrated improvement over other models like RoBERTa-GLG, BiLSTM(P), and SVM. These results highlight the DJC model's effectiveness in handling diverse sentiment analysis tasks and its potential for real-world applications. [ABSTRACT FROM AUTHOR]

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Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system's ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems. [ABSTRACT FROM AUTHOR]

ABSTRACT Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. These challenges will be addressed by the bidirectional gated recurrent unit (Bi‐GRU) model, which forecasts power‐generating outcomes more efficiently. The investigation is done over a health data set from 2000 to 2023, including the energy states of the United Kingdom, Finland, Germany, and Switzerland. The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. The performance remains better with a mean absolute percentage error (MAPE) of 2.75%, root mean square error (RMSE) of 0.0414, mean squared error (MSE) of 0.0017, and authentify that Bi‐GRU performs much better than others. This model's superior prediction accuracy significantly enhances our ability to forecast renewable and nonrenewable energy outputs in European states, contributing to more effective energy management strategies.

Many areas of image identification and classification for medical imaging diagnostics have greatly benefited from deep learning (DL). Diabetic retinopathy (DR) will become the most common cause of blindness worldwide, making diabetes a major threat to public health. This research proposes an automated identification system using deep recurrent neural networks (RNNs) to identify and classify four categories of diabetic eye diseases: DR, cataract, glaucoma, and diabetic macular edema (DME). We use three different model architectures based on RNN and their types, we called our proposed system RNN Diabetic framework. These models are combined with one of the commonly used architectures that support sufficient accuracy and speed for the model which is residual network (ResNet)152V2. The three model architectures are RNN+ResNet152V2, gated recurrent unit (GRU)+ResNet152V2, and bidirectional GRU (Bi-GRU)+ResNet152V2. The proposed models were assessed as collected datasets: DIARETDB0, DIARETDB1, messidor, HEI-MED, ocular, and retina. A full analysis and evaluation of these three deep RNN architectures are presented. The experiments showed that the Bi-GRU+ResNet152V2 model worked better than the other two proposed models. In addition, we compare these three proposed models with the previous studies and find that the proposed Bi-GRU+ResNet152V2 model achieves the highest results with accuracy equal to 99.8%, 98.1% sensitivity, 98.6% specificity, 99.8% precision, 99.8% F1 score, and 99.8% areas under the curve (AUC). [ABSTRACT FROM AUTHOR]

Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for comparison.This ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data—common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds" and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. [ABSTRACT FROM AUTHOR]

In marine ranching aquaculture, dissolved oxygen (DO) is a crucial parameter that directly impacts the survival, growth, and profitability of cultured organisms. To effectively guide the early warning and regulation of DO in aquaculture waters, this study proposes a hybrid model for spatiotemporal DO prediction named PCA-ISSA-DAM-Bi-GRU. Firstly, principal component analysis (PCA) is applied to reduce the dimensionality of the input data and eliminate data redundancy. Secondly, an improved sparrow search algorithm (ISSA) based on multi strategy fusion is proposed to enhance the optimization ability and convergence speed of the standard SSA by optimizing the population initialization method, improving the location update strategies for discoverers and followers, and introducing a Cauchy-Gaussian mutation strategy. Thirdly, a feature and temporal dual attention mechanism (DAM) is incorporated to the baseline temporal prediction model Bi-GRU to construct a feature extraction network DAM-Bi-GRU. Fourthly, the ISSA is utilized to optimize the hyperparameters of DAM-Bi-GRU. Finally, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected from a self-built LoRa+5G-based marine ranching aquaculture monitoring system. The results show that: (1) Compared with the baseline model Bi-GRU, the addition of PCA, ISSA and DAM module can effectively improve the prediction performance of the model, and their fusion is effective; (2) ISSA demonstrates superior capability in optimizing model hyperparameters and convergence speed compared to traditional methods such as standard SSA, genetic algorithm (GA), and particle swarm optimization (PSO); (3) The proposed hybrid model achieves a root mean square error (RMSE) of 0.2136, a mean absolute percentage error (MAPE) of 0.0232, and a Nash efficient (NSE) of 0.9427 for DO prediction, outperforming other similar data-driven models such as IBAS-LSTM and IDA-GRU. The prediction performance of the model meets the practical needs of precise DO prediction in aquaculture. [ABSTRACT FROM AUTHOR]

The ability to accurately predict the stock market is a crucial financial topic. The basic assumption is that future stock returns can be somewhat predicted based on publicly available data from the past. Economic variables like interest rates and currency rates, information related to political influences, and company-specific data like statements of income and dividends are some examples of this information. Yet, attempting to forecast stock returns goes against the idea that markets are generally efficient. A prediction model has been created that leverages machine learning, big data analytics, and social media analytics to predict stock market trends regularly. The main objective of this work is to propose a novel approach for stock market prediction for handling big data. This research develops a new SP-PDA (Stock Price Prediction with Political Data Analysis) which includes preprocessing, feature extraction, and prediction. In this work, we employed three types of data (Stock data, News data and Political data). For the stock data, preprocessing was done using Z-score normalization, while tokenization and lemmatization were used for the news and political data. Next, to handle the big data, we used a Map-Reduce architecture. Here, feature extraction from preprocessed stock, news, and political data occurs in the mapper function. The reduction face yields the final extracted feature set. Commodity channel index (CCI), Chaikin Volatility (CV), and Donchian Channel (DC) are examples of technical indicator-based features that are extracted for stock data during the feature extraction phase. Mutual Information (MI), Improved Pointwise mutual information (IPMI), Term Frequency-Inverse Document Frequency (TF-IDF), and correlation features are retrieved for news and political data. Stock market prediction is based on the features extracted. To predict stock prices, we employed an ensemble classification model that incorporates classifiers such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long-short term memory (LSTM), and Deep Maxout. An improved score level fusion is carried out to define the final prediction outcome from the obtained intermediate prediction results. The suggested Dwarf Mongoose Updated War Strategy-based Generalized Normal Distribution (DMUWS-based GND) Optimization Algorithm is used to optimizing the weights of ensemble classifiers. Finally, the performance of the proposed model is evaluated over existing models in terms of error measures like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Squared Log Error (MSLE). The SP-PDA achieved the desired MAE of 0.012, which is low when compared to the other traditional systems for accurate prediction of the stock market. [ABSTRACT FROM AUTHOR]

Simple Summary: Phosphorylation is a crucial process that regulates various cellular activities. Detecting phosphorylation sites, especially in cells infected by the SARS-CoV-2 virus, is challenging due to technical limitations. To address this, we developed GBMPhos, an advanced tool combining deep learning techniques, to accurately identify these sites. GBMPhos outperformed traditional methods and current state-of-the-art approaches in identifying phosphorylation sites. We have developed a free web server, which helps researchers gain a better understanding of protein modifications during a SARS-CoV-2 infection, potentially aiding in the development of therapeutic strategies and contributing to the fight against COVID-19. Phosphorylation, a reversible and widespread post-translational modification of proteins, is essential for numerous cellular processes. However, due to technical limitations, large-scale detection of phosphorylation sites, especially those infected by SARS-CoV-2, remains a challenging task. To address this gap, we propose a method called GBMPhos, a novel method that combines convolutional neural networks (CNNs) for extracting local features, gating mechanisms to selectively focus on relevant information, and a bi-directional gated recurrent unit (Bi-GRU) to capture long-range dependencies within protein sequences. GBMPhos leverages a comprehensive set of features, including sequence encoding, physicochemical properties, and structural information, to provide an in-depth analysis of phosphorylation sites. We conducted an extensive comparison of GBMPhos with traditional machine learning algorithms and state-of-the-art methods. Experimental results demonstrate the superiority of GBMPhos over existing methods. The visualization analysis further highlights its effectiveness and efficiency. Additionally, we have established a free web server platform to help researchers explore phosphorylation in SARS-CoV-2 infections. The source code of GBMPhos is publicly available on GitHub. [ABSTRACT FROM AUTHOR]

Over 50% of India's population relies on crop growing, making it the foundation of the country's economy. Differences in weather, temperature, and other environmental factors are now a significant threat to the continued success of agriculture. This makes it difficult to predict the crop yield appropriately. Hence, computer-based models are believed to be more predictive in analyzing crop yields. Machine learning (ML) and deep learning (DL) models play a vital role as the supporting tool for Crop Yield Prediction (CYP), which includes assisting decisions on which crops to plant and the measures to be taken for the growth of crops. This paper aims to propose a deep ensemble model for predicting crop yields more accurately. The steps followed are: Improved Log scaling-based pre-processing of data. Then, from the preprocessed data, the input features like higher-order features, Enhanced entropy-based features and correlation-based features are derived. Further, the optimal features are elected using the Integrated Bird Swarm and BOA (IBS-BOA) algorithm. Based on the selected features, prediction is done by the Deep Ensemble Model (DMO, Bi-GRU and CNN). Also, one of the models of Ensemble classifiers, CNN is trained by the IBS-BOA algorithm. Finally, the performance of the proposed DEC-IBSBOA is evaluated concerning varied error factors namely, MEAE, MAE, MALE, MAPE, ME and MSE. For the suggested system minimum error is obtained for MAE (~ 1.0) when compared to the other existing methods such as Bi-GRU, DMO, CNN, PLMDC, SVM, LSTM, SSTNN, EC + WOA, EC + BES, EC + BOA, EC + BMO and EC + BSA. [ABSTRACT FROM AUTHOR]

Accurate tracking of tropical cyclones (TCs) can provide regions of interest for intelligent forecasting of TC tracks and intensity. There have been few studies on algorithms for automatic TC tracking. This study proposes an effective TC tracking method based on deep learning combined with infrared satellite images. The study first constructed a TC tracking dataset based on the infrared images of the China Fengyun-2D geostationary satellite covering six different TC intensity levels between 2009 and 2012. This included 47 complete cases (video sequences) of TCs from generation to extinction. Based on deep learning, the visual tracking algorithm SiamRPN was used as the model framework. Combining Bi-GRU and TC cloud spatiotemporal evolution characteristics to improve the performance of the SiamRPN network, the SiamTCNet target-tracking model was designed to track TCs automatically. Considering that the shape and scale of TC changes with time, a TC is regarded as a typical non-rigid object with obvious timing characteristics, so the first frame of a TC video sequence is combined with the satellite images of the first three frames of the current frame as inputs to the proposed SiamTCNet model, which then extracts the evolution of the TC's spatial structure and its bidirectional temporal change information. The experimental results show that the TC tracking of the proposed model is a significant improvement over the original SiamRPN model. [ABSTRACT FROM AUTHOR]

Most diabetes patients are liable to have diabetic retinopathy (DR); however, the majority of them might not be even aware of the ailment. Therefore, early detection and treatment of DR are necessary to prevent vision loss. But, avoiding DR is not a simple process. An ophthalmologist can typically identify DR through an optical evaluation of the fundus and through the evaluation of color pictures. However, due to the increased count of DR patients, this could not be possible as it consumes more time. To rectify this problem, a novel deep ensemble-based DR classification technique is developed in this work. Initially, a Wiener filter (WF) is applied for preprocessing the image. Then, the enhanced U-Net-based segmentation process is done. Subsequent to the segmentation process, features are extracted that include statistical features, inferior superior nasal temporal (ISNT), cup to disc ratio (CDR), and improved LGBP as well. Further, deep ensemble classifiers (DEC) like CNN, Bi-GRU, and DMN are used to recognize the disease. The outcomes from DMN, CNN, and Bi-GRU are then subjected to improved SLF. Additionally, the weights of DMN, CNN, and Bi-GRU are adjusted via pelican updated Tasmanian devil optimization (PU-TDO). Finally, outputs on DR (microaneurysms, hemorrhages, hard exudates, and soft exudates) are obtained. The performance of DEC + PU-TDO for diabetic retinopathy is computed over extant models with regard to different measures for four datasets. The results on accuracy using the DEC + PU-TDO scheme for the IDRID dataset is maximum around 0.975 at 90th LP while other models have less accuracy. The FPR of DEC + PU-TDO is less around 0.039 at the 90th LP for the SUSTech-SYSU dataset, while other extant models have maximum FPR. [ABSTRACT FROM AUTHOR]

Deoxyribonucleic acid (DNA) profiling is an important branch of forensic science that aids in the identification of missing people, particularly in mass disasters. This study presents an artificial intelligence system that utilizes DNA-Short Tandem Repeat (STR) data to identify victims using Deep Neural Network (DNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (Bi-GRU) deep learning models. The identification of STR information for living family members, such as parents or brothers, poses a significant challenge in victim identification. Familial data are artificially generated based on the actual data of distinct Iraqi individuals from the province of Al-Najaf. Two people are selected as male and female to create a family of 10 members. As a result of this action, 151,580 individuals were generated from 106 different people, which helps to overcome the lack of datasets caused by restrictive policies and the confidentiality of familial datasets in Iraq. These datasets are prepared and formatted for training deep learning models. Based on various reference datasets, the models are built to handle five different scenarios where both parents are alive, only one parent is alive, or the siblings are available for reference. The three models' performances were compared: Bi-GRU performed the best, with a loss of 0.0063 and an accuracy of 0.9979, followed by GRU with a loss of 0.0102 and an accuracy of 0.9964, and DNN with a loss of 0.2276 and an accuracy of 0.9174. The evaluation makes use of a confusion matrix and receiver operating characteristic curve. Based on the literature, this is the first attempt to introduce deep learning in DNA profiling, which reduces both time and effort. Despite the fact that the proposed deep learning models have good results in identifying missing persons according to their families, these models have limitations that can be confined to the availability of familial DNA profiles. The system doesn't work well if no relative samples are available as references, such as a father, mother, or brother. In the future, DNN, GRU, and Bi-GRU models will be applied to mini-STR sequences that are used in cases of degraded victims of incomplete STR sequences. [ABSTRACT FROM AUTHOR]

Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), aims to extract subjective information from textual data. With the proliferation of social media platforms like Twitter, accurately determining public sentiment has become increasingly important for businesses, policymakers, and researchers. This study introduces the Dual Joint Classifier (DJC), which integrates the strengths of RoBERTa and BERT architectures. The DJC model leverages Bidirectional Gated Recurrent Units (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) layers to capture complex sequential dependencies and nuanced sentiment expressions. Advanced training techniques such as Focal Loss and Hard Sample Mining address class imbalance and improve model robustness. To further validate the DJC model’s robustness, the larger TweetEval Sentiment dataset was also included, on which DJC outperformed conventional models despite increased training time. Evaluations were conducted on the Twitter US Airlines and Apple Twitter Sentiment datasets to verify experiments. The DJC model achieved 87.22% and 93.87% accuracies, respectively, and demonstrated improvement over other models like RoBERTa-GLG, BiLSTM(P), and SVM. These results highlight the DJC model’s effectiveness in handling diverse sentiment analysis tasks and its potential for real-world applications.

Dynamic hand gesture recognition continues to be an interesting field in computer vision applications. Occlusion and background clutter make dynamic hand gesture recognition challenging. In this study, we proposed two parallel pipelines. The first pipeline uses skeleton data to generate a skeleton point trajectory video where the fingertips are tracked across the frame and a trajectory video is created. The use of skeleton data overcomes the challenges of occlusion and complex background. Similarly, in the second pipeline optical flow videos are calculated from RGB/Depth data that capture the motion information of the moving hand. Creation of an optical flow video filters out irrelevant data and concentrates on the gesturing hand that helps in extracting spatio-temporal information. Then, features are extracted parallelly from both pipelines using pre-trained Xception-Net. The created feature vector is passed to the Bi-GRU unit for sequence-to-sequence learning. At the feature level, features of both Bi-GRU networks are averagely fused and flattened at the FC layer and the Softmax classifier is used to classify the gesture. We tested our proposed model on two benchmark datasets, namely NWUHG dataset and the DHG-14/28 dataset. The proposed model achieved 99.2 % accuracy on NWUHG, 98.1 % on DHG-14, and 94.2 % on DHG-28, i.e., comparable with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs. [ABSTRACT FROM AUTHOR]

Enhancing the prognosis of brain tumors through early detection is crucial for enhancing patient survival through improved treatment results. It is a challenging task to manually assess the Magnetic Resonance Imaging (MRI) generated on a regular basis in the clinic. Thus, more accurate computer-aided techniques are desperately needed for early tumor diagnosis. Tumor identification, segmentation and classification procedures are part of computer-aided brain tumor diagnosis from MRI. In this model, uses the RS-ESIHE preprocessing approach to acquire enhanced MRI images. Pre-processed MRI is segmented using the FANET and segmented images are extracted using the GLRLM technique. The ensemble learning classifier is trained to predict different types of brain tumors. This proposed model achieves performance metrics of 98.5%, 97.5%, 96.8%, 1.5%, and 99% for Accuracy, precision, F1-Score, error and specificity. The comparisons are conducted between the assessed values and existing approaches like Mask-RCNN, DCNN, and SENET. Thus, the detection of brain tumor types based on FANET segmentation and GLRLM with an ensemble learning classifier performs better prediction than the existing model. [ABSTRACT FROM AUTHOR]

Automatically detecting unusual behavior in a crowded environment greatly improves public safety. Unusual behaviors are those that depend on the rules established in the environment under consideration and cannot be properly described. This paper proposes a new deep feature-based crowd anomaly detection method. Priorly, the input image is preprocessed using the Weiner filtering method. Subsequently, AlexNet, VGGnet, and ResNet-based deep features are extracted. During this process, all three models were optimally tuned. For optimization, a new hybrid optimization method called Hybrid COOT and Bald Eagle with Bernoulli Map Evaluation (HCBEBME) is introduced in this work. This improves the performance of extracting features from the input. Finally, based on the proposed feature set, anomalies are detected by the hybrid detection model that combines LSTM and Bi-GRU models, respectively. Finally, the performance of the proposed model is validated over the conventional models. The detection accuracy of the suggested approach is 96.59%, whereas the minimal accuracy scores for the other methods BRCASO, GNNN, CNN-BILSTM, LSTM, BIGRU, BILSTM, CNN, and RNN are 93.63%, 79.16%, 87.57%, 73.85%, 70.59%, 77.08%, 81.44%, and 84.66% respectively. [ABSTRACT FROM AUTHOR]

• We proposed a novel computational method for detect RNA m6A sites. • The proposed method is of powerful ability to predict RNA m6A sites in multiple species or tissues, and of across species or tissue ability. • A user-friendly webserver facilitates recognition of RNA m6A for biologists. N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection over the past decades, there were rooms left to improve the predictive accuracy and the efficiency. In this paper, we proposed an improved method for predicting m6A modification sites, which was based on bi-directional gated recurrent unit (Bi-GRU) and convolutional neural networks (CNN), called Deepm6A-MT. The Deepm6A-MT has two input channels. One is to use an embedding layer followed by the Bi-GRU and then by the CNN, and another is to use one-hot encoding, dinucleotide one-hot encoding, and nucleotide chemical property codes. We trained and evaluated the Deepm6A-MT both by the 5-fold cross-validation and the independent test. The empirical tests showed that the Deepm6A-MT achieved the state of the art performance. In addition, we also conducted the cross-species and the cross-tissues tests to further verify the Deepm6A-MT for effectiveness and efficiency. Finally, for the convenience of academic research, we deployed the Deepm6A-MT to the web server, which is accessed at the URL http://www.biolscience.cn/Deepm6A-MT/. [ABSTRACT FROM AUTHOR]

ObjectiveThe widespread prevalence of sclerotinia disease poses a significant challenge to the cultivation and supply of oilseed rape, not only results in substantial yield losses and decreased oil content in infected plant seeds but also severely impacts crop productivity and quality, leading to significant economic losses. To solve the problems of complex operation, environmental pollution, sample destruction and low detection efficiency of traditional chemical detection methods, a Bi-directional Gate Recurrent Unit (Bi-GRU) model based on space-spectrum feature fusion was constructed to achieve hyperspectral images (HSIs) segmentation of oilseed rape sclerotinia infected area.MethodsThe spectral characteristics of sclerotinia disease from a spectral perspective was initially explored. Significantly varying spectral reflectance was notably observed around 550 nm and within the wavelength range of 750-1 000 nm at different locations on rapeseed leaves. As the severity of sclerotinia infection increased, the differences in reflectance at these wavelengths became more pronounced. Subsequently, a rapeseed leaf sclerotinia disease dataset comprising 400 HSIs was curated using an intelligent data annotation tool. This dataset was divided into three subsets: a training set with 280 HSIs, a validation set with 40 HSIs, and a test set with 80 HSIs. Expanding on this, a 7×7 pixel neighborhood was extracted as the spatial feature of the target pixel, incorporating both spatial and spectral features effectively. Leveraging the Bi-GRU model enabled simultaneous feature extraction at any point within the sequence data, eliminating the impact of the order of spatial-spectral data fusion on the model's performance. The model comprises four key components: an input layer, hidden layers, fully connected layers, and an output layer. The Bi-GRU model in this study consisted of two hidden layers, each housing 512 GRU neurons. The forward hidden layer computed sequence information at the current time step, while the backward hidden layer retrieves the sequence in reverse, incorporating reversed-order information. These two hidden layers were linked to a fully connected layer, providing both forward and reversed-order information to all neurons during training. The Bi-GRU model included two fully connected layers, each with 1 000 neurons, and an output layer with two neurons representing the healthy and diseased classes, respectively.Results and DiscussionsTo thoroughly validate the comprehensive performance of the proposed Bi-GRU model and assess the effectiveness of the spatial-spectral information fusion mechanism, relevant comparative analysis experiments were conducted. These experiments primarily focused on five key parameters—ClassAP(1), ClassAP(2), mean average precision (mAP), mean intersection over union (mIoU), and Kappa coefficient—to provide a comprehensive evaluation of the Bi-GRU model's performance. The comprehensive performance analysis revealed that the Bi-GRU model, when compared to mainstream convolutional neural network (CNN) and long short-term memory (LSTM) models, demonstrated superior overall performance in detecting rapeseed sclerotinia disease. Notably, the proposed Bi-GRU model achieved an mAP of 93.7%, showcasing a 7.1% precision improvement over the CNN model. The bidirectional architecture, coupled with spatial-spectral fusion data, effectively enhanced detection accuracy. Furthermore, the study visually presented the segmentation results of sclerotinia disease-infected areas using CNN, Bi-LSTM, and Bi-GRU models. A comparison with the Ground-Truth data revealed that the Bi-GRU model outperformed the CNN and Bi-LSTM models in detecting sclerotinia disease at various infection stages. Additionally, the Dice coefficient was employed to comprehensively assess the actual detection performance of different models at early, middle, and late infection stages. The dice coefficients for the Bi-GRU model at these stages were 83.8%, 89.4% and 89.2%, respectively. While early infection detection accuracy was relatively lower, the spatial-spectral data fusion mechanism significantly enhanced the effectiveness of detecting early sclerotinia infections in oilseed rape.ConclusionsThis study introduces a Bi-GRU model that integrates spatial and spectral information to accurately and efficiently identify the infected areas of oilseed rape sclerotinia disease. This approach not only addresses the challenge of detecting early stages of sclerotinia infection but also establishes a basis for high-throughput non-destructive detection of the disease.

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of production, the label on the product provides the basic product information. Due to the complex text contained on the product labels, the high accuracy recognition for online production labels has always been a challenging problem. To address this issue, a more effective method for complex text detection by improving the convolutional recurrent neural network has been proposed to enhance the recognition accuracy of complex text. Firstly, the SE-DenseNet feature extraction network has been introduced for feature extraction, aiming to improve the model’s depth and feature extraction capacity. Then, the Bi-GRU network is utilized to learn and model the hidden states and spatial features extracted by SE-DenseNet, anticipate preliminary sequence results, reduce model parameters, and improve the model’s calculation performance. Finally, the CTC network is employed for transcription to convert each feature sequence prediction output by Bi-GRU into a label sequence, achieving complex text recognition. Experimental results on the SVT, IIIT-5K, ICDAR2013 public dataset, and a self-built dataset demonstrate that the proposed model achieves superior outcomes on both public and self-built datasets. Remarkably, the model exhibits the highest recognition accuracy of 93.2% on the ICDAR2013 public dataset, demonstrating its potential to support complex text recognition for online production labels. [ABSTRACT FROM AUTHOR]

This paper suggests a hybrid method that combines the strengths of a bidirectional gated recurrent unit with a stacked denoising autoencoder to enhance the precision and effectiveness of diagnosing transmission faults in electric vehicles. The bidirectional gated recurrent unit makes advantage of these deep features for efficient fault pattern identification and classification. The results revealed that the hybrid algorithm had the best feature extraction ability for gear fault signals, and the signal features extracted by the algorithm were more concentrated and crossed each other less. The neurons in the hidden layer of the stacked denoising autoencoder was 180, and the number of neurons in the bidirectional gated recurrent unit was 160, and the hybrid algorithm performed best when the neurons in the hidden layer was 180 and the neurons in the bidirectional gated recurrent unit was 160. The hybrid algorithm performed best when the number of neurons was 160. The hybrid algorithm had the highest diagnostic accuracy for the faults, with the highest diagnostic accuracy of 97.98% in the balanced samples and 94.86% in the unbalanced samples. The hybrid algorithm constructed in the study effectively improves the diagnostic accuracy of transmission gear faults in electric vehicles.. [ABSTRACT FROM AUTHOR]

Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model's robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model's generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability. [ABSTRACT FROM AUTHOR]

Due to its important role in smart grids, power system management, and smart buildings, energy consumption forecasting has gained a lot of interest in recent years, further achieving energy efficiency objectives, decreasing CO 2 emissions, and reducing energy bill. Because of the nonlinear and non-smooth characteristics of residential building electricity consumption time series data, developing an accurate energy consumption model is a crucial task. To solve this constraint, this research proposes a short-term, hybrid model that combines variational mode decomposition and Bi-GRU with the aim to predict household energy consumption forecasting of the next 24 hours with a time granularity of 15 minutes. The VMD algorithm in this model decomposes the power consumption time series into distinct signals called IMFs, and the Bi-GRU is used to predict each IMF separately. To produce the final prediction output, the prediction results of each model are summed and rebuilt. The conclusive findings indicate that the forecasting model based on VMD-BI-GRU demonstrates exceptional performance, with a mean squared error of 0.0038 KW, a mean absolute error of 0.046 KW, a mean absolute percentage error of 0.11%, and a notably high R 2 score of 0.98. These results collectively signify its precision as a prediction model. [ABSTRACT FROM AUTHOR]

Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the "curse of dimensionality," the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics. [ABSTRACT FROM AUTHOR]

Deep learning promotes the breakthrough of emotion recognition in many fields, especially speech emotion recognition (SER). As an important part of speech emotion recognition, the most relevant acoustic feature extraction has always attracted the attention of existing researchers. Aiming at the problem that the emotional information contained in the current speech signals is distributed dispersedly and cannot comprehensively integrate local and global information, this paper presents a network model based on a gated recurrent unit (GRU) and multi-head attention. We evaluate our proposed emotion model on the IEMOCAP and Emo-DB corpora. The experimental results show that the network model based on Bi-GRU and multi-head attention is significantly better than the traditional network model at detecting multiple evaluation indicators. At the same time, we also apply the model to a speech sentiment analysis task. On the CH-SIMS and MOSI datasets, the model shows excellent generalization performance. [ABSTRACT FROM AUTHOR]

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Amidst the ongoing wave of economic globalization, the societal demand for English proficiency is escalating, particularly for individuals adept in practical applications of the language. Recognizing the pivotal role of English reading as a cornerstone in language acquisition, there arises a need for personalized approaches tailored to individual interests, thereby necessitating an in-depth analysis of text emotions. Addressing the challenges in text classification within English reading courses, this study presents a novel method for text emotion analysis. Integrating sentiment dictionaries with BI-GRU networks, the proposed approach significantly enhances the efficiency of text emotion recognition while simultaneously fostering students’ engagement. By segmenting the emotion dictionary based on polarity and extracting pertinent features, the study amalgamates these with BI-GRU features at the feature level. This fusion facilitates emotion classification within reading texts through sophisticated activation functions. Notably, the precision of recognizing positive, negative, and neutral emotions reaches an impressive 92.5%, marking a notable improvement over methods devoid of dictionary feature integration. This framework offers novel insights for future English reading material development and intelligent learning strategies to bolster student enthusiasm and chart a promising trajectory for cultivating practical English talents.

The widespread use of the Internet and the tremendous growth of social media have enabled people to connect with each other worldwide. Individuals are free to express themselves online, sharing their photos, videos, and text messages globally. However, such freedom sometimes leads to misuse, as some individuals exploit this platform by posting hateful and abusive comments on forums. The proliferation of abusive language on social media negatively impacts individuals and groups, leading to emotional distress and affecting mental health. It is crucial to automatically detect and filter such abusive content in order to effectively tackle this challenging issue. Detecting abusive language in text messages is challenging due to intentional word concealment and contextual complexity. To counter abusive speech on social media, we need to explore the potential of machine learning (ML) and deep learning (DL) models, particularly those equipped with attention mechanisms. In this study, we utilized popular ML and DL models integrated with attention mechanism to detect abusive language in Urdu text. Our methodology involved employing Count Vectorizer and Term Frequency-Inverse Document Frequency (TF/IDF) to extract n-grams at the word level: Unigrams (Uni), Bigrams (Bi), Trigrams (Tri), and their combination (Uni + Bi + Tri). Initially, we evaluated four traditional ML models—Logistic Regression (LR), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF)—on both proposed and established datasets. The results highlighted that RF model outperformed other conventional models in terms of accuracy, precision, recall, and F1-measure on both datasets. In our implementation of deep learning models, we employed various models integrated with custom fastText and Word2Vec embeddings, each equipped with an attention layer, except for the Convolutional Neural Network (CNN). Our findings indicated that the Bidirectional Long Short-Term Memory (Bi-LSTM) + attention model, utilizing custom Word2Vec embeddings, exhibited improved performance in detecting abusive language on both datasets.

In the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redundancy of current rumor detection methods based on deep learning model, a social network rumor detection method based on bidirectional gated recurrent unit (Bi-GRU) and double self-attention (DSA) mechanism is suggested. First, a combination of application program interface and third-party crawler approach is used to obtain microblogging data from publicly available fake microblogging information pages, including both rumor and non-rumor information. Second, Bi-GRU is used to capture the tendency of medium- and long-term dependence of data and is flexible enough to deal with variable length input. Finally, the DSA mechanism is introduced to help reduce the redundant information in the dataset, thereby enhancing the model’s efficacy. The results of the experiments indicate that the proposed method outperforms existing advanced methods by at least 0.114, 0.108, 0.064, and 0.085 in terms of accuracy, precision, recall, and F1-scores, respectively. Therefore, the proposed method can significantly enhance the ability of social network rumor detection.

Accurate and real-time traffic flow prediction is crucial in intelligent transportation systems (ITS), and the traditional shallow prediction methods are challenging to capture the nonlinearity and uncertainty of traffic data effectively. To this end, this paper proposes a hybrid deep-learning method based on Residual Self-Attention and Bidirectional Gated Recurrent Unit combined with a Convolution-Gated Recurrent Unit (RSAB-ConvGRU) network to improve the accuracy of traffic flow prediction. The method consists of an RSA-ConvGRU module and two Bidirectional GRU (Bi-GRU) modules. The RSA-ConvGRU module includes a convolution-gated recurrent unit (Conv-GRU) module and a residual self-attention mechanism (RSA) module. Specifically, the Conv-GRU utilizes the convolutional and gated recurrent unit to extract spatial and temporal features. Moreover, the residual self-attention mechanism is used to determine the contribution of traffic features at different periods and stabilize the network's training process to improve Conv-GRU's prediction performance. Finally, the Bi-GRU module obtains the periodic characteristics and forward and backward variance trends in traffic flow data. The experimental results show that the accuracy of the RSAB-ConvGRU method is superior to state-of-the-art methods, such as SVR, LSTM, GRU, DCRNN, CNN-GRU-Attention, Conv-LSTM, AT-Conv-LSTM, Stacked-LSTM, and LSTM-RNN with Attention. Compared to the above nine methods, with a prediction time of 60 minutes and the urban traffic data, the MAPE values of RSAB-ConvGRU are reduced by 56.01%, 22.97%, 25.64%, 16.55%, 7.57%, 11.11%, 12.58%, 7.64%, and 6.3%, respectively. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a deadly neurological condition. Deep learning approaches (DL) techniques have just been utilized to track the evolution of Alzheimer's disease. These studies only employed baseline neuro imaging data. Because of the high cost of neuro imaging data, it is constantly restricted or unavailable. As a result, this research developed a novel, four-phase early Alzheimer's disease detection approach: "(a) pre-processing, (b) feature extraction, (c) feature selection, and (d) classification". Data cleaning and normalization is used in pre-processing. Consequently, features like "Weighted Geometric Mean Principle Component Analysis (WGM-PCA), Statistical Features, higher-order statistical features, and Weighted modified correlation-based features" are retrieved from the pre-processed data. Employing the Improved Attribute Ranker (IAR), the most relevant characteristics are chosen. Furthermore, the disease classification phase is represented by a deep learning model based on an ensemble of classifiers, containing optimized "Bi-GRU, Multi-Layer Perceptron (MLP), and Quantum Neural Network (QDNN)", respectively. The ultimate decision is obtained via optimal Bi-GRU, which is trained using MLP and QDNN outcomes. Both the MLP and the QDNN would be trained using the chosen IAR-based features. Interestingly, to improve the network's detection accuracy, the weight of the QDNN model is adjusted using the recently proposed Enhanced Math Optimizer Accelerated Arithmetic Optimization (EMOAOA) technique. Particularly, the proposed EMOAOA+EC achieved detecting accuracies of 95% at the 60th LR, 95.5% at the 70th LR, 98% at the 80th LR, and 98.7% at the 90th LR. The development of the optimized ensemble classifier is responsible for this improvement. [ABSTRACT FROM AUTHOR]

There are many methods to diagnose heart disease; the most effective way is to analyze electrocardiogram (ECG) signals. Generally, the automatic classification techniques based on ECG analysis consist of three steps: data preprocessing, feature extraction, and classification. This study designed eight hybrid model architectures using several types of deep neural networks, including Convolution Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (Bi-GRU), four of them without Fast Fourier Transform (FFT) and the rest using FFT. Firstly, the MIT-BIH arrhythmia database is cleaned using the wavelet (WT) thresholding method that separates the combined noise and signal frequencies, making it ideal for processing nonstationary ECG signals. Additionally, the imbalance problem in this database was addressed using the synthetic minority over-sampling technique (SMOTE), which is more suitable for medical data than random synthesis methods. Secondly, hybrid models FFT-CNN, FFT-GRU, FFT-CNN-GRU, and FFT-CNN-Bi-GRU are constructed using the new proposed architecture by concatenating resultant features from two paths, the first path using ECG in the time domain and the second path using the resultant spectrum of ECG from FFT as input. A comparative study of the performance of all models was created in terms of accuracy, training time, number of trainable parameters, and robustness against noise. The results show that the proposed CNN, GRU, CNN-GRU, and CNN-Bi-GRU models without WT and FFT achieved 90%, 93%, 95%, and 96% accuracies, while the proposed FFT-CNN, FFT-GRU, FFT-CNN-GRU, and FFT-CNN-Bi-GRU models achieved 97%, 95%, 96%, and 97% accuracies with WT. So, the proposed FFT-CNN model was the best, with less training time and parameters than other models, which significantly impacts designing a high-efficiency model with less complexity for a practical medical diagnosis system. On the other hand, using FFT improved all models' performance, accuracy and robustness against noise. [ABSTRACT FROM AUTHOR]

Intracranial hemorrhage (ICH) is a dangerous condition that needs prompt diagnosis and treatment. Computed tomography (CT) images are employed in examination of individuals with ICH, which produces better results and cost‐effective than MRI. The existing convolutional neural network (CNN) models are unable to consider inter‐pixel dependency, which leads to false predictions while considering the input CT Images. In this study, we implemented an efficient model of a stack of bidirectional gated recurrent unit (Bi‐GRU) with a bidirectional long short‐term memory (Bi‐LSTM) based CNN to improve detection accuracy in the case of 2D slices. The proposed model holds slice‐wise information by accessing the properties of both Bi‐LSTM and Bi‐GRU modules in a single unit. As a result, the model attained a testing and training accuracy of 96.2% and 93.4%, respectively, with a test loss score of 0.126. In addition, the proposed model could outperform the state‐of‐the‐art CNN in identifying brain hemorrhages. [ABSTRACT FROM AUTHOR]