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18 articles for “Root Mean Square Error (RMSE)”
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Indoor Localization of Mobile Device Using Fingerprinting Technique
Abstract: AbstractIndoor localization technology is real-time tracking of any device or person in an indoor area via a control device. In this paper, the fingerprinting method is utilized to track mobile devices in a specific indoor area. Fingerprinting, also known as pattern matching or database correlation method (DCM), needs a powerful received signal strength indication (RSSI) database which helps to make signal strength maps as well as used for matching. The …
Published in Recent Trends in Electronics Communication Systems · Vol. 4, Issue 3, 2017 · pp. 1–8 Read article
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Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC
Abstract: Several engineering fields have increased their use of metal matrix composites (MMCs) in the past few years. Due to the increase in composites, the demand for accurate machining has also become important. Specifically, pertaining to biomaterial applications, accuracy factor with desired surface finish is critical. While the near-net shape manufacturing process has advanced, MMCs frequently require post-mould machining to achieve surface quality, and dimensional tolerances. In the present study, a …
Published in Journal of Polymer & Composites · Vol. 11, Issue 1, 2023 · pp. 01–13 Read article
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The Influence of Fresh Raffia Palm Trunk on the Bioremediation of Oil-Based Drill
Abstract: This study determines the effect of fresh raffia palm for biodegradation of oil-based drill cuttings bioremediation. The experiment was conducted at the rivers institute of Agricultural research and training (RIART) located at the Rivers State University, Port Harcourt. Samples of oil-based drill cuttings were bulked in eleven reactors with four replications (T1, T2, T3-T11). The physiochemical properties of the initial drill cuttings were analyzed. Also, the physiochemical properties of the …
Published in International Journal of Pollution: Prevention & Control · Vol. 1, Issue 2, 2023 · pp. 41–48 Read article
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Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA
Abstract: In this study, the liquid metal nanocomposites were investigated using artificial neural network (ANN) prediction capabilities for Compression Ignition (CI) engine performance. The independent input variables selected were load (20-100%), Liquid-metal nanocomposites Doped Rate (NDR, 0-50 ppm), and Biogas Flow Rate (BFR, 0.5-1.0 kg/h). The Central Composite Face-Centered Design (CCFCD) was used in conjunction with the selected input variables and output parameters to assist in the preparation of the Design …
Published in Journal of Polymer & Composites · Vol. 11, Issue 11, 2023 · pp. 12–27 Read article
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The Strength of Dry, Fresh and Decomposed Raffia Palm Trunk in the Bioremediation of Oil-based Drill Cutting
Abstract: In this research, the impact of dry drill cuttings and compost tea on the environment's ability to degrade oil-based drill cutting contamination is investigated. The experiment was conducted in the research center located at the workshop of Agricultural and Environmental Engineering Department, Rivers State University, Port Harcourt. Oil-based drill cutting samples were placed in bulk in eleven reactors (T1, T2, T3-T11) with four replications. The original drill cuttings' physiochemical characteristics …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 15, Issue 3, 2024 · pp. 26–33 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 282–297 Read article
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Application of Compressive Sensing for Sampling and Reconstruction of MRI Images
Abstract: In recent years, a new theory of compressive sensing has evolved which asserts that super resolved signals and images can be recovered with far fewer samples than that demanded by the Nyquist sampling theorem. It is required that the signal being sensed has a low information-rate meaning that it is sparse in original or some transform domain. Former approaches capture the complete signal and process it to extract the information. …
Published in Current Trends in Signal Processing · Vol. 6, Issue 2, 2016 · pp. 42–48 Read article
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ECG De-noising Techniques and Optimal Feature Selection Using Principle Component Analysis
Abstract: AbstractECG (Electrocardiography) is used to record and determine the condition of the heart. This paper provides an overview of various ECG de-noising techniques that are used to eliminate different type of noises; therefore, noise reduction procedure to be performed to eliminate different type of noises such as baseline wander, dc offset and high frequency interference, and then the pre-processed signal is used to extract features from the ECG signal. This …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 5, Issue 1, 2018 · pp. 14–20 Read article
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Rainfall-runoff Modeling using HEC-HMS Hydrologic Model for Guder River Watershed, Blue Nile Basin, Ethiopia
Abstract: Rainfall-runoff modeling is important for a number of hydrologic applications including flood forecasting, water resource planning and management. This study presents the result of a watershed rainfall-runoff modeling for Guder river watershed having area of 6597 km 2 using Hydrologic Engineering Center-Hydrologic Modeling Systems (HEC-HMS). The watershed runoff is varying spatially and temporally due to manmade factors and natural factors on the watershed. These issue needs efficient water resource planning …
Published in Journal of Water Resource Engineering and Management · Vol. 8, Issue 2, 2021 · pp. 62–77 Read article
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Reduction of Ocular Artifacts in Single Channel EEG by EEMD-IMF Thresholding
Abstract: AbstractElectroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to the movements of eye, heart, muscles and power line interference. Among these ocular activities creates significant artifacts and makes the analysis difficult. In this paper, ensemble empirical mode decomposition (EEMD) inspired by wavelet thresholding is used for the correction of ocular artifacts (OA) in EEG signals. Unlike the conventional …
Published in Recent Trends in Electronics Communication Systems · Vol. 5, Issue 1, 2018 · pp. 17–25 Read article
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Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites
Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Physics-Informed Machine Learning and Multiscale Modeling for Structure–Property Quantification of Polymer Composites
Abstract: The growing need for light-weight, high strength, and sustainable polymer composites has led to the development of smart methods that enable accurate structural-property quantification and material design. However, conventional methods have been predominantly data-based, thus ignoring physical constraints as well as multi-scale interactions involving fiber, matrix, interface, and process parameters, leading to lower accuracy and poor robustness and interpretability of the models. In this study, a Cat Swarm Optimization-Tuned Physics-Informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models
Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article