Average Mean Absolute Percentage Error Research Articles

A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve

Accurately estimating the state-of-health of batteries is critical for effective battery monitoring and management. However, the estimation remains challenging due to dynamic operation environments and complex battery degradation patterns. In this study, a feature fusion-based convolutional neural network is proposed for battery state-of-health estimation based on voltage measurements obtained during a partial cycle. Instead of directly feeding the voltage data as input, three feature sequences are first extracted, including the capacity versus voltage curve and its differentiation with respect to voltage and life cycle. The objective is to exploit more effective information from intra-cycle and inter-cycle perspectives. Then, an element-wise addition is embedded as a feature fusion operation in the proposed convolutional neural network to generate more efficient feature maps when dealing with multiple model inputs. To validate the performance of the proposed methodology, eighteen batteries from three battery datasets are utilized for comparative studies. Experimental results demonstrate that the data preprocessing from both intra-cycle and inter-cycle perspectives, along with the adoption of the feature fusion operation, significantly improve the accuracy of battery state-of-health estimation, with an average mean absolute error and mean absolute percentage error being no more than 0.0028 and 0.32 %, respectively.

Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

Prediction of carbon emissions from municipal solid waste treatment in 31 provinces and cities in China under the shared socio-economic pathways

In order to reduce greenhouse gas (GHG) emissions from the municipal solid waste (MSW) sector, it is essential to describe its greenhouse gas emission patterns and propose appropriate mitigation measures. Therefore, this study predicts the greenhouse gas emissions of urban domestic waste treatment by combining the Intergovernmental Panel on Climate Change (IPCC), shared socio-economic pathways and in-depth learning model. Under the hypothetical policy scenario and the shared socio-economic pathways (SSPs), the greenhouse gas emissions from municipal solid waste treatment in 31 provinces and cities in China by 2030 are analyzed. Combined with the model comparison results, the bidirectional long and short-term memory neural network model has better prediction accuracy than the other models, with an average mean absolute percentage error of 9.68% for 31 provinces and cities, indicating good applicability. Scenario analysis shows that greenhouse gas emissions from municipal solid waste disposal can be significantly reduced only after the implementation of municipal solid waste classification. Scenario 3 (S3) of waste segregation will produce the most GHG emissions, and switching to Scenario 5 (S5) of waste segregation will produce significantly less GHG emissions. Finally, the paper makes policy recommendations on the priority areas of urban domestic waste classification, waste source classification and recycling, and the structure of urban municipal solid waste treatment.

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study.

Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Our machine learning-based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Our findings show the feasibility of using machine learning-based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions-an important aspect in clinical practice.

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.

This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels' tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects' three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels' diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model's age predictions in patients with intracranial vessel diseases.

State of health estimation of lithium-ion batteries using EIS measurement and transfer learning

Accurately estimating the state of health (SOH) of lithium-ion batteries in real-world scenarios, especially for electric vehicles (EVs) is challenging due to dynamic operating conditions and limited battery usage data. To address these challenges, transfer learning, as an effective approach that uses the knowledge gained from a source task to improve efficiency and accuracy on a similar target task, has been combined with deep neural networks (DNN) to determine the SOH of Li-ion batteries at various temperatures with a limited amount of target data. In this study, the SOH of Li-ion batteries has been estimated using one of the largest Electrochemical Impedance Spectroscopy (EIS) datasets collected at three different temperatures, 25, 35, and 45 degrees Celsius. The source task is to estimate the SOH using DNN models at one or two operating temperatures (25, 35, and 45 degrees Celsius), while the target task is to estimate the SOH at the other temperature. The results demonstrate that the average mean squared error (MSE) and mean absolute percentage error (MAPE) for estimating the SOH using transfer learning-based DNN (DNN-TL) at 35 degrees Celsius have been reduced up to 72.63% and 48.43%, respectively, compared with the DNN model without transfer learning. Similarly, the average MSE and MAPE for estimating the SOH at 45 degrees Celsius using DNN-TL have been reduced up to 79.51% and 55.11%, respectively. Also, our findings demonstrate the robustness of DNN-TL models, requiring target data with only 6% of the size of source data for retraining, leveraging previously learned knowledge from the source data. Furthermore, the exclusion of fixed layers in the retraining process enhances the performance of the DNN-TL model.

A Data-Driven Rutting Depth Short-Time Prediction Model with Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94\% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning (ML) for battery capacity estimation has attracted widespread attention. However, a clear classification, fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives: charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory (LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error (MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 mV voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

ANALISIS KEAKURATAN FORECAST RESIN ABS GRADE 500 322 MENGGUNAKAN PENDEKATAN MEAN ABSOLUTE PERCENTAGE ERROR (MAPE): STUDI KASUS INDUSTRI MANUFAKTUR PLASTIC COLORING DAN COMPOUNDING

The plastic resin coloring industry has a very important role in the molder supply chain. Materials must be imported from abroad with a long enough lead time, so material purchases are made based on the forecast provided by the molder. The constraints that occur are forecast inaccuracies which result in an increase in inventory level of material and a potential shortage of material at the customer. This research is a descriptive research with a quantitative approach, where the research method is carried out by interviews and literature studies. This research is aimed at measuring the extent to which forecast accuracy is carried out by molders using the MAPE (Mean Absolute Percentage Error) method, the results of the study show whether or not a forecast is feasible. The results of this study show an average MAPE of 36.5% so that it can be categorized as feasible. Even though the forecast is categorized as feasible, PT X must respond properly to this forecasting inaccuracy so that the supply to the molder is not disrupted. In this study, PT X had to make adjustments to the material arrival planaccording to the current month's material needs. On the other hand, it is no less important to carry out active communication and provide education to molders about the importance of forecast accuracy, so that it can provide benefits for the company itself and the molder.Keywords: Forecasting; MAPE; Molder

Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models

Increasing the renewable energy penetration, especially photovoltaic systems, requires accurate and short-term load forecasting for every individual electricity customer. This can significantly help distributed network service providers (DNSP) plan and operate electrical networks and provide better quality and more reliable electricity services to their customers. Due to the strong volatility of the load data, an optimised deep-ensemble learning methodology is proposed to develop several deep learning models to forecast the load data in individual and aggregate load scenarios. The adaptive wind-driven optimisation (AWDO) algorithm is used to tune the hyperparameters of four of the developed deep learning models, significantly improving their performance. Tuning the deep learning models’ hyperparameters shows significant improvements in the accuracy, as well as training, and testing times. In addition, a hybrid ensemble strategy that contains bagging, random Subspace, and boosting (BRSB) with ensemble pruning is adapted in the optimised deep learning-based models to extract deep features from multivariate data. In this context, the bidirectional long-short-term memory optimised by the AWDO algorithm (Bi-LSTM-AWDO) based hybrid ensemble learning forecasting model is compared exhaustively with several benchmark models, including the recently developed models in the state-of-the-art. The average root mean square error (RMSE), and the average mean absolute percentage error (MAPE) of the Bi-LSTM-AWDO model for individual loads are 0.121 kW, and 7.55%, respectively, while they are 0.025 kW, and 1.51% for aggregate loads, respectively. Accordingly, the Bi-LSTM-AWDO model outperforms other models in forecasting short-term load data for individual and aggregate households using actual smart meters’ measurements.

Performance of the autoregressive integrated moving average model with exogenous variables statistical model on the intraday market for the Denmark-West bidding area

This article aims to investigate whether a statistical model known as Autoregressive Integrated Moving Average with Explanatory Variables can aid better predictability of volume-weighted average electricity prices compared to a commonly used forecasting method. This analysis was conducted for a specific bidding area, the Denmark-West bidding area (DK1). Autoregressive integrated moving average model with exogenous variable's performance was tested on the DK1 intraday market over a two-year period starting from 1 January 2019 until 31 December 2020. An explanatory variable used to support better the accuracy of the forecast is the day-ahead price for a corresponding intraday delivery hour. To ensure the validity of the paper, a well-known forecasting methodology was applied, and the results of the analysis show superior performance over the benchmark forecasting method. The autoregressive integrated moving average model with exogenous variables model developed was found to significantly outperform other commonly used forecasting methods, with an average mean absolute percentage error of 1.5%. The model was able to accurately predict intraday volume-weighted average prices up to 24 h in advance, using only publicly available data on day-ahead prices and historical intraday prices. Energy traders and other market players may find the developed autoregressive integrated moving average model with exogenous variables model to be a useful resource when looking to make more informed decisions in the intraday market.

Physics-Integrated Machine Learning for Efficient Design and Optimization of a Nanoscale Carbon Nanotube Field-Effect Transistor

We propose an efficient framework for optimizing the design of Carbon Nanotube Field-Effect Transistor (CNTFET) through the integration of device physics, machine learning (ML), and multi-objective optimization (MOO). Firstly, we leverage the calibrated TCAD model based on experimental data to dissect the physical mechanisms of CNTFET, gaining insights into its operational principles and unique physical properties. This model also serves as a foundation, enabling multi-scale performance evaluations essential for dataset construction. In the ML phase, a chain structure of Support Vector Regression (SVR Chain) guided by a comprehensive statistical analysis of the design metrics is utilized to predict the design metrics. The surrogate model based on the SVR Chain achieves an average mean absolute percentage error (MAPE) of 1.59% across all design metrics without overfitting, even with limited data. The established ML model exhibits its competence in rapidly producing a global response surface for multi-scale CNTFET. Remarkably, an anomalous equivalent oxide thickness (EOT) and ON-state current (I on ) relationship is observed in CNTFET behavior due to extreme gate length scaling in long channel devices. This intriguing observation is further elucidated through a physics-based explanation. We further compare shallow and deep learning-based TCAD digital twins for model selection guidance. Using the Non-Dominated Sorted Genetic Algorithm-II (NSGA-II) in MOO, we harmonize metrics at both device and circuit levels, significantly reducing the design space. The closed-loop framework expedites the early-stage development of advanced transistors, overcoming the challenges posed by limited data.

Rainfall prediction in coastal hilly areas based on VMD–RSA–DNC

Abstract Highly accurate rainfall prediction can provide a reliable scientific basis for human production and life. For the characteristics of occasional and sudden changes of rainfall in coastal hilly areas, this article chooses four cities in the eastern Zhejiang province as the object of the study and establishes a rainfall prediction model based on variational mode decomposition (VMD), reptile search algorithm (RSA), and differentiable neural computer (DNC). The VMD algorithm reduces the complexity of the sequence data; RSA is used to find the best-fit function; and DNC combines the advantages of the recurrent neural network and computational processing to improve the problem of memory forgetting of long short-term memory. To verify the prediction accuracy of the model, the prediction results are compared with the other three models, and the results show that the VMD–RSA–DNC model has the best prediction with the maximum and minimum relative errors of 9.62 and 0.17%, respectively, the average root-mean-square error of 5.43, the average mean absolute percentage error of 3.59%, and the average Nash–Sutcliffe efficiency of 0.95 for predicting four cities in the coastal hilly area. This study provides a new reference method for the construction of rainfall prediction models.

Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

Public services such as public transportation are closely related to user satisfaction. Busway DKI Jakarta is one of the first public transportation services in Southeast and South Asia. In order to maintain passenger satisfaction, the management of Busway continues to improve services such as adding buses and opening a new line. Opening a new lane or adding buses must necessarily be adjusted also with the increasing number of passengers. So to know the number of passengers in the future, it is necessary to forecast the number of passengers through existing historical data. The historical data used is time series data from January 2015 to January 2016. The method used in forecasting is Long Short-Term Memory (LSTM), one of the machine learning methods. The method is measured in accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). As a comparison of LSTM accuracy in forecasting, we also use the Exponential Smoothing method. Based on the results of forecasting, the most and least dominant method of producing RMSE and MAPE is the LSTM method. Only in corridor 3 LSTM can not provide RMSE and MAPE values below baseline values. While corridor 5 LSTM can give better results after the data transformation process by using exponential smoothing. However, overall the LSTM method provides the best accuracy based on the lowest average RMSE and MAPE values, namely RMSE of 2640.53 and MAPE of 9.14%.

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin.

Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin's biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins.

Artificial Neural Network Model to Predict Final Construction Contract Duration

Forecasting the final construction contract duration at an early stage plays a vital role in the progress of a project. An inaccurate project duration prediction may lead to the project’s benefits being lost. It is essential to precisely predict the duration due to the presence of several different factors. This paper contributed to developing a model to predict final construction contract duration (FCCD) in the early stages based on parameters characterized as few and shared for any contract. (contract cost, contract duration, and sector). This paper developed an Artificial Neural Network (ANN) model based on 135 Saudi construction project data. The development model has three stages. The first stage was standardization and augmentation using Zavadskas and Turskis’ logarithmic and Pasini methods. The second and third stages were the first and second analyses of the ANN models, respectively. The first analysis aimed to promote the used data and integrate them into the second analysis to develop the ANN model. The ANN models were compared with three linear regression (LR) models (LR1, LR2, and LR3) and other models in the literature. The results revealed that the accuracy of the ANN model provides reasonable accuracy with an average mean absolute percentage error (MAPE) of 12.22%, which is lower than the LR3′s MAPE by 27.03%. The accuracy of the ANN model is similar to that of earned value management (EVM) in the previous study. This paper supports research to deal with relatively little data and integrate them into a neural network. The ANN model assists the stakeholder in making appropriate decisions for the project during the pre-tendering phase by predicting the actual contract duration based on the CC, CD, and project sector.

Deep learning approaches for MIMO time-series analysis

This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior.

Predicting Commercial Building Energy Consumption Using a Multivariate Multilayered Long-Short Term Memory Time-Series Model

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most commonly used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate, multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE of 30%, RMSE of 25%, and MAPE of 20% compared with the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE of 10%, RMSE of 20%, and MAPE of 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%.These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions

Towards sustainable port management: Data-driven global container ports turnover rate assessment

Accurate assessment of port turnover rate is essential for port operators and shipping carriers to benchmark and improve their operations. This study proposes a standardized method to estimate the port turnover rate based on satellite data of ocean ships. This method can be generalized to accommodate ports of different geographic and operational characteristics with minimum input and running times. To achieve the research objective, we first construct berth polygon areas for terminals based on Greatmaps (GMap) visual technique. Then, two tailor-made algorithms are proposed to estimate the berthing time of ship in a berthing event. Finally, we assess the port turnover rate with aggregate berthing time at a port and its historical port throughput. Assuming that the turnover rate is unchanged in the short term, we can use the estimated turnover to estimate the monthly throughput of global ports. The findings suggest the average Mean Absolute Percentage Error (MAPE) of our estimation is 3.84%. Standardized and high-frequency port statistics are highly valued by the industry but very costly to access. The proposed method makes high-frequency port turnover rate and throughput available for a wide range of users. The statistics and findings will enhance standardization and transparency of port statistics and promote the sustainable development of port industry.