Demand forecasting at retail stage for selected vegetables: a performance analysis

The rapid spread of contagious diseases poses a colossal threat to human existence. Presently, the emergence of coronavirus COVID-19 which has rightly been declared a global pandemic resulting in so many deaths, confusion as well as huge economic losses is a challenge. It has been suggested by the World Health Organization (WHO) in conjunction with different Government authorities of the world and non-governmental organizations, that efforts to curtail the COVID-19 pandemic should rely principally on measures such as social distancing, identification of infected persons, tracing of possible contacts as well as effective isolation of such person(s) for subsequent medical treatment. The aim of this study is to provide a framework for monitoring Movements of Pandemic Disease Patients and predicting their next geographical locations given the recent trend of infected COVID-19 patients absconding from isolation centres as evidenced in the Nigerian case. The methodology for this study, proposes a system architecture incorporating GPS (Global Positioning System) and Assisted-GPS technologies for monitoring the geographical movements of COVID-19 patients and recording of their movement Trajectory Datasets on the assumption that they are assigned with GPS-enabled devices such as smartphones. Accordingly, fifteen (15) participants (patients) were selected for this study based on the criteria of residency and business activity location. The ensuing participants movements generated 157, 218 Trajectory datasets during a period of 3 weeks. With this dataset, mining of the movement trace, Stay Points (hot spots), relationships, and the prediction of the next probable geographical location of a COVID-19 patient was realized by the application of Artificial Intelligence (AI) and Data Mining techniques such as supervised Machine Learning (ML) algorithms (i.e., Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting regression(XGBR) as well as density-based clustering methods (i.e., DBSCAN) for the computation of Stay Points (hot spots) of COVID-19 patient. The result of this study showed clearly that it is possible to determine the Stay Points (hot spots) of a COVID-19 patient. In addition, this study demonstrated the possibility of predicting the next probable geographical location of a COVID-19 patient. Correspondingly, Six Machine Learning models (i.e., MLR, kNN, DTR, RFR, GBR, and XGBR) were compared for efficiency, in determining the next probable location of a COVID-19 patient. The result showed that the DTR model performed better compared to other models (i.e., MLR, kNN, RFR, GBR, XGBR) based on four evaluation matrices (i.e., ACCURACY, MAE, MSE, and R2) used. It is recommended that less developed Countries consider adopting this framework as a policy initiative for implementation at this burgeoning phase of COVID-19 infection and beyond. The same applies to the developed Countries. There is indication that GPS Trajectory dataset and Machine Learning algorithms as applied in this paper, appear to possess the potential of performing optimally in a real-life situation of monitoring a COVID-19 patient. This paper is unique given its ability to predict the next probable location of a COVID-19 patient. In the review of extant literature, prediction of the next probable location of a COVID-19 patient was not in evidence using the same Machine Learning algorithms.

COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series.

This chapter aims to predict the foreign currency exchange rate on the basis of the US dollar over twenty-two different currencies. This chapter proposes two machine learning algorithms like Support Vector Regression (SVR), Random Forest Regression (RFR) and one deep learning algorithm named Long Short-Term Memory (LSTM), for the technical analysis of currency exchange rate prediction. The authors use Mean absolute error (MAE), Mean squared error (MSE), Root Mean Squared Error (RMSE), and mean absolute percentage error (MAPE) to measure the performance of the algorithms. Empirical findings specify that the overall performance of the algorithms is outstanding, but the Long Short-Term Memory (LSTM) shows less error than others. This study is useful for the stakeholders to set a wide range of approaches for the foreign exchange market.KeywordsCurrency exchange rate predictionMachine learningDeep learningTime series analysis

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

Demand forecasting is a critical function in Enterprise Resource Planning (ERP) systems, directly influencing inventory management, production planning, and overall operational efficiency. Traditional statistical models often fall short in handling the complexity and variability of modern supply chains. This study investigates the application of Artificial Intelligence (AI), specifically Machine Learning (ML) algorithms, to enhance demand forecasting accuracy within ERP environments. I conduct a comparative analysis of four widely used ML models: Linear Regression, Random Forest, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. Using real-world ERP datasets, each model is evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational performance. The results reveal that while Random Forest and LSTM models outperform others in terms of accuracy, their complexity and training time vary significantly. My findings highlight the trade-offs between model accuracy and computational efficiency, offering practical insights for ERP stakeholders. This study contributes to the growing field of AI-driven enterprise analytics and provides guidance on selecting appropriate ML techniques tailored to specific forecasting needs within ERP systems.

With the rapid expansion of deep‐sea development activities, the performance requirements for buoyancy materials have become increasingly stringent. Carbon fiber reinforced epoxy resin composites (CFRP) are a leading candidate due to their superior mechanical properties; however, the degradation mechanisms of the epoxy matrix and CFRP under high‐pressure water absorption remain poorly understood. In this study, standard epoxy and CFRP specimens were prepared and subjected to high‐pressure water absorption tests, followed by comprehensive mechanical characterization. Pearson correlation analysis revealed a strong association between water uptake and mechanical degradation. Eight machine learning algorithms—including Ridge Regression, Decision Tree, Support Vector Regression, Random Forest Regressor, Gradient Boosting Regressor, AdaBoost Regressor, XGBoost, and Neural Networks—were employed to construct predictive models; after data preprocessing and hyperparameter optimization, Gradient Boosting and AdaBoost achieved R 2 values above 0.89 and root mean square error (RMSE) below 1 for epoxy strength prediction, while ensemble methods for CFRP all exceeded R 2 of 0.90. Model forecasts indicate that the strength degradation of epoxy and CFRP stabilizes after approximately 36 and 40 days, respectively. These findings provide a theoretical basis for the design, performance optimization, and lifespan prediction of deep‐sea buoyancy materials and introduce a robust machine learning framework for material performance forecasting.

Wind speed forecasting has proven to be crucial for many domains like power generation. However, it is quite convoluted and demanding due to the unpredictability of the wind. Numerous physical and statistical methods have been employed in the past by the researchers in India and over the years, these methods have been improvised. While the physical methods prove to be computationally expensive and the statistical methods represent weaker generalizations of non-linear features, there arises a need to develop much more efficient and robust approaches that can augment with the traditional methodologies. Artificial Intelligence has shown tremendous potential in providing solutions to time-series forecasting tasks, akin to the precise task that this work aims to address. Various machine learning and deep learning algorithms have been found to be a prominent choice for forecasting of various weather parameters. This work aims at implementing two machine learning algorithms, namely, Light Gradient Boosting Machine (LGBM) and Long Short-Term Memory (LSTM) network to obtain short-term forecasts of wind speed of Indian weather stations. Robust feature engineering approaches like Miss Forest to fill in the missing values, a novel feature representation for giving importance to recent data, and Fast Fourier Transform (FFT) with Digital Filters for removal of outliers and noises in the data have been used. Finally, stacking, an ensemble learning has been implemented using LGBM and LSTM as base learners and Random Forest as the metalearner. The average Root Mean Square Error (RMSE) (in m/s to tenths) obtained by individual models is high. 24-hour wind forecast errors of LGBM and LSTM are 8.19 and 21.17, whereas the forecast error using the proposed ensemble framework is 5.74.

This study investigates the use of various machine learning algorithms for predicting rice and sugarcane yields for Navsari district of Gujarat, India. Recognizing the critical role of weather in crop productivity, accurate forecasting becomes essential for effective resource management. In methodology, weekly averages and weighted weather indices were computed based on daily weather data to develop forecast models using machine learning algorithms such as Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), XGBoost (XGB), Gradient Boost Regression (GBR), and Decision Tree (DT). Results show that RF and GBR algorithms outperform others in rice yield forecasting, while Gradient Booster and XGBoost demonstrate high accuracy in sugarcane yield prediction. However, the Mean Absolute Percentage Error (MAPE) values remained above 8%, indicating room for improvement. The study also emphasizes the importance of tuning hyperparameters for each machine learning algorithms (MLA) to achieve the most accurate predictions. Overall, the findings contribute valuable insights for stakeholders, including agricultural planners, policymakers, and researchers, emphasizing the need for continued refinement and validation of models to optimize agricultural planning and decision-making in this region. MLA highlight that features associated with temperature and relative humidity (RH) play a crucial role as the most significant contributors to the forecasting models for both rice and sugarcane yield. Introducing additional features, particularly remote sensing data, holds the potential to decrease the current error range of 8 to 10% to a more favourable and lower value.

Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regression (SVR) and long short-term memory (LSTM) networks. These models are optimized via the K-Nearest Neighbor (KNN) algorithm. Initially, cumulative displacement data were separated into trend and cyclic elements using a smoothing approach. SVR and LSTM were then used to predict the components, and KNN was introduced to optimize input factors and classify the results, improving accuracy. The final KNN-optimized SVR-LSTM model effectively integrates static and dynamic features, addressing limitations of traditional models. The results show that LSTM performs better than SVR, with an RMSE and MAPE of 24.73 mm and 1.87% at monitoring point ZG111, compared to 30.71 mm and 2.15% for SVR. The sequential hybrid model based on KNN-optimized SVR and LSTM achieved the best performance, with an RMSE and MAPE of 23.11 mm and 1.68%, respectively. This integrated model, which combines multiple algorithms, offers improved prediction of landslide displacement and practical value for disaster forecasting in the Three Gorges area.

Accurate rainfall forecasting is crucial for effective urban planning and disaster management in Dhaka, the capital of Bangladesh, a city highly vulnerable to urban flooding and extreme weather events. Traditional forecasting methods often struggle to capture the region's complex rainfall patterns, resulting in inaccurate rainfall forecasts. This study evaluates the performance of two traditional machine learning algorithms, Random Forest Regression and Multi-layer Perceptron (MLP), alongside one deep learning algorithm, the Long Short-Term Memory (LSTM) network. These models are trained and tested to forecast rainfall over 1 to 5-day lead times, emphasizing their ability to handle temporal dependencies in time series data. Atmospheric and hydrologic variables, including temperature, surface pressure, evaporation, solar surface radiation, total column rainwater, large-scale precipitation, and total cloud cover, from the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) dataset, were used as model inputs. Model forecasts were validated against ERA5 rainfall data and compared with the forecasts from the Global Forecast System (GFS) model. Results indicate that the Random Forest model outperforms all others, achieving an RMSE of 6.11 mm and Pearson’s correlation coefficient (R) of 0.74 for a 1-day lead time. The LSTM model achieved an RMSE of 7.46 mm, while the MLP performed less effectively than both RF and LSTM, with an RMSE of 7.61 mm. In comparison, the GFS forecasts displayed an RMSE of 9.16 mm. The RF model outperformed the other models at all lead times; however, its accuracy decreased as the lead time increased. This study highlights the potential of machine learning to improve short to medium range rainfall forecasts, contributing to timely decision-making for urban resilience and resource management.

The load forecasting is the significant task carried out by the electricity providing utility companies for estimating the future electricity load. The proper planning, scheduling, functioning, and maintenance of the power system rely on the accurate forecasting of the electricity load. In this paper, the clustering-based filter feature selection is proposed for assisting the forecasting models in improving the short term load forecasting performance. The Recurrent Neural Network based Long Short Term Memory (LSTM) is developed for forecasting the short term load and compared against Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR) and Random Forest (RF). The performance of the forecasting model is improved by reducing the curse of dimensionality using filter feature selection such as Fast Correlation Based Filter (FCBF), Mutual Information (MI), and RReliefF. The clustering is utilized to group the similar load patterns and eliminate the outliers. The feature selection identifies the relevant features related to the load by taking samples from each cluster. To show the generality, the proposed model is experimented by using two different datasets from European countries. The result shows that the forecasting models with selected features produce better performance especially the LSTM with RReliefF outperformed other models.

In the realm of retail supply chain management, accurate forecasting is paramount for informed decision making, as it directly impacts business operations and profitability. This study delves into the application of tree-based ensemble forecasting, specifically using extra tree Regressors (ETRs) and long short-term memory (LSTM) networks. Utilizing over six years of historical demand data from a prominent retail entity, the dataset encompasses daily demand metrics for more than 330 products, totaling 5.2 million records. Additionally, external variables, such as meteorological and COVID-19-related data, are integrated into the analysis. Our evaluation, spanning three perishable product categories, reveals that the ETR model outperforms LSTM in metrics including MAPE, MAE, RMSE, and R2. This disparity in performance is particularly pronounced for fresh meat products, whereas it is marginal for fruit products. These ETR results were evaluated alongside three other tree-based ensemble methods, namely XGBoost, Random Forest Regression (RFR), and Gradient Boosting Regression (GBR). The comparable performance across these four tree-based ensemble techniques serves to reinforce their comparative analysis with LSTM-based deep learning models. Our findings pave the way for future studies to assess the comparative efficacy of tree-based ensembles and deep learning techniques across varying forecasting horizons, such as short-, medium-, and long-term predictions.

Due to the rapid adoption of renewable energy sources and the increasing complexity of residential electricity consumption, there is a growing need for intelligent, scalable, and context-aware energy management systems in smart homes. This paper presents an artificial intelligence (AI)-based model that integrates high-resolution household power forecasting with dynamic demand response (DR) simulations to optimize energy consumption, enhance grid stability, and support sustainable energy transitions. Using the publicly available UCI Smart Home Energy dataset, a seven-step framework was implemented, including data preprocessing, exploratory data analysis (EDA), feature engineering, and the development of various predictive models—linear regression (LR), random forest regressor (RFR), support vector regression (SVR), k-nearest neighbors (k-NN), and long short-term memory (LSTM) neural networks. Each model was evaluated under seven DR strategies to capture temporal dependencies and nonlinear load behaviors: Peak Clipping, Valley Filling, Load Shifting, Load Leveling, Time-of-Use (ToU) Optimization, Price-Based Control, and Behavioral DR. Model performance was assessed using the mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination ( R ²). Results revealed that the LSTM model achieved the highest accuracy, with the lowest MAE (18.95 Wh), lowest RMSE (24.83 Wh), and highest R ² (0.94) under the Price-Based DR strategy. Random forest emerged as the most effective traditional model, yielding an MAE of 25.13 Wh and R ² of 0.89, particularly for Behavioral and ToU-based DR strategies. The SVR and k-NN models provided moderate accuracy, while LR performed poorly, underscoring its limitation in modeling nonlinear and dynamic energy patterns. DR simulations indicated that Price-Based, Behavioral, and ToU Optimization strategies achieved the best alignment between household loads and grid requirements while maintaining user flexibility. Visualization tools such as heatmaps, grouped bar plots, and R ² comparisons further confirmed the superior temporal modeling capability of deep learning (DL) methods. Overall, the proposed framework offers a scalable and interpretable AI-driven platform that integrates load forecasting with DR evaluation, demonstrating that DL—particularly LSTM—can substantially enhance forecasting accuracy, enable smarter DR programs, and promote sustainable energy management in smart homes.

Gradient boosted regression model for the degradation analysis of prismatic cells

This work evaluates machine learning (ML) and deep learning (DL) models for Photovoltaic (PV) power output prediction based on two real-world datasets. Eight models – Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Decision Tree (DT), one Dimensional Convolutional Neural Network (1DCNN) and Artificial Neural Network (ANN) – were evaluated using Root Mean Squared Error (RMSE), R-squared (R²), Mean Absolute Error (MAE), and Mean Squared Error (MSE). For Dataset1 (region of Cacak) 1DCNN achieved the optimal performance, with the reduced errors and the largest R² values at three spatial locations, and it was closely followed by RFR and GBR. Likewise, in Dataset2 (region of Kraljevo), 1DCNN, ANN, and RFR give the best results. Conventional models, such as LR and DT, performed poorly in both data tribes. The results highlight the ability of state-of-the-art ensemble and DL models in learning nonlinear patterns in solar energy data and thus, the importance of choosing the right prediction tools to provide accurate and reliable PV power forecasts.