Mean Square Error Research Articles

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes is one of the key factors that is interlinked with a plethora of diseases. Health management can be achieved through balanced diet, physical exercise, and periodic examination of blood glucose level and hematocrit volume. Our study developed a model to estimate the hematocrit volume (red blood cells) from the correlation of the glucose concentration obtained from a glucometer by employing machine learning techniques. This Article explores the prediction of hematocrit volume in whole blood by applying various machine learning (ML) models such as linear regression (LR), support vector regressor (SVR), decision tree (DT), random forest regressor (RFR), artificial neural network (ANN), and extreme gradient boosting regressor model (XGBoost). We used amperometric signals generated from an electrochemical glucose sensor or glucose strip, which produces current values on glucose concentration. We estimated the hematocrit volume via processing of the amperometric signals to enhance diagnostic capabilities with the least error in the field of biomedical signal processing. The ML models were trained on the data set comprising 80% training set and 20% testing set in the Python programming language. The models were evaluated based on the metrics such as R-squared (R2), mean squared error (MSE), and root mean squared error (RMSE) values, and their reliability was assessed through the three validation mechanisms, namely, the relative error, K-fold cross-validation, and analysis of confidence interval. We observed that the XGBoost regression results were comparatively better than the LR and ANN results as corroborated through reliability analysis. It was concluded that XGBoost demonstrated 15% relative error between actual and predicted data and 68% accuracy with 6% standard deviation in the prediction obtained via a 5-fold cross-validation technique. The XGBoost model demonstrates comparatively better performance in terms of flexibility in tuning and interpretability options, which make it suitable for the regression task in the predictive biomedical analytics.

A method for reconstruction of structural health monitoring data using WGANGP with U-net generator

Data loss issue often occurs in structural health monitoring (SHM), which can undermine the reliability of structural condition diagnosis and prognosis. Neural networks are commonly employed for reconstruction of missing SHM data by modeling the correlation between intact and missing signals. The existing neural network methods use deeper architectures to capture complex data correlations. As network depth increases, the ability to preserve both low- and high-level features diminishes, and network training becomes challenging, which reduces data reconstruction accuracy. This study proposes a data reconstruction method that utilizes a Wasserstein generative adversarial network containing a gradient penalty term with a U-net generator. Multiple improvements are made to the generative adversarial network to enhance the reconstruction performance. First, the U-net is used as a generator, and the signal features at low and high levels are preserved based on the skip-connection technique. The U-net is pre-set with multiple layers to enhance the reconstruction accuracy. Second, a mean square error (MSE) term is added to the loss function. The MSE coefficient is proposed to balance adversarial training and reconstruction feature learning. Third, the Wasserstein distance is introduced to replace the cross-entropy loss function of traditional generative adversarial networks, avoiding gradient vanishing and exploding. The reconstruction performance of the proposed method is evaluated on a computational bridge model and Canton Tower, and the influence of reconstruction parameters on the accuracy of reconstruction results is discussed in detail. The reconstructed data by the proposed method closely matches the original data in both the time domain and frequency domain. The time–frequency characteristics of the acceleration data can be accurately reconstructed, demonstrating the effectiveness of the reconstructed signals in data analysis. By comparing with typical neural network methods, it is found that the proposed method has higher reconstruction accuracy.

Weighted past and paired dynamic varentropy measures, their properties, usefulness and inferences

We introduce two uncertainty measures, say weighted past varentropy (WPVE) and weighted paired dynamic varentropy (WPDVE). Several properties of these proposed measures, including their effect under the monotone transformations are studied. An upper bound of the WPVE using the weighted past Shannon entropy and a lower bound of the WPVE are obtained. Further, the WPVE is studied for the proportional reversed hazard rate (PRHR) models. Upper and lower bounds of the WPDVE are derived. In addition, the non-parametric kernel estimates of the WPVE and WPDVE are proposed. Furthermore, the maximum likelihood estimation technique is employed to estimate WPVE and WPDVE for an exponential population. A numerical simulation is provided to observe the behaviour of the proposed estimates. A real data set is analysed, and then the estimated values of WPVE are obtained. Based on the bootstrap samples generated from the real data set, the performance of the non-parametric and parametric estimators of the WPVE and WPDVE is compared in terms of the absolute bias and mean squared error (MSE). Finally, we have reported an application of WPVE. Abbreviations RV: Random variable; PDF: Probability density function; IC: Information content; SE: Shannon entropy; VE: Varentropy; MSE: Mean squared error; RVE: Residual varentropy; CDF: Cumulative distribution function; PVE: Past varentropy; WVE: Weighted varentropy; WPSE: Weighted past Shannon entropy; WRVE: Weighted residual varentropy; WPVE: Weighted past varentropy; WPRE: Weighted past Rényi entropy; PRHR: Proportional reversed hazard rate; WPDE: Weighted paired dynamic entropy; WPDVE: Weighted paired dynamic varentropy entropy; AB: Absolute bias; CRHR: Cumulative reversed hazard rate; VPL: Variance past lifetime; MPL: Mean past lifetime; MRL: Mean residual lifetime; VRL: Variance residual lifetime; GMB-II: Gumbel-II; GXE: Generalised X-exponential; EXP: Exponential; lnL: Log-likelihood criterion; AIC: Akaikes information criterion; BIC: Bayesian information criterion; MLE: Maximum likelihood estimate

Prediction of mechanical characteristics of shearer intelligent cables under bending conditions.

The frequent bending of shearer cables during operation often leads to mechanical fatigue, posing risks to equipment safety. Accurately predicting the mechanical properties of these cables under bending conditions is crucial for improving the reliability and service life of shearers. This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. This method leverages TCN's causal and dilated convolution operations to capture long-term sequential features, BiLSTM's bidirectional information processing to ensure the completeness of sequence information, and the SEAttention mechanism to assign adaptive weights to features, effectively enhancing the focus on key features. The model's performance is validated through comparisons with multiple other models, and the contributions of input features to the model's predictions are quantified using Shapley Additive Explanations (SHAP). By learning the stress variation patterns between the optical fiber, power conductor, and control conductor in the shearer cable, the model enables accurate prediction of the stress in other cable conductors based on optical fiber stress data. Experiments were conducted using a shearer optical fiber cable bending simulation dataset with traction speeds of 6 m/min, 8 m/min, and 10 m/min. The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. The maximum deviation between predicted and actual values is only 0.86%, demonstrating outstanding prediction accuracy. SHAP feature analysis reveals that the control conductor features have the most substantial influence on predictions, with a SHAP value of 0.095. The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.

Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach.

Computed tomography attenuation correction (CTAC) is commonly used in cardiac SPECT imaging to reduce soft-tissue attenuation artifacts. However, CTAC is prone to inaccuracies due to CT artifacts and SPECT-CT mismatch, along with additional radiation exposure to patients. Thus, these limitations have led to increasing interest in CT-free AC, with deep learning (DL) offering promising solutions. We proposed a new DL-based CT-free AC methods for cardiac SPECT. We developed a feature alignment attenuation correction network (FA-ACNet) based on the 3D U-Net framework to generate predicted DL-based AC SPECT (Deep AC). The network was trained on 167 cardiac SPECT/CT studies using 5-fold cross validation and tested in an independent testing set (n = 35), with CTAC serving as the reference. During training, multi-scale features from non-attenuation-corrected (NAC) SPECT and CT were processed separately and then aligned with the encoded features from NAC SPECT using adversarial learning and distance metric learning techniques. The performance of FA-ACNet was evaluated using mean square error (MSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Additionally, semi-quantitative evaluation of Deep AC images was performed and compared to CTAC using Bland-Altman plots. FA-ACNet achieved an MSE of 16.94 ± 2.03 × 10- 6, SSIM of 0.9955 ± 0.0006 and PSNR of 43.73 ± 0.50 after 5-fold cross validation. Compared to U-Net, MSE and PSNR improved by aligning multi-scale features from NAC SPECT and CT with those from NAC SPECT. In the testing set, FA-ACNet achieved an MSE of 11.98 × 10- 6, SSIM of 0.9976 and PSNR of 45.54. The 95% limits of agreement (LoAs) between the Deep AC and CTAC images for the summed stress/rest scores (SSS/SRS) were [- 2.3, 2.8] and [-1.9,2.1] in the training set and testing set respectively. Changes in perfusion categories were observed in 4.19% and 5.9% of studies assessed for global perfusion scores in the training set and testing set. We propose a novel DL-based CT-free AC approach for cardiac SPECT, which can generate AC images without the need for a CT scan. By leveraging multi-scale features from both NAC SPECT and CT, the performance of CT-free AC is significantly enhanced, offering a promising alternative for future DL-based AC strategies.

Inverse Properties Estimation of Methanol Adsorption in Activated Carbon to Utilise in Adsorption Cooling Applications: An Experimental and Numerical Study

The precise estimation of influential parameters in adsorption is a key point in conducting simulations for the sensitivity analysis and optimal design of cooling systems. This study explores the critical role of a new type of granular activated carbon (GAC-208C) in adsorption refrigeration systems. By fitting experimental and numerical models to the thermophysical properties of GAC/methanol as a working pair, an advanced methodology is established for the thermal analysis of the adsorption bed, addressing the various operating conditions overlooked in prior studies. The physical properties of the studied carbon sample are determined in a laboratory using surface area and pore volume tests, thermal adsorption analysis, and weight loss. To determine the thermal properties of GAC/methanol, the adsorption process is experimentally tested inside an isolated heat exchanger. A three-dimensional (3D) model is created to simulate the procedure and then coupled with the particle swarm optimisation (PSO) algorithm in MATLAB. The optimal thermal parameters for adsorption are determined by minimising the mean square error (MSE) of the adsorption bed temperature between the numerical and experimental data. The laboratory studies yielded accurate results for the physical properties of GAC, including adsorption capacity, porosity, permeability, specific heat capacity, density, activation energy, and the heat of adsorption. The thermal analysis of the adsorption process identified the ideal values for the Dubinin–Astakhov equation constants, diffusion coefficients, heat transfer coefficients, and contact resistance. The numerical model demonstrated strong agreement with the experimental results, and the dynamic behaviour of pressure and uptake distribution showed good agreement with 1.2% relative error. This research study contributes to the improved estimation of adsorption parameters to conduct more accurate numerical simulations and design new adsorption systems with enhanced performance under different operating conditions.

DeiT and Image Deep Learning-Driven Correction of Particle Size Effect: A Novel Approach to Improving NIRS-XRF Coal Quality Analysis Accuracy

Coal, as a vital global energy resource, directly impacts the efficiency of power generation and environmental protection. Thus, rapid and accurate coal quality analysis is essential to promote its clean and efficient utilization. However, combined near-infrared spectroscopy and X-ray fluorescence (NIRS-XRF) spectroscopy often suffer from the particle size effect of coal samples, resulting in unstable and inaccurate analytical outcomes. This study introduces a novel correction method combining the Segment Anything Model (SAM) for precise particle segmentation and Data-Efficient Image Transformers (DeiTs) to analyze the relationship between particle size and ash measurement errors. Microscopic images of coal samples are processed with SAM to generate binary mask images reflecting particle size characteristics. These masks are analyzed using the DeiT model with transfer learning, building an effective correction model. Experiments show a 22% reduction in standard deviation (SD) and root mean square error (RMSE), significantly enhancing ash prediction accuracy and consistency. This approach integrates cutting-edge image processing and deep learning, effectively reducing submillimeter particle size effects, improving model adaptability, and enhancing measurement reliability. It also holds potential for broader applications in analyzing complex samples, advancing automation and efficiency in online analytical systems, and driving innovation across industries.

A Deep Learning CNN-GRU-RNN Model for Sustainable Development Prediction in Al-Kharj City

This study introduces an advanced Deep Learning (DL) framework, the Convolutional Neural Network-Gated Recurrent Unit-Recurrent Neural Network (CNN-GRU-RNN). This model is engineered to forecast climate dynamics extending to the year 2050, with a particular focus on four pivotal scenarios: temperature, air temperature dew point, visibility distance, and atmospheric sea level pressure, specifically in Al-Kharj City, Saudi Arabia. To address the data imbalance problem, the Synthetic Minority Over-Sampling Technique was employed for Regression along with the Gaussian Noise (SMOGN). The efficacy of the CNN-GRU-RNN model was benchmarked against five regression models: the Decision Tree Regressor (DTR), the Random Forest Regressor (RFR), the Extra Trees Regressor (ETR), the Bayesian Ridge Regressor (BRR), and the K-Nearest Neighbors Regressor (KNNR). The models were evaluated using five distinct metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). The experimental outcomes demonstrated the superiority of the CNN-GRU-RNN model, which surpassed the traditional regression models across all four scenarios.

Harnessing Deep Learning and Technical Indicators for Enhanced Stock Predictions of Blue-Chip Stocks on the Indonesia Stock Exchange (IDX)

Given the limitations of existing models in accurately predicting stock prices, particularly in emerging markets such as Indonesia, this study aimed to evaluate the effectiveness of deep learning models in forecasting stock prices using blue-chip company shares traded on the Indonesia Stock Exchange (IDX). The main focus lies in combining historical stock data with a series of existing technical indicators, optimizing their integration to improve prediction accuracy. The accuracy of this method is reflected in a comprehensive evaluation of model performance using robust metrics, including R2, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Empirical results show the superiority of models integrating technical indicators compared to models relying only on historical data. The LSTM model showed the most significant improvement, with R2 for ASII stock jumping by 14.59% after incorporating technical indicators. The prediction accuracy of the GRU model for BBCA shares increased significantly, as shown by a decrease of 45.16% in MSE. These findings underscore the critical role of feature selection in developing prediction models. Integrating technical indicators with historical stock data increases prediction accuracy and provides additional tools for informed decision-making.

An efficient sparse code shrinkage technique for ECG denoising using empirical mode decomposition

Accurate denoising of Electrocardiogram (ECG) signals is essential for reliable cardiac diagnostics, but traditional methods often struggle with high-frequency noise and artifacts, leading to potential misinterpretations. It is often impeded by interference such as power line interference (PLI) and Gaussian noise. To address this challenge, we suggest a novel ECG denoising technique that combines empirical mode decomposition (EMD) with wavelet domain sparse code shrinking. Our approach first decomposes the noisy ECG signal into Intrinsic Mode Functions (IMFs) using EMD. These IMFs are then transformed into the wavelet domain, where a sparse code shrinking function is applied to effectively reduce both Gaussian noise and PLI while preserving the integrity of the original signal. The effectiveness of the technique is assessed on the MIT-BIH database, where it shows marked improvements in Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Percentage Root Mean Square Difference (PRD). The suggested approach demonstrates improved SNR and reduced MSE when compared to prior approaches, which suggests that the ECG signals are clearer and more precise. This method presents a rather effective approach to enhancing ECG analysis as it is important for diagnosis and interpretation. At 10 dB SNR, the suggested technique achieves an MSE of 0.005, which is much less than the 0.076 and 0.0025 MSEs obtained by EMD wavelet adaptive thresholding and soft thresholding correspondingly. This indicates that the proposed approach effectively eliminates noise while preserving significant signal characteristics, leading to an improved and less erroneous signal reconstruction. Furthermore, the proposed method outperformed conventional techniques and demonstrated improved noise reduction and signal clarity, achieving an SNR of 19.24 and a PRD of 20.38 at 10 dB SNR.

On-Site Implementation of External Wrench Measurement via Non-Linear Optimization in Six-Axis Force–Torque Sensor Calibration and Crosstalk Compensation

This study introduces a novel calibration method for accurate external wrench measurement using a six-axis FT (force–torque) sensor. We propose a sensor model and calibration method for FT sensors that enable precise separation of the force and torque components without the need for additional devices or sensors by estimating essential parameters: bias, crosstalk, CoM (center of mass), and inclination. By directly utilizing manufacturer-provided data, our approach eliminates the complexities of traditional calibration processes while achieving higher accuracy in force–torque measurements. This method simplifies the calibration workflow and enhances the practicality of FT sensor applications. A mobile manipulator installed with an FT sensor and a gripper is used to demonstrate calibration effectiveness across varying postures and incline conditions, with non-linear optimization based on the gradient descent method applied to minimize sensor-data errors. The tilt of the base is implemented by placing a step under the wheels of the mobile base to simulate roll or pitch scenarios. A digital level was used to measure the angle and verify that our predicted results were accurate. The proposed method addresses typical calibration challenges, including the effects of the end tool and base incline, which are not commonly covered in existing methods. The results show that, on a non-inclined base, crosstalk and CoM calibration reduces the MSE (mean squared error) by 55.8%, 56.2%, and 14.5% for the external force with respect to data without any calibration conducted. On an inclined base, our full calibration process reduces the MSE by a maximum of 98.6% for external mass measurement with respect to no calibration method applied. These findings highlight the importance of incline calibration for achieving accurate external force estimations, especially in mobile manipulator applications where the environment frequently changes.

Enhancing Banking Transaction Security with Fractal-Based Image Steganography Using Fibonacci Sequences and Discrete Wavelet Transform

The growing reliance on digital banking and financial transactions has brought significant security challenges, including data breaches and unauthorized access. This paper proposes a robust method for enhancing the security of banking and financial transactions. In this context, steganography—hiding information within digital media—is valuable for improving data protection. This approach combines biometric authentication, using face and voice recognition, with image steganography to secure communication channels. A novel application of Fibonacci sequences is introduced within a direct-sequence spread-spectrum (DSSS) system for encryption, along with a discrete wavelet transform (DWT) for embedding data. The secret message, encrypted through Fibonacci sequences, is concealed within an image and tested for effectiveness using the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The experimental results demonstrate that the proposed method achieves a high PSNR, particularly for grayscale images, enhancing the robustness of security measures in mobile and online banking environments.

AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry

The need to develop ecologically friendly sustainable building materials is made apparent by the worldwide construction industry's substantial contribution to global greenhouse gas emissions. The use of supplemental materials in concrete is one potential solution to lessen the environmental footprint. Thus, the purpose of this work is to use Machine Learning (ML) algorithms to forecast and create an empirical formula for the Compressive Strength (CS) of concrete with supplemental materials. Six distinct ML models—XGBoost, Linear Regression, Decision Tree, k-Nearest Neighbors, Bagging, and Adaptive Boosting—were trained and tested using a dataset that included 359 experimental data of varying mix proportions. The most significant factors used as input parameters are cement, aggregates, water, superplasticizer, silica fume, ambient curing, and supplemental material. Several statistical measures, such as Mean Absolute Error (MAE), coefficient of determination (R2), and Mean Square Error (MSE), were used to evaluate the models. XGBoost model outperformed the other models with R2 values of 0.99 at the training stage. To ascertain how the input parameters affected the outcome, feature importance analysis using Shapely Additive exPlanations (SHAP) was conducted. It was demonstrated that curing age and cement type significantly affected the strength of concrete with high SHAP values. By eliminating experimental procedures, reducing the demand for labor and resources, increasing time efficiency, and offering insightful information for enhancing sustainable manufacturing of concrete, this research advances the low-cost production of concrete in the USA construction industry.

Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction

Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA).

An Approach to Forecast Quality of Water Effectively Using Machine Learning Algorithms

Background:: The quality of water directly or indirectly impacts the health and environmental well-being. Data about water quality can be evaluated using a Water Quality Index (WQI). Computing WQI is a quick and affordable technique to accurately summarise the quality of water. Objective:: The objective of this study is to find strategies for data preparation to categorize a dataset on the water quality in two remote Indian villages in different geographic locations, to predict the quality of water, and to identify low-quality water before it is made accessible for human consumption. Methods:: To accomplish this task, four water quality features Nitrate, pH, Residual Chlorine, and Total Dissolved Solids which are crucial for human consumption, are considered to dictate the quality of water. Methods used in handling these features include five steps that are data preprocessing with min-max normalization, finding WQI, using feature correlation to identify parameter importance with WQI, application of supervised machine learning regression models such as Random Forest (RF), Multiple Linear Regression (MLR), Gradient Boosting (GB) and Support Vector Machine (SVM) for WQI prediction. Then, a variety of machine learning classification models, including K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Multi-layer Perceptron (MLP), are ensembled with Logistic Regression (LR), acting as a meta learner, to create a stack ensemble model classifier to predict the Water Quality Class (WQC) more accurately. Results:: The examination of the testing model revealed that RF regression and MLR algorithms performed best in predicting the WQI with mean absolute error (MAE) of 0.003 and 0.001 respectively. Mean square error (MSE), root mean square error (RMSE), R squared (R2), and Explained Variance Score (EVS) findings are 0.002,0.005,0.988 and 0.998 respectively with RF while 0.001,0.031,0.999 and 0.999 respectively with MLR. Meanwhile, for predicting WQC, the stack model classifier showed the best performance with an Accuracy of 0.936, F1 score of 0.93, and Matthews Correlation Coefficient (MCC) of 0.893 for the dataset of Lalpura and Accuracy of 0.991, F1 Score of 0.991 and MCC of 0.981 respectively for the dataset of Heingang. Conclusion:: This study explores a method for predicting water quality that combines easy and feasible water quality measurements with machine learning. The stack model classifier performed best for multiclass classification, according to this study. To ensure that the highest quality of water is given throughout the year, information from this study will motivate researchers to look into the underlying root causes of the quality variations.

Iterative frequency-domain equalization with multipath interference cancellation for aeronautical telemetry

The increasing transmitted signal bandwidth of aeronautical telemetry systems leads to the channel’s frequency selectivity. Equalization techniques are always adopted to conquer such problems. To optimize the equalization performance, we pursue minimizing the mean square error (MSE) of the equalizers. The solution shows that equalizing the line-of-sight (LOS) signal results in a lower MSE than equalizing the received signal directly. Thus, we proposed an iterative equalization algorithm with multipath interference cancellation (MIC) in this letter. In each iteration, the MIC is used to extract the LOS signal from the received signal. Then, we equalize the LOS signal to reduce the MSE of the equalization. Finally, the symbols are detected from the equalized LOS signal to achieve bit-error-rate (BER) performance improvement. The numerical results demonstrate that the proposed method provides a superior BER performance with lower complexity compared to the state-of-the-art equalizers.

Comparison Between the Partial Least Squares Method and Principal Components Using Genetic Algorithm with an Application

The problem of multicollinearity among independent variables in a regression model was addressed in this study using Partial Least Squares (PLS) and Principal Component Analysis (PCA). The influence exerted by multicollinearity can deteriorate the results of regression modeling; that is, it makes the traditional technique OLS less reliable. The research is centered on applying advanced algorithms for Partial Least Squares (SIMPL and O-PLS) as well as for PCA (NIPALS and SVD) on real-life data scenarios, as well as integrating genetic algorithm (GAs) with these algorithms to optimize predictive performance. The relative efficiency of these methods is evaluated primarily through the amplitude of the Mean Square Error (MSE) used as a criterion for comparison. The results show the effectiveness of PLS-OPLS above PCA in terms of the lowest before and after embedding genetic algorithms into the MSE. All this underpins the effectiveness of PLS in minimizing multicollinearity thereby allowing for the formulation and prediction of very highly predictive models. More and more indication of subtle class was revealed with regard to fine tuning advanced GA technique in favor of enhancing regression modeling for complex data analysis. The ongoing research will help in opening numerous possibilities to reinforce regression methodology, especially when one factor in an array of applications representing a relatively significant level of prediction accuracy.

Dự báo giá cổ phiếu CATL đóng cửa và các chiến lược giao dịch sử dụng XGBoost và thuật toán tối ưu hóa Optuna

This study employs the XGBoost algorithm, enhanced through hyperparameter optimization with Optuna, to forecast the closing stock prices of CATL and develop effective trading strategies. The XGBoost model achieved superior performance, with an R² score of 0.9201, a Mean Absolute Error (MAE) of 0.1982, and a Mean Squared Error (MSE) of 0.0825, outperforming benchmark models such as ElasticNet and Decision Tree. The resulting trading strategy generated actionable buy, sell, and hold recommendations over a 30-day horizon, aiding investors in profit optimization. The findings highlight the high prediction accuracy and stability of the XGBoost model, particularly when optimized with Optuna, thereby enhancing decision-making efficiency in stock trading. Nonetheless, the study underscores the potential for further improvement through the integration of advanced deep learning models, alternative optimization techniques, or ensemble learning approaches. Future research could also explore incorporating unstructured data to refine forecasting accuracy and expand the applicability of trading strategies.

Predicting mortality in upper gastrointestinal tract cancer using long short-term memory neural networks.

340 Background: Upper gastrointestinal (GI) tract cancers represent a significant global health burden, characterized by high morbidity and mortality rates, with an increasing incidence among aging populations and varying outcomes based on gender and ethnicity. Early prediction of mortality rates in upper gastrointestinal cancer can significantly enhance treatment strategies and patient outcomes. This study describes current trends in upper gastrointestinal cancer and proposes a predictive model, developed using Long Short-Term Memory (LSTM) neural networks to project future trends using demographic data. Methods: We extracted mortality data from the Center for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiological Research (WONDER) Database spanning 1999, normalized it and encoded categorical variables such as age, sex, ethnicity, and race numerically. Using Studio R to analyze mortality trends, we developed an LSTM model known for its ability to capture and utilize long term data patterns. The model, trained with a sequence length of 20, predicted future data points based on the previous 20. We then assessed its accuracy and generalization with a test set. Ultimately, our model is able to utilize demographic variables to forecast annual mortality and survival rates. Results: The mortality rates for upper GI tract cancers are notably higher among elderly individuals, particularly those aged 80 and above, with males and certain ethnic groups, such as Hispanics. The accuracy of the trained model was evaluated by calculating the Mean Squared Error (MSE), a measure of the difference between the predicted and actual value, with zero indicating no difference between the two. Our LSTM model achieved a training MSE of 0.00085 and a validation MSE of 0.0016, indicating good reliability and accuracy. Conclusions: Our analysis highlighted clear mortality trends and demonstrated the model’s effectiveness in improving mortality predictions in upper gastrointestinal cancer. However, limitations due to the dataset’s lack of clinical variables (such as comorbidities, cancer stage, and prior treatments) and its reliance on a single dataset may affect external validity. Future research is needed to determine if the model can be applied for clinical prognosis or strategic planning. We ultimately created and validated an LSTM neural network model with the CDC WONDER dataset, showing promising results. Future development will aim to include clinical characteristics to create a more personalized predictive tool.

Artificial neural networks classification of s-band absorption performance in eco-friendly microwave absorbers

Microwave absorbers are essential for applications such as radar stealth and electromagnetic compatibility. Nevertheless, traditional materials encounter obstacles related to cost and sustainability, which has led to the exploration of new options such as materials derived from agricultural waste. This study focuses on the classification challenge of evaluating the absorption performance of eco-friendly microwave absorbers in the S-band (2 to 4 GHz) frequency. Three multilayer perceptron (MLP) algorithms, namely levenberg marquardt (LM), resilient backpropagation (RBP) and scale conjugate gradient (SCG) are assessed for classification accuracy. The dataset consists of 135 absorption performance values of microwave absorbers that were taken from experimental measurements using the naval research laboratory (NRL) arch free. The MLP algorithms will be divided into three divisions, which are training, validation and testing, evaluating important criteria such as accuracy, precision, sensitivity and specificity. The performance of three types of algorithms will be compared using two basic inputs: the absorption values and the single slot sizes. The RBP algorithm achieved 100% accuracy, and a lower mean squared error (MSE) of 0.02500 compared to the LM and SCG. This study provides valuable insights for designing better microwave absorbers and highlights the commercial potential of agricultural waste materials in such applications.