Lowest Mean Absolute Error Research Articles

A novel application of waveform matching algorithm for improving monthly runoff forecasting using wavelet–ML models

ABSTRACT The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.

N-DEPTH: Neural Depth Encoding for Compression-Resilient 3D Video Streaming

Recent advancements in 3D data capture have enabled the real-time acquisition of high-resolution 3D range data, even in mobile devices. However, this type of high bit-depth data remains difficult to efficiently transmit over a standard broadband connection. The most successful techniques for tackling this data problem thus far have been image-based depth encoding schemes that leverage modern image and video codecs. To our knowledge, no published work has directly optimized the end-to-end losses of a depth encoding scheme sandwiched around a lossy image compression codec. We present N-DEPTH, a compression-resilient neural depth encoding method that leverages deep learning to efficiently encode depth maps into 24-bit RGB representations that minimize end-to-end depth reconstruction errors when compressed with JPEG. N-DEPTH’s learned robustness to lossy compression expands to video codecs as well. Compared to an existing state-of-the-art encoding method, N-DEPTH achieves smaller file sizes and lower errors across a large range of compression qualities, in both image (JPEG) and video (H.264) formats. For example, reconstructions from N-DEPTH encodings stored with JPEG had dramatically lower error while still offering 29.8%-smaller file sizes. When H.264 video was used to target a 10 Mbps bit rate, N-DEPTH reconstructions had 85.1%-lower root mean square error (RMSE) and 15.3%-lower mean absolute error (MAE). Overall, our method offers an efficient and robust solution for emerging 3D streaming and 3D telepresence applications, enabling high-quality 3D depth data storage and transmission.

Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

The current flood forecasting models heavily rely on historical measured data, which is often insufficient for robust predictions due to practical challenges such as high measurement costs and data scarcity. This study introduces a novel hybrid approach that synergistically combines the outputs of traditional physical-based models with historical data to train Long Short-Term Memory (LSTM) networks. Specifically, the NAM hydrological model and the HD hydraulic model are employed to simulate flood processes. Focusing on the Jinhua basin, a typical plains river area in China, this research evaluates the efficacy of LSTM models trained on measured, mixed, and simulated datasets. The LSTM architecture includes multiple layers, with optimized hyperparameters tailored for flood forecasting. Key performance indicators such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak-relative Error (PRE) are employed to assess the predictive accuracy of the models. The findings demonstrate that LSTM models trained on mixed datasets with a simulated-to-measured data ratio of less than 2:1 consistently achieve superior performance, exhibiting significantly lower RMSE and MAE values compared to models trained on mixed data with larger data ratios. This highlights the advantage of integrating measured and simulated data, leveraging the strengths of both data types to enhance model accuracy. Despite its advantages, the approach has limitations, including dependence on the quality of simulated data and potential computational complexity. However, the development of this hybrid model marks a significant advancement in flood forecasting, offering a promising solution to the challenges of computational efficiency and data scarcity. Potential applications of this approach include real-time flood prediction and risk management in other flood-prone regions, providing a robust framework for integrating diverse data sources to improve forecasting accuracy.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep.

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

Indirect deformable image registration using synthetic image generated by unsupervised deep learning

Background and purpose3D image registration is now common in many medical domains. Multimodal registration implies the use of different imaging modalities, which results in lower accuracy compared to monomodal registration. The aim of this study was to propose a novel approach for deformable image registration (DIR) that incorporates an unsupervised deep learning (DL)-based generation step. The objective was to reduce the challenge of multimodal registration to monomodal registration. Material and methodsTwo datasets from prostate radiotherapy patients were used to evaluate the proposed method. The first dataset consisted of Computed Tomography (CT)/ Cone Beam Computed Tomography (CBCT) pairs from 23 patients using different CBCT devices. The second dataset included Magnetic Resonance Imaging (MRI)/CT pairs from two different care centers, utilizing different MRI devices (0.35 T MRIdian MR-Linac, 1.5 T GE lightspeed MRI). Following a preprocessing step essential for ensuring DL synthesis accuracy and standardizing the database, synthetic CTs (sCTreg) were generated using an unsupervised conditional Generative Adversarial Network (cGAN). The generated sCTs from CBCT or MRI were then utilized for deformable registration with CT scans. This registration method was compared to three standard methods: rigid registration, Elastix registration based on BSplines, and VoxelMorph-based registration (applied exclusively to CBCT/CT). The endpoints of comparison were the dice coefficients calculated between delineated structures for both datasets. ResultsFor both datasets, intermediary sCT generation provided the highest dice coefficients. Dices reached 0.85, 0.85 and 0.75 for the prostate, bladder and rectum for the dataset 1 and 0.90, 0.95 and 0.87 respectively for the dataset 2. When the sCT were not used, dices reached 0.66, 0.78, 0.66 for the dataset 1 and 0.93, 0.87 and 0.84 for the dataset 2. Furthermore, the evaluation of the impact of registration on sCT generation showed that lower Mean Absolute Errors were obtained when the registration was conducted with a sCT. ConclusionsUsing unsupervised deep learning to synthesize intermediate sCT has led to improved registration accuracy in radiotherapy applications employing two distinct imaging modalities.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

BackgroundAbdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use.PurposeTo develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan.MethodsTotally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed.ResultsThe 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson’s and Spearman’s correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI.ConclusionCI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

IoT based solar power forecasting using SSA-ELM technique

Abstract The optimizing of renewable energy use and grid integration relies on accurate solar power predictions. In order to predict the amount of power that solar photovoltaic (PV) systems would produce inside an IoT framework, this study suggests a new method that integrates Singular Spectrum Analysis (SSA) with Extreme Learning Machine technology. The SSA algorithm makes sense of solar power data by separating it into its component parts, such as trend, seasonality, and noise. The ELM model, a quick and effective feedforward neural network with a single hidden layer, takes these broken-down parts as input characteristics. In order to enhance the accuracy of solar power forecasts, the suggested strategy combines the decomposition skills of SSA with the predictive capability of ELM. Data acquired by solar PV sensors is input into the IoT-based forecasting model, which then undergoes preprocessing with SSA, feature extraction, model training with ELM, and performance evaluation. The SSA-ELM methodology has been successfully tested on real solar power data and has shown promising results in terms of accuracy measures such as low mean absolute error and mean absolute percentage error. By implementing the suggested method, accurate projections of solar output can be made, leading to better energy management, lower costs, and the smooth incorporation of renewables into smart grids. A dependable and computationally efficient method for solar forecasting in Internet of Things applications is provided by the combination of SSA and ELM.

Machine learning optimization for hybrid electric vehicle charging in renewable microgrids

Renewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.

Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L−1, 3.13, 117.65 min, and 0.19 g L−1) and (5 mg L−1, 3, 120 min, and 0.19 g L−1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.

Improved dendritic learning: Activation function analysis

This study conducted a thorough evaluation of an improved dendritic learning (DL) framework, focusing specifically on its application in power load forecasting. The objective was to optimise the activation functions within the synapses and somas of DL to enhance their adaptability across diverse real-world scenarios. Through a rigorous analysis involving 25 experiments across five activation functions (sigmoid, hyperbolic tangent (tanh), rectified linear unit (ReLU), leaky ReLU, and exponential linear unit (ELU)), we elucidated their impacts on both regression and classification performance. Notably, the leaky ReLU–tanh combination demonstrated exceptional mean performance and effectiveness across 14 benchmark datasets from the University of California Irvine Machine Learning Repository, surpassing alternative combinations. When applied to power load forecasting, this combination outperformed other models, particularly transformer and LSTM. These findings underscore the significant advantages of the leaky ReLU–tanh-based DL framework in accurately predicting electricity load in smart grids, as evidenced by the lowest mean absolute error (39.27), root mean squared error (29.13), and mean absolute percentage error (2.84).

The impact of perceived vaginal and urinary symptoms and treatment burden in predicting sexual functioning among perimenopausal and postmenopausal women.

This study aimed to explore the relationship between urogenital symptom frequency and severity, perception of vaginal treatment burden, and female sexual desire, arousal, and satisfaction. A cross-sectional study was conducted with a sample of 326 patients from three tertiary care hospitals in the United Arab Emirates. The frequency and severity of urogenital symptoms, emotional and physical functioning, and treatment burden were assessed using the validated genitourinary syndrome of menopause symptoms and vaginal treatments acceptability questionnaire (GSM-SVATQ). To examine the mediating roles of emotional and physical functioning, as well as the perceived treatment burden on sexual functioning, a partial least squares-structural equation model was developed using the SmartPLS 4 Software. The measurement model was successfully established. All constructs had a reliability of > 0.70 and discriminant validity of < 0.90. Emotional, physical and sexual functioning showed an adjusted R2 values of 0.377, 0.282 and 0.169, respectively.The multistep multiple mediator model revealed a full mediation effect of both emotional and physical functioning between symptom, treatment burden and sexual functioning. The model showed high predictive performance with all manifest variables showing lower mean absolute errors compared to the naiive benchmark model. This study enhances our understanding of the relationships between urogenital symptoms, perceived treatment burden, emotional functioning, and sexual well-being. The findings emphasize the importance of addressing emotional well-being in managing urogenital symptoms and in addressing emotional factors associated with the use of vaginal treatments.

Advancing Retail Predictions: Integrating Diverse Machine Learning Models for Accurate Walmart Sales Forecasting

In the rapidly evolving landscape of retail analytics, the accurate prediction of sales figures holds paramount importance for informed decision-making and operational optimization. Leveraging diverse machine learning methodologies, this study aims to enhance the precision of Walmart sales forecasting, utilizing a comprehensive dataset sourced from Kaggle. Exploratory data analysis reveals intricate patterns and temporal dependencies within the data, prompting the adoption of advanced predictive modeling techniques. Through the implementation of linear regression, ensemble methods such as Random Forest, Gradient Boosting Machines (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), this research endeavors to identify the most effective approach for predicting Walmart sales. Comparative analysis of model performance showcases the superiority of advanced machine learning algorithms over traditional linear models. The results indicate that XGBoost emerges as the optimal predictor for sales forecasting, boasting the lowest Mean Absolute Error (MAE) of 1226.471, Root Mean Squared Error (RMSE) of 1700.981, and an exceptionally high R-squared value of 0.9999900, indicating near-perfect predictive accuracy. This model's performance significantly surpasses that of simpler models such as linear regression, which yielded an MAE of 35632.510 and an RMSE of 80153.858. Insights from bias and fairness measurements underscore the effectiveness of advanced models in mitigating bias and delivering equitable predictions across temporal segments. Our analysis revealed varying levels of bias across different models. Linear Regression, Multiple Regression, and GLM exhibited moderate bias, suggesting some systematic errors in predictions. Decision Tree showed slightly higher bias, while Random Forest demonstrated a unique scenario of negative bias, implying systematic underestimation of predictions. However, models like GBM, XGBoost, and LGB displayed biases closer to zero, indicating more accurate predictions with minimal systematic errors. Notably, the XGBoost model demonstrated the lowest bias, with an MAE of -7.548432 (Table 4), reflecting its superior ability to minimize prediction errors across different conditions. Additionally, fairness analysis revealed that XGBoost maintained robust performance in both holiday and non-holiday periods, with an MAE of 84273.385 for holidays and 1757.721 for non-holidays. Insights from the fairness measurements revealed that Linear Regression, Multiple Regression, and GLM showed consistent predictive performance across both subgroups. Meanwhile, Decision Tree performed similarly for holiday predictions but exhibited better accuracy for non-holiday sales, whereas, Random Forest, XGBoost, GBM, and LGB models displayed lower MAE values for the non-holiday subgroup, indicating potential fairness issues in predicting holiday sales. The study also highlights the importance of model selection and the impact of advanced machine learning techniques on achieving high predictive accuracy and fairness. Ensemble methods like Random Forest and GBM also showed strong performance, with Random Forest achieving an MAE of 12238.782 and an RMSE of 19814.965, and GBM achieving an MAE of 10839.822 and an RMSE of 1700.981. This research emphasizes the significance of leveraging sophisticated analytics tools to navigate the complexities of retail operations and drive strategic decision-making. By utilizing advanced machine learning models, retailers can achieve more accurate sales forecasts, ultimately leading to better inventory management and enhanced operational efficiency. The study reaffirms the transformative potential of data-driven approaches in driving business growth and innovation in the retail sector.

Autoencoder-based composite drought indices

Depending on the type, drought events are described using different indices, such as meteorological, agricultural, and hydrological. The use of different indices often causes confusion for making water-related management decisions. One simple summarized index which can describe the different aspects of drought is desired. Several methods have therefore been proposed, especially with the linear combination method which does not adequately describe drought characteristics. Meanwhile, autoencoders, nonlinear transformation in dimensional reduction, have been applied in the deep learning literature. The objective of this study, therefore, was to derive autoencoder-based composite drought indices (ACDIs). First, a basic autoencoder was directly applied as ACDI, illustrating a negative relation with the observed drought indices which was further multiplied by a negative. Also, the hyperbolic tangent function was adopted instead of the sigmoid transfer function due to its higher sensitivity to drought conditions. For better expression of drought indices, positive and unity constraints were applied for weights, denoted as ACDI-C. Further simplification was made as sACDI by excluding the decoding module since it was not necessary. All applied weights of different sites over a country can be unified into one weight, and the same weights were made for all the sites, called as sACDI1. In the context of model evaluation, a comprehensive analysis was undertaken employing metrics as root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficients. The collective findings underscore the superior performance of both the sACDI and sACDI1 models over their counterparts. Notably, these simplified models manifestly diminished RMSE and MAE values, indicating their enhanced predictive capabilities. Of particular note, sACDI1 exhibited a discernibly lower MAE in comparison to alternative models. Further alarm performance metrics was conducted including the false alarm ratio, probability of detection, and accuracy (ACC). The investigations revealed superiority of the simplified models in terms of alarm ACC, especially in the case of SRSI(A). The developed ACDI can comprehensively summarize multiple drought aspects and provide summarized information about drought conditions.

Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

AbstractDecentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric measurements. We analyzed 237 patients (474 individual breasts) who underwent reduction mammoplasty at our institution. Anthropometric variables included body surface area (BSA), body mass index, sternal notch-to-nipple (SN-N), and nipple-to-inframammary fold values. Four different ML algorithms (linear regression, ridge regression, support vector regression, and random forest regression) either including or excluding the Schnur Scale prediction for the same data were trained and tested on their ability to recognize the relationship between the anthropometric variables and total resection weights. Resection weight prediction accuracy for each model and the Schnur scale alone were evaluated based on using mean absolute error (MAE). In our cohort, mean age was 40.36 years. Most patients (71.61%) were African American. Mean BSA was 2.0 m 2 , mean body mass index was 33.045 kg/m 2 , mean SN-N was 35.0 cm, and mean nipple-to-inframammary fold was 16.0 cm. Mean SN-N was found to have the greatest variable importance. All 4 models made resection weight predictions with MAE lower than that of the Schnur Scale alone in both the training and testing datasets. Overall, the random forest regression model without Schnur scale weight had the lowest MAE at 186.20. Our ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.

Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids

This paper presents an implementation of metaheuristic algorithms to optimise deep learning neural networks (DNNs) for sensor-less control of photovoltaic (PV) Converters in DC nanogrids. Using a Fixed Forward Neural Network (FFNN), it estimates PV output current (IPV ) based on three days of real data. Given the vulnerability of current sensors in DC system measurements, accurately replicating current sensor data is vital. The data exhibits dynamic nonlinear relationships with inputs like solar irradiance, temperature, and voltage. The study assesses the effectiveness of Evolutionary Mating Algorithm (EMA) and Sand Cat Swarm Optimisation (SCSO), comparing them with Adaptive Moment Estimation (ADAM), Genetic Algorithm (GA), and Particle Swarm Optimisation (PSO). The research leverages these metaheuristic algorithms to optimise machine learning integration, addressing regression estimation problems, and enhancing system reliability by eliminating sensors. Key findings indicate that the EMA-DNN combination achieved remarkable performance, with the lowest Mean Squared Error (MSE) of 0.5906, the lowest Mean Absolute Error (MAE) of 0.4680, and the lowest Mean Absolute Percentage Error (MAPE) of 12.1780%. These results offer valuable insights into how the metaheuristic-DNN approach can solve regression estimation problems and reduce the number of sensors in PV battery-based nanogrids’ control layers.

Exploring microstructure evolution and machine-learning methods based on SCAT-CIWOA-BP-DMM theory during hot deformation of 56Ni–32Ti–12Hf alloy

Hot compression simulation tests were carried out on a 56Ni–32Ti–12Hf alloy at temperatures ranging from 800 to 1000 °C and strain rates ranging from 0.001 to 1 s−1. The study aimed to explore the hot deformation behavior and microstructure evolution mechanism of the alloy. Two flow stress prediction models were developed: a strain-compensated Arrhenius constitutive (SCAT) model based on phenomenology and a chaotic mapping adaptive inertia weight whale optimization algorithm-optimized back propagation neural network (CIWOA-BPNN) model based on machine learning. In comparison to the SCAT model, the CIWOA-BPNN model demonstrates higher prediction accuracy, improved error correlation coefficient, decreased average relative error, and lower root mean square error and mean absolute error. The hot processing map of the 56Ni–32Ti–12Hf alloy was developed using dynamic material modeling (DMM) theory and flow stress data predicted by the CIWOA-BPNN model. The optimal hot processing range was found to be between temperatures of 875–975 °C and strain rates of 0.001–1 s−1. The study found that within the optimal processing interval, the softening mechanisms observed were discontinuous dynamic recrystallization (DDRX) and continuous dynamic recrystallization (CDRX). An increase in power dissipation efficiency (η) led to an increase in the dynamic recrystallization (DRX) fraction from 27.1 % to 41.9 %, while the substructure fraction decreased from 34.2 % to 19.8 %. The decrease in the number of dislocations around the DDRX grains further validated the accuracy of the identified optimum machining interval in this research.

Resting Energy Expenditure, comparison of predictive formulas with bioimpedance: Peruvian population

ntroduction: In areas with limited access to healthcare systems, Resting Energy Expenditure (REE) estimation is performed using predictive equations to calculate an individual’s caloric requirement. One problem is that these equations were validated in populations with different characteristics from those in Latin America, such as race, height, or body mass, leading to potential errors in the prediction of this parameter. Objective: To determine the REE using predictive formulas compared with bioimpedance in Peruvians. Materials and methods: A comparative analytical cross-sectional study with secondary database analysis of the CRONICAS cohort. Results: we worked with a total of 666 subjects. The Mjeor equation was the one with the highest rating of 0.95, a lower mean absolute percentage error (MAPE) of 4.69%, and equivalence was found with the REE values. In the multiple regression, it was observed that the Mjeor equation was the one that least overestimated the REE, increasing 0.77 Kcal/day (95% CI: 0.769-0.814; p&lt;0.001) for each point that increased the REE determined by bioimpedance. The strength of association between Mjeor and bioimpedance was 0.9037. Furthermore, in the regression of the data (weight, height, age) in the Mjeor equation it was observed that the coefficients obtained were the same as those used in the original equation. Conclusions: The Mjeor equation seems to be the most adequate to estimate the REE in the Peruvian population. Future prospective studies should confirm the usefulness of this formula with potential utility in primary health care.

Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models

For discovering uncharted chemical space of ionic liquids (ILs) for CO2 dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by t-distributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO2 solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO2 solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO2 solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO2 solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.