Mean Square Error Research Articles

Developing a neural network model to predict the optimal minimum flow rate for effective hole cleaning

The object of the study is effective well cleaning during drilling. The subject of the study is the development of a machine learning model based on a neural network for predicting the optimal minimum drilling fluid flow rate. The challenge is the need to improve well cleaning efficiency to prevent stuck pipes and the associated downtime and costs. During the study, a neural network model was developed and tested to predict the minimum flow rate for cleaning wells. The model was trained and tested on data showing its high accuracy and reliability. The mean square error (MSE) reached 0.019169 for LSTM and 0.0828 for GRU, indicating the accuracy of the predictions. Neural network architectures such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to efficiently process time series of data and consider long-term dependencies. The results are explained using advanced neural network architectures and machine learning algorithms, which made it possible to achieve good accuracy of predictions. These architectures enable efficient model training on large amounts of data, allowing complex dependencies and influencing factors to be considered. Distinctive features of the results include good accuracy of predictions and the ability to use the model in real-world conditions. The model demonstrates good performance and reliability in predicting the minimum flow rate. The results of the study can be used to optimize the processes of well drilling. Practical applications include using the model to predict the optimal minimum flow rate in various conditions, which will reduce the risks of stuck pipes and increase the efficiency of drilling operations. The model can be integrated into existing monitoring and control systems for drilling processes to improve their performance

On using the penalized regression estimators to solve the multicollinearity problem

The paper compares coefficient parameter estimation efficiency using penalized regression approaches. Five estimators are employed: Ridge Regression, LASSO regression, Elastic Net (ENET) Regression, Adaptive Lasso (ALASSO) regression, and Adaptive Elastic Net (AENET) regression methods. The study uses a multiple linear regression model to address multicollinearity issues. The comparison is based on average mean square errors (MSE) using simulated data with varying sizes, numbers of independent variables, and correlation coefficients. The results are expected to be useful and will be applied to real data to determine the best-performing estimator.

Hardware Implementation of a 2D Chaotic Map-Based Audio Encryption System Using S-Box

This paper presents a hardware-based audio encryption system using a 2D chaotic map and dynamic S-box design implemented on an Artix-7 FPGA platform. Three distinct chaotic maps—logistic–fraction (2D-LF), logistic–sine (2D-LS), and fraction–sine (2D-FS)—were investigated and implemented on an FPGA. The 2D-LF map was employed in the encryption system for its throughput and power efficiency performance. The proposed encryption system benefits from the randomness of chaotic sequences for block permutation and S-box substitution to enhance the diffusion and confusion properties of the encrypted speech signal. The system’s encryption strength is validated through performance evaluations, using the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficients, and NIST randomness tests, which confirm the unpredictability of the encrypted speech signal. The hardware implementation results show a throughput of 2880 Mbps and power consumption of 0.13 W.

An Effective PDE-based Thresholding for MRI Image Denoising and H-FCM-based Segmentation

Image denoising and segmentation play a crucial role in computer graphics and computer vision. A good image-denoising method must effectively remove noise while preserving important boundaries. Various image-denoising techniques have been employed to remove noise, but complete elimination is often impossible. In this paper, we utilize Partial Differential Equation (PDE) and generalised cross-validation (GCV) within Adaptive Haar Wavelet Transform algorithms to effectively denoise an image, with the digital image serving as the input. After denoising, the image is segmented using the Histon-related fuzzy c-means algorithm (H-FCM), with the processed image serving as the output. The proposed method is tested on images exposed to varying levels of noise. The performance of image denoising and segmentation techniques is evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) of 77.42, Mean Squared Error (MSE) of 0.0011, and Structural Similarity Index (SSIM) of 0.7848. Additionally, segmentation performance is measured with a sensitivity of 99%, specificity of 98%, and an accuracy of 98%. The results demonstrate that the proposed methods outperform conventional approaches in these metrics. The implementation of the proposed methods is carried out on the MATLAB platform.

Time series forecasting of rain-induced attenuation using coupled ARIMA-SARIMA models for radio propagation applications over subtropical locations

This present research aimed to identify the optimum model for predicting the rain-induced attenuation along the terrestrial and satellite communication networks and employed the time series forecasting models of Autoregressive integrated moving average (ARIMA) and Seasonal Autoregressive integrated moving average (SARIMA) to examine and contrast several techniques for predicting rain-induced attenuation across a sub-tropical area of South Africa. In this research, the ARIMA was developed using the Box and Jenkins technique to predict long-term rain-induced attenuation in the following South African provinces: Kwazulu-Natal, Eastern Cape, Gauteng, and Northern Cape. The datasets used for the research were obtained from the South African Weather Station from 1994–2023 were used to build and check the model after generating rain-induced attenuation using synthetic storm techniques (SST). The forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model and that of autoregressive integrated moving average (ARIMA) were compared with four forecast performance measures: - Mean Squared Error (MSE), Mean Absolute Error (MAE), R- squared or the coefficient of determination (R2) and Root Mean Squared Error (RMSE). The results of the study showed that due to its lower forecast performance error indicators, SARIMA performed better than ARIMA in forecasting rain-induced attenuation in South Africa. There is no statistically significant difference between the two projected values, according to the results of a Ljung-box test for significant difference. The authors draw the inference that the two approaches can effectively be employed in their proper locations. Rain-attenuation prediction data indicates that this long-term projection can help decision makers to improve the performance of microwave networks in the face of random fluctuations and avoiding unexpected signal loss with efficient installation of communications infrastructure. It also has applications in prediction of floods, urban planning, and crop management.

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

Integrating Radial Basis Networks and Deep Learning for Transportation

Introduction This research focuses on the concept of integrating Radial Basis Function Networks with deep learning models to solve robust regression tasks in both transportation and logistics. Methods It examines such combined models as RNNs with RBFNs, Attention Mechanisms with Radial Basis Function Networks (RBFNs), and Capsule Networks with RBFNs and clearly shows that, in all cases, compared to the others, the former model has a Mean Squared Error (MSE) of 0.010 to 0.013, Mean Absolute Error (MAE) – 0.078 to 0.088, and R-squared (R2) – 0.928 to 0.945, across ten experiments. In the case of Attention Mechanisms with RBFNs, the models also demonstrate strong performance in terms of making predictions. The MSE ranges from 0.012 to 0.015, the MAE from 0.086 to 0.095, and the R2 from 0.914 to 0.933. Results However, it is critical to note that the Capsule Networks with RBFNs outperform other models. In particular, they offer the lowest MSE, which is between 0.009 and 0.012, the smallest MAE, which ranges from 0.075 to 0.083, and the highest R2, from 0.935 to 0.950. Conclusion Overall, the results indicate that the use of RBFNs in combination with different types of deep learning networks can provide highly accurate and reliable solutions for regression problems in the domain of transportation and logistics.

Cauchy-Schwarz bounded trade-off weighting for causal inference with small sample sizes

The difficulty of causal inference for small-sample-size data lies in the issue of inefficiency that the variance of the estimators may be large. Some existing weighting methods adopt the idea of bias-variance trade-off, but they require manual specification of the trade-off parameters. To overcome this drawback, in this article, we propose a Cauchy-Schwarz Bounded Trade-off Weighting (CBTW) method, in which the trade-off parameter is theoretically derived to guarantee a small Mean Square Error (MSE) in estimation. We theoretically prove that optimizing the objective function of CBTW, which is the Cauchy-Schwarz upper-bound of the MSE for causal effect estimators, contributes to minimizing the MSE. Moreover, since the upper-bound consists of the variance and the squared ℓ2-norm of covariate differences, CBTW can not only estimate the causal effects efficiently, but also keep the covariates balanced. Experimental results on both simulation data and real-world data show that the CBTW outperforms most existing methods especially under small sample size scenarios.

Biological activities of Hypericum spectabile extract optimized using artificial neural network combined with genetic algorithm application

Optimizing extraction conditions can help maximize the efficiency and yield of the extraction process while minimizing negative impacts on the environment and human health. For the purpose of the current study, an artificial neural network (ANN) combined with a genetic algorithm (GA) was utilized for that the extraction conditions of Hypericum spectabile were optimized. In this particular investigation, the main objective was to get the highest possible levels of total antioxidant status (TAS) for the extracts that were obtained. In addition to this, conditions of the extract that exhibited the maximum activity have been determined and the biological activity of the extract that was obtained under these conditions was analyzed. TAS values were obtained from extracts obtained using extraction temperatures of 30–60 °C, extraction times of 4–10 h, and extract concentrations of 0.25-2 mg/mL. The best model selected from the established ANN models had a mean absolute percentage error (MAPE) value of 0.643%, a mean squared error (MSE) value of 0.004, and a correlation coefficient (R) value of 0.996, respectively. The genetic algorithm proposed optimal extraction conditions of an extraction temperature of 59.391 °C, an extraction time of 8.841 h, and an extraction concentration of 1.951 mg/mL. It was concluded that the integration of ANN-GA can successfully be used to optimize extraction parameters of Hypericum spectabile. The total antioxidant value of the extract obtained under optimum conditions was determined as 9.306 ± 0.080 mmol/L, total oxidant value as 13.065 ± 0.112 µmol/L, oxidative stress index as 0.140 ± 0.001. Total phenolic content (TPC) was 109.34 ± 1.29 mg/g, total flavonoid content (TFC) was measured as 148.34 ± 1.48 mg/g. Anti-AChE value was determined as 30.68 ± 0.77 µg/mL, anti-BChE value was determined as 41.30 ± 0.48 µg/mL. It was also observed that the extract exhibited strong antiproliferative activities depending on the increase in concentration. As a result of LC-MS/MS analysis of the extract produced under optimum conditions in terms of phenolic content. The presence of fumaric, gallic, protocatechuic, 4-hydroxybenzoic, caffeic, 2-hydoxycinamic acids, quercetin and kaempferol was detected. As a result, it was determined that the H. spectabile extract produced under optimum conditions had significant effects in terms of biological activity.

Predictive coding compressive sensing optical coherence tomography hardware implementation

Compressed sensing (CS) is an approach that enables comprehensive imaging by reducing both imaging time and data density, and is a theory that enables undersampling far below the Nyquist sampling rate and guarantees high-accuracy image recovery. Prior efforts in the literature have focused on demonstrations of synthetic undersampling and reconstructions enabled by compressed sensing. In this paper, we demonstrate the first physical, hardware-based sub-Nyquist sampling with a galvanometer-based OCT system with subsequent reconstruction enabled by compressed sensing. Acquired images of a variety of samples, with volume scanning time reduced by 89% (12.5% compression rate), were successfully reconstructed with relative error (RE) of less than 20% and mean square error (MSE) of around 1%.

Comparative Analysis of Machine Learning Models for Weather Forecasting: A Heathrow Case Study

Accurate weather forecasting, especially temperature prediction, is fundamental for various segments within the UK, including agriculture, energy, and policy planning, as the nation adapts to the effects of climate change. This study addresses the limitations of conventional linear models in capturing the complex, non-linear relationships within meteorological data by comparing the effectiveness of different Machine Learning (ML) strategies. This study evaluates the performance of baseline ML models, such as Linear Regression (LR), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN). It also examines advanced ensemble and boosting models such as Decision Tree (DT), Random Forest (RF), XGBoost (XGB), LightGBM (LGBM), and CatBoost, using a comprehensive dataset from Heathrow Airport. Detailed preprocessing, model training, and optimization through cross-validation were conducted, with performance assessed using Mean Squared Error (MSE) and Coefficient of Determination (R²) metrics. The results demonstrate that ensemble methods, particularly XGB and LGBM, offer superior predictive accuracy for weather forecasting tasks, highlighting their potential to enhance predictive models in meteorological applications.

Bayesian quantum phase estimation with fixed photon states

We consider a two-mode bosonic state with fixed photon number n∈N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n \\in \\mathbb {N}$$\\end{document}, whose upper and lower modes pick up a phase ϕ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi $$\\end{document} and -ϕ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-\\phi $$\\end{document}, respectively. We compute the optimal Fock coefficients of the input state, such that the mean square error (MSE) for estimating ϕ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi $$\\end{document} is minimized, while the minimum MSE is always attainable by a measurement. Our setting is Bayesian, i.e., we consider ϕ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi $$\\end{document} to be a random variable that follows a prior probability distribution function (PDF). Initially, we consider the flat prior PDF and we discuss the well-known fact that the MSE is not an informative tool for estimating a phase when the variance of the prior PDF is large. Therefore, we move on to study truncated versions of the flat prior in both single-shot and adaptive approaches. For our adaptive technique, we consider n=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n=1$$\\end{document} and truncated prior PDFs. Each subsequent step utilizes as prior PDF the posterior probability of the previous step, and at the same time we update the optimal state and optimal measurement.

Deep Convolutional Transformer Network for Stock Movement Prediction

The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning and deep learning approaches, have been introduced for the purpose of forecasting stock price fluctuations, yielding numerous successful outcomes. Nonetheless, the identification of effective features for predicting stock movements is considered a complex challenge, primarily due to the non-linear characteristics, volatility, and inherent noise present in financial data. This study introduces an innovative Deep Convolutional Transformer (DCT) model that amalgamates convolutional neural networks, Transformers, and a multi-head attention mechanism. It features an inception convolutional token embedding architecture alongside separable fully connected layers. Experiments conducted on the NASDAQ, Hang Seng Index (HSI), and Shanghai Stock Exchange Composite (SSEC) employ Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), accuracy, and Matthews Correlation Coefficient (MCC) as evaluation metrics. The findings reveal that the DCT model achieves the highest accuracy of 58.85% on the NASDAQ dataset with a sliding window width of 30 days. In terms of error metrics, it surpasses other models, demonstrating the lowest average prediction error across all datasets for MAE, MSE, and MAPE. Furthermore, the DCT model attains the highest MCC values across all three datasets. These results suggest a promising capability for classifying stock price trends and affirming the DCT model’s superiority in predicting closing prices.

Oleanolic acid purified from the stem bark of Olax subscorpioidea Oliv. inhibits the function and catalysis of human 17β-hydroxysteroid dehydrogenase 1

Cancer is a leading cause of global death. Medicinal plants have gained increasing attention in cancer drug discovery. In this study, the stem bark extract of Olax subscorpioidea, which is used in ethnomedicine to treat cancer, was subjected to phytochemical investigation leading to the isolation of oleanolic acid (OA). The structure was elucidated by 1-dimensional and 2-dimensional nuclear magnetic resonance spectroscopic (NMR) data, and by comparing its data with previously reported data. Molecular docking was used to investigate the interactions of OA with nine selected cancer-related protein targets. OA docked well with human 17β-hydroxysteroid dehydrogenase type-1 (17βHSD1), caspase-3, and epidermal growth factor receptor tyrosine kinase (binding affinities: −9.8, −9.3, and −9.1 kcal/mol, respectively). OA is a triterpenoid compound with structural similarity to steroids. This similarity with the substrates of 17βHSD1 gives the inhibitor candidate an excellent opportunity to bind to 17βHSD1. The structural and functional dynamics of OA-17βHSD1 were investigated by molecular dynamics simulations at 240 ns. Molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) studies showed that OA had a binding free energy that is comparable with that of vincristine (–52.76, and −63.56 kcal/mol, respectively). The average C-α root mean square of deviation (RMSD) value of OA (1.69 Å) compared with the unbound protein (2.01 Å) indicated its high stability at the protein’s active site. The binding energy and stability at the active site of 17βHSD1 recorded in this study indicate that OA exhibited profound inhibitory potential. OA could be a good scaffold for developing new anti-breast cancer drugs.

An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices.

Deep learning approaches for image restoration in invasive coronary angiography

Abstract Background Despite being the gold standard for diagnosing coronary artery disease and guiding percutaneous coronary interventions (PCI), image restoration during invasive coronary angiography (ICA) has rarely been explored. Generative adversarial network (GAN), a deep learning model, has demonstrated its effectiveness in image restoration. Our hypothesis was that we could restore ICA images, damaged or deleted to varying degrees, using the inpainting GAN method, and we undertook efforts to test this hypothesis. Methods We extracted 22,659 ICA patch images (128 × 128 size) from 60 patients, focusing on sparse vessels for model training. To assess our inpainting GAN model, we degraded the extracted images to varying degrees: minimal, mild, moderate, severe, and completely blocked. The performance was quantitatively analyzed using metrics such as the peak signal-to-noise ratio (PSNR), structured similarity indexing methods (SSIM), and mean squared error (MSE). Results The inpainting GAN method significantly improved the image quality in all degraded images. For the blocked images, PSNR and SSIM were enhanced by 161.9% and 14.3%, respectively, with a 98% MSE reduction. In severely, moderately, mildly, and minimally damaged images, PSNR and SSIM increased by 14.9% and 14.8%, 15.7% and 14.7%, 19.3% and 15.6%, and 12.7% and 5.3%, respectively, with MSE reductions of 61.5%, 64.1%, 72.7%, and 33.9%, respectively. Conclusions Inpainting GAN can restore ICA images with varying damage levels, including deletions. Our deep-learning model can instantaneously improve ICA image quality, potentially aiding in emergency or chronic total occlusion PCI.Table 2.Quantitative Analysis of ImageFigure 1.Sample Cases Pre and Post-Rest

Rancang Bangun Aplikasi Untuk Prediksi Harga Bitcoin Menggunakan Algoritma Long Short-Term Memory

Bitcoin, as the first cryptocurrency launched in 2009, has experienced rapid growth and significant volatility.The high price fluctuations of Bitcoin have attracted the attention of investors and traders worldwide.Predicting Bitcoin prices poses a challenge due to its inherent complexity and volatility. Various methodsare employed to predict Bitcoin prices, including fundamental analysis, technical analysis, and deeplearning techniques. This study explores the use of neural networks, specifically Long Short-Term Memory(LSTM), as a tool for predicting Bitcoin prices. Long Short-Term Memory is a type of recurrent neuralnetwork (RNN) designed to address the vanishing and exploding gradient problems present in traditionalRNNs and can learn long-term dependencies in data. The LSTM model used in this research comprises 50neurons, 400 epochs, a batch size of 32, and utilizes Adam optimization. Model evaluation indicates thatLSTM performs well, with a Mean Square Error (MSE) of 2688. Additionally, the model achieves a MeanAbsolute Percentage Error (MAPE) accuracy of 97.77%. Based on these predictions, Bitcoin's price isexpected to decrease over the next month, with an estimated price of approximately 66,191.00 on June 15,2024, and around 64,271.64 on July 15, 2024

Using Machine Learning to identify the social determinants of the mid-life decline in mental health

Abstract Background The mid-life decline in mental well-being, depicted as a U-shaped pattern, is debated across disciplines. This study seeks to identify the social determinants of this decline and the overall contribution of social determinants to changes in mid-life mental health. Methods We use data from the 1970 British Cohort Study, from the ages 26, 34, and 42 (N = 6992, 51.5% female). Mental distress was measured using the 9-score Malaise Inventory. Mental distress increased from age 34 (M = 1.67) to 42 (M = 1.86; p < 0.001), with 17.8% declining by at least 1 SD on the inventory. We contrasted this group of decliners with those exhibiting stable mental health. We included socioeconomic and family factors, as well as physical health and health behaviours as potential determinants of mental health decline (at ages 26, 34, or at birth). We combine Random Forest and logistic regressions. Results The Random Forest’s Variable Importance (VI; root Mean Squared Error (MSE)), revealed social class at birth (VI = 0.023), physical multimorbidity at age 26 (VI = 0.22), and income quintile at age 34 (VI = 0.022) as most important predictors. Regressions confirmed that low birth social class (OR = 1.51 [1.25-1.83]), high-income at age 34 (OR = 0.68 [0.53-0.88]), and physical multimorbidity at age 26 (OR = 1.25 [1.06-1.48]) predict mental health decline. For those with high income and social class, the predicted probability of mid-life mental health decline is 0.109 (CI = [0.08-0.139]), rising to 0.277 (CI = [0.227-0.324]) for high risk individuals. Social class does not predict changes in mental health between ages 26 - 34. Interpretation We show that various social determinants of health contribute to the observed decline in mental health during mid-life. Social class at birth, income, and physical health are the most relevant predictors; they reduce the risk of decline from up to 27% to 11%. The contribution of social class appears specific to mid-life, and it could help explain the U-shaped pattern. Key messages • Social Factors Matter: Social class, income, and health predict mid-life mental decline. • Mid-life Class Impact: Social class uniquely affects mental health decline in mid-life.

A Study of Recommendation Methods Based on Graph Hybrid Neural Networks and Deep Crossing

In the face of complex user behavior patterns and massive data, improving the performance of recommender system models is an urgent challenge. Traditional methods often struggle to effectively handle feature interactions and complex user-item relationships. Combining the advantages of graph neural networks and the Deep Crossing network, this paper proposes a recommendation method based on hybrid neural networks with Deep Crossing (Deep Crossing with Graph Convolution and GRU, DCGCN-GRU). First, by constructing the graph structure of users and items, higher-order feature representations are extracted, and node features are updated using a multilayer graph convolution operation. Then, the higher-order features learned by the graph convolution network are spliced and weighted with the original features to form new feature inputs. Next, a Gated Recurrent Unit (GRU) is introduced to capture the inter-feature temporal dynamic relationships and sequence information. Finally, the Deep Crossing model is utilized to learn the interactions between the fused features at multiple levels and enhance the interactions between the features. Comparative experiments on three public datasets, MovieLens-ml-25m, Book-Crossings, and Amazon Reviews’23, show that the model achieves significant improvements in accuracy, mean square error (MSE), and mean absolute error (MAE).