Bidirectional Long Short-term Memory Model Research Articles

Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions

Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions.

Time series prediction of sea surface temperature based on BiLSTM model with attention mechanism

With the advancement of technology, ocean observation techniques have become increasingly prevalent in estimating marine variables such as Sea Surface Temperature (SST). This progress has led to a substantial surge in the volume of marine data. Presently, the abundance of available data presents a remarkable opportunity for training predictive models. The prediction of SST poses a challenge due to its temporal-dependent structure and multi-level seasonality. In this study, we propose a deep learning approach that combines the Bidirectional Long Short-Term Memory (BiLSTM) model with the attention mechanism to forecast SST. By leveraging the BiLSTM's ability to effectively capture long-term dependencies through both forward and backward LSTM processing, the attention mechanism accentuates salient features, thereby enhancing the model's evaluation accuracy.To evaluate the effectiveness of the Attention-BiLSTM model in predicting SST, we conducted a case study in the Moroccan Sea, focusing on four distinct regions. We compared the performance of the Attention-BiLSTM model against alternative models such as LSTM, Attention-BiGRU, XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Transformers in forecasting the SST time series.The experimental results unequivocally demonstrate that the Attention-BiLSTM model achieves significantly superior prediction outcomes and is a good candidate for deployment in the field.

Optimizing classification of diseases through language model analysis of symptoms

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine.

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.

A Deep Bidirectional LSTM Model Enhanced by Transfer-Learning-Based Feature Extraction for Dynamic Human Activity Recognition

Dynamic human activity recognition (HAR) is a domain of study that is currently receiving considerable attention within the fields of computer vision and pattern recognition. The growing need for artificial-intelligence (AI)-driven systems to evaluate human behaviour and bolster security underscores the timeliness of this research. Despite the strides made by numerous researchers in developing dynamic HAR frameworks utilizing diverse pre-trained architectures for feature extraction and classification, persisting challenges include suboptimal performance accuracy and the computational intricacies inherent in existing systems. These challenges arise due to the vast video-based datasets and the inherent similarity in the data. To address these challenges, we propose an innovative, dynamic HAR technique employing a deep-learning-based, deep bidirectional long short-term memory (Deep BiLSTM) model facilitated by a pre-trained transfer-learning-based feature-extraction approach. Our approach begins with the utilization of Convolutional Neural Network (CNN) models, specifically MobileNetV2, for extracting deep-level features from video frames. Subsequently, these features are fed into an optimized deep bidirectional long short-term memory (Deep BiLSTM) network to discern dependencies and process data, enabling optimal predictions. During the testing phase, an iterative fine-tuning procedure is introduced to update the high parameters of the trained model, ensuring adaptability to varying scenarios. The proposed model’s efficacy was rigorously evaluated using three benchmark datasets, namely UCF11, UCF Sport, and JHMDB, achieving notable accuracies of 99.20%, 93.3%, and 76.30%, respectively. This high-performance accuracy substantiates the superiority of our proposed model, signaling a promising advancement in the domain of activity recognition.

DeBERTa-BiLSTM: A multi-label classification model of Arabic medical questions using pre-trained models and deep learning

It is wise to investigate past and present epidemics in the hopes of profiting from them and being better prepared for future ones. COVID-19 is one of the most recent and well-known pandemics; its effects are still felt today. Most or nearly all governments have announced various measures to combat the virus, making it challenging to keep people aware of the most up-to-date and relevant information. As a result, many websites have created and maintained Frequently Asked Questions (FAQs) regarding the pandemic. People naturally tend to ask about multiple points in one question, leading to multi-label questions. Multi-label questions classification is one of Natural Language Processing’s (NLP) most common and complicated tasks. One of classification’s most significant contributions to advancing medical care and facilities is the development of automated question-and-answer systems. These systems can improve the efficiency of healthcare by reducing the burden on healthcare professionals and providing patients with timely and reliable answers to their questions. Due to the Arabic language’s intricate morphology and structure, such a task becomes more challenging when dealing with Arabic text. This study aims to build a multi-label classification model for Arabic medical questions. The investigation of pre-trained neural models significantly improved NLP performance. Recently, pre-trained models have been used in multi-label classification. This study proposes a deep learning model for classifying Arabic multi-label COVID-19 questions by combining the strengths of DeBERTa (Decoding-enhanced BERT with Disentangled Attention) and BiLSTM (Bidirectional Long Short-Term Memory) networks. Deep learning methods are prevalent because they generate dense feature representations automatically and implicitly capture hidden relationships. The DeBERTa model is fine-tuned to generate the representation of word vectors. The BiLSTM model is fed word vectors to extract and represent features deeply. The proposed multi-label classification model categorizes questions into one or more available ten categories. The deep learning model is evaluated using hamming loss, micro-precision, micro-recall, micro-F1, subset accuracy, AUC, and Jaccard index. It showed an effective classification for Arabic questions with encouraging performance. The proposed model achieved values of 0.042 for hamming loss, 0.84 for micro-precision, micro-recall, and micro-F1, 0.71 for subset accuracy, 0.89 for AUC, and 0.72 for Jaccard index. Therefore, this paves the way for adopting an automated multi-label classification model for medical questions in health facilities. Which can help telehealth medical providers present more reliable and effective consultations.

Forecasting Stock Prices via Deep Learning During COVID-19: A Case Study from an Emerging Economy

In this study we apply a Deep Learning Technique to predict stock prices for the 30 stocks that compose the BIST30, Turkish Stock Market Index before and after the onset of Covid-19 crises. Specifically, we utilize the Bi-Directional Long-Short Term Memory (BiLSTM) model which is a variation of the Long-Short-Term Memory (LSTM) model to predict stock prices for the BIST30 stocks. We compare the performance of the model to other commonly used machine learning models like decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), artificial neural networks (ANN), and other deep Leaning models like recurrent neural network (RNN), and the Long-Short-Term Memory (LSTM) model. The BiLSTM model seems to have better performance compared to conventional models used for predicting stock prices and continues to have superior performance in the Covid19 period. The LSTM model seems to have a good overall performance and is the next best model.

Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model

In order to accurately predict the lane‐changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane‐changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short‐term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. Firstly, the vehicle trajectory data are filtered and smoothed, and it is divided into three categories: left lane change, right lane change, and straight driving, and a lane change trajectory sample set is constructed. Secondly, CNN‐BiLSTM model is constructed to identify the sample set of lane‐changing trajectory. Considering the interaction between vehicles in the driving process, the information of predicted vehicle, and surrounding vehicles is taken as the input of the model. The extracted feature vector is input to the BiLSTM layer for prediction after the CNN layer feature extraction, and the horizontal and vertical coordinates of the target vehicle at the next time are output. Thirdly, the trajectory data of the US‐101 dataset in NGSIM is selected to verify the performance of the CNN‐BiLSTM model, and at the same time, it is compared with models such as CNN‐LSTM, long short‐term memory (LSTM), BiLSTM, and CNN‐GRU‐Att. Finally, the verification result shows that the overall fitting degree of the vehicle lane change trajectory prediction of the proposed model reaches 99.50%, and the mean square error and mean absolute error are 0.0003076 and 0.01417, which are improved compared with other models. In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.

Learning Analytics Model for Predictive Analysis of Learners Behavior for an Indigenous MOOC Platform (Tadakhul System) in Oman

The Massive Open Online Courses (MOOCs) platforms are widely used by the learner community all over the world. Using MOOC, the learners can choose the course of interest and learn it in their own pace. The main problem encountered in most MOOC based learning is the lack of a learning analytic system that monitors the learners’ interaction with their enrolled courses. This problem leads to incompletion or discontinuation of the course. In this paper, ‘Tadakul’ system, an original bilingual (English and Arabic) MOOC platform is developed for students of higher education institutions of the Sultanate of Oman. Using the Tadakhul system, learners of various higher education institutions of Oman can enroll themselves in various courses of their interest and complete the course at their own pace. This research aims in understanding the impact of learning analytics system used in the Tadakhul system. A novel deep-learning approach used in the system monitors the learning process of the learners based on their interaction with the enrolled courses. The feedback thus obtained, using the deep-learning approach, can be used to improve the student learning experience. To analyze the feedback obtained from the learners, an innovative approach of combining Bidirectional Long Short-Term Memory (BiLSTM) network with a Convolutional Neural Network (CNN) is proposed. The BiLSTM model performs well in analyzing the sequential data whereas the CNN model is good in extracting the spatial features at each hidden layer that are important for evaluating the student’s learning patterns. Thus the proposed model can identify learner’s learning behavior and learning styles that help teachers better understand the individual needs. The results of the experimental study revealed that the proposed model outperformed conventional machine learning approaches in predicting learner’s learning behavior. Hence, the results obtained by integrating BiLSTM and CNN models on the Tadakhul platform can improve the student experience by making teaching more efficient and effective.

A Hybrid GARCH and Deep Learning Method for Volatility Prediction

Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse models. Thus, the study integrated four powerful methods: seasonal autoregressive (AR) integrated moving average (MA), generalized AR conditional heteroskedasticity (ARCH) family models, convolutional neural network (CNN), and bidirectional long short‐term memory (LSTM) network. The hybrid model was developed using the residuals generated by the seasonal AR integrated MA model as input for the generalized ARCH model. Following this, the estimated volatility obtained was utilized as an input feature for both the hybrid CNNs and bidirectional LSTM models. The model’s forecasting performance was assessed using key evaluation metrics, including mean absolute error (MAE) and root mean squared error (RMSE). Compared to other hybrid models, our new proposed hybrid model demonstrates an average reduction in MAE and RMSE of 60.35% and 60.61%, respectively. The experimental results show that the model proposed in this study has good performance and accuracy in predicting the volatility of stock prices. These findings offer valuable insights for financial data analysis and risk management strategies.

Stock Prediction System Based on Bi-directional LSTM Model

Stock Prediction System Based on Bi-directional LSTM Model

Internet of Things Enabled Based Arrhythmia Classification using Dandelion Optimization Algorithm with Ensemble Learning

Internet of Things (IoT) based Arrhythmia Classification is a cutting-edge algorithm that amalgamates the abilities of the IoT and advanced medical diagnosis to revolutionize the detection and classification of arrhythmias—irregular heartbeats that may indicate fundamental cardiovascular issues. This technique leverages IoT devices, namely connected health monitors and wearable sensors, to continuously gather electrocardiogram (ECG) information from individuals. This information, streamed in real-time, provides a great opportunity for timely and remote monitoring of cardiac health. Leveraging the abilities of deep learning and IoT, this technique provides an automated and more sophisticated means of classifying and detecting arrhythmias, improving the efficiency and accuracy of diagnoses. This article presents an Internet of Things Enabled Based Arrhythmia Classification using the Dandelion Optimization Algorithm with Ensemble Learning (AC-DOAEL) method. The presented AC-DOAEL technique utilizes IoT-based data collection with an ensemble learning-based classification process. For the arrhythmia detection and classification process, the AC-DOAEL technique follows an ensemble learning algorithm such as long short-term memory (LSTM), autoencoder (AE), and bidirectional LSTM (BiLSTM) models. To improve the recognition rate of the ensemble models, the AC-DOAEL technique uses DOA as a hyperparameter optimizer. The simulation outcomes of the AC-DOAEL method are well-studied on benchmark ECG data. The experimental result analysis inferred the greater performance of the AC-DOAEL algorithm with other techniques.

Short-term power load forecasting using informer encoder and bi-directional LSTM

An innovative model called InE-BiLSTM is proposed here, which combines the Informer Encoder with a bidirectional LSTM (Bi-LSTM) network. The goal is to enhance the precision and efficacy of short-term electricity load forecasting. By integrating the long-term dependency capturing capability of the informer encoder with the advantages of Bi-LSTM in handling dynamic features in time series data, the InE-BiLSTM model effectively addresses complex patterns and fluctuations in electricity load data. The study begins by analyzing the current state of short-term electricity load forecasting, followed by a detailed introduction to the structure and principles of the InE-BiLSTM model. Results of the experiment demonstrate that, compared to the Informer, traditional Bi-LSTM, and Transformer models, the InE-BiLSTM model consistently outperforms them across various evaluation metrics.

E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments.

Machine vision and artificial intelligence hold promise across healthcare applications. In this paper, we focus on the emerging research direction of infant action recognition, and we specifically consider the task of reaching which is an important developmental milestone. We develop E-babyNet, a lightweight yet effective neural-network-based framework for infant action recognition that leverages the spatial and temporal correlation of bounding boxes of infants' hands and objects to reach for to determine the onset and offset of the reaching action. E-babyNet consists of two main layers based on two LSTM and a Bidirectional LSTM (BiLSTM) model, respectively. The first layer provides a pre-evaluation of the reaching action for each hand by providing onset and offset keyframes. Then, the biLSTM model merges the previous outputs to deliver an outcome of the reaching actions detection for each frame including the reaching hand. We evaluated our approach against four other lightweight structures using a dataset comprising 5,865 annotated images resulting in 16,337 bounding boxes from 375 distinctive infant reaching actions performed while sitting by different subjects in unconstrained (home/clinic) environments. Results illustrate the effectiveness of our approach and ability to provide reliable reaching action detection and offer onset and offset keyframes with a precision of one frame. Moreover, the biLSTM layer can handle the transition between reaching actions and help reduce false detections.

Exploring Character-Level Deep Learning Models for POS Tagging in Assamese Language

The proposed research investigates a novel approach of character-level Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) for part-of-speech (POS) tagging in the Assamese language. The proposed work contributes to Natural Language Processing (NLP) by exploring these models’ ability to assign grammatical labels (POS tags) to individual words within Assamese sentences. The corpus encompasses 60,000 Assamese words and utilizes the LDCIL Assamese tagset. For training and testing, the corpus is divided into an 80:20 ratio where 80% of the corpus is used for training the models, and the rest of 20% is used for evaluation. The character-level LSTM model achieves an accuracy of 92.80%, while the character-level Bi-LSTM model surpasses it by achieving an accuracy of 93.36%. The performance of the proposed research outperforms the existing research works in the Assamese language. The results of this work broaden the understanding of POS tagging in Assamese, offering valuable findings that could be applied to other languages with similar characteristics

Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.

This study introduced a novel approach for predicting occupational injury severity by leveraging deep learning-based text classification techniques to analyze unstructured narratives. Unlike conventional methods that rely on structured data, our approach recognizes the richness of information within injury narrative descriptions with the aim of extracting valuable insights for improved occupational injury severity assessment. Natural language processing (NLP) techniques were harnessed to preprocess the occupational injury narratives obtained from the US Occupational Safety and Health Administration (OSHA) from January 2015 to June 2023. The methodology involved meticulous preprocessing of textual narratives to standardize text and eliminate noise, followed by the innovative integration of Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) word embeddings for effective text representation. The proposed predictive model adopts a novel Bidirectional Long Short-Term Memory (Bi-LSTM) architecture and is further refined through model optimization, including random search hyperparameters and in-depth feature importance analysis. The optimized Bi-LSTM model has been compared and validated against other machine learning classifiers which are naïve Bayes, support vector machine, random forest, decision trees, and K-nearest neighbor. The proposed optimized Bi-LSTM models' superior predictability, boasted an accuracy of 0.95 for hospitalization and 0.98 for amputation cases with faster model processing times. Interestingly, the feature importance analysis revealed predictive keywords related to the causal factors of occupational injuries thereby providing valuable insights to enhance model interpretability. Our proposed optimized Bi-LSTM model offers safety and health practitioners an effective tool to empower workplace safety proactive measures, thereby contributing to business productivity and sustainability. This study lays the foundation for further exploration of predictive analytics in the occupational safety and health domain.

Leveraging transfer learning with deep learning for crime prediction.

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

Bidirectional Long Short-Term Memory (Bi-LSTM) Hourly Energy Forecasting

The growing demand for energy, especially in urban and densely populated areas, has driven the need for smarter and more efficient approaches to energy resource management. One of the main challenges in energy management is fluctuations in energy demand and production. To overcome this challenge, accurate and careful forecasting of hourly energy fluctuations is required. One method that has proven effective in time series forecasting is using deep learning. The research phase uses the CRISP-DM data mining methodology as a common problem solver for business and research. The scenarios tested in the study used 5 attribute selection scenarios based on correlation values based on target attributes and 2 normalization scenarios. Then, the deep learning model used is Bi-LSTM with hyperparameter tuning grid search. Performance measurement evaluation is performed with MAPE, RMSE, and R2. Based on the tests conducted, it was found that the Bi-LSTM model produced the best MAPE of 7.7256%. RMSE of 0.1234. and R2 of 0.6151 at min-max normalization. In comparison, the results on the z-score normalization are lower with the best MAPE value produced at 10.5525%. RMSE of 0.7627. and R2 of 0.4186.

Deep learning model for state of health estimation of lithium batteries based on relaxation voltage

Lithium-ion batteries have been widely used in many fields due to their high energy density, long cycle life and low self-discharge rate. However, most existing SOH estimation methods require a lengthy amount of time to determine features, the acquisition of multi-dimensional raw data is challenging, and the application scenarios are limited, making SOH estimation algorithms less practical in developing battery management systems (BMS). To face the challenge, this paper develops a new battery SOH estimation method and investigates the transferability of machine learning models under multiple operating conditions using transfer learning. First, features are extracted from the 15 s relaxation voltage using tsfresh, a Python package for time series feature extraction. Subsequently, four statistical features were obtained using a feature selection method based on the Pearson correlation coefficient. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) model is established and hyperparameters are adjusted using Bayesian optimization. The root mean square error (RMSE) and the mean absolute percentage error (MAPE) of battery SOH estimation on battery data set are 1.210 % and 1.225 %, respectively. Finally, based on transfer learning, the Bi-LSTM model is applied to battery datasets under multiple operating conditions, reducing the model's dependence on original training data while ensuring model accuracy. Considering the easy accessibility of short-term relaxation battery data, the battery SOH estimation method proposed in this paper is expected to facilitate the estimation of battery SOH under various operating conditions.

Enhancing source code retrieval with joint Bi-LSTM-GNN architecture: A comparative study with ChatGPT-LLM

Retrieving relevant source code from large repositories is a significant and ongoing challenge in the field of software engineering, primarily due to the vast and ever-expanding amount of available code. Existing deep learning methods, although effective to some extent, exhibit limitations in capturing the intricate and complex structural information embedded within source code, which hinders their ability to provide highly accurate retrieval results. This study endeavors to tackle this prominent issue by introducing a novel and innovative approach known as the Joint Bi-directional LSTM and Graph Neural Networks (JBLG) model for source code retrieval. The central aim is to harness the combined strengths and capabilities of Bi-directional Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the model’s capacity to capture and interpret the complex structural characteristics intrinsic to source code. The proposed JBLG model employs a unique fusion of Bi-directional LSTM, which excels in capturing sequential and temporal dependencies within code, and GNN, which is adept at modeling the intricate graph structure of the code. By leveraging this hybrid architecture, the model aims to provide a comprehensive and highly effective solution for source code retrieval tasks. To assess the efficacy of the JBLG model, extensive experiments are conducted, and the model’s performance is evaluated against well-established benchmarks, including LSTM, GNN, and ChatGPT, using two diverse datasets: CodeSearchNet and CosBench datasets. These evaluations span multiple programming languages, ensuring a comprehensive and robust assessment of the model’s capabilities. The experimental results indicate that the JBLG model consistently outperforms its counterparts, including Bi-LSTM, GNN, ChatGPT, and DGMS, across various evaluation metrics. the JBLG model showcases an exceptional ability to handle and extract the intricate structural information inherent in source code, resulting in significantly enhanced retrieval accuracy. The JBLG model emerges as a highly promising solution for real-world source code retrieval applications, with the potential to revolutionize the field. The success of this model underscores the importance of combining deep learning techniques like Bi-directional LSTM and GNNs for tackling complex software engineering challenges. Furthermore, future research directions could involve exploring advanced techniques such as attention mechanisms and extending the model’s applicability to other software engineering tasks like code summarization and code completion. The findings of this study are expected to have a lasting impact on the advancement of source code retrieval methodologies.