Measurement of Visitor Behavioral Engagement in Heritage Informal Learning Environments Using Head-Mounted Displays.

This study introduces a hybrid AutoRegressive Integrated Moving Average (ARIMA)—Long Short-Term Memory (LSTM) model for predicting and managing sugarcane pests and diseases, leveraging big data for enhanced accuracy. The ARIMA component efficiently captures linear patterns in time-series data, while the LSTM model identifies complex nonlinear dependencies. By integrating these two approaches, the hybrid model effectively handles both linear trends and nonlinear fluctuations, improving predictive performance over conventional models. The model was trained on 33 years of meteorological and pest occurrence data, and its effectiveness was evaluated using mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ARIMA-LSTM model achieves an MSE of 2.66, RMSE of 1.63, and MAE of 1.34, outperforming both the standalone ARIMA model (MSE = 4.97, RMSE = 2.29, MAE = 1.79) and LSTM model (MSE = 3.77, RMSE = 1.86, MAE = 1.45). This superior performance highlights its ability to effectively capture seasonal variations and complex nonlinear patterns in pest outbreaks. Beyond accurate forecasting, this model provides valuable decision-making support for agricultural management, aiding in early intervention strategies. Future enhancements, including the integration of additional variables and climate change factors, could further expand its applicability across diverse agricultural sectors, improving crop yield stability and pest control strategies in an increasingly unpredictable climate.

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

Cocoa is a leading commodity from Indonesia. Cocoa prices from time to time fluctuate. Accurate Cocoa price predictions are very important to ensure future prices and help decision making. Cocoa price data is non-stationary and nonlinear, so to make accurate predictions, an Artificial Neural Network (ANN) model is applied. One type of ANN is Long Short-Term Memory (LSTM). LSTM has superior performance for time series based prediction. Optimization methods used are Root Mean Square Propagation, and Adaptive Moment Estimation. The best model was selected based on the Means Square Error (MSE) and Mean Absolute Percentage Error (MAPE) values. This study uses the R-Shiny GUI to facilitate the use of LSTM for users who are less proficient in programming languages. Based on the results, the Long Short-Term Memory model with the Adaptive Moment Estimation optimization method is more optimal than the Long Short-Term Memory with Root Mean Square Propagation seen from the smaller MSE and MAPE values. This study used 27 combinations of hyperparameters. Prediction results with LSTM using the R-Shiny GUI have different levels of accuracy in each experiment. The best accuracy value is experiment with MSE value of 491505.1 and MAPE value of 1.739155% . Cocoa Price Forecasting for the period November to December 2021 tends to decline.Keywords : Cocoa Prices, Forecasting, Long Short-Term Memory, Root Mean Square Propagation, Adaptive Moment Estimation, GUI R-Shiny

The quality and reliability of internet of thing (IoT) ecosystems heavily rely on accurate and dependable sensor data. However, resource limited sensors are prone to failure due to various factors like environmental disturbances and electrical noise in which they can produce erroneous and faulty measurements. These can have significant consequences across different domains, including a threat to safety in critical systems. Though many researches have been conducted, the existing literature primarily focuses on fault detection in the sensor data, while fault detection is useful, it is still a reactive approach that identifies the faults after they have occurred, meaning that actions are taken after the fault has already impacted the system, potentially leading to negative consequences. In this study, a proactive approach has been proposed by developing a two-stage solution. In the first stage, a hybrid convolutional neural network-long short term memory (CNN-LSTM) model was trained to forecast sensor measurements based on historical data, while in the second stage, the forecasted measurements were passed to a hybrid convolutional neural network-multi layer perceptron (CNN-MLP) model that has been trained to recognize different types of sensor faults and classify the new measurements accordingly. By passing the forecasted sensor values as input to the classification model and categorizing them as normal, bias, drift, random or poly-drift, anticipated the potential faults before they manifest. The publicly available Intel Lab data raw dataset is used, which has been annotated and fault-injected. For regression, gated recurrent unit (GRU), Long short term memory (LSTM), bidirectional long short term memory (BiLSTM), convolutional neural network-gated recurrent unit (CNN-GRU), convolutional neural network-long short term memory (CNN-LSTM), and convolutional neural network-bidirectional long short term memory (CNN-BiLSTM), were evaluated and compared their performance using root mean squared error (RMSE), mean squared error (MSE) and mean absolute error (MAE) with 2-split time series cross-validation. CNN-LSTM outperformed the other models with a Mean Absolute Error of 2.0957 for a 45 time steps forecast. For the classification task, convolutional neural network (CNN), multi-layer perceptron (MLP), and convolutional neural network-multi layer perceptron (CNN-MLP) evaluated using the metrics accuracy, precision, recall, and F1-score with 5 and tenfold cross-validations. CNN-MLP outperformed the others with accuracy of 96.11% for bias, 99.33% for drift, and 98.61% for random and 98.81% for poly-drift. The average accuracy across the 4 faults is 98.21%, which is a 0.3% increase from the baseline work 97.91%. By adopting a proactive approach to sensor fault prediction and classification, this research aims to enhance the reliability and efficiency of IoT systems, allowing for preventive measures to be taken before faults have a detrimental impact.

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO2 emissions that combines a multi-strategy improved particle swarm optimization (MSPSO) algorithm with a long short-term memory (LSTM) model. Firstly, the particle swarm optimization (PSO) algorithm is enhanced by combining tent chaotic mapping, mutation for the least-fit particles, and a random perturbation strategy. Subsequently, the performance of the MSPSO algorithm is evaluated using a set of 23 internationally recognized test functions. Finally, the predictive performance of the MSPSO-LSTM hybrid model is assessed using data from the building industry in the Yangtze River Delta region as a case study. The results indicate that the coefficient of determination (R2) of the model reaches 0.9677, which is more than 10% higher than that of BP, LSTM, and CNN non-hybrid models and demonstrates significant advantages over PSO-LSTM, GWO-LSTM, and WOA-LSTM hybrid models. Additionally, the mean square error (MSE) of the model is 2445.6866 Mt, and the mean absolute error (MAE) is 4.1010 Mt, both significantly lower than those of the BP, LSTM, and CNN non-hybrid models. Overall, the MSPSO-LSTM hybrid model demonstrates high predictive accuracy for CO2 emissions in the building industry, offering robust support for the sustainable development of the industry.

Increasingly intense climate change has increased the frequency and intensity of extreme weather, making weather prediction critical for mitigation and adaptation. This research focuses on long-term prediction of extreme weather using the Long ShortTerm Memory (LSTM) model, as well as evaluating the influence of climate change on prediction accuracy. In this study, historical weather data is used to train and test an LSTM model combined with a RandomForestClassifier. Analysis was carried out using the Mean Squared Error (MSE) evaluation technique for 50 epochs and 8 trials at various threshold values (26, 29, 32, 35, 38, 41, 44, 47). The research results show that the LSTM model is able to predict extreme weather with an accuracy of up to 100%. Apart from that, this research also predicts daily rainfall in Bandung City through the process of data collection, preprocessing, normalization and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). This model produces an RMSE of 4.24 and MAE value of 2.72%, indicating quite good predictions. It is hoped that this research can make a significant contribution to the field of meteorology and can be developed further by adding parameters or other methods to improve the quality of predictions. Suggestions are given to increase the amount of data used to obtain better prediction results in the future.

The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30) meteorological sub-divisions in India. The model was compiled using the Mean Square Error (MSE) loss function and Adam optimizer. The models' performances were evaluated using statistical metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings: This study discovered that the proposed Bidirectional LSTM model achieved an RMSE of 240.79 and outperformed an existing Recurrent Neural Network (RNN), Vanilla LSTM and Stacked LSTM by 8%, 4% and 2% respectively. The study also finds that increasing the input time step and increasing the number of cells in the hidden layer enhanced the prediction performance of the proposed model, and the Bidirectional LSTM converges at a lower epoch compared to RNN and LSTM models. Novelty: This study applied the Bidirectional LSTM for the first time in predicting all-India monthly average rainfall and provides a new benchmark for this dataset. Keywords: Deep Learning, LSTM, Rainfall prediction, Stacked LSTM, Bidirectional LSTM

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

In recent years, the air quality in China has become a matter of serious concern. Among the available indicators for evaluating air quality, PM2.5 is one of the most important. It comprises a complex mixture of extremely small particles and liquid droplets emitted into the air, whose diameters are no more than 2.5 μm. Environments with a high PM2.5 index are extremely harmful to human health. Once inhaled, these particles can affect the heart and lungs and cause serious health problems. Air pollution is closely related to meteorological conditions such as wind speed, wind direction, atmospheric stability, temperature, and air humidity. With the development of various machine learning methods, deep learning models based on neural networks are increasingly applied in air pollution research. In this study, the temperature, humidity, wind velocity data at different pressure altitudes from 8 locations around Beijing and average of PM2.5 data in Beijing were analyzed and normalized. Multi-dimensional data was ideal for research applications using machine learning methods. and three neural network models were built, including the back propagation (BP), convolutional neural network (CNN), and long short-term memory (LSTM) models, and trained them using the meteorological and PM2.5 data.The results indicate that the accuracies of the back propagation and convolutional neural network models in predicting the PM2.5 pollution level in the next hour is much lower than that of the long short-term memory model. The PM2.5 pollution index predicted for the next hour by the long short-term memory model is very close to the actual value. This result reveals the strong relationship between the PM2.5 pollution index of Beijing and the local meteorological conditions. The long short-term memory model is trained using meteorological data from different pressure altitudes, and found it to be more accurate in predicting pollution levels when using near-surface meteorological data than that obtained from multiple altitudes.

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships.

Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer dengan arsitektur terpilih menunjukkan kinerja unggul dengan nilai Mean Square Error (MSE) sebesar 5,844 x10⁻⁵, Mean Absolute Error (MAE) sebesar 0,005971, dan Root Mean Square Error (RMSE) sebesar 0, 007645. Arsitektur Environment Intelligence versi (a) dengan Nadam Optimizer terbukti paling efektif dalam memproses data sensor, sehingga dipilih untuk integrasi dengan perangkat LSTM-IOT. Implementasi LSTM-IOT dalam skenario dunia nyata dilakukan pada wadah web lokal yang memungkinkan akses real-time ke data suhu dan kelembaban di berbagai lokasi. Halaman web berbasis Streamlit ini menampilkan visualisasi data, performa LSTM, dan hasil prediksi. Uji fungsional menunjukkan bahwa LSTM-IOT memenuhi kebutuhan perusahaan, termasuk penyimpanan data dalam database internal serta prediksi kondisi lingkungan hingga 150 menit ke depan. Dengan fitur prediksi dan pemantauan yang canggih, perangkat ini memberikan solusi efisien dan bernilai tinggi bagi perusahaan dalam memantau kondisi lingkungan secara akurat dan proaktif. Abstract Problems in the industry related to temperature and humidity data loss are often caused by device interference or loss of connection. This data is important to determine the feasibility of the product to be distributed. To overcome these problems, an LSTM-IOT innovation was developed, namely an IoT device that is integrated with the Long Short-Term Memory (LSTM) model in the Environment Intelligence architecture. This architecture has been optimized through experiments using different types of optimizers, such as Adam, RMSprop, AdaGrad, SGD, Nadam, and Adadelta. From the optimization results, the combination of Nadam Optimizer with the selected architecture shows superior performance with a mean square error (MSE) value of 5.844 x 10⁻⁵, a mean absolute error (MAE) of 0.005971, and a root mean square error (RMSE) of 0.007645. The Environment Intelligence architecture version (a) with Nadam Optimizer proved to be the most effective in processing sensor data, so it was chosen for integration with LSTM-IOT devices. The implementation of LSTM-IOT in real-world scenarios is carried out on a local web container that allows real-time access to temperature and humidity data in various locations. This Streamlit-based webpage displays data visualizations, LSTM performance, and prediction results. Functional tests show that LSTM-IOT meets the needs of the company, including data storage in an internal database and prediction of environmental conditions for up to the next 150 minutes. With advanced prediction and monitoring features, these devices provide efficient and high-value solutions for companies to monitor environmental conditions accurately and proactively.

Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer dengan arsitektur terpilih menunjukkan kinerja unggul dengan nilai Mean Square Error (MSE) sebesar 5,844 x10⁻⁵, Mean Absolute Error (MAE) sebesar 0,005971, dan Root Mean Square Error (RMSE) sebesar 0, 007645. Arsitektur Environment Intelligence versi (a) dengan Nadam Optimizer terbukti paling efektif dalam memproses data sensor, sehingga dipilih untuk integrasi dengan perangkat LSTM-IOT. Implementasi LSTM-IOT dalam skenario dunia nyata dilakukan pada wadah web lokal yang memungkinkan akses real-time ke data suhu dan kelembaban di berbagai lokasi. Halaman web berbasis Streamlit ini menampilkan visualisasi data, performa LSTM, dan hasil prediksi. Uji fungsional menunjukkan bahwa LSTM-IOT memenuhi kebutuhan perusahaan, termasuk penyimpanan data dalam database internal serta prediksi kondisi lingkungan hingga 150 menit ke depan. Dengan fitur prediksi dan pemantauan yang canggih, perangkat ini memberikan solusi efisien dan bernilai tinggi bagi perusahaan dalam memantau kondisi lingkungan secara akurat dan proaktif. Abstract Problems in the industry related to temperature and humidity data loss are often caused by device interference or loss of connection. This data is important to determine the feasibility of the product to be distributed. To overcome these problems, an LSTM-IOT innovation was developed, namely an IoT device that is integrated with the Long Short-Term Memory (LSTM) model in the Environment Intelligence architecture. This architecture has been optimized through experiments using different types of optimizers, such as Adam, RMSprop, AdaGrad, SGD, Nadam, and Adadelta. From the optimization results, the combination of Nadam Optimizer with the selected architecture shows superior performance with a mean square error (MSE) value of 5.844 x 10⁻⁵, a mean absolute error (MAE) of 0.005971, and a root mean square error (RMSE) of 0.007645. The Environment Intelligence architecture version (a) with Nadam Optimizer proved to be the most effective in processing sensor data, so it was chosen for integration with LSTM-IOT devices. The implementation of LSTM-IOT in real-world scenarios is carried out on a local web container that allows real-time access to temperature and humidity data in various locations. This Streamlit-based webpage displays data visualizations, LSTM performance, and prediction results. Functional tests show that LSTM-IOT meets the needs of the company, including data storage in an internal database and prediction of environmental conditions for up to the next 150 minutes. With advanced prediction and monitoring features, these devices provide efficient and high-value solutions for companies to monitor environmental conditions accurately and proactively.

Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

River stage prediction is indispensably a challenging task in flood-prone river basins to disseminate accurate early warning in advance. In this study, multivariate wavelet-based long short-term memory (WLSTM) models have been developed to predict river stage at six gauging stations of the Teesta River basin in India for 1, 3, and 5-day lead time, the comparison of which has been done with long short-term memory (LSTM) models. Various combinations of wavelet decomposed components were utilized to form different sub-series that were fed as input in WLSTM models. In terms of statistical indicators, both the models yielded exceptionally good results, but the root-mean-square error values of the WLSTM model for 1- and 3-day lead time were minimal compared to the LSTM model. However, the accuracy of the LSTM model in longer lead time prediction is noticeable. Specifically, the WLSTM model predicted the peak stage values more precisely compared to the LSTM model, indicating the potential of wavelet analysis to capture the variations and periodicities of the data by removing the noise. Though the WLSTM model marginally outperformed the LSTM model in prediction accuracy, the results highlight both models as feasible alternatives for longer lead time water level prediction.