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This paper discussed a novel recurrent neural network architecture for electricity price forecasting in a distribution system. Uncertainty in price forecasts misleads utilities in making bidding plans, and investments, and being aware of the risks involved. Accurate price forecasting assists utilities in developing effective bidding strategies and making appropriate investment decisions. Hence, in this paper, a novel feature combination such as the day ahead, similar day, and the combination of both day ahead and similar day using PSO-based LSTM and GRU network models are presented. The proposed method involves the optimal selection of the recurrent network model for electricity price forecasting. Finally, an experimental study is carried out to select the layers and nodes of the network model. In this analysis, the performance of the proposed model is evaluated using the mean absolute percentage error. [ABSTRACT FROM AUTHOR]

The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved. [ABSTRACT FROM AUTHOR]

Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by external factors. For predicting the stock market, several approaches have been put forward. Many academics have successfully forecasted stock prices using soft computing models. Recently, there has been growing interest in applying deep learning techniques in combination with technical indicators to forecast stock prices, attracting attention from both investors and researchers. This paper focuses on developing a reliable model for anticipating future stock prices in one day advance using Long Short-Term Memory (LSTM). Three steps make up the suggested model. The approach begins with ten technical indicators computed from previous data as feature vectors. The second phase involves data normalization to scale the feature vectors. Finally, in the third phase, the LSTM model analyzes the closing price for the next day using the normalized characteristics as input. Two stock markets, NASDAQ and NYSE are chosen to evaluate the efficacy of the developed model. To demonstrate how effective the new model is in making predictions, its performance is compared to earlier models. Comparing the suggested model to other models, the findings revealed that it had a high level of prediction accuracy. [ABSTRACT FROM AUTHOR]

Objective: To recalculate the estimated fetal weight (EFW) based on ultrasound measurements in patients complicated with isolated polyhydramnios, using 14 current formulas to observe which formula better predicts the EFW. Methods: This study examined pregnant women who gave birth in the hospital between January 2015 and January 2020. Maximum vertical pocket (MVP) was classified as, mild, moderate, and severe polyhydramnios, and the patients' measurements were reanalyzed using 14 formulas. The estimation of birth weight (EBW) alongside observed birth weight (OBW) facilitated the computation of statistical indices, namely the mean absolute percentage error (MAPE) expressed as [(EBW – OBW)/OBW × 100], the mean percentage error (MPE) denoted as (EBW – OBW)/(OBW × 100), and their corresponding 95% confidence intervals. Results: A total of 564 polyhydramnios patients were included in the study. When looking at the MAPE, the lowest rate (7.65) was found in the Hadlock 2 formula. Hadlock 1, Hadlock 3, and Shinozuka formulas demonstrated MAPE values most closely aligned with Hadlock 2. Weiner I and Thurnau were the formulas with the highest MAPE values. When the cut‐off values for MAPE were taken as 10%, 4/14 of the formulas (Weiner I–II, Vintzleos and Thurnau) gave results above 10%. Among 14 formulas, 3 (21.4%) had positive (sonographic overestimation) (Hadlock 3, Shinozuka, and Vintzleos) and the other 11 (78.6%) had negative MPE (sonographic underestimation). Conclusion: The Hadlock 2 formula had the lowest MAPE in predicting birth weight in patients with polyhydramnios, closely followed by the Hadlock 1, Hadlock 3, and Shinozuka formulas. [ABSTRACT FROM AUTHOR]

The primary objective of this manuscript is to find the service quality factors, which influence customer satisfaction, promotional efforts taken by the retailer, and the perception of customers towards the organized retail sector. This investigation uses both offline and online retail sales datasets. Particularly, in the case of the offline retail sale dataset, an energy‐efficient analysis is conducted to collect scalable, accurate, and real‐time data. The collected data is used in enhancing several aspects of e‐commerce operations, including customer experience optimization and inventory management, which results in better decision‐making with increased efficiency. After data collection, the outliers are eliminated by implementing the z‐score technique. The removal of outliers increases the variability of collected data which reduces statistical power. The statistically sufficient data are given to the Randomized Grasshopper Optimization Algorithm (RGOA) for optimal instance selection. Finally, the selected optimal instances are given to the deep neural network (DNN) model for future customer behaviour prediction. The efficacy of the RGOA‐DNN model is analysed by using evaluation measures like mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean percentage error (MPE) and mean absolute percentage error (MAPE). The numerical analysis states that the RGOA‐DNN model obtained a minimal error rate with MSE of 0.09 and 0.10, RMSE of 0.13 and 0.14, MAE of 0.12 and 0.16, MPE of 0.10 and 0.11 and MAPE of 0.08 and 0.10 on the offline and online retail sales datasets. The RGOA‐DNN model has a minimal error rate in future customer behaviour prediction related to the conventional regression models. Furthermore, the elimination of inactive instances or selection of optimal instances reduces the model complexity to linear and computational time to 22.10 and 23.12 s on the offline and online retail sales datasets. [ABSTRACT FROM AUTHOR]

Abstract Background The majority of studies that explore changes in musculature following resistance training interventions or examine atrophy due to immobilization or sarcopenia use ultrasound imaging. While most studies assume acceptable to excellent reliability, there seems to be unawareness of the existing absolute measurement errors. As early as 1998, methodological research addressed a collective unawareness of the random measurement error and its practical indications. Referring to available methodological approaches, within this work, we point out the limited value of focusing on relative, correlation-based reliability indices for the interpretability in scientific research but also for clinical application by assessing 1,512 muscle thickness values from more than 400 ultrasound images. To account for intra- and inter-day repeatability, data were collected on two consecutive days within four testing sessions. Commonly-stated reliability values (ICC, CV, SEM and MDC) were calculated, while evidence-based agreement analyses were applied to provide the accompanied systematic and random measurement error. Results While ICCs in the range of 0.832 to 0.998 are in accordance with the available literature, the mean absolute percentage error ranges from 1.34 to 20.38% and the mean systematic bias from 0.78 to 4.01 mm (all p ≤ 0.013), depending on the measurement time points chosen for data processing. Conclusions In accordance with prior literature, a more cautious interpretation of relative reliability values should be based on included systematic and random absolute measurement scattering. Lastly, this paper discusses the rationale for including different measurement error statistics when determining the validity of pre-post changes, thus, accounting for the certainty of evidence.

Background: The majority of studies that explore changes in musculature following resistance training interventions or examine atrophy due to immobilization or sarcopenia use ultrasound imaging. While most studies assume acceptable to excellent reliability, there seems to be unawareness of the existing absolute measurement errors. As early as 1998, methodological research addressed a collective unawareness of the random measurement error and its practical indications. Referring to available methodological approaches, within this work, we point out the limited value of focusing on relative, correlation-based reliability indices for the interpretability in scientific research but also for clinical application by assessing 1,512 muscle thickness values from more than 400 ultrasound images. To account for intra- and inter-day repeatability, data were collected on two consecutive days within four testing sessions. Commonly-stated reliability values (ICC, CV, SEM and MDC) were calculated, while evidence-based agreement analyses were applied to provide the accompanied systematic and random measurement error. Results: While ICCs in the range of 0.832 to 0.998 are in accordance with the available literature, the mean absolute percentage error ranges from 1.34 to 20.38% and the mean systematic bias from 0.78 to 4.01 mm (all p ≤ 0.013), depending on the measurement time points chosen for data processing. Conclusions: In accordance with prior literature, a more cautious interpretation of relative reliability values should be based on included systematic and random absolute measurement scattering. Lastly, this paper discusses the rationale for including different measurement error statistics when determining the validity of pre-post changes, thus, accounting for the certainty of evidence. Key Points: • While reliability of a testing protocol is most often determined via relative reliability indices such as the intraclass correlation coefficient, further reliability values such as the systematic bias and the random error have been described as valuable for results interpretation. • This study used the most frequently employed procedure to determine muscle hypertrophy (ultrasound) as an example linking relative reliability values to absolute measurement errors under special consideration of appropriate calculation models using three scenarios (best case, worst case, stability). • Overall, 504 ultrasound images were examined showing excellent relative reliability, but the corresponding measurement errors suggest that caution must be exercised when interpreting pre-post settings in cases where the measurement error exceeds the expected changes. [ABSTRACT FROM AUTHOR]

The stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investors. [ABSTRACT FROM AUTHOR]

Stock market prediction is a vital task with high attention for gaining attractive profits with proper decisions to invest. Predicting the stock market is becoming a major challenge nowadays due to chaotic data, non-stationary data, and blaring data. Hence, it's challenging for investors to invest money to make profits. Many techniques are developed to predict stock market trends, but each differs based on time and year. In this paper, hybridised optimisation algorithm, namely the proposed Gannet Ladybug Beetle Optimisation (GLBO), is used for training Transfer Learning (TL), which considers Convolutional Neural Network-enabled Long Short-Term Memory (CNN-enabled LSTM). This TL is responsible for predicting the stock market from augmented data. Here, the stock market dataset of two companies is used as input time series data. Moreover, many features are extracted from those input data and then from those features, necessary features are selected based on Motyka similarity. The bootstrap method is used in this paper for data augmentation. Also, GLBO is hybridised with Gannet Optimisation Algorithm (GOA) along Ladybug Beetle Optimisation (LBO) algorithm. Furthermore, the proposed model is verified for its performance capability based on three metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), as well as Mean Absolute Percentage Error (MAPE) with values of 5.8%, 24.1%, and 78.8%. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of the United Arab Emirates (UAE) electrical sector, accurately predicting long-term trends is paramount for sustainable energy planning. Owing to the consistently elevated temperatures experienced in the Gulf region during the summer months, there is a notable surge in demand for electricity in the UAE, leading to an increased energy consumption per capita. This heightened demand during the summer renders the power system particularly susceptible, posing a substantial risk of power outages, production shortfalls, and a subsequent escalation. This study delves into the realm of forecasting using Non-Homogeneous Discrete Grey Modeling (NDGM), a cutting-edge approach tailored to the unique challenges of the UAE's electrical domain. By leveraging NDGM, this research aims to provide a robust framework for anticipating electrical trends over an extended period. Precise load demand forecasting would impact energy-generating capacity scheduling and power grid management. The results promise valuable insights for policymakers, industry stakeholders, and energy planners, facilitating informed decision-making to meet the ever-evolving demands of the UAE's power sector. The findings of this study were unique in that it avoids increasing generation capacity in mid- and long-term plans, which will assist in avoiding load shedding and meeting energy demands in various sectors. [ABSTRACT FROM AUTHOR]

The stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investors.

Abstract In response to the growing demand for fast-charging electric vehicles (EVs), this study presents a novel hybrid multimodule DC–DC converter based on the dual-active bridge (DAB) topology. The converter comprises eight modules divided into two groups: four Insulated-Gate Bipolar Transistor (IGBT) modules and four Metal–Semiconductor Field-Effect Transistor (MESFET) modules. The former handles high power with a low switching frequency, while the latter caters to lower power with a high switching frequency. This configuration leverages the strengths of both types of semiconductors, enhancing the converter’s power efficiency and density. To investigate the converter’s performance, a small-signal model is developed, alongside a control strategy to ensure uniform power sharing among the modules. The model is evaluated through simulation using MATLAB, which confirms the uniformity of the charging current provided to EV batteries. The results show an impressive power efficiency of 99.25% and a power density of 10.99 kW/L, achieved through the utilization of fast-switching MESFETs and the DAB topology. This research suggests that the hybrid multimodule DC–DC converter is a promising solution for fast-charging EVs, providing high efficiency, power density, and switching speed. Future studies could explore the incorporation of advanced wide bandgap devices to handle even larger power fractions.

In this paper, we propose a forecasting system of sunshine radiation for planners to quickly and accurately predict the output of solar power. The field data, including observation time, temperature, relational humidity, wind speed and global radiation, were collected, and the data clusters were embedded in the Excel Database. To improve the computational performance, the data selection technique was used in the stage of data cleaning, data integration and data reduction. Using the Integration of the Radial Basis Function Network (RBFN) and Sliding Mode Control (SMC), a Sliding Mode Radial Basis Function Network (SMRBFN) was proposed to solve this forecasting problem. Since the Sliding Mode Control has the design's sense of optimal parameters, three parameters in the SMRBFN were dynamically adjusted to promote the accurate and reliability of forecasting system. Linking the SMRBFN and Excel database, the learning stage and testing stage of SMRBFN retrieved the input data from Excel Database to perform and analyze the forecasting system. The proposed algorithm was tested on Kaohsiung district in summer and winter. The average prediction error of MAPE and RMSE obtained from the forecasting results are about 9% and 0.223, respectively. It can be proved that SMRBFN can efficiently forecast the sunshine radiation and accurately provide the output of solar power in an uncertainty environment.

In response to the growing demand for fast-charging electric vehicles (EVs), this study presents a novel hybrid multimodule DC–DC converter based on the dual-active bridge (DAB) topology. The converter comprises eight modules divided into two groups: four Insulated-Gate Bipolar Transistor (IGBT) modules and four Metal–Semiconductor Field-Effect Transistor (MESFET) modules. The former handles high power with a low switching frequency, while the latter caters to lower power with a high switching frequency. This configuration leverages the strengths of both types of semiconductors, enhancing the converter's power efficiency and density. To investigate the converter's performance, a small-signal model is developed, alongside a control strategy to ensure uniform power sharing among the modules. The model is evaluated through simulation using MATLAB, which confirms the uniformity of the charging current provided to EV batteries. The results show an impressive power efficiency of 99.25% and a power density of 10.99 kW/L, achieved through the utilization of fast-switching MESFETs and the DAB topology. This research suggests that the hybrid multimodule DC–DC converter is a promising solution for fast-charging EVs, providing high efficiency, power density, and switching speed. Future studies could explore the incorporation of advanced wide bandgap devices to handle even larger power fractions. [ABSTRACT FROM AUTHOR]

In today's world the cryptocurrencies have taken a special place in the financial market with the aid of time series forecasting and with a market value of almost USD 1.5 Trillion as of August 2021. These financial commodities show more volatility and effect of different internal and external forces. The prediction of such cryptocurrencies is a time consuming and difficult affair. In this paper, an attempt is made with a multilayer Gated Recurrent Unit based model for price prediction of cryptocurrencies. The currencies considered in this work are Bitcoin, Ethereum and Dogecoin and are pre-processed to deal with the NaN values. The LSTM model, one variant of recurrent neural network is used initially for predication. Further, the Gated Recurrent Unit is used with single feature. However, it is observed that for multiple features with three layers of Gated Recurrent Units based model is working well with error minimization. The performance of the proposed model is compared with other two models over a 21-day forecasting window. The proposed model is found to provide better performance in terms of different parameters like mean square error, root mean square error, mean absolute error, mean absolute percentage error, p-value, and precision values than the other two models. [ABSTRACT FROM AUTHOR]

This article aims to propose short-term forecasting for a combined model of the hourly electricity price and the hourly load. Where both the independent system operators and the market participants must deal with the uncertainty and volatility of the day-ahead electricity price. Therefore, an accurate prediction model has been needed for a combined short-term forecasting model for both the hourly electricity price and the hourly load in the deregulated market. To get a better prediction, real-time data for the day-ahead electricity price and the hourly load are provided from the ISO New England Control Area (ISO-NE-CA) market. This study presents the combined model based on four featured machine and deep learning algorithms: Feed Forward Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, Long Short-Term Memory, and Gated Recurrent Units. The results are obtained from two scenarios, In the first scenario, the data has been trained and tested without depending on other factors such as temperatures and calendar. In the second scenario, the data has been trained and tested depending on the previously mentioned factors. The obtained results from the four algorithms have been compared to show their performance and their efficiency in the values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE). The four featured algorithms prove their good performance with less error in the forecasting model.

The SARS-CoV-2 virus was first detected in December 2019, which prompted many researchers to investigate how the virus spreads. SARS-CoV-2 is mainly transmitted through respiratory droplets. Symptoms of the SARS-CoV-2 virus appear after an incubation period. Moreover, the asymptomatic infected individuals unknowingly spread the virus. Detecting infected people requires daily tests and contact tracing, which are expensive. The early detection of infectious diseases, including COVID-19, can be achieved with wastewater-based epidemiology, which is timely and cost-effective. In this study, we collected wastewater samples from wastewater treatment plants in several cities in North Dakota and then extracted viral RNA copies. We used log-RNA copies in the model to predict the number of infected cases using Quantile Regression (QR) and K-Nearest Neighbor (KNN) Regression. The model's performance was evaluated by comparing the Mean Absolute Percentage Error (MAPE). The QR model performs well in cities where the population is >10000. In addition, the model predictions were compared with the basic Susceptible-Infected-Recovered (SIR) model which is the golden standard model for infectious diseases.

In this paper, a new machine learning (ML) technique is proposed that uses the fine-tuned version of support vector regression for stock forecasting of time series data. Grid search technique is applied over training dataset to select the best kernel function and to optimize its parameters. The optimized parameters are validated through validation dataset. Thus, the tuning of this parameters to their optimized value not only increases model's overall accuracy but also requires less time and memory. Further, this also minimizes the model from being data overfitted. The proposed method is used to analysis different performance parameters of stock market like up-to-daily and up-to-monthly return, cumulative monthly return, its volatility nature and the risk associated with it. Eight different large-sized datasets are chosen from different domain, and stock is predicted for each case by using the proposed method. A comparison is carried out among the proposed method and some similar methods of same interest in terms of computed root mean square error and the mean absolute percentage error. The comparison reveals the proposed method to be more accurate in predicting the stocks for the chosen datasets. Further, the proposed method requires much less time than its counterpart methods. [ABSTRACT FROM AUTHOR]

Micro, small and medium enterprises (UMKM) is one of the important aspects to support the improvement of the economy in Indonesia. Zee Mart's business is one of the UMKM shop in Pematang Siantar City with sales and purchase transaction activities for supplies. The purpose of this study is to predict the sales of Zee Mart store goods in the coming month using the adaptive response rate single exponential smoothing (ARRSES) method. ARRSES is a method with the advantage of having two parameters, alpha and beta, where alpha will change every period when the data pattern changes. The dataset obtained will be pre-processed through data selection, cleaning, and transformation. The best beta is determined based on the level of accuracy calculated using the mean absolute percentage error (MAPE). Model development using the ARRSES method will produce forecasting percentages and errors for each product using MAPE. The number of sales data is 23,092 before preprocessing and 23,021 after pre-processing, with the total quantity of goods sold being 149,764 of 1,492 products. The results obtained using sales data 23,021 show the lowest MAPE value of 9.85 at the best beta of 0.6 with the highest accuracy of 90.15% and the model is implemented into a web interface. [ABSTRACT FROM AUTHOR]

The time series is considered as one of the most important tools to investigate the microorganisms infection and interpretation their behavior through a specific period of time. This research aims to determine the most efficient statistical model by applying the Box and Jenkins methodology to predict the number of people recovering from the Corona virus in the State of Libya, for developing plans, measures and necessary precautions. The proposed model was compared with several different models using the indicators of the average absolute error and the average relative absolute error. It was found, that the optimal model is a model (0,1, 1), due to passing the stage of examination and diagnosis, and accordingly, this model could be used to predict the independent values for the period (2/11/2022) until (4/11/2002(. [ABSTRACT FROM AUTHOR]

The availability of electrical energy is an important issue. Along with the growth of the human population, electrical energy also increases. This study addresses problems in the operation of the electric power system. One of the problems that occur is the power imbalance due to scale growth between demand and generation. Alternative countermeasures that can be done are to prepare for the possibility that will occur in the future or what we are familiar with forecasting. Forecasting using the multiple linear regression method with this research variable assumes the household sector, business, industry, and public sectors, and is considered by the influence of population, gross regional domestic product, and District Minimum Wage. In forecasting, it is necessary to evaluate the accuracy using mean absolute percentage error (MAPE). MAPE evaluation results show a value of 0.142 % in the household sector, 0.085 % in the business sector, 1.983 % in the industrial sector, and 0.131 % in the total customer sector.

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature. [ABSTRACT FROM AUTHOR]

Residential load forecasting plays an important role in management and planning in modern smart grids. In planning to keep demand and supply balanced, accurate residential load forecasting is needed. This paper presents a mid-term residential load forecasting method based on feature selection for solving the linear regression problem. Utilization of neighborhood component analysis Method is proposed in this paper. For this purpose, an optimization problem is designed and the problem is solved using LBFGS algorithm. The AMPds2 dataset is used to implement the proposed method and the results were compared with the results of the other six forecasting methods. Comparisons were made by means of mean squared error, root mean squared error, and mean absolute percentage error. The simulation results confirm the effectiveness of the proposed method for accurate residential load forecasting.

Aiming at minimizing the ratio of error with respect to the response variable, the least squares ratio is a relatively new method for estimating the regression parameters. In the current article, the performance of this new approach is compared with the traditional OLS approach in the case when homoscedasticity of errors assumption is violated. A comparison is made through a simulation study using mean square error, mean absolute percentage error, and false acceptance rate as performance measures. It is observed that the least square ratio method outperforms the OLS method in case of moderate or severe heteroscedasticity for all sample sizes and in case of weak or mild heteroscedasticity for relatively small samples. Generally, it is noted that the efficiency of the least squares ratio method increases with an increase in the severity of heteroscedasticity as well as an increase in values of common error variance. The use of the least squares ratio method is recommended in case of mild or moderate to severe heteroscedasticity. Similar results were obtained from two real-life examples. [ABSTRACT FROM AUTHOR]

Starch-containing foods such as bread, pastries, and cakes are usually baked at a moderately high temperature in an oven. When these products are later exposed to room temperature, the associated gelatinized starch begins to harden which causes retrogradation and molecular realignment. Due to this circumstance, manufacturers need to have a fairly accurate estimate of products demand in order to determine the precise amount of baking powder and additives for use in their production so as not to incur losses in their business arising from the stale and consequentially unsalable products. This research was therefore focused on selecting the best forecasting model using a prominent confectionery firm in Abeokuta, Ogun State, Nigeria as a case study. The study was based on 24-week operational period sales data collected from the company. The moving average model and the exponential smoothing model were the two forecasting models considered in this research. The data obtained was thoroughly reviewed and the results of the forecasting models were compared. The most effective model was the exponential smoothing model as it produced the lowest mean absolute percentage error on the average of 3.7347 for the cumulative days of sales under review as against the 15.1713 for the moving average model. However, the exponential smoothing model was considered the best forecasting model for minimizing forecasting error in this study. [ABSTRACT FROM AUTHOR]

Prediction of the infectious disease is a potential research area from the decades. With the progress in medical science, early anticipation of the disease spread becomes more meaningful when the resources are limited. Also spread prediction with limited data pose a deadly challenge to the practitioners. Hence, the paper presents a case study of the Corona virus (COVID-19). COVID-19 has hit the major parts of the world and implications of this virus, is life threatening. Research community has contributed significantly to understand the spread of virus with time, along with meteorological conditions and other parameters. Several forecasting techniques have already been deployed for this. Considering the fact, the paper presents a proposal of two Rolling horizon based Cubic Grey Models (RCGMs). First, the mathematical details of Cubic Polynomial based simple grey model is presented than two models based on time series rolling are proposed. The models are developed with the time series data of different locations, considering diverse overlap period and rolling values. It is observed that the proposed models yield satisfactory results as compared with the conventional and advanced grey models. The comparison of the performance has been carried out with calculation of standard error indices. At the end, some recommendations are also framed for the authorities, that can be helpful for decision making in tough time. [ABSTRACT FROM AUTHOR]

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Stock prices tend to show trends or seasonality or have random walk movements. Time series statistical models developed over time aid prediction of stock prices to assist informed decision-making for investors. These models provide quantitative information to financial specialists at the time of placing their buy-sell orders. The paper compares the movement of two univariate time series using two forecasting models--exponential smoothing and autoregressive integrated moving average (ARIMA) (p; d; q). We predict stock prices of selected 15 companies across three sectors (banking, pharmaceuticals, and Information technology) from NIFTY 50 data for the period April 01, 2016 to March 31, 2021. All these 15 companies are representative constituents of the three sectors within the Nifty 50 index. Performances of models were assessed through forecasting error measures such as root mean square error and mean absolute percentage error. Performances of both models were identical for nine stocks. Prediction based on ARIMA was more accurate for six stocks, whereas exponential smoothing model was a better indicator of stock prices in the case of one stock. However, the differences in error measures of the both the models are marginal, and parsimony principle may drive the choice of model. [ABSTRACT FROM AUTHOR]

Dimethyl carbonate (DMC) has emerged as a promising candidate for sustainable chemical processes due to its remarkable versatility and low toxicity. From a green chemistry perspective, the direct synthesis of DMC has been considered the most promising route, as water is the only byproduct generated in the reaction between CO2 and methanol. However, this synthetic route has faced significant thermodynamic limitations, even at elevated pressure conditions. Therefore, a two-part study explored low-pressure synthesis of DMC via the direct route, and a low-pressure kinetic model for the CeO2 catalyst was developed based on the results. Proposed Langmuir–Hinshelwood mechanisms were verified using experimental data generated in our labs. The investigation suggests that DMC formation in the direct synthetic route is a surface reaction of CO2 and methanol on the catalyst. The kinetic model predictions closely aligned with experimental data, demonstrating a 17% mean absolute percentage error and indicating a high level of predictability. Additionally, a rigorous assessment was conducted on CO2 fixations in DMC synthesis, quantifying CO2 capture and its conversion into stable or high-value products, formally designated as CO2 Fixation (CO2Fix). The CO2Fix analysis revealed that, at a conversion rate of 27%, the process can achieve a "net zero" state when operated at an approximate pressure of 30 bar, thereby supporting the viability of low-pressure synthesis. Increasing the conversion rate to levels exceeding 95% significantly enhances the CO2Fix metric, potentially surpassing 3.5 or higher. [ABSTRACT FROM AUTHOR]

${}^1\text{H}$ NMR chemical shifts for 30 organic compounds (396 data points) were predicted using four NMR predictor software and compared with the experimental data from SDBS. The NMR predictors involved were MestReNova, ChemDraw, NMRShiftDB and ACD Workbook Suite. Root mean square deviation (RMSD) and mean absolute percentage error (MAPE) were calculated from the data obtained. One-way analysis of variance (ANOVA), Tukey’s honestly significant difference (HSD) and $t$-test were carried out to analyse the statistical significance of the differences between the predictors. The results from the statistical analysis were used to predict chemical shifts of three organic compounds.

The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques among which hybrid methods are most popular. The present technologies of load forecasting and present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages are discussed in this paper. The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are compared and discussed with literature of different models to support the researchers to select the best model for load prediction. This comparison validates the fact that the hybrid forecasting models will provide a more optimal solution.

Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was (26.24%), and (5.5%), and AA was (74%), and (94.5%), for cost and time model, respectively. The researcher concluded that the ANN model has a strong correlation and high accuracy, indicating that these models are characterized by high efficiency and good performance in predicting cost and time.

This paper aimed to compare the multi-layer perceptron as an artificial neural network and the decision tree model for predicting OPEC crude oil production. Machine learning is about designing algorithms that automatically extract valuable information from data, and it has seen many success stories. The accuracy of these two models was assessed using symmetric mean absolute percentage errors, mean absolute scaled errors, and mean absolute percentage errors. Achieved were the OPEC crude oil production's maximum projected figures. The OPEC crude oil output was also represented by certain descriptive scales and graphs; A comparison was made between the results and the earlier results acquired by the others after the study of the association between the variables revealed statistical significance. [ABSTRACT FROM AUTHOR]

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported. [ABSTRACT FROM AUTHOR]

Traditional techniques of the Helmert model are known to be used for the purpose of smoothing the data. Therefore, the special Helmert robust technique is adopted in this research to reach the internal and external reliability and suitability for the data using observed points and corresponding weights, which form the objective of this study. The least-squares method poorly performed in the presence of outliers, in comparison with the use of the robust traditional techniques, to reduce the outliers’ impact. Geodetic reliability consists of two basic components: internal reliability and external reliability, which are critical measures of validating the model. Through the analyses, the internal reliability values represented by the minimum detectable bias become larger, increasing the reliability value and decreasing the probability of error. After comparing the two methods, the best method was chosen based on the final value of the probability of error. The values of the measuring parameters were modified accordingly. This method is essential to determine the reliability of the model and is bound by the effect of observations on the estimated variance of the parameters. Depending on the using various methods to estimate the reliability of the Helmert model, the results indicated that the suggested robust M method is the most accurate. And in this contribution, the minimum detectable bias was used to determine the outliers in the Gauss-Helmert model. The results showed that the robust M method has the ability to detect outliers and reduce their impact by calculating the value of the Mean Absolute Percentage Error. [ABSTRACT FROM AUTHOR]

Residential load forecasting plays an important role in management and planning of modern smart grids. Accurate residential load forecasting is needed in planning to keep demand and supply balanced. This paper presents a mid-term residential load forecasting method based on feature selection to solve the linear regression problem. In this way, for feature selection to perform the regression, the neighborhood component analysis method is used. For this purpose, an optimization problem is designed, and the problem is solved using the LBFGS algorithm. The AMPds2 dataset is used to implement the proposed method, and the results were compared with the results of the other six forecasting methods. Comparisons were made through mean squared error, root mean squared error, and mean absolute percentage error. The simulation results confirm the effectiveness of the proposed method for accurate residential load forecasting. [ABSTRACT FROM AUTHOR]

The objectives of this work are to find the suitable forecasting model and forecasting period of the number of foreign tourists traveling to Thailand. The monthly data is gathered during January 2008 to December 2019 and is divided into two sets. The first set is the data from January 2008 to December 2018 for the modelling by the method of decomposition, Holt–Winter's exponential smoothing method and the Box–Jenkins. The second is the monthly data in 2019 for comparing the performance of the forecasting models via the criteria of the lowest mean absolute percentage error (MAPE) and the root mean square error (RMSE). The results show that, in term of forecasting, the multiplicative decomposition is the most accurate technique for the short-term (3 months) forecasting period with the lowest MAPE and RMSE of 1.04% and 42,054.29 international tourists, respectively. [ABSTRACT FROM AUTHOR]