Bidirectional Long Short-term Memory Research Articles

Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1.

Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application

Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.

Unsupervised fault detection approach based on depth auto-encoder for photovoltaic power generation system

Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems.

Fast Recurrent Neural Network with Bi-LSTM for Handwritten Tamil Text Segmentation in NLP

Tamil text segmentation is a long-standing test in language comprehension that entails separating a record into adjacent pieces based on its semantic design. Each segment is important in its own way. The segments are organised according to the purpose of the content examination as text groups, sentences, phrases, words, characters or any other data unit. That process has been portioned using rapid tangled neural organisation in this research, which presents content segmentation methods based on deep learning in natural language processing (NLP). This study proposes a bidirectional long short-term memory (Bi-LSTM) neural network prototype in which fast recurrent neural networks (FRNNs) are used to learn Tamil text group embedding and phrases are fragmented using text-oriented data. As a result, this prototype is capable of handling variable measured setting data and gives a vast new dataset for naturally segmenting text in Tamil. In addition, we develop a segmentation prototype and show how well it sums up to unnoticeable regular content using this dataset as a base. With Bi-LSTM, the segmentation precision of FRNN is superior to that of other segmentation approaches; however, it is still inferior to that of certain other techniques. Every content is scaled to the required size in the proposed framework, which is immediately accessible for the preparation. This means, each word in a scaled Tamil text is employed to prepare neural organisation as fragmented content. The results reveal that the proposed framework produces high rates of segmentation for manually authored material that are nearly equivalent to segmentation-based plans.

UNet-BiLSTM: A Deep Learning Method for Reconstructing Electrocardiography from Photoplethysmography

Electrocardiography (ECG) is generally used in clinical practice for cardiovascular diagnosis and for monitoring cardiovascular status. It is considered to be the gold standard for diagnosing cardiovascular diseases and assessing cardiovascular status. However, it is not always easy to obtain. Unlike ECG devices, photoplethysmography (PPG) devices can be placed on body parts such as the earlobes, fingertips, and wrists, making them more comfortable and easier to obtain. Several methods for reconstructing ECG signals using PPG signals have been proposed, but some of these methods are subject-specific models. These models cannot be applied to multiple subjects and have limitations. This study proposes a neural network model based on UNet and bidirectional long short-term memory (BiLSTM) networks as a group model for reconstructing ECG from PPG. The model was verified using 125 records from the MIMIC III matched subset. The experimental results demonstrated that the proposed model was, on average, able to achieve a Pearson‘s correlation coefficient, root mean square error, percentage root mean square difference, and Fréchet distance of 0.861, 0.077, 5.302, and 0.278, respectively. This research can use the correlation between PPG and ECG to reconstruct a better ECG signal from PPG, which is crucial for diagnosing cardiovascular diseases.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.

Construction of a painting image classification model based on AI stroke feature extraction

Abstract A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features; and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color features with stroke features, and uses weighted K-means clustering algorithm to extract sample features. The experiment illustrates that the K-CNN hybrid model proposed in the study achieved an accuracy of 94.37% in extracting image information, which is higher than 78.24, 85.69, and 86.78% of C4.5, K-Nearest Neighbor (KNN), and Bi directional Long short-term Memory (BiLSTM) algorithms. In terms of image classification information recognition, the algorithms with better performance from good to poor are: the mixed model > BiLSTM > KNN > C4.5 model, with corresponding accuracy values of 0.938, 0.897, 0.872, and 0.851, respectively. And the number of fluctuation nodes in the mixed model is relatively small. And the sample search time is significantly shorter than other comparison algorithms, with a maximum recognition accuracy of 92.64% for the style, content, color, texture, and direction features of the image, which can effectively recognize the contrast and discrimination of the image. This method effectively provides a new technical means and research direction for digitizing image information.

A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

Ensemble Meta-Learning-Based Robust Chipping Prediction for Wafer Dicing

Our previous study utilized importance analysis, random forest, and Barnes–Hut t-SNE dimensionality reduction to analyze critical dicing parameters and used bidirectional long short-term memory (BLSTM) to predict wafer chipping occurrence successfully in a single dicing machine. However, each dicing machine of the same type may produce unevenly distributed non-IID dicing signals, which may lead to the undesirable result that a pre-trained model trained by dicing machine #1 could not effectively predict chipping occurrence in dicing machine #2. Therefore, regarding the model robustness, this study introduces an ensemble meta-learning-based model that can evaluate many dicing machines for chipping prediction with high stability and accuracy. This approach constructs several base learners, such as the hidden Markov model (HMM), the variational autoencoder (VAE), and BLSTM, to form an ensemble learning. We use model-agnostic meta-learning (MAML) to train and test the ensemble learning model by several prediction tasks from machine #1. After MAML learning, we call the trained model a meta learner. Then, we successfully apply a retrieved data set from machine #2 to the meta learner for training and testing wafer chipping occurrence in this machine. As a result, our contribution to the robust chipping prediction on cross-machines can improve the yield of wafer dicing with a prediction accuracy of 93.21%, preserve the practical wearing of dicing kerfs, and significantly cut wafer manufacturing costs.

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Abstract. Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a “gray zone”, where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone.

A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting

Accurate wind power forecasting is essential for (i) the management of wind energy, (ii) increasing the integration of generated power into the electrical grid, and (iii) enhancing maintenance efficiency. This paper proposes a novel hybrid model for short-term (1-hour ahead) wind power forecasting. The model integrates the echo-state network (ESN) architecture with empirical mode decomposition (EMD) to improve forecast accuracy. Unlike existing approaches that use separate models for each decomposed signal after EMD method, proposed model uses a single ESN structure to predict wind power by incorporating all decomposed signals and their past values. The main advantage of this architecture is that it eliminates the need to train multiple models, thereby streamlining the forecasting process. To evaluate the performance of the proposed model, one year of data from the West of Duddon Sands, Barrow, and Horns Power offshore wind farms is utilized. Firstly, the classical ESN model and the EMD-ESN hybrid model are compared for the three datasets. Then, a comprehensive evaluation is performed by comparing the results of the proposed model with commonly used standalone and hybrid forecasting models such as Multi-Layer Perceptron (MLP), Adaptive-Neuro Fuzzy Inference System (ANFIS), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), EMD-LSTM, EMD-BiLSTM, Variational Mode Decomposition based ESN (VMD-ESN), and Wavelet Decomposition based ESN (WD-ESN). The results show that the EMD-ESN hybrid model outperforms implemented models in predicting wind power in all three datasets. Furthermore, the proposed EMD-ESN model for an onshore wind turbine power data in Germany is compared with the SOTA models, such as Transformer, Informer, Autoformer, and Graph Patch Informer (GPI), in the literature. This study highlights the superior predictive capabilities of proposed model, making it a valuable tool for enhancing the accuracy of wind power forecasts, thereby contributing to the reliable integration of wind energy into the power grid.

Deteksi Cyberbullying Menggunakan BERT dan Bi-LSTM

Cyberbullying is a digital problem that is not a new phenomenon. This existed before the advent of social networks, and cyberbullying has a wide impact, including a person's mental and physiological conditions such as sadness, anxiety and depression. The main objective of this research is to develop an effective cyberbullying detection system using natural language processing techniques. The method used in this research includes the application of the BERT (Bi-Directional Encoder Representations from Transformers) and Bi-LSTM (Bi-Directional Long Short-Term Memory) models as a deep learning approach to analyze text and detect cyberbullying behavior. This approach allows the system to understand complex language contexts and capture patterns that traditional methods may find difficult to identify. Testing was carried out using a dataset that included various types of Indonesian language texts containing cyber bullying acts. The research results show that the combination of BERT and Bi-LSTM is able to provide superior detection performance with a high accuracy rate of 90% and the ability to identify variations of cyber bullying. This research makes a significant contribution to efforts to protect individuals from the negative impacts of cyber bullying through the development of a sophisticated and adaptive detection system.

Xai-driven knowledge distillation of large language models for efficient deployment on low-resource devices

Large Language Models (LLMs) are characterized by their inherent memory inefficiency and compute-intensive nature, making them impractical to run on low-resource devices and hindering their applicability in edge AI contexts. To address this issue, Knowledge Distillation approaches have been adopted to transfer knowledge from a complex model, referred to as the teacher, to a more compact, computationally efficient one, known as the student. The aim is to retain the performance of the original model while substantially reducing computational requirements. However, traditional knowledge distillation methods may struggle to effectively transfer crucial explainable knowledge from an LLM teacher to the student, potentially leading to explanation inconsistencies and decreased performance. This paper presents DiXtill, a method based on a novel approach to distilling knowledge from LLMs into lightweight neural architectures. The main idea is to leverage local explanations provided by an eXplainable Artificial Intelligence (XAI) method to guide the cross-architecture distillation of a teacher LLM into a self-explainable student, specifically a bi-directional LSTM network.Experimental results show that our XAI-driven distillation method allows the teacher explanations to be effectively transferred to the student, resulting in better agreement compared to classical distillation methods,thus enhancing the student interpretability. Furthermore, it enables the student to achieve comparable performance to the teacher LLM while also delivering a significantly higher compression ratio and speedup compared to other techniques such as post-training quantization and pruning, which paves the way for more efficient and sustainable edge AI applications

Toward better atmospheric polycyclic aromatic hydrocarbons pollution control in the Northern Hemisphere: Process analysis based on interpretable deep learning models

The accurate prediction of atmospheric polycyclic aromatic hydrocarbons (PAHs) concentrations in the Arctic is crucial in guiding the development of pollution control measures for atmospheric PAHs. However, the complex formation process and strong time-dependence of PAHs in the Arctic increase the difficulty of prediction. Herein, deep learning (DL) models were developed to predict monthly air concentrations of 16 priority PAHs at Alert monitoring station by incorporating the inherent periodicity of PAH concentrations and ancillary data including PAH emissions, meteorological information, fire emissions, and sea ice area. The prediction performance of long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (Bi LSTM) algorithms were compared by multiple model evaluation metrics. The results showed that Bi LSTM models outperformed LSTM and GRU in predicting PAHs concentrations because it exhibited higher accuracy in terms of average R2ext (0.09) and errors (average MAPEext = 1.45). The DL interpretation based on SHAP values suggested that the main drivers of PAHs concentrations were PAHs emissions and sea ice area, with a more prominent contribution from meteorological conditions. The interpretable deep learning approach provides a potential shortcut for predicting time-delayed PAHs concentrations in the Arctic, promoting the management and control of global PAHs pollution.

Predicting Transcription Factor Binding Sites with Deep Learning.

Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with different approaches, Machine Learning being one of the most successful. Nevertheless, we note that many such approaches fail to propose a robust and meaningful method to embed the genetic data under analysis. We try to overcome this problem by proposing a bidirectional transformer-based encoder, empowered by bidirectional long-short term memory layers and with a capsule layer responsible for the final prediction. To evaluate the efficiency of the proposed approach, we use benchmark ChIP-seq datasets of five cell lines available in the ENCODE repository (A549, GM12878, Hep-G2, H1-hESC, and Hela). The results show that the proposed method can predict TFBS within the five different cell lines very well; moreover, cross-cell predictions provide satisfactory results as well. Experiments conducted across cell lines are reinforced by the analysis of five additional lines used only to test the model trained using the others. The results confirm that prediction across cell lines remains very high, allowing an extensive cross-transcription factor analysis to be performed from which several indications of interest for molecular biology may be drawn.

IoT-based waste management: hybrid optimal routing and waste classification model.

Internet of Things (IoT) makes connectivity between physical devices which are embedded with sensors, software, and connectivity that let them to communicate and transfer data. This technology makes it possible to collect and transfer data from a vast network device, opening the door to the development of automatic and more efficiency systems. The term "waste management" refers to all of the responsibilities essential to regulate trash, from the point of gathering through reusing and monitoring. Reducing the hazardous consequences of such garbage on the environment and human health is the goal of waste management. By considering these hazardous consequences, this research work is interested in working on an efficient waste management system. The utilization of IoT devices enables municipalities to optimize waste management operations by leveraging data insights. This information aids in scheduling waste collections more effectively and planning optimal routes. Therefore, the research work proposes an IoT-based waste management system with two vital processes such as IoT routing and waste management. At first, routing in IoT is done by proposing hybrid optimization algorithm named Snake Optimization Updated Beluga Whale Optimization algorithm (SOUBWO) under constraints such as distance, energy, link quality, delay, and trust. Secondly, waste management is worked on by following steps including pre-processing, segmentation, feature extraction, and classification. The waste images collected by IoT devices are transmitted from source node (SN) to destination node (DN) by optimal routing. Those transmitted waste images are pre-processed by Wiener filtering process. Consequently, the pre-processed images are segmented by employing proposed Balanced Iterative Reducing and Clustering Using Hierarchies-Altered Distance Metrics (BIRCH-ADM) algorithm. Subsequently, features such as multi-text on histogram feature, proposed Local Gabor XOR Pattern (LGXP)-based feature using novel image processing techniques, and statistical features are extracted. Finally, these extracted features are efficiently classified by hybrid classification model which is formed by integrating conventional deep maxout and Bidirectional-Long Short Term Memory (Bi-LSTM) networks. The effectiveness of the proposed approach is validated through various analyses, including performance and statistical analyses. Moreover, the proposed scheme demonstrates minimal energy consumption, with a recorded value of 0.123. In contrast, conventional methodologies exhibit higher energy consumption, with values such as SOA = 0.237, BWO = 0.146, BES = 0.183, SMO = 0.158, CHOA = 0.174, and PSO = 0.189, respectively. By this hybrid classification model, the process of classification on waste is effectively done and moreover its effectiveness is proved by various analyses.

Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification

Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification

Intercity Traffic Travel Mode Identification Method Based on Mobile Signalling Data

With the popularity of mobile devices, the signalling data generated by them provides significant opportunities for studying intercity travel behaviour in terms of data scale and information continuity. However, due to the low quality of the data in spatial accuracy, temporal frequency, and traffic semantics, the accuracy of identifying individual travel modes is low and it is difficult to extend to complex traffic scenarios. In this paper, we propose a new framework for identifying individual intercity travel modes based on mobile signalling data. The framework includes components for data pre-processing, geo-information mapping, feature and attribute extraction, and travel mode recognition. We utilize a comprehensive detection model to identify users’ multimodal intercity transport behaviour. Using two modules, Random Forest Embedding (RFE) and Bidirectional Long Short-Term Memory (Bi-LSTM), the model can capture the spatiotemporal characteristics and complex multi-stage associations in intercity travel chains. A large-scale mobile phone dataset from Jiangsu Province, China, was used for verification. The results showed that, on average, the method was able to detect travel mode with 92% accuracy. This study provides valuable support for further research on individual travel behaviour and the enhancement of transportation planning.

Partial discharge fault identification method for GIS equipment based on improved deep learning

AbstractAiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.