Performance Assessment Metrics Research Articles

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models.

THE ROLE OF GAMES IN CONTEMPORARY EDUCATION: CHALLENGES FOR THE TEACHERS AND STUDENTS IN THE ERA OF BLENDED LEARNING – CORRELATION BETWEEN GRADES AND GAMES PERFORMANCE

In recent years, there has been a surge in information and communications technology, propelled by the pandemic’s emphasis on remote learning. Interactive technologies like computers, tablets, and mobile phones hold promise for digitizing work and education, fostering creativity and immersion. Educators are increasingly exploring the integration of interactive and game-based tools into learning, inspired by the widespread popularity of gaming across age groups. Immersive game-based platforms, capable of integrating curricula and functioning in multiplayer settings, are emerging as a new trend in online learning, offering enhanced engagement for both students and teachers and improving test results. However, integrating games still presents challenges for teachers and students. Understanding the correlation between students’ grades and their performance in educational games is a significant challenge. While research suggests that game-based learning can positively impact academic achievement, further exploration is needed to grasp the nuanced relationship between in-game performance and traditional assessment metrics. Formative assessments through game-based solutions have gained popularity, but there is a lack of multiplayer classroom tools offering varied gameplay mechanics and content integration for creative learning. Research into the motivational aspects of multiplayer gaming, the impact of different gameplay dynamics on learning outcomes, and a comparison of multiplayer versus traditional approaches are areas for further exploration. Our study introduces a novel multiplayer game-based platform (Multiplayer Team Training Platform or MTT) addressing the challenges of uniform gameplay, offering a variety of gameplay dynamics and automated tracking of student performance in both single and multiplayer modes. A comparison between traditional assessment and game-based assessment in similar settings was undertaken with different classes, subjects, and age groups, and a comparative study was done to analyze the results of both experiments. The investigations showed that the Control Groups consistently outperformed, suggesting potential benefits of traditional teaching methods, especially under teacher’s supervision. However, it was also observed that repeated testing in the control groups did not necessarily result in enhanced retention of knowledge. Conversely, the game-based environment showed gradual improvement with gameplay repetition and reduced concerns about cheating among students. Strong positive correlations between subject grades and performance were found, indicating that higher-graded students excel in both environments, with lower-grade students showing a trend of better and continuous improvement, particularly in the game-based environment.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.

Recent Advances in Multi‐Scale Piezoresistive Interfaces for MXene‐Based Flexible Sensors

AbstractFlexible piezoresistive sensors hold great promise in biomedicine and personal motion detection, attracting significant attention from researchers. Particularly, two‐dimensional (2D) MXene has made remarkable strides in biomedicine, electromagnetic shielding, and other practical applications, due to its exceptional hydrophilicity, biocompatibility, mechanical properties, surface functional groups, and large specific surface area. The ease of composite material formation further makes it ideal for high‐sensitivity flexible sensors. This paper summarizes the principal structural configurations for the multi‐scale interfaces, performance assessment metrics, and the corresponding sensing mechanisms of flexible piezoresistive sensors. Subsequently, the main characteristics and synthesis methods of MXene, especially the related applications and parameters are introduced in detail. The research trends and typical applications of MXene‐based flexible piezoresistive sensors focused on film structures, fiber structures, 3D structures, and hydrogel structures, are highlighted systematically. In addition, the challenges and prospects that promoting the advancement of MXene‐based flexible piezoresistive sensors in future are also discussed, which will revolutionize the sensing field and offer a wealth of opportunities in biomedicine and beyond.

Efficiency Comparison of Floating, Ground, and Rooftop Solar Systems: Environmental and Social Impacts of Floating Solar Grids

The global fossil fuel shortage and environmental concerns underscore the imperative for transitioning to renewables. Developing nations like Sri Lanka are prioritizing green technologies, notably solar energy, to bridge the gap between current usage and its substantial potential. This study compares technical efficiency among floating, ground, and rooftop solar in Sri Lanka. Analyse local perceptions of floating solar. Assess the environmental impact and sustainability of floating solar systems in Sri Lanka. This study compares the energy production efficiency of floating solar, ground solar, and rooftop solar systems in Sri Lanka. This study compares the technical efficiency of floating, ground-mounted, and rooftop solar systems. Kilinochchi, chosen for floating solar, a nearby ground solar site facilitates a direct comparison, and Mass Vidiyal in Kilinochchi represents rooftop solar. The study strategically gathers data from inverters to assess performance metrics. The goal is a representative analysis, minimizing solar irradiance variations for accurate comparisons. Conducting a literature review to analyse the environmental impact of floating solar systems, drawing insights from past published research papers, and A socio-economic survey was conducted among the local community to assess the socioeconomic viability of a proposed floating solar plant near Chandrika Lake. The survey considered factors such as job creation, local employment opportunities, skill development, and alignment with national energy policies. Floating solar systems were found to have the highest energy generation capability and efficiency, followed by rooftop solar systems, while ground-mounted solar systems had lower energy output. The advantages of floating solar systems over ground-mounted systems were significant (43.69% advantage) and outperformed rooftop solar systems (30.81% advantage). According to the survey, stakeholder engagement and mitigation measures were identified as crucial, considering concerns related to water quality, safety, fisheries, agriculture, and the aesthetic value of the lake This study highlights technical efficiency, socioeconomic viability, and environmental impact of solar systems in Sri Lanka, emphasizing responsible deployment. While floating solar exhibits energy efficiency, addressing altered water flow and aquatic habitat concerns through mitigation measures is crucial for sustainable implementation.
 
 Keywords: Solar energy, Floating solar, Environmental impact, Social impact, Renewable energy

Stock Price Prediction based on Time Series Model and Long Short-term Memory Method

This study conducts a comparative analysis of two prominent methodologies, Time Series Analysis and Long Short-Term Memory Neural Networks (LSTM), for the prediction of stock prices, utilizing historical data from Netflix. The primary purpose of conducting this research is to evaluate their efficacy in terms of predictive accuracy. Time Series Analysis encompasses stationarity tests, rolling statistics, and the application of the Autoregressive Integrated Moving Average model. In contrast, LSTM Neural Networks involve data normalization, reshaping, and the development of LSTM-based models. Performance assessment metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and visual comparisons are utilized. The results prominently favor long short-term memory Neural Network, which consistently outperforms in predictive accuracy, yielding reduced forecasting errors. This study contributes significant insights into stock price prediction methodologies and offers implications for refining model parameters, bolstering adaptability to evolving market dynamics, and addressing computational efficiency concerns in both Time Series Analysis and LSTM Neural Networks. In summary, LSTM model emerges as the preferred approach, advancing understanding of effective strategies for stock price prediction in financial markets.

A novel python-based floating offshore wind turbine simulation framework

The expansion of floating offshore wind brings the industry closer to achieving commercial viability. However, the challenging environment characterised by strong winds, waves, and currents, along with the growing size of wind turbines and the dynamic behaviour of floaters, introduces concerns about power production efficiency and system durability due to increased fatigue loads, which subsequently impacts overall costs. In an attempt to mitigate the financial implications coming from alterations made to control strategies and structural elements during the initial design phase, this paper propounds an all-encompassing simulation framework for offshore wind turbines. The current study thoroughly explores the various capabilities of the tool, with a focus on its simulation models. Importantly, the paper highlights the complex interactions between tool models and different controllers. Carefully designed, this tool offers users a variety of functions to enhance system design, fine-tune control strategies, and thoroughly assess performance metrics. The paper elaborates on these aspects, providing an explanation of the tool's capabilities and enhancing the dynamic comparison between the models.

Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

<abstract> <p>Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.</p> </abstract>

An Efficient Systematic Approach for Adaptability Synthesis of IOT's Performance

The Internet of Things (IoT) has profoundly impacted various facets of contemporary society, transforming the ways in which individuals live, work, travel, and conduct business. Given its significance, it becomes imperative to ensure that IoT systems perform as intended and anticipated. This necessitates the availability of a comprehensive set of IoT performance metrics for assessment and management. This research endeavor's primary objective is to methodically catalog and categorize recent explorations into Internet of Things measurements. The writers executed a review of the literature encompassing research findings published from January 2010 until December 2021, guided by five research questions in all. Through this review, 158 in total distinct IoT measurements were unearthed and systematically grouped into 12 distinct groups, each pertaining to different facets and elements of IoT systems. To holistically assess IoT system performance, these twelve categories were carefully arranged in ontology. The outcomes unveiled the network metrics emerged as the most prevalent category of discussion, appearing 43 percent of the analyzed research, and boasting the greatest percentage of metrics, 37%. This research stands as a valuable resource for both researchers and practitioners, offering guidance when choosing the right metrics for Internet of Things systems. Additionally, it provides priceless insights into topics ripe for enhancement and optimization in the realm of IoT performance evaluation.

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.

Climate-active building enclosures

The building energy demand and anthropogenic greenhouse gas emissions have risen since the preindustrial period, reaching the highest levels. Brazil is the eighth largest consumer of primary energy globally, with buildings accounting for 51.2% of the total electric energy consumption. In this sense, the building enclosure has substantial potential and the lowest cost for reducing energy expenditure. The dynamicity of environmental factors allows for many design approaches, and since the user comfort analysis evolved, time-varying building skin configurations emerged. When coupled with computational design, the building's skins no longer must compromise to one stationary condition that is never optimal to any particular condition. These climate-active envelopes need a seemingly conveyed characterization or a straightforward design process as a relatively new technique. We aimed to differentiate climate-active building typologies and gather the latest compositions and performance assessment metrics, rendering an integrative literature review, state-of-the-art, and bibliometric analysis. As the main results, we assembled tabular data on 100 research pieces considering various study methodologies, climate-active typologies, movement categories, actuation styles, simulation engines, and performance criteria, demonstrating that most studies evaluated facade typologies, concerned temperate climates and adopted simple, binary movement characterizations. Furthermore, the design process for active building enclosures needs to be clearer and well-structured, and the available computational tools still need improvement.

Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy.

Assessing the smart city: A review of metrics for performance assessment, risk assessment and construction ability assessment

Assessing the smart city: A review of metrics for performance assessment, risk assessment and construction ability assessment

Utilising random forests in the modelling of Eragrostis curvula presence and absence in an Australian grassland system

Eragrostis curvula is an agronomically and ecologically undesirable perennial tussock grass dispersed across Australia. The objective of this study is to investigate relationships of ecologically relevant abiotic variables with the presence of E. curvula at a landscape scale in the Snowy Monaro region, Australia. Through vegetation surveys across 21 privately owned properties and freely available ancillary data on E. curvula presence, we used seven predictor variables, including Sentinel 2 NDVI reflectance, topography, distance from roads and watercourses and climate, to predict the presence or absence of E. curvula across its invaded range using a random forest (RF) algorithm. Assessment of performance metrics resulted in a pseudo-R squared of 0.96, a kappa of 0.97 and an R squared for out-of-bag samples of 0.67. Temperature had the largest influence on the model’s performance, followed by linear features such as highways and rivers. Highways’ high importance in the model may indicate that the presence or absence of E. curvula is related to the density of human transit, thus as a vector of E. curvula propagule dispersal. Further, humans’ tendency to reside adjacent to rivers may indicate that E. curvula’s presence or absence is related to human density and E. curvula’s potential to spread via water courses.

Experiences of sexual minorities on social media: A study of sentiment analysis and machine learning approaches

<p>Nowadays, social media has become a forum for people to express their views on issues such as sexual orientation, legislation, and taxes. Sexual orientation refers to individuals with whom you are attracted and wish to be engaged. In the world, many people are regarded as having different sexual orientations. People categorized as lesbian, gay, bisexual, transgender, queer, and many more (LGBTQ+) have many sexual orientations. Because of the public stigmatization of LGBTQ+ persons, many turn to social media to express themselves, sometimes anonymously. The present study aims to use natural language processing (NLP) and machine learning (ML) approaches to assess the experiences of LGBTQ+ persons. To train the data, the study used lexicon-based sentiment analysis (SA) and six distinct machine classifiers, including logistic regression (LR), support vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF), and gradient boosting (GB). Individuals are positive about LGBTQ concerns, according to the SA results; yet, prejudice and harsh statements against the LGBTQ people persist in many regions where they live, according to the negative sentiment ratings. Furthermore, using LR, SVM, NB, DT, RF, and GB, the ML classifiers attained considerable accuracy values of 97%, 96%, 88%, 100%, 92%, and 91%, respectively. The performance assessment metrics used obtained significant recall and precision values. This study will assist the government, non-governmental organizations, and rights advocacy groups make educated decisions about LGBTQ+ concerns in order to ensure a sustainable future and peaceful coexistence.</p>

River flow modeling for flood prediction using machine learning techniques in Godavari river, India

Floods are highly impactful natural calamities, inflicting significant damage to infrastructure and causing numerous fatalities. These devastating events occur when rivers exceed their capacity or breach their banks due to intense precipitation.Forecasting river flow in the minimum Godavari river basin of eastern India allowed researchers to examine the potential of four data-driven techniques, including the artificial neural network (ANN), support vector machine (SVM), nonlinear autoregressive network with exogenous input (NARX) and Gaussian process regression (GPR), and compare the outcomes to those of the proposed neuro-tree method. By combining values of the antecedent river flow from two gauging stations, various models were built utilizing the methodologies, and the results were compared to see which models had the best match. The performances of the generated models were examined using mean square error, coefficient of correlation (R), and Nash-Sutcliffe coefficient, three widely used statistical performance assessment metrics (NS).An extensive analysis of the overall performance indicators revealed that the proposed neuro-tree algorithm models were more effective in flood prediction than the other four techniques employed in this study.

Evaluation and selection of CMIP6 GCMs for long-term hydrological projections based on spatial performance assessment metrics across South Korea

Abstract The selection of an appropriate subset of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) for long-term hydrological simulation at the basin scale is a necessity. This study selected high-performing GCMs among 32 available CMIP6 GCMs on the basis of reproducing the observed precipitation over the main watershed in South Korea during the historical period. An integrated selection approach based on four spatial performance assessment metrics was proposed to better estimate changes in precipitation using the simulated GCMs precipitation. Results revealed that the spatial performance over different GCMs could provide an effective means of selecting GCMs for hydrological study over the major river watersheds in South Korea. The four top-ranked GCMs are FIO-ESM-2-0, CESM2-WACCM, CESM2, and CMCC-ESM2, and they overestimated precipitation in South Korea during the historical period, with a bias of 10–25%. However, this study confirmed that a formal bias correction for these GCMs is not recommended prior to model selection, and the ranking of GCMs under the bias correction could be problematic. The proposed approach in this study can be applied to numerous GCMs, climate variables, and other regions to select representative GCMs to reduce the uncertainties in terms of the spatial patterns observed.

Circular Economy in Automotive Manufacturing: Recycling and Sustainability

This paper investigates the integration of Circular Economy principles within the automotive manufacturing sector, with a particular focus on recycling and sustainability practices. Findings indicate a substantial shift towards Circular Economy adoption, with 68% of surveyed automotive companies initiating these practices. These efforts are exemplified by a commendable recycling rate of 45% and the widespread implementation of sustainability metrics (72%) for environmental performance assessment. Moreover, Circular Economy adopters report significant reductions in carbon emissions (average: 27%), improved resource efficiency (average: 34%), and decreased waste generation (average: 23%). These results underscore the industry's commitment to sustainability and emphasize the need for continued adoption, data-driven decision-making, innovation, policy support, and industry collaboration to further advance environmental responsibility and resource efficiency in automotive manufacturing, contributing to a more sustainable and responsible future.

Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications.