Hybrid Long Short-term Memory Research Articles

LSTM-MLP BASED UNCERTAINTY MODELLING APPROACH FOR COMPLEX HUMAN INDOOR TRAJECTORY

Abstract. Modelling the movement uncertainty of human indoor trajectory consist of an essential part in promoting the performance of smart city related applications. At this stage, the existing uncertainty modelling algorithms usually take the constant sampling error or measurement error into consideration and cannot adapt well to the changeable human motion modes and complex handheld modes of smartphones. To fill this gap, this paper applied the Long Short-Term Memory (LSTM) network for continuous prediction of uncertainty error of human indoor trajectory with complex motion modes and detected indoor landmark points. The human motion information including handheld modes, walking speed, and heading information in extracted and fused with detected landmark points for reconstruction of human indoor trajectory under large-scale areas using Gradient Descent (GD) algorithm. In addition, the hybrid LSTM and Multilayer Perceptron (MLP) network is adopted for uncertainty error prediction, by considering both sampling error and measurement error in a specific time period, and the reconstructed trajectory with human motion features are modelled as the input vector for model training with the ground-truth uncertainty error as reference. Comprehensive experiments on real-world collected dataset indicate that the proposed LSTM-assisted uncertainty modelling algorithm has robust outperformance in uncertainty error prediction and uncertainty region definition compared with traditional uncertainty modelling approaches.

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.

Hybrid model combining LSTM with discrete wavelet transformation to predict surface methane concentration in the Arctic Island Belyy

In this paper, we propose a hybrid model that combines wavelet transformation of the raw data and an artificial neural network with long short-term memory (LSTM). This model allows researchers to increase the accuracy of time series forecasting.The model is based on data from environmental monitoring of greenhouse gases on the Belyy Island of the Yamal-Nenets Autonomous Okrug, Russia.The raw data for building the proposed model were obtained in July–August 2016. The CH4 concentration time series was decomposed using a discrete wavelet transform into four components - one approximating and three detailing. These components, along with a timed temperature series, were used to train six ANNs—two exogenous input autoregressive networks (NAR) and four LSTM networks. The forecast was calculated as the sum of forecasts for each of the components. Forecast accuracy was assessed using several indices (mean absolute error (MAE), root mean square error (RMSE), mean square relative error (RMSRE), Willmott's agreement index (IA1, IA2)) and a Taylor diagram. The hybrid model based on LSTM showed the best accuracy. The hybrid model based on LSTM showed the best accuracy. MAE, RMSE, and RMSRE errors decreased by more than 70%. In terms of IA1 and IA2, the models improved by 11% and 30% (when comparing the best hybrid models LSTM and NAR. For the hybrid LSTM model errors RMSE, RMSRE, and MAE are more than 20% less than for the base LSTM model for training in which data without wavelet transform were used. Willmott agreement indices (IA1, IA2) rose from 32% to 59%, depending on the models being compared. So, the increase in accuracy after applying the described approach was up to 79% for models based on LSTM (more precisely, from 20% to 79%, depending on the indicator). Applying wavelet transform data to train the NAR-based model reduced MAE, RMSE, and RMSRE errors by 27%, 30%, and 31%, respectively. The Willmott agreement indices (IA1, IA2) rose from 41% to 45% respectively. The Taylor diagram also shows the advantage of the proposed approach.

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance to evacuate and save lives. Deep learning is used in flood forecasting to improve the timeliness and accuracy of flood water level predictions. While various deep learning models similar to Long Short-Term Memory (LSTM) have achieved notable results, they have complex structures with low computational efficiency, and often lack generalizability and stability. This study applies a spatiotemporal Attention Gated Recurrent Unit (STA-GRU) model for flood prediction to increase the models’ computing efficiency. Another salient feature of our methodology is the incorporation of lag time during data preprocessing before the training of the model. Notably, for 12-h forecasting, the STA-GRU model’s R-squared (R2) value increased from 0.8125 to 0.9215. Concurrently, the model manifested reduced root mean squared error (RMSE) and mean absolute error (MAE) metrics. For a more extended 24-h forecasting, the R2 value of the STA-GRU model improved from 0.6181 to 0.7283, accompanied by diminishing RMSE and MAE values. Seven typical deep learning models—the LSTM, the Convolutional Neural Networks LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the spatiotemporal Attention Long Short-Term Memory (STA-LSTM), the GRU, the Convolutional Neural Networks GRU (CNNGRU), and the STA-GRU—are compared for water level prediction. Comparative analysis delineated that the use of the STA-GRU model and the application of the lag time pre-processing method significantly improved the reliability and accuracy of flood forecasting.

HY-LSTM: A new time series deep learning architecture for estimation of pedestrian time to cross in advanced driver assistance system

Advanced driver assistance systems (ADASs), particularly pedestrian protection systems (PPSs), have emerged as a hot research topic with the goal of enhancing traffic safety. The development of reliable on-board pedestrian detection systems is a critical problem for PPSs. It is extremely difficult to provide the required resilience of this type of system due to the fluctuating look of pedestrians (e.g., varied attire, changing size, aspect ratio, and dynamic shape) and the unstructured surroundings. The detection of pedestrians has gained huge focus among researchers because of its huge applications in the domain of automated vehicles. In the previous decades, the majority of examinations are done to obtain better solutions for detecting pedestrians, but fewer of them concentrated on determining the pedestrian. It is one of crucial interest to study regarding transport safety as it implies minimizing the count of traffic collisions and protecting pedestrians who are more susceptible to accidents. Predicting pedestrian conduct is critical for road safety, traffic management systems, ADAS, and autonomous vehicles in general. The fundamental problem in the field of self-driving and smart automobiles is identifying impediments, particularly people, and taking action to avoid collisions with them. Various studies have been conducted in this sector by many researchers, yet there are still many mistakes in the proper identification of pedestrians. Hence, this paper devises an approach to estimate pedestrian time for crossing in an advanced driving assistance system (ADAS). The inputted videos undergo keyframe extraction wherein crucial keyframes are extracted. The deep joint segmentation is further applied for identifying the pedestrian followed by intention classification. Then, the estimation of pedestrian time to cross is predicted using Hybrid-Long short term memory (HY-LSTM), and it is a new time series model obtained by unifying LSTM and Object-based convolution neural network (OCNN), where the layer and hyperparameters of OCNN are optimally derived using Gradient Chef Based Optimization (GCBO). The proposed GCBO-HY-LSTM outperformed showing the least Mean absolute error (MAE) of 0.038, Mean square error (MSE) of 0.029, and Root Mean square error (RMSE) of 0.170.

Prediction of ultimate bearing capacity of shallow foundations on cohesionless soil using hybrid LSTM and RVM approaches: An extended investigation of multicollinearity

This research presents the optimum performance model for predicting the shallow foundation ultimate bearing capacity (UBC). Twenty-one models are employed, trained, tested, and compared to find the best architecture computational model. Thirteen performance metrics, including three new index metrics, are used to calculate and analyze the model's performance. Five kernels, linear, polynomial, gaussian, laplacian, and exponential, have been implemented in RVM (mentioned by SRVM). These RVM models have been optimized by each particle swarm optimization (PSO) and genetic (GA) algorithm. Moreover, the kernel function of the higher-performance SRVM model has been used as kernel 1 to develop dual (parallel) kernel function-based RVM (mentioned by HRVM) models. Thus, four combinations have developed four HRVM models optimized by each GA and PSO algorithm. The Adam, root mean square propagation, and stochastic gradient descent with momentum algorithms have optimized long short-term memory (LSTM) models. The analysis of performance metrics reveals that (i) the GA-optimized laplacian SRVM model UBC3 has performed better than the PSO-optimized exponential SRVM model UBC10, (ii) PSO-optimized dual kernel-based HRVM model UBC18 has attained higher performance than GA-optimized dual kernel-based HRVM model UBC14, i.e., 0.9985. Adam-optimized model UBC19 based on the LSTM approach has gained higher performance (R = 0.9999) and the least residuals (RMSE = 5.9132 kPa, MAE = 3.7037 kPa, MAPE = 2.5314 kPa, and WMAPE = 0.0118 kPa). Also, model UBC19 has outperformed models UBC3, UBC10, UBC14, and UBC18 and published models available in the literature with higher index metrics. This study reveals that the HRVM models are less affected by multicollinearity.

Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants

In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introducing an approach to improve the best possible solution by using the optimal ranking of the wolves. The proposed model combines the GWO with Adam Optimizer to train the LSTM. Apart from the LSTM, we have also implemented the Adaptive GWO on other variants of Recurring Neural Networks (RNN) like LSTM, Bi-Directional LSTM, Gated Recurrent Units (GRU), and Bi-Directional GRU and computed the corresponding results. The Adaptive GWO here evolves the initial weights and biases of the above-discussed neural networks. In this research, we have also compared the forecasting efficiency of our proposed work with a particle-warm optimization (PSO) based hybrid LSTM model, simple Grey-wolf Optimization (GWO), and Adaptive PSO. According to the experimental findings, the suggested model has effectively used the best initial weights, and its results are the best overall.

Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management.

Investigation of a Hybrid LSTM + 1DCNN Approach to Predict In-Cylinder Pressure of Internal Combustion Engines

The control of internal combustion engines is becoming increasingly challenging to the customer’s requirements for growing performance and ever-stringent emission regulations. Therefore, significant computational efforts are required to manage the large amount of data coming from the field for engine optimization, leading to increased operating times and costs. Machine-learning techniques are being increasingly used in the automotive field as virtual sensors, fault detection systems, and performance-optimization applications for their real-time and low-cost implementation. Among them, the combination of long short-term memory (LSTM) together with one-dimensional convolutional neural networks (1DCNN), i.e., LSTM + 1DCNN, has proved to be a promising tool for signal analysis. The architecture exploits the CNN characteristic to combine feature classification and extraction, creating a single adaptive learning body with the ability of LSTM to follow the sequential nature of sensor measurements over time. The current research focus is on evaluating the possibility of integrating virtual sensors into the on-board control system. Specifically, the primary objective is to assess and harness the potential of advanced machine-learning technologies to replace physical sensors. In realizing this goal, the present work establishes the first step by evaluating the forecasting performance of a LSTM + 1DCNN architecture. Experimental data coming from a three-cylinder spark-ignition engine under different operating conditions are used to predict the engine’s in-cylinder pressure traces. Since using in-cylinder pressure transducers in road cars is not economically viable, adopting advanced machine-learning technologies becomes crucial to avoid structural modifications while preserving engine integrity. The results show that LSTM + 1DCNN is particularly suited for the prediction of signals characterized by a higher variability. In particular, it consistently outperforms other architectures utilized for comparative purposes, achieving average error percentages below 2%. As cycle-to-cycle variability increases, LSTM + 1DCNN reaches average error percentages below 1.5%, demonstrating the architecture’s potential for replacing physical sensors.

Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model

High-frequency financial data is more difficult to predict than low-frequency data because it possesses nonlinearity, nonstationarity, higher volatility, and long memory and is frequently accompanied by the jump phenomena. In this paper, the nonparametric regression (NR) model based on kernel function is used to fit the nonlinear relationship between the nonstationary series Yt and its lagging series to model the trend of high frequency financial time series. Furthermore, the deep learning LSTM (long short-term memory) model is applied to capture the high volatility and frequent jumps of high frequency financial data and to improve the forecasting accuracy of the residual series. The results demonstrate that the hybrid NR and LSTM model has greatly improved the forecasting accuracy in several evaluation criteria. In comparison to NR, support vector machine (SVM), LSTM, ARIMA and NR-SVM models, the mean absolute error (MAE) of NR-LSTM has reduced by 89.78%, 97.85%, 86.48%, 32.47% and 89%, respectively. In addition, we have constructed the trading strategy for the Shanghai-Shenzhen 300 index by using the NR-LSTM model. The NR-LSTM model can continue to provide good returns even during a bear market, which can serve as a guide for investors. Furthermore, the NR-LSTM model also exhibits the best forecasting effect when we model the high-frequency data of Ping An bank in China, the FTSE 100 index in the UK, and the S&P 500 index in the US.

Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro

Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. The focus of this paper is applying and comparing the performance of Support Vector Machine and hybrid LSTM regression models for air pollution prediction. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, our approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution of this paper is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures.

Novel Method for Sentiment Analysis in Social Media Data Using Hybrid Deep Learning Model

It is common practise to employ a contextual mining approach called sentiment analysis (SA) to glean subjective but potentially helpful information from textual data. In order to recognise, analyse, and extract answers, states, or emotions from the data, it employs Natural Language Processing (NLP), language understanding, forensics, and cognitive science. Significant progress towards a better SA model may be made using the features analysis method. In recent years, feature extractions have made extensive use of GloVe and Word2vec embedding models. They require a huge corpora of text data for training and creating accurate vectors, but they ignore emotional and contextual information in the text. Out-of-Vocabulary (OOV) words are not considered while creating these vectors, hence some information may be lost if these methods are used. The limited availability of annotated data is another obstacle to sentiment categorization. Misclassification may occur when there is a discrepancy between the review and the label. In this research, we offer a hybrid SA model capable of overcoming the challenges posed by missing ratings and reviews due to noise, out-of-context phrases, and context. To investigate and conduct sentiment and appropriate analysis, this study proposes an Autoencoder Bi-directional Recurrent Neural Network (ABRNN) based on Bi-directional Encoding from Transformers (BET). The reviews are initially categorised by their polarity ratings using the zero-shot categorization. Then, the data is fed into a pre-trained BET system to extract embeddings based on the semantics and context of sentences. The neural network, made up of expanded and hybrid LSTM, was then fed the acquired contextual embedded vectors. For extracting both global and local spatial contextual characteristics from the embedded data, this approach employs expanded approach in place of traditional approach. The complete phrase sequencing is performed with the help of Hybrid Long Short-Term Memory (HLSTM). Measures of accuracy, precision, recall, f1-score, and area under the curve (AUC) are used to assess the ABRNN model on four separate text datasets from different domains. Because of this, ABRNN may be utilised well for SA processes on social media evaluations, with no loss of data.

A Hybrid LSTM and MLP Scheme for COVID-19 Prediction: A Case Study in Thailand

After the COVID-19 epidemic, Thailand was affected in a variety of ways, with the most obvious being the economic downturn and the huge impact on health, including the loss of medical and human resources to combat the epidemic. However, Thailand still lacks analysis and prediction tools required to prepare for future epidemic situations. Therefore, we present development models for predicting the spread of the COVID-19 epidemic. In particular, the application of a long short-term memory (LSTM) and multilayer perceptron (MLP) model was investigated to predict new cases, total cases, new deaths, and total deaths. There are a total of 77 provinces in Thailand. The data used in this trial were obtained from the Department of Disease Control (DDC) of the Thai government. The modeling employed 2 types of data: dynamic (time series) and static. There were 2 phases: 1) the LSTM was used to manipulate time series data and 2) the MLP model was used to manipulate static data. Then, the models were merged for further analysis. We evaluated the performance of the combined model, yielding an accuracy of 99.72 % based on R2 values, higher than the values obtained for state-of-the-art methods. In addition, the prediction results can be further combined with GIS data in each province and displayed via an easy-to-use web application for mapping. HIGHLIGHTS A new architecture that can be used as a tool to predict the COVID-19 epidemic situation is proposed Deep learning is applied to create predictive models with LSTM for time series data and MLPs for static data A tool used for displaying predictions using a web application in the form of a map of each province in Thailand is developed to show the prediction results in detail GRAPHICAL ABSTRACT

Using machine learning methods to predict electric vehicles penetration in the automotive market

Electric vehicles (EVs) have been introduced as an alternative to gasoline and diesel cars to reduce greenhouse gas emissions, optimize fossil fuel use, and protect the environment. Predicting EV sales is momentous for stakeholders, including car manufacturers, policymakers, and fuel suppliers. The data used in the modeling process significantly affects the prediction model’s quality. This research’s primary dataset contains monthly sales and registrations of 357 new vehicles in the United States of America from 2014 to 2020. In addition to this data, several web crawlers were used to gather the required information. Vehicles sale were predicted using long short-term memory (LSTM) and Convolutional LSTM (ConvLSTM) models. To enhance LSTM performance, the hybrid model with a new structure called “Hybrid LSTM with two-dimensional Attention and Residual network” has been proposed. Also, all three models are built as Automated Machine Learning models to improve the modeling process. The proposed hybrid model performs better than the other models based on the same evaluation units, including Mean Absolute Percentage Error, Normalized Root Mean Square Error, R-square, slope, and intercept of fitted linear regressions. The proposed hybrid model has been able to predict the share of EVs with an acceptable Mean Absolute Error of 3.5%.

Classification of health care products using hybrid CNN-LSTM model

Automated healthcare product classification is a challenging field of research that has recently gained a lot of interest. This process is beneficial in terms of time and cost but problematic in obtaining annotated data and lacks uniformity. The high volume of healthcare products and their categories raise the need for machine learning models that can decrease the time and cost spent by human editors. Deep learning techniques that have recently emerged are applied to automated healthcare data classification. The efficacy of the deep learning model depends on the training data and the learning model's suitability for the data domain. When the dataset is large, training a model requires potent processors, including GPUs, and might take hours. However, when such a large volume of data is unavailable, the Conventional Neural Network (CNN) does not train well for the lack of enough samples. To overcome this issue, an effective classification method is proposed to classify the products with a contemporary architecture that integrates the data selection, transformation, and filtering processes with the training of CNN and long short-term memory (LSTM) with limited labeled data and has an imbalance among the classes. The efficiency of the hybrid LSTM approach is evaluated using ResNet, Google Net, and Alex Net. The models were trained using different hyperparameters and the accuracy of the network trained on this data and the accuracy of AlexNet is 94.38, GoogleNet is 94.82 and ResNet-50 is 95.37. Finally, the proposed approach demonstrates that using an efficient classifier at the end of the CNN structure delivers the desired performance even when the CNN model is not intensely trained.

Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs

Excessive external vibrations could affect the normal functioning and integrity of sensitive buildings such as laboratories and heritage buildings. Usually, these buildings are exposed to multiple external vibration sources simultaneously, so the monitoring and respective evaluation of the vibration from various sources is necessary for the design of targeted vibration mitigation measures. To classify the sources of vibration accurately and efficiently, the advanced hybrid models of the convolutional neural network (CNN) and long short-term memory (LSTM) network were built in this study, and the models are driven by the extensive data of external vibration recorded in Beijing, and the parametric studies reveal that the proposed optimal model can achieve an accuracy of over 97% for the identification of external vibration sources. Finally, a real-world case study is presented, in which external vibration monitoring was carried out in a laboratory and the proposed CNN+LSTM model was used to identify the sources of vibration in the monitoring so that the impact of vibration from each source on the laboratory was analyzed statistically in detail. The results demonstrate the necessity of this study and its feasibility for engineering applications.

Energy forecasting of the building-integrated photovoltaic façade using hybrid LSTM.

Effective building energy management systems need a reliable approach to estimating future energy needs using renewable energy sources. However, nonlinear and nonstationary trends in building energy use data make prediction more challenging for integrating the photovoltaic system. To estimate future energy forecast, this work presents a hybrid approach based on random forest (RF) and long short-term memory (LSTM) using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Initial steps in our suggested procedure include utilizing CEEMDAN to translate the raw energy usage data into multiple components. Then, the component with the most significant frequency is predicted using RF, and the other components are forecasted using hybrid LSTM. Finally, all of the individual parts' predictions are combined to form a whole. Real-world output energy usage data has been predicted to test the suggested strategy. Results from the experiments show that the suggested strategy outperforms the reference methods.

A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks. Further, the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent. In this research work, DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory (LSTM) model have been proposed with NSL-KDD dataset. In Hybrid LSTM method, the Particle Swarm Optimization (PSO) technique, which is combined to optimize the weights of the LSTM neural network, reduces the prediction error. This deep belief network method is used to extract the features of IP packets, and it identifies DDoS attacks based on PSO-LSTM model. Moreover, it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks. The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine (SVM) and LSTM in terms of attack detection performance along with the results of the measurement of accuracy, recall, f-measure, precision.

Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit

There is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by proper ventilation system across buildings. Nevertheless, different buildings require different mechanistic approaches and it may not be an effective solution for the buildings. Thus, statistical approaches have great potential to evaluate the IAQ in real occupied buildings. Numerous machine learning (ML) techniques were introduced to forecast the indoor environmental risk across buildings. However, there is inadequate data available on how well these ML techniques perform in indoor environments. Recurrent neural network (RNN) is a ML technique that deals with sequential data or time series data. However, the RNN model gradient tends to explode and vanish, leading to inaccurate prediction outcomes. Therefore, this study presents the development of a time based prediction model, Long Short-Term Memory (LSTM) with adaptive gated recurrent units for the prediction of IAQ. Using an advanced LSTM model, the study focuses on the performance of the prediction accuracy and the loss during training and validation. Also, the developed model will be assessed with other RNN models for data validation and comparisons. A set of particulate matter (PM2.5) dataset from commercial buildings is assessed, preprocessed and clean to ensure quality prediction outcomes. This study demonstrates the performance of the hybrid LSTM model to remember past information, minimize gradient error and predict the future data precisely, ensuring a healthier indoor building environment.

Volatility Prediction via Hybrid LSTM Models with GARCH Type Parameters

Since the establishment of financial models for risk prediction, the measurement of volatility at risky market has improved, and its significance has also grown. For high-frequency financial data, the degree of investment risk, which has always been the focus of attention, is measured by the variance of residual sequence obtained following model regression. By integrating the long short-term memory (LSTM) model with multiple generalized autoregressive conditional heteroscedasticity (GARCH) models, a new hybrid LSTM model is used to predict stock price volatility. In this paper, three GARCH models are used, and the model that can best fit the data is determined.