Linear Tree Research Articles

Which fields drive the h-index?

The measurement of the quality of academic research is often done by means of the h-index measure. Although widely accepted, the h-index has some issues and, in particular, it may depend on the scientific field in which a researcher operates. To date there is not a definitive answer as to whether this difference holds, and to what extent it varies. To fill the gap, we propose to operationaly measure the difference in h-index across the sectors of a relatively homogeneous population of all scientists of a nation. To answer the heterogeneity issue we apply three different explainable machine learning models: linear regression, Poisson regression and tree models. Our results show that the latter two models better explain the data. They show that the only sectors for which a difference in h-index is significant are Physics, Biology and Clinical Sciences.

Parametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads: A Finite Element and Machine Learning Approach.

This research focuses on investigating the buckling strength of thin-walled composite structures featuring various shapes of holes, laminates, and composite materials. A parametric study is conducted to optimize and identify the most suitable combination of material and structural parameters, ensuring the resilience of structure under both mechanical and thermal loads. Initially, a numerical approach employing the finite element method is used to design the C-section thin-walled composite structure. Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. Various ML techniques such as linear regression, lasso regression, decision tree, random forest, and gradient boosting are employed to assess their accuracy in comparison with finite element results. As a result, the simulation model showcases the variation in critical buckling load when altering the structural and material properties. Additionally, the machine learning models successfully predict the optimal critical buckling load under mechanical and thermal loading conditions. In summary, this paper delves into the study of the stability of C-section thin-walled composite structures with holes under mechanical and thermal loading conditions using finite element analysis and machine learning studies.

Noninvasive brain stimulation during EEG improves machine learning classification in chronic stroke.

In individuals with chronic stroke and hemiparesis, noninvasive brain stimulation (NIBS) may be used as an adjunct to therapy for improving motor recovery. Specific states of movement during motor recovery are more responsive to brain stimulation than others, thus a system that could auto-detect movement state would be useful in correctly identifying the most effective stimulation periods. The aim of this study was to compare the performance of different machine learning models in classifying movement periods during EEG recordings of hemiparetic individuals receiving noninvasive brain stimulation. We hypothesized that transcranial direct current stimulation, a form of NIBS, would modulate brain recordings correlating with movement state and improve classification accuracies above those receiving sham stimulation. Electroencephalogram data were obtained from 10 participants with chronic stroke and 11 healthy individuals performing a motor task while undergoing transcranial direct current stimulation. Eight traditional machine learning algorithms and five ensemble methods were used to classify two movement states (a hold posture and an arm reaching movement) before, during and after stimulation. To minimize compute times, preprocessing and feature extraction were limited to z-score normalization and power binning into five frequency bands (delta through gamma). Classification of disease state produced significantly higher accuracies in the stimulation (versus sham) group at 78.9% (versus 55.6%, p < 0.000002). We observed significantly higher accuracies when classifying stimulation state in the chronic stroke group (77.6%) relative to healthy controls (64.1%, p < 0.0095). In the chronic stroke cohort, classification of hold versus reach was highest during the stimulation period (75.2%) as opposed to the pre- and post-stimulation periods. Linear discriminant analysis, logistic regression, and decision tree algorithms classified movement state most accurately in participants with chronic stroke during the stimulation period (76.1%). For the ensemble methods, the highest classification accuracy for hold versus reach was achieved using low gamma frequency (30-50 Hz) as a feature (74.5%), although this result did not achieve statistical significance. Machine learning algorithms demonstrated sufficiently high movement state classification accuracy in participants with chronic stroke performing functional tasks during noninvasive brain stimulation. tDCS improved disease state and movement state classification in participants with chronic stroke.

OPTIMIZING RETAIL DEMAND FORECASTING: A PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS INCLUDING LSTM AND GRADIENT BOOSTING

Effective demand forecasting is vital for inventory management in retail. This study evaluates five machine learning models—Linear Regression (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting (GB), and Long Short-Term Memory (LSTM)—for predicting retail demand. Utilizing a dataset with transactional sales, promotions, calendar events, and external factors like weather and economic indicators, we applied rigorous preprocessing and feature engineering. Performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). Results show that LSTM outperforms other models with an MAE of 9.53, RMSE of 14.67, and R² of 0.90, excelling in capturing temporal dependencies and complex demand patterns. Gradient Boosting and Random Forest also performed well, while Linear Regression and Decision Tree Regressor showed limitations. This study highlights the effectiveness of advanced models, particularly LSTM, for enhancing demand forecasting accuracy and offers valuable insights for optimizing retail inventory and operations.

Robustness Investigation of ML Models for Stock Price Prediction During Market Volatility: A Case Study of the Japanese Stock Market Amidst the COVID-19 Pandemic

In the evolving realm of financial markets, precise stock price prediction remains a pivotal challenge. This paper investigates the efficacy of traditional machine learning models for stock price forecasting, particularly during the high volatility phase induced by the COVID-19 pandemic, using the Japanese stock market as a case study. This study utilized a dataset from the JPX Tokyo Stock Exchange Prediction competition, selecting two stocks at random to avoid bias. After standardizing the data for consistency, this study employed linear regression and decision trees to project stock prices, comparing their performance during stable periods and the economic upheaval of the pandemic. The analysis revealed that both models' predictive capabilities were compromised during the pandemic, with the decision tree model particularly underperforming. In normal times, linear regression showed improved accuracy for stock 1301 in 2021, while for stock 6758, the model's performance declined. These findings underscore the necessity for robust models that can withstand market turbulences like those observed during the COVID-19 crisis. The research contributes to both academic discussions on financial forecasting and practical strategies for market participants navigating complex stock environments. Future work will focus on developing more resilient forecasting methods to address these challenges.

A machine learning framework for predictive electron density modelling to enhance 3D NAND flash memory performance

Data storage in electronic devices has been revolutionised by 3D NAND flash memory. However, polycrystalline silicon and grain boundaries offer issues that greatly affect memory performance in terms of string current and Program-Erase Threshold Voltage window (Vt –Window). Scientists need to learn more about how grain size, channel thickness, and trap density affect electron behaviour to improve the efficiency of memory chips. Regression models are used in this work to forecast fluctuations in electron density along the channel in 3D NAND string devices. The dataset, which was derived using TCAD simulations, has a sizable number of samples that show the electron density as a function of channel length. We assess their performance using R2 scores and RMSE values using regression models such as Linear Regression, Random Forest, K-Neighbour Regressor, Decision Tree, Gradient Boosting, XGBRegressor, CatBoosting Regressor, and AdaBoost Regressor. By improving our knowledge of how electrons behave in transistor channels, this work contributes to the optimisation of 3D NAND flash memory.

Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security

Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. Doi: 10.28991/HIJ-2024-05-03-018 Full Text: PDF

Analyzing Stock Price Prediction Models: A Comparative Study of Linear Regression and Decision Trees During the COVID-19 Pandemic

Stock market forecasting plays a vital role in financial decision making. In a volatile and uncertain situation like the COVID-19 pandemic, it is important for us to compare the accuracy of different models for forecasting. This study compares the performance of the Linear Regression (LR) algorithm and the Decision Tree (DT) algorithm in predicting the stock price of Pfizer Inc. before and during a pandemic. The study evaluates the accuracy and stability of the predictions using datasets before and during the pandemic. The results show that the prediction accuracy of LR is better than that of DT in both cases, although both accuracies decrease during the pandemic. The results of the study emphasize the need to consider external factors in the selection of forecasting models and suggest some lessons for future research, leading to the selection of more stable and accurate models under turbulent market conditions. It is worth noting that overfitting occurs when a model learns to capture noise in the data rather than the underlying patterns, leading to reduced generalization performance on unseen data. Future research should address this by exploring techniques such as regularization or cross-validation to mitigate overfitting and enhance the robustness of predictive models in dynamic market environments.

Stock Return Prediction under US Federal Funds Rate Hike Based on Supervised Machine Learning

As US Federal Reverse Systems has continued to raise federal funds in the past few years in order to curb inflation, this has had a huge impact on chinese capital market. Investors and financial institude are actively engaged in predicting stock returns to maximize their investment income. This study constructs a model to analyze and predict the financial factor of stock income based on supervised machine learning methods and evaluate the results. The datasample is extracted from wind, for the period half a year before and after the recent federal funds rate hike. Utilizing machine learning packages in R, this paper construct models based on linear discriminant analysis, decision tree and random forest models. After constructing estimation models, this paper visualizes the prediction result by utilizing drawing package in R. It has been found that the random forest algorithm predicting model generates very successful results for the financial factor prediction as the model accuracy reaches 99%. According to the analysis, relevant model to predict financial factors and non-financial factors based on non-linear supervised machine learning algorithm can be built by investors or hedge fund agency to estimate stock performance in the future. This study also investigates the economic result of the Federal Funds Rate hikes, which is instructive for investors to make well-informed decisions.

Comparative Analysis of Financial Forecasting Techniques in Machine Learning for Netflix Stock Price Prediction

Abstract - In the stock market, predicting the price of stocks is still a hot research issue. One of the most challenging tasks in the field of computation is prediction of the stock market. The prediction depends on a number of variables, including physiological vs. Physical elements, rational vs. irrational investor behavior, investor attitude, market rumors, etc. All these factors work together to make stock values unpredictable and very challenging to predict accurately. Supervised Machine Learning (SML) algorithms is used to predict future value by using past stock price data. ML approaches have the ability to reveal trends and knowledge that hadn't previously noticed, and they can be used to predict that are incredibly accurate. The primary objective of this project is to predict stock prices of Netflix for analyzing profit on day closing. Data was gathered between the years of 2002 and 2022, and separated into two parts: training set and testing set. Only the testing portion is to be used for the final forecast. Four machine learning algorithms such as linear regression, decision tree, MLP, and KNN are used to forecast the close value of the Netflix stock price. This project's output adds to the body of knowledge on predicting stock prices and offers investors some new information for accurate prediction of stock prices.

Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective.

Development of a Predictive Model to Determine Appropriate Length of Profile.

Musculoskeletal injuries are one of the primary causes of Soldiers' inability to be medically ready, comprising over 80% of such causes. The electronic profile (e-Profile) is the way that musculoskeletal injuries are documented so that commanders will know the type of injury as well as the length of the time that the Soldier will need limited duty. A previous study of e-Profiles in an Army MTF Integrated Pain Management Center showed that the median length of an e-Profile was 30 days. It is in the best interest of the Army to have the Soldier out of the fight the minimum amount of time for recovery to ensure the unit readiness. The goal of this study was to utilize e-Profile data to see if a machine learning model can be developed to determine the appropriate time a Soldier needs to be on profile for a given diagnoses. Institutional Review Board approval was obtained from the USAMRDC (protocol #M-10966). The initial dataset provided to the research team consisted of a single pipe delimited ("|") text file containing 2.9 million rows of e-Profile data. Linear regression, decision trees, and random forests (RFs) were evaluated to see which model would best predict the number of days needed for an e-Profile. Linear regression had an adjusted R-squared of 0.165. The positive predictive value of decision trees (0-to-30-day range of e-Profiles) was 73.6%, and the negative predictive value (30-90 days) was 60.9% with an area under the receiver operating characteristic curve (AUC) of 0.694 for the model. The positive predictive value of RFs was 85.3% (for the 0-30 range), and the negative predictive value was 58.7% (for the 30-90 range) with an AUC of 0.794. An AUC that approaches 1 indicates a more accurate prediction. The 3 models (linear regression, decision trees, and RF) studied as part of this project did not predict the days on e-Profile with a high degree of certainty. Future research will focus on adding additional data to the e-Profile dataset in order to improve model accuracy.

Machine learning-based predictive approach for pitting and uniform corrosion in geothermal energy systems

Geothermal energy is a sustainable source from Earth's crust, and holds great potential for electricity generation, but corrosion poses a significant challenge. In this study, we employed a range of machine learning models, including linear regression, decision tree, k-nearest neighbors, random forest, and support vector regression to predict uniform and pitting corrosion. Despite limited data performance issues, virtual samples from a synthetic data generator improved results when combined with actual data. Fine-tuning with various hyperparameters enhanced model performance, with the decision tree proving the most effective. Exhaustive feature selection identifies key factors influencing uniform and pitting corrosion, validated by the models.

Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging

The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction.

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

This study introduces machine learning (ML) algorithms to predict hydrogen (H2) thermodynamic properties for geological storage, focusing on its mixtures with natural gas, nitrogen (N2), and carbon dioxide (CO2). Employing 1167 data samples, this research utilizes multiple linear regression (MLR), random forest (RF), gradient boosting (GB), and decision tree (DT) models, enhanced by sensitivity analysis for feature engineering. GB model's superior accuracy and efficiency over conventional statistical linear regression methods. The finding reveals that the GB model achieves superior performance, with an R2 value of 0.9998 for thermal capacity and the lowest mean absolute error (MAE)/root mean square error (RMSE) across all H2 properties—density (1.51%, 2.06 kg/m3), viscosity (0.000078%, 0.000117 cp), thermal conductivity (0.000427%, 0.000849 W/m.K), and thermal capacity (8.79%, 26.06 J/g.K). Moreover, this ML-based approach not only demonstrates remarkable accuracy and efficiency but also suggests the potential of smart paradigms to further enhance the safety and transportability of underground hydrogen storage projects. Ultimately, this work will help the reservoir simulation at field scale to be more robust and reliable.

Comparative Analysis of Linear Regression, Decision Tree and Gradient Boosting for Predicting Stock Price of Bank Rakyat Indonesia

An investment is the placement of a current amount of funds in the hope of generating a profit in the future. There are several types of investments, including stocks, which are attractive options as they can bring a huge return to investors. However, rapidly fluctuating stock prices are influenced by various factors, such as company performance, interest rates, economic conditions, and government policies. In Indonesia, PT Bank Rakyat Indonesia Tbk (BBRI) had the largest profit among the 10 largest banks by the end of March 2024, with a profit of IDR 13.8 trillion. The higher the bank's return, the greater the investor's interest in purchasing the stock, influencing the stock price. The goal of stock price prediction is to forecast the stock's future price in order to increase investors' potential profits. Various methods, such as Linear Regression, Decision Tree, and Gradient Boosting, have been developed for stock price prediction. Comparative analysis is needed to determine the most accurate method in predicting the stock price of People's Bank of Indonesia, using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared metrics (R2). The analysis showed that Linear Regression achieved the highest accuracy with R2 of 0.96, MAE of 65.72 and RMSE of 86.74 compared to the Decision Tree and Gradient Boosting models.

Degrees of Freedom: Search Cost and Self-consistency

Model degrees of freedom ( df ) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap between df and the number of independent variables in the fitting procedure, Tibshirani (2015) introduced the search degrees of freedom ( sdf ) concept to account for the search cost during model selection. However, this definition has two limitations: it does not consider fitting procedures in augmented spaces and does not use the same fitting procedure for sdf and df . We propose a modified search degrees of freedom ( msdf ) to directly account for the cost of searching in either original or augmented spaces. We check this definition for various fitting procedures, including classical linear regressions, spline methods, adaptive regressions (the best subset and the lasso), regression trees, and multivariate adaptive regression splines (MARS). In many scenarios when sdf is applicable, msdf reduces to sdf . However, for certain procedures like the lasso, msdf offers a fresh perspective on search costs. For some complex procedures like MARS, the df has been pre-determined during model fitting, but the df of the final fitted procedure might differ from the pre-determined one. To investigate this discrepancy, we introduce the concepts of nominal df and actual df , and define the property of self-consistency, which occurs when there is no gap between these two df ’s. We propose a correction procedure for MARS to align these two df ’s, demonstrating improved fitting performance through extensive simulations and two real data applications.

Performance enhancement of classifiers through Bio inspired feature selection methods for early detection of lung cancer from microarray genes

Gene expression in the microarray is assimilated with redundant and high-dimensional information. Moreover, the information in the microarray genes mostly correlates with background noise. This paper uses dimensionality reduction and feature selection methods to employ a classification methodology for high-dimensional lung cancer microarray data. The approach is enforced in two phases; initially, the genes are dimensionally reduced through Hilbert Transform, Detrend Fluctuation Analysis and Least Square Linear Regression methods. The dimensionally reduced data is further optimized in the next phase using Elephant Herd optimization (EHO) and Cuckoo Search Feature selection methods. The classifiers used here are Bayesian Linear Discriminant, Naive Bayes, Random Forest, Decision Tree, SVM (Linear), SVM (Polynomial), and SVM (RBF). The classifier's performances are analysed with and without feature selection methods. The SVM (Linear) classifier with the DFA Dimensionality Reduction method and EHO feature selection achieved the highest accuracy of 92.26 % compared to other classifiers.

Multiscale Models to Evaluate the Impact of Chemical Compositions and Test Conditions on the Mechanical Properties of Cement Mortar for Tile Adhesive Applications.

This study aims to develop systematic multiscale models to accurately predict the compressive strength of cement mortar for tile adhesive applications, specifically tailored for applications in the construction industry. Drawing on data from 200 cement mortar tests conducted in previous studies, various factors such as cement/water ratios, curing times, cement/sand ratios, and chemical compositions were analyzed through static modeling techniques. The model selection involved utilizing various approaches, including linear regression, pure quadratic, interaction, M5P tree, and artificial neural network models to identify the most influential parameters affecting mortar strength. The analysis considered the water/cement ratio, testing ages, cement/sand ratio, and chemical compositions, such as silicon dioxide, calcium dioxide, iron (III) oxide, aluminum oxide, and the pH value. Evaluation metrics, such as the determination coefficient, mean absolute error, root-mean-square error, objective function, scatter index, and a-20 index, were employed to ensure the accuracy of the compressive strength estimates. Additionally, empirical equations were utilized to predict flexural and tensile strengths based on the compressive strength of the cement mortar for tile adhesive applications.

Renewable Energy Prediction Using Different ML Algorithms

Abstract: The global community is grappling with numerous obstacles in fulfilling its energy requirements, stemming from the limited availability of fossil fuel reserves. It is, at present, rapidly shifting its focus towards Renewable Energy Sources to overcome the limitations of fossil fuels and generate environmentally friendly energy. Earlier, their unpredictable nature made them less appealing, but recent progress in Artificial Intelligence (AI) and Machine Learning (ML) has allowed for accurate forecasts of their energy production. With a well-planned approach to balancing energy loads, renewable energy sources can satisfy energy needs without the necessity for extra storage. In this piece, we conducted a machine learning analysis on a dataset obtained from the Kaggle Platform by utilizing various algorithms, such as Random Forest (RF), Linear Support Vector Regression (SVM), Decision Tree (DT), and Linear Regression (LR).