Deep LSTM Research Articles

Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty

Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty

The Research on the Application of Deep Learning in Education

With the in-depth reform of education, deep learning is gradually being applied to education, which can stimulate students’ initiative in learning and improve their learning efficiency. This article explores some specific applications of deep learning in education, uses the deep learning model LSTM to predict students’ learning abilities and outcomes, and obtains high accuracy. Through deep learning models, we have abstracted the characteristics of students’ learning states and made relatively accurate predictions on their learning outcomes. This paper provides some useful explorations on the application of artificial intelligence and deep learning in education, which can provide valuable references for better research in the future.

Traffic speed prediction of regional complex road network based on CapsNet and D-BiLSTM

Accurate and efficient short-term traffic prediction is of great significance in the study of regional traffic network. However, the complex and dynamic spatiotemporal correlation of traffic patterns makes the existing methods insufficient in learning traffic evolution in terms of structural depth and prediction scale. Therefore, this paper proposes a deep learning model combining capsule network (CapsNet) and deep bidirectional LSTM (D-BiLSTM). CapsNet is used to identify the spatial topological structure of the road network and extract spatial features, and D-BiLSTM network is integrated. The forward and backward dependencies of traffic states are considered at the same time, and the bidirectional temporal correlation of different historical periods is captured to predict the traffic of large-scale complex road networks in the target area. Experiments on real traffic network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average , which is better than other methods. It has high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

Dynamic Response Prediction of Railway Bridges Considering Train Load Duration Using the Deep LSTM Network

Dynamic Response Prediction of Railway Bridges Considering Train Load Duration Using the Deep LSTM Network

A hybrid CNN-LSTM approach for intelligent cyber intrusion detection system

As the technology is advancing more and more in the era of increasing digitalization, safeguarding networks from cyber threats is crucial. As cyber-attacks on critical infrastructure are becoming more and more sophisticated, enhancing cyber intrusion detection systems (IDS) is imperative. This paper proposes and evaluates a deep learning-based IDS using the NSL-KDD dataset, a benchmark for intrusion detection. The system pre-processes data with Recursive Feature Elimination (RFE) and a Decision Tree classifier to identify the most significant features, optimizing model performance. Various deep learning models, including ANN, LSTM, BiLSTM, CNN-LSTM, GRU, and BiGRU, have been evaluated. The CNN-LSTM model outperformed the others, with 95 % accuracy, 0.89 recall, and 0.94 f1-score. These results prove the effectiveness of the proposed IDS in accurately distinguishing between malicious and benign network traffic. Future research can explore ensemble techniques like boosting or bagging to further enhance IDS performance.

Anomaly Detection in Video Surveillance: A Comparative Analysis of Deep Learning Models

Anomaly detection in video surveillance is critical for enhancing security and public safety across various applications, including traffic monitoring, public spaces, and industrial settings. Traditional methods often struggle with the complexity and variability of real-world data, prompting a shift towards advanced machine learning models. This paper presents a comprehensive analysis of deep learning algorithms, including YOLOv5, 3D CNNs, LSTM, Deep SVDD, Vision Transformers, Temporal Transformers, and Autoencoders, applied to three benchmark datasets: CIFAR-10, MVTec AD, and UCSD Anomaly Detection. We compare these algorithms based on accuracy, precision, recall, and F1-score, providing insight into their strengths and weaknesses. The results suggest that Vision Transformers and CNN-LSTM hybrids offer superior performance across spatial and temporal anomaly detection tasks.

Prediction of Solar Radiation using Deep LSTM-based Machine Learning Algorithm

Solar radiation, a critical parameter for various applications such as solar energy systems and weather forecasting, exhibits complex temporal patterns influenced by numerous environmental factors. In the quest to enhance the accuracy of solar radiation predictions, this study introduces a novel approach utilizing Deep Long Short-Term Memory (Deep LSTM) networks, a type of recurrent neural network (RNN) known for its capability to model sequential data. Traditional prediction methods often struggle to capture these intricacies, leading to sub-optimal performance. This investigation is based on a Deep LSTM-based machine learning algorithm designed to predict solar radiation with elevated exactitude. The model is meticulously trained on copious datasets encompassing historical meteorological data, including temperature, humidity, wind velocity, and antecedent solar radiation metrics. Pivotal stages encompass data preprocessing, judicious feature selection, and hyperparameter optimization to enhance the model’s predictive efficacy. Empirical results clearly illustrate that the Deep LSTM model surpasses traditional methodologies, attaining superior accuracy and resilience across diverse meteorological scenarios. The ramifications of this endeavor portend significant advancements in the strategic planning and administration of solar energy resources, thereby contributing to more dependable and efficacious renewable energy systems.

Enhancing Agricultural Commodity Price Forecasting Using Generative Models: A Deep Learning Approach

Predicting stock market prices is a critical yet challenging task in finance. Technical analysis, a widely used methodology in investment theory, involves forecasting price movements by analyzing historical market data. Recently, deep learning has gained prominence for its exceptional ability to process complex data, making it a popular tool for financial applications such as stock prediction, portfolio optimization, financial information analysis, and trade execution strategies. In this study, we propose a novel deep learning architecture that integrates a Generative Adversarial Network (GAN) with a Convolutional Neural Network (CNN) as the discriminator and Gated Recurrent Units (GRU) as the generator. This framework generates distributions of daily stock prices through adversarial learning to predict stock closing prices. Using daily trading data from Ruchi Soya Industries Limited, an empirical analysis was performed across a broad time frame. Results show that the proposed GAN-based architecture significantly outperforms traditional deep learning models, including GRU, LSTM, and Bi-LSTM, in predicting stock closing prices. These findings demonstrate the potential of this novel approach for improving stock price forecasting accuracy.

Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain

This paper introduces a novel approach for hyperparameter optimization of long short-term memory networks (LSTMs) to achieve highly accurate hourly streamflow and water level predictions in the realm of regional rainfall-runoff modeling. Leveraging simultaneous systematic hyperparameter optimization of 10 distinct hyperparameters by Random Search, the study achieves high accuracy in terms of predictions across 40 humid flashy catchments in Basque Country, north of Spain. By carefully designing the search space and incorporating domain expertise, the approach quickly converges to optimal and highly accurate network configurations with both efficiency and efficacy. LSTMs ingested precipitation, temperature, and potential evapotranspiration as inputs to predict 2 targets of streamflow and water level, in an hourly timestep. On the test set, the optimized LSTM networks accurately predicted streamflow and water level with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) efficiencies as high as 0.97, in one of the catchments. Across all 40 studied catchments, the overall average NSE and KGE values for streamflow were 0.89 and 0.87, respectively; water level exhibited average NSE and KGE scores of 0.91 and 0.92.Moreover, statistical analysis reveals significant differences in the performance of the 2 distinct optimized network architectures in different hydrological catchments, underscoring the importance of deliberate network configuration selection post-random search. This selection process is vital for achieving higher performance in as many catchments as possible. The findings highlight opportunities for enhancing the “learning maturity” of regional hydrological deep learning LSTM networks. This research provides valuable insights for researchers and practitioners involved in optimizing regional hydrological deep learning models for a variety of applications and on new datasets.

Advanced Ocular Artifact Removal In EEG For Enhanced Schizophrenia Diagnosis Using Wavelet Shrinkage And LSTM Deep Learning Techniques

Advanced Ocular Artifact Removal In EEG For Enhanced Schizophrenia Diagnosis Using Wavelet Shrinkage And LSTM Deep Learning Techniques

Analysis of Food Nutrients Impact for Improving Public Health Using Multi Features Deep Recurrent Neural Network for Health Care Management

The food nutrients in daily life is incredible source to make good food for good life cycle. In recent analysis, the new food styles and ingredients affects the public health especially children to cause various disease and impacts which leads dangerous to healthier life. By analyzing food nutrients and presence healthy ingredients in daily life is important. World health organization pays number of research to healthcare management and suggestions to improve the public health by recommending different protocols. The risk is identifying presence of nutrients scaling is tedious due to prevailing techniques machine learning techniques does not provide good results to make better good food recommendation. The problem is more data labels and features are need to analyze which increase the false rate to reduce accuracy. To address the issue, to propose a deep LSTM gated recurrent neural network (LSTM-GRNN) based on Support vector feature selection (SVFS) to identify the presence of food nutrients to recommend the good food to improve the public health. Initially the food product and nutrients scaling logs are collected and to make preprocessing based on C-score normalization form feature labels. Then nutrients scaling impact rate is analyzed for each food ingredients presence by extracting the margins from features list. The important feature is selected by using SVSR to reduce the feature margins based on support vector. Then the selected features are trained into LSTM unit with recurrent neural network to identify the presence of nutrients rate in each food and to categorize the class by presence. The higher scaling rate of class is considered as good food to recommend for healthy facts. The proposed system attains higher detection accuracy in precision, recall rate and f1 measure, also the lower false negative rate increase the performance of accuracy compared to the existing system.

Optimized Hybrid Deep Learning for Real-Time Pandemic Data Forecasting: Long and Short-Term Perspectives

Background: With new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective: This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are designed to capture spatial and temporal patterns in COVID-19 data, thereby improving the accuracy and timeliness of predictions. An output forecasting framework integrates these models, and an optimization algorithm automatically selects the hyperparameters for the 13 baselines and the three proposed hybrid models. Methods: Real-time time series data from the five most affected countries were used to test the effectiveness of the proposed models. Baseline models were compared, and optimization algorithms were employed to improve forecasting capabilities. Results: CNN-GRU and CNN-LSTM are the top short- and long-term forecasting models. CNNGRU had the best performance with the lowest SMAPE and MAPE values for long-term forecasting in India at 3.07% and 3.17%, respectively, and impressive results for short-term forecasting with SMAPE and MAPE values of 1.46% and 1.47%. Conclusion: Hybrid deep learning models, like CNN-GRU, can aid in early COVID-19 assessment and diagnosis. They detect patterns in data for effective governmental strategies and forecasting. This helps manage and mitigate the pandemic faster and more accurately.

Self configuring mobile agent-based intrusion detection using hybrid optimized with Deep LSTM

Sensor nodes can be deployed in harsh or hostile environments in many applications, making these nodes more prone to failure. The illegal movement monitoring within the sensor networks is a most challenging problem. The mobile malicious nodes are preferred by the attacker to maximize his impact. For a dynamic environment, a promising technology of sensor networks is expected to Intrusion detection. Multi-mobile agents utilize many approaches, after verification that collects data from sensor nodes. However, these approaches are inefficient to verify all the sensor nodes (SNs) of the network, due to its high delay, energy consumption, and mobility. The proposed Dunnock Ibis optimization LSTM model (DIO opt LSTM) solves this problem. Here, the sensor nodes are grouped into clusters; hence, mobile agent performs verification only the cluster heads instead of verifying all the SNs. The proposed DIO optimization combines the unique behavior of Egret Swam and Ibis optimization algorithm which efficiently tunes the LSTM classifier, resulting in the model providing better convergence. The simulation results show the proposed system shows a better result than the existing system by utilizing the database IDS 2018 Intrusion CSVs, the analysis is done based on performance metrics such as End-end-delay (ED), normalized energy (NE), and throughput. At 200 nodes and 1500 rounds, the DIO opt LSTM method has efficiently performed 146 numbers of alive nodes, 0.46 ms of delay, 0.15 J of normalized energy, and 0.89 bps of throughput.

Bitcoin returns and YouTube news: a behavioural time series analysis

ABSTRACT This study investigates whether investor’s sentiment and attention information collected via YouTube can improve bitcoin return forecasts. Accordingly, we collected daily data over the period 2017–2023, covering calm and turbulent periods marked by different types and episodes of emotions. Unlike previous studies, we used YouTube videos to propose two sentiment proxies: investor attention to YouTube (daily number of YouTube video views) and investor sentiment on YouTube (number of positive and negative videos on YouTube). Interestingly, we break down both attention and sentiment per subject. Econometrically, we assess lead-lag effects between sentiment/attention and bitcoin return using causality tests and Vector Auto-regressive (VAR) model. We also evaluate the forecasting power of YouTube attention/sentiment data using a deep learning LSTM model. Our study shows two main results. First, we find lead-lag effects between bitcoin returns and per subject investor’s attention and sentiment proxies. Second, we show that our deep learning LSTM model relying on the information provided by attention and sentiment supplants benchmark Buy and Hold Strategy to forecast future bitcoin returns.

Optimized Deep LSTM-Based Multi-Object Tracking with Occlusion Handling Mechanism

The multi-object tracking is a basic computer vision process having a huge class of real-life tools that range from monitoring of medical video to surveillance. The goal of tracking numerous items is to place numerous objects in a scene, handle their identities throughout time, and construct trajectories for analysis. However, this is a complex task, because of certain issues like occlusions, complicated object dynamics, and variations in the appearance of objects. In this research, a new technique named TPRO-based Deep LSTM is developed for tracking multi-object with occlusion handling. Here, the videos are considered as input wherein the extraction of frames is done from each video. Each frame undergoes pre-processing with filtering to eliminate noise from frames. By using a sparse Fuzzy c-Means (FCM) and Local Optimal-Oriented Pattern (LOOP) features, the localization of objects is done. Moreover, the visual and spatial trackings are considered for hybrid tracking. The second derivative model and neighborhood search model are used to perform visual tracking. Then the occlusion handling is performed. Concurrently, with the use of Deep Long Short-Term Memory (Deep LSTM) the spatial tracking is performed and the Taylor Poor Rich Optimization (TPRO) algorithm assigns the weight and bias of the Deep LSTM. The TPRO is obtained by the unification of the Taylor series along with the Poor and Rich Optimization algorithm. By combining visual and spatial tracking, the final tracked output is generated. The devised method achieves a performance with the highest value of 88.9% for Multiple Object Tracking Precision (MOTP), smallest tracking distance (TD) of 4.185, average MOTP of 0.889, average TD of 4.201, and highest tracking number (TN) of 14.

Occlusion enhanced pan-cancer classification via deep learning

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces “GENESO,” a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called “Symmetrical Occlusion (SO)”. It collaborates with the baseline LSTM network, mimicking the “gain of function” and “loss of function” of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

Application of Machine Learning in Stock Market Return Forecasting

With the development of our country's financial market, people pay more attention to the stock market return forecast, which can bring the investors a lot of income and also become a tool of risk management. Machine learning (ML) and artificial intelligence (AI) are becoming increasingly sophisticated and have achieved amazing results in many fields. Machine learning and deep learning algorithm models can process huge amounts of data, from which they can quickly process and analyze laws, many scholars try to apply various machine learning models to the financial field. This paper first expounds the theoretical basis of stock market return prediction, and summarizes the main literature in this field in recent years, from the traditional stock prediction model to the machine learning prediction model. Finally, the traditional stock prediction model ARIMA model and the deep learning LSTM neural network model are selected for comparative empirical research. The sample data are from the Tushare big data community, taking the new energy vehicle stock BYD (002594) as an example. The empirical results show that the prediction accuracy and stability of LSTM neural network are far higher than ARIMA model, and it will have broad application prospects in financial prediction and other fields in the future.

Examining the Application of Deep LSTM Neural Networks in Steganography of Textual Information in Digital Images

Information security has emerged as a critical concern alongside the development of multimedia technology. Among the myriad security challenges, the secure transmission of sensitive information between parties has become a focal point of researchers. Encryption, involving mathematical techniques to ensure data security, is explored in this study. Specifically, the application of deep LSTM neural networks in concealing textual information within digital images is investigated. The approach involves embedding one image within another in a manner that prevents detection of the hidden image within the cover image, while textual content is covertly embedded within the image. The proposed method demonstrates superior performance based on three evaluation metrics—Peak Signal-to-Noise Ratio (PSNR) in decibels, Mean Squared Error (MSE), and accuracy rate in percentage—compared to three other benchmark images (lena.png, peppers.png, mandril.png, and monkey.png), achieving values of 93.665275 dB, 0.6945 MSE, and 97.23% accuracy, respectively.

Covid-19 Analysis Using Deep Learning LSTM Model

Covid-19 Analysis Using Deep Learning LSTM Model

Multi-sensor signal fusion for tool wear condition monitoring using denoising transformer auto-encoder Resnet

Multi-sensor signal fusion is commonly used in associate with the artificial intelligence model to monitor tool wears. However, AI models equipped with limited multi-sensor training samples still exist problems: 1) The limitation of training samples may cause the AI model failure due to the intrinsic wear features contaminated by heavy noises. 2) The selection of multi-sensor signals as training samples is a difficult task for the negative impact on the recognition accuracy using inappropriate sensor features. Therefore, this paper proposes a denoise transformer Auto-Encoder (DTAE) as pre-processor for the tool condition monitoring (TCM) classifiers. The reconstruction task of the DTAE allows the model to pay more attention to the intrinsic wear features and the appropriate selection of them from the multi-sensor signals during feature extraction. Moreover, the loss function for DTAE ResNet is formed by summing the reconstruction loss from DTAE with the classification loss from ResNet. Compared DTAE ResNet to stacked sparse auto-encoder network, deep stacked auto-encoder network, ResNet-18, VGG-16, and LSTM, experiments demonstrated that the present method would attain the highest classification accuracies for tool wear suitable for TCM. And a comparative experiment was conducted to verify the effectiveness of the DTAE preprocessor in improving the anti-noise performance of the classifier.