Proposed Long Short-term Memory Model Research Articles

Photometric redshift estimation for CSST survey with LSTM neural networks

ABSTRACT Accurate estimation of photometric redshifts (photo-zs) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on recurrent neural networks with long short-term memory (LSTM) to predict photo-z. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo-z estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalogue, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo-z estimation. For instance, when trained on the same data set and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers $f_{\mathrm{out}}$ observed with a multi-layer perceptron neural network (MLP) model, while the normalized median absolute deviation $\rm \sigma _{NMAD}$ is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo-zs of galaxies using photometric data from large-scale survey projects.

A study on music therapy aimed at psychological trauma recovery for bereaved families driven by artificial intelligence.

This study explores the effectiveness of music therapy in repairing psychological trauma in bereaved families, aiming to provide a comprehensive understanding of its potential therapeutic impact. It begins with an analysis of the current situation faced by bereaved families, identifying the psychological challenges they experience. The research design included the recruitment of participants from bereaved families, who were then divided into an experimental group and a control group. An optimized Long Short-Term Memory (LSTM) network model was constructed to analyze music therapy data, tailored specifically to capture the nuances of this therapeutic process. The experimental procedure detailed the specific operations involved in the music therapy sessions and established a clear comparison framework between the two groups. The performance of the proposed LSTM model demonstrated high accuracy (0.85), precision (0.86), recall (0.84), and F 1-score (0.85), indicating its effectiveness in predicting treatment outcomes. When compared to the Self-Reporting Inventory-90 (SCL-90) scale, the model captured the trend of treatment effects with a high level of accuracy and reliability. Notably, participants numbered 2, 6, and 8 in the experimental group showed substantial improvement rates of 67.21%, 71.45%, and 75.67%, respectively, in their psychological health issues. The comparative analysis between the experimental and control groups confirmed that the music therapy approach, as guided by the proposed LSTM model, led to a more significant improvement in psychological health issues for bereaved families. This suggests that the model offers a promising avenue for enhancing the effectiveness of music therapy in this context.

Abnormal high water temperature prediction in nearshore waters around the Korean Peninsula using ECMWF ERA5 data and a deep learning model

The abnormally high-water temperature (AHWT) phenomena have caused the mass stranding of farmed fish in the Korean coastal waters, leading to a substantial monetary loss in recent decades. It is most important to predict the HWT occurrence and take responsive measures before the HWT arrival to prevent such loss, we proposed a methodology to predict HWT occurrences using a deep-learning technology. Firstly, we trained a long short-term memory (LSTM) deep-learning model using the sea surface temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 product to estimate future water temperature in advance. Secondly, we used the estimated water temperature data to predict HWT occurrences from 1 day to 7 days later. We computed root mean square error (RMSE), mean absolute percentage error (MAPE) metrics, and F1-scores to evaluate the accuracy of the proposed LSTM model. In the cases of 1-day and 7-day water temperature predictions, RMSE and MAPE values between the estimated data and the sea-truth data were 0.293 degrees Celsius with 1.313 % and 0.854 degrees Celsius with 4.175 %, respectively. The F1-scores of the classification algorithm of 1- and 7-day HWT predictions were 0.96 and 0.74, respectively. This study contributes to developing measures to reduce the monetary loss of HWT damage on fish farms.

Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning

Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.

Forecasting international tourist arrivals in South Korea: a deep learning approach

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

Long short-term memory model for COVID-19 forecasting

Every time new variants of COVID-19, back to the pandemic, could bring massive loss to human society and traditional models have failed to catch the complexities of COVID-19. Thus, to handle the unpredictable challenges posed by the dynamic nature of COVID-19, Long Short-Term Memory (LSTM) models were utilized to make accurate short and long-term predictions about different variants and prepare the model for another variant or virus similar to COVID-19 by learning the data of different COVID-19 variants. The dataset, sourced from owid-covid-data, is cleaned and divided into original, alpha, delta, and mixed variants in the United States from March 1, 2020, to April 30, 2022. MSE, RMSE, MAE, and R2 are used to compare the difference between real and predicted values to evaluate how accurately the model performs. Results demonstrate the model excels in long-term and short-term predicting COVID-19 cases and deaths for various variants, and mixed variants even promote accuracy. Thus, the proposed LSTM model shows promise for infectious disease forecasting, providing a foundation for anticipating future outbreaks to help policymakers make better decisions or early prevention.

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

AbstractIn smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.

Construction of a recurrent neural network-based electrical load forecasting model for a 110 kV substation: a case study in the Western Region of The Republic of Kazakhstan

This paper presents an approach for using a long short-term memory (LSTM)-based recurrent neural network with various configurations to construct a forecasting model for electrical load prediction of a 110 kV substation. The issues of unbalances arising in energy management systems due to discrepancies between generated and consumed energy can lead to power outages and blackouts. With the introduction of the most accurate forecasts, the task of maintaining electrical reliability for grid operators can be greatly simplified. Through an investigation into 81 different parameter combinations, the research revealed the optimal setup for an LSTM model in the task of electrical load forecasting. This configuration comprised 15 neurons, a batch size of 16, and employed the Adamax optimization algorithm. Applying this specific setup yielded a mean squared error (MSE) of 5.584 MW2 and an R2 value of 0.99. High R2 values and low MSE values indicate that the LSTM model accurately captures changes in electricity consumption with minimal deviation between predicted and actual consumption values. Selection of appropriate parameters significantly enhances the performance of the predictive model and resulted in a reduction of the MSE error from 12.706 to 5.584 MW2. The optimized configuration of the LSTM model, tailored through extensive experimentation, enhances its predictive capabilities. The proposed LSTM model holds practical utility for integrating into systems for monitoring and forecasting mode reliability of electrical networks, particularly in the Western energy hub of the Republic of Kazakhstan. Its accuracy and reliability make it valuable for energy resource management and infrastructure planning

State of health estimation embedded with hardware accelerator based on long short-term memory combined with Bayesian optimization considering extracted health indicator in charging conditions

Recently, the importance of renewable energy has increased as fossil fuel use is regulated worldwide to protect the environment. Accordingly, the use of electric vehicles (EVs) that use a battery as a power source is increasing and estimating the battery state for an efficient and safe operation of EV is a very significant task. This study proposes a method to estimate the state-of-health (SOH) in which batteries are a major state indicator. A health indicator (HI) capable of determining the state of the battery is derived from the charging part by considering the stopping situation in which there is a slight variation in the battery characteristics of the EV. As an integrity indicator, the partial capacity according to the voltage range set by the user can be considered, and the correlation between the SOH and HI is analyzed for validity verification. To learn the derived HI, a long short-term memory (LSTM) model, a type of deep-neural network, is designed, and model tuning was performed through Bayesian optimization. The absolute error of SOH estimation for the proposed LSTM model is 1.1 %, compared and validated against recurrent neural network (RNN) (1.7 %) and gated recurrent unit (GRU) (1.85 %). After validating the performance of the LSTM algorithm, an analysis of SOH estimation error (1 %) based on LSTM was conducted following pack-level experiments to assess scalability. Finally, for evaluation of operation within a hardware accelerator (HA) for integration into EVs, NVIDIA Jetson Nano was utilized. Upon mounting the algorithm on Jetson Nano, the mean absolute error (MAE) was 0.6581, mean absolute percentage error (MAPE) was 0.7209, and root mean square error (RMSE) was 0.7133, demonstrating excellent estimation performance.

LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach

The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.

A coupled data-physics computational framework for temperature, residual stress, and distortion modeling in autoclave process of composite materials

It is challenging to obtain the full-field temperature profile during autoclave processes to control the temperature uniformity and minimize the residual stress and distortion of cured composite structures. This paper proposes a coupled data-physics computational framework for full-field temperature reconstruction and the subsequent residual stress/distortion modeling by using limited monitoring temperature data. Firstly, a Long Short-Term Memory (LSTM) model is developed for full field temperature reconstruction. In this LSTM model, a cross-recombination method is proposed to maximize the value of monitored temperature data. The method effectively cuts through the bottleneck of neural network training with limited labeled data. The LSTM model’s prediction stability is enhanced based on the mean-teacher and ensemble learning strategy. To train and validate the proposed method, we perform experiments using an autoclave at the National Institute for Aviation Research (NIAR). The LSTM model’s accuracy is assessed by comparing its predicted results with the thermocouple (TC) data from measurements and high-fidelity simulation data from computational fluid dynamics (CFD). The study shows that the proposed LSTM model can effectively reconstruct the full-field temperature using limited monitoring data and significantly improve accuracy and efficiency compared with the CFD-based counterpart. Then, we create a coupled data-physics computational framework by embedding the data-driven LSTM model into a physics-based thermo-mechanical finite element model to predict residual stress and distortion. The simulation results show that the coupled data-physics framework provides an effective way for process-to-performance modeling and simulation of autoclave curing processes.

Diabetic Drug ontology mapping for individual Diabetic Person and predict Insulin Dosage on Daily Basis

Managing diabetes requires a personalized treatment plan based on the patient's health records and drug usage effectively. In this paper, we have proposed accurate prediction of insulin dosage for diabetic patients daily by using ontology mapping with deep learning recurrent neural network models such as long short-term memory (LSTM), Gated recurrent unit( GRU), and Bidirectional long short-term memory(BiLSTM). The patient's information is arranged in a structured format. The structured ontology data provides a meaningful connection between patients' personal information, physical activity, insulin intake, carbohydrate intake and glucose level. The structured data is beneficial to take decisions effectively to recommend the insulin dosage level daily to avoid a sudden decrease in sugar level. The proposed LSTM model outperforms well on insulin prediction compared to GRU and BiLSTM models. The proposed model is very effective for diabetic personal care and improves patient outcomes and quality of life.

Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model

The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.

Daily average load demand forecasting using LSTM model based on historical load trends

AbstractLoad demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart micro grid. This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends. The main objective of this research work is estimating monthly electricity demand of an Indian state Chhattisgarh, in terms of per day average load demand using a machine learning model—Long Short‐Term Memory (LSTM). This framework considers average of each day load demand for every month of years 2018–2022 and forecasted per day average load demand for each month of the year 2023. Furthermore, the predicting accuracy is evaluated for training and testing phase, in terms of error metrics like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The MAPE values under the training and testing phase are in the range of 0.010%–0.652% and 0.378%–10.54%, respectively. A comparative study of LSTM model with Artificial Neural Network (ANN) model indicates the proposed LSTM model is more accurate and can be applied for real time load demand forecasting.

Wind power deviation charge reduction using long short term memory network

High penetration of variable generation like wind in modern power systems results in frequent load-generation imbalances. Additional spinning reserves and balancing services are deployed to reduce or avoid such imbalances. However, these additional balancing services result in increased total operation costs and it is generally shifted to Wind Power Generation Companies (WPGC) as Deviation Charges (DCs). The high DCs diminish the profitability of WPGCs. DCs are directly proportional to forecasting errors and can be reduced by enhancing forecasting accuracy. This paper proposes a Long Short-Term Memory (LSTM) network to reduce DCs. LSTM is a special kind of recurrent neural network and it is capable of learning long-term and short-term dependencies effectively. Therefore, the LSTM network can produce minimal error wind power forecasts and this helps to reduce DCs. The performance of the proposed LSTM network is realized using the real system data. Results obtained from the proposed LSTM model are compared with various forecasting models like ARIMA, ANN, and SVR. The comparison shows that the proposed LSTM model shows the least total DCs on most of the days followed by ANN, SVR, and ARIMA. The proposed LSTM, ANN, SVR, and ARIMA models show an average total DC of 6194.25$, 8459.32$, 15822.22$, and 38269.89$ respectively. Thus, the proposed LSTM model is capable of reducing 517.82%, 155.43%, and 36.56% DCs from ARIMA, SVR, and ANN models respectively, and helps the WPGCs to enhance their profitability.

Detection of Autism Spectrum Disorder (ASD) Symptoms using LSTM Model

Autistic children will often exhibit certain behaviors that are unique to them and that are not typical of neurotypical children. Parents will become familiar with these patterns over time and will be able to use this knowledge to answer questions about their child's behavior. Deep learning models are very useful to solve critical problems in the healthcare domain. Detection of ASD at the early age of a child is a challenging task. Recent research reveals that there is an increasing trend of ASD among children. Communication, eye contact, social behavior, and education are very poor for those who suffer from ASD. The proposed research work has been done to detect ASD symptoms in a child. Data has been collected from the various autism groups from social sites and organizations that are working on special children. A Deep learning model like the Long-Short Term Memory (LSTM) model has been used to detect the sentiment of parents’ dialog. LSTM is the most popular deep learning model that can able to solve complex natural language problems. The proposed LSTM model has been trained with prepared data and accuracy is 97% according to the prepared data.

DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2.

Deep causal speech enhancement and recognition using efficient long-short term memory Recurrent Neural Network.

Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to capture long-term temporal correlations by reducing the feature resolutions without data loss. We have used skip connections in non-adjacent layers to avoid gradient decay. In addition, an attention process is incorporated into skip connections to emphasize the essential spectral features and spectral regions. The proposed LSTM model is applied to speech enhancement and recognition applications. The proposed LSTM model uses no future information, resulting in a causal system suitable for real-time processing. The combined spectral feature sets are used to train the LSTM model for improved performance. Using the proposed model, the ideal ratio mask (IRM) is estimated as a training objective. The experimental evaluations using short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) have demonstrated that the proposed model with robust feature representation obtained higher speech intelligibility and perceptual quality. With the TIMIT, LibriSpeech, and VoiceBank datasets, the proposed model improved STOI by 16.21%, 16.41%, and 18.33% over noisy speech, whereas PESQ is improved by 31.1%, 32.9%, and 32%. In seen and unseen noisy situations, the proposed model outperformed existing deep neural networks (DNNs), including baseline LSTM, feedforward neural network (FDNN), convolutional neural network (CNN), and generative adversarial network (GAN). With the Kaldi toolkit for automated speech recognition (ASR), the proposed model significantly reduced the word error rates (WERs) and reached an average WER of 15.13% in noisy backgrounds.

Weather Prediction: Predicting Rain Using Weather Conditions

Weather forecasting is a major discipline that plays an important role in fields such as agriculture, transport, and emergency management, and it largely depends on accurate forecasts. Concerning this problem, this work aimed to analyze the effectiveness of recurrent neural networks, particularly the Long Short-Term Memory (LSTM), for estimating rainfall depending on precipitation, maximum temperature, minimum temperature, and wind speed. We will therefore use a large database containing recorded weather data obtained over several years to calibrate accurate predictive models designed to distinguish between drizzle, rain, sun, snow, and fog. The main idea of the work is to teach LSTM models that are capable of revealing temporal relations and patterns in sequential data, which makes them suitable to work on various time series forecasting such as weather prediction. The data is preprocessed effectively to clean it and make it ideal for our analysis to accurately compare the performance of one model against the others, we have divided the data into training, validation, and testing sets. The concurrency of the proposed LSTM model is then evaluated with the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) to measure the forecasting accuracy. The findings show a better predictive performance uplift whereby the best-performing LSTM model has an MSE of 8.74, RMSE of 2.96, MAE of 2.35, and R² of 0.83. Such metrics represent logical dependence between the predicted and actual weather conditions, proving thus the efficiency of the model. Also, the evaluation shows how hyper parameters’ optimization, features’ selection, and normalization, make a huge difference in the model’s performance and indicate that the precise management of weather parameters can result in better forecasts. However, the contributors of this research are not recluded to theoretical perspective; the present study can be useful for various subjects since the dependability of weather forecasts can be improved. They will be advantaged to have more precise weather data for crop growing, road networks, and other transport systems to prepare for the worst conditions, and emergency, rescue operations to be in a better position to handle certain disasters. Consequently, this study improves the academic literature on weather peculiarities with unforeseen downpours through a demonstration and explanation of the potential of LSTM networks to analyze key meteorological characteristics for rainfall prediction. Possible future study directions are outlined, proposing the expansion of features beyond those analyzed in the existing study to improve the predictive models, the usage of continuous rather than weekly data, as well as considering the mixed-ingredients approach for increasing the prediction accuracy. This inclusive strategy seeks to enhance the realistic stages in the phased meteorological prognosis and also timely resource allocation and management tactics within climate volatility.