Classical Regression Research Articles

Enhancing COVID-19 forecasting through deep learning techniques and fine-tuning

In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.

The Influence of Service Quality on Train Passenger Satisfaction at Rantau Prapat Station PT KAI Persero Divre I North Sumatra

In order to keep clients using this kind of transportation and to keep the business afloat, PT KAI places a high priority on their satisfaction. Employees of KAI are required to utilize five dimensions in its implementation: tangibles, assurance, responsiveness, empathy, and consistency. It is undoubtedly not ideal in its implementation at each station, as some travelers continue to be dissatisfied with the offers that are made. Examples of this include the fact that some people are still unable to board the train without a ticket and the indifference of staff members. Following basic observations by the researchers, there was a decline in staff responsiveness and overall satisfaction. The goal of this study is to determine how much the independent variable affects the dependent variable. In this quantitative analysis, primary and secondary data sources are consulted. Three techniques were employed to gather data: questionnaire completion, documentation, and observation. Using SPSS (Statistical Product and Service Solution), the instrument test, classical assumption test, hypothesis test, and simple linear regression method are the data analysis techniques employed. Once the researcher had finished using the data that had been collected. With a significance value (Sig) of 0.000, it may be inferred that the quality of the services offered significantly affects passenger happiness. Less than 0.05. The T test decision-making framework indicates that H1 is accepted and H0 is rejected, indicating that service quality has an impact on passengers' pleasure with trains. When the coefficient of determination hypothesis is evaluated using the SPSS version 26 software, a value of 0.906 can be obtained. This indicates that there is a 90.6% link between passenger satisfaction and service quality, with the remaining 9.4% coming from factors that have not been thoroughly investigated.

Spatio-Temporal Dual Kriging with Adaptive Coefficient Drift Function

Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods.

Discriminative and robust least squares regression for semi-supervised image classification

Due to the ability to leverage information from both unlabeled and labeled data, semi-supervised classification has found extensive applications in various practical scenarios. However, there are two major drawbacks: (1) Traditional graph construction leads to high algorithmic complexity, which limits efficiency. (2) The decision boundary might be blurred by boundary points in common cases. To cope with these issues, inspired by classical Least Squares Regression (LSR), we present a novel semi-supervised classification algorithm termed as Discriminative and Robust LSR (DRLSR) for semi-supervised image classification. First of all, a manifold regularization term is designed and introduced to an LSR-based semi-supervised method to preserve local manifold structures and smooth structures of the subspace, which strengthens the discrimination ability. Meanwhile, preservation of local manifold structures also contributes to restrain decision boundaries from being blurred by boundary points, which strengthens robustness of the algorithm. After that, an efficient alternative optimization method is applied to our algorithm. Evidence of the effectiveness of DRLSR are compelled by extensive experimental results.

Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.

The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.

Logistic regression versus XGBoost for detecting burned areas using satellite images

Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant problem: the identification of burned areas through satellite imagery, that is a clear example of imbalanced data. The methods are illustrated in the North-Central Portugal and the North-West of Spain in October 2017 within a multi-temporal setting of satellite imagery. Daily satellite images are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Our analysis shows that a classical Logistic regression (LR) model competes on par, if not surpasses, a widely employed machine learning algorithm called the extreme gradient boosting algorithm (XGBoost) within this particular domain.

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates in classification tasks, it remains uncertain whether its use significantly enhances the potential for early warning of impending problems. The following question arises: will classical methods eventually replace the effectiveness of these advanced techniques? This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis. This paper aims to develop bankruptcy prediction models for the chemical industry in Slovakia and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks (ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed methods is the most efficient. The research sample consists of circa 600 enterprises operating in the Slovak chemical industry. The selection of eleven financial indicators used for bankruptcy prediction was grounded in prior research and existing literature. The results show that all of the explored methods yielded highly similar outcomes. Therefore, determining the clear superiority of any single method is a difficult task. This might be partially due to the potentially reduced quality of the input data. In addition to classical statistical methods employed in econometrics, there is an ongoing development of AI-based models and their hybrid forms. The following question arises: to what extent can these newer approaches enhance accuracy and effectiveness?

Regression with continuous mixture of Gaussian distributions for modeling the memory time of water treatment

. In this article, a new model of regression with a continuous mixture of Gaussian distributions is developed. The proposed model is applied for the prediction of the time memory of water treatment corresponding to the minimal conductivity. The model training is performed on the basis of a dataset concerning the conductivity of the water and its variations. In order to estimate the regression parameters, we suggested an extension of the expectation maximization (EM) algorithm. More precisely, we explicitly computed the conditional expectation of the complete data log-likelihood in the EM algorithm. The results of this algorithm provide consistent estimators of the parameters. The calibrated regression model with a continuous mixture of Gaussian distributions outperforms the classical regression model, as shown in the numerical analysis. We prove that the EM algorithm outperforms the two-Stage Least Squares method. These results are presented to illustrate the performance and applicability of this proposed model in water treatment.

The effect of liquidity and leverage on financial performance with company size as a moderating variable: A study on companies listed in Jakarta Islamic Index 30

IntroductionThe Jakarta Islamic Index is an index on the Indonesia Stock Exchange which contains companies that contribute to the halal industry in Indonesia. However, research on financial performance specifically using liquidity, leverage and company size is still limited.ObjectivesThe research aims to examine the effect of liquidity and leverage on performance financial with company size as a moderating variable. This research aims to find out which companies in the Jakarta Islamic Index 30 are influenced by factors that can improve their financial performance so that they have a more positive impact on companies and investors.MethodThe subject of this research companies listed on Jakarta Islamic Index 30 for 2020 – 2022. Data collection techniques used in this study were purposive sampling and with a sample size of 114 samples. While the data analysis techniques used in this study is classic multiple regression analysis with interaction.ResultsThe analysis show that liquidity has a significant effect on financial performance, leverage has no effect on financial performance, company size moderates liquidity on financial performance but does not moderate leverage to financial performance.ImplicationsCompanies registered on Jakarta Islamic Index 30 can utilize their debt policy to obtain additional capital as long as the use is reasonable and does not burden the company.Originality/NoveltyThis study contributes to broaden the knowledge on financial performance of companies listed in Jakarta Islamic Index 30.

Determinants of profitability of insurance companies in Ethiopia: evidence from insurance companies from 2011 to 2020 years

The profitability of insurance companies has been the focus of several studies within the rise of economies across the countries worldwide. This study aimed to identify determinants of profitability of insurance companies in Ethiopian. The study used nine insurance companies selected by purposive sampling technique among the total 17 insurance companies in Ethiopia from period of 2011 to 2020 based on their establishment. Descriptive, causal research design and quantitative research approach were adopted in carrying out this study. Classical linear regression model under estimation of ordinary least square was employed to identify the determinants of profitability of insurance companies in Ethiopia at 5% level of significance. The classical linear regression model revealed that variable age of the company, tangibility of an asset, size of the company, managerial efficiency, leverage ratio, premium growth and GDP have a positive coefficient relationship with return on asset while loss ratio and inflation have a negative coefficient relationship with return on asset. On the other hand, age of the company size, managerial efficiency, leverage ratio, liquidity ration inflation and premium growth have statistically significant at 5% confidence interval level, whereas the other variables such tangibility of asset and GDP have no statistical significance at 5% confidence interval level. The insurance companies’ previous profit, age of the company, company size, managerial efficiency, leverage ratio, liquidity ratio, loss ratio, premium growth and inflation rate variables are significant key drivers of profitability of Ethiopian insurance companies. Giving due attention to the sector in line with key factors affecting the profitability will improve the overall performance of the insurance companies in Ethiopia.

Guidewire Endpoint Detection Based on Pixel‐Adjacent Relation during Robot‐Assisted Intravascular Catheterization: In Vivo Mammalian Models

Existing surgical guidewire endpoint localization methods in X‐ray images face challenges owing to their small size, simple appearance, nonrigid nature of objects, low signal‐to‐noise ratio of X‐ray images, and imbalance between the number of guidewire and background pixels, which lead to errors in surgical navigation. An eight‐neighborhood‐based method for increasing the localization accuracy of guidewire endpoint to improve the safety of interventional procedures is proposed herein. The proposed method includes two stages: 1) An improved U‐Net network is employed for segmenting the data of the guidewire to extract regions of interest containing guidewire endpoints with higher precision and to reduce interference from other anatomical structures and imaging artifacts. 2) The proposed method detects guidewire endpoints using the adjacent relationship between pixels in the eight‐neighborhood regions. This stage covers skeletonization extraction, removal of bifurcation points, and repair of fracture points. This study achieves mean pixel errors of 2.02 and 2.13 pixels in an in vivo rabbit and porcine X‐ray fluoroscopy images, outperforming ten classic heatmap and regression methods, achieving state‐of‐the‐art detection results. The proposed method can also be applied to detect other tiny surgical instruments such as stents and balloons, while preserving the flexibility of the guidewire bending angle.

Deep learning approaches for instantaneous laser absorptance prediction in additive manufacturing

The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high-speed synchrotron X-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%.

Machine learning and multi-criteria decision analysis for polyethylene air-gasification considering energy and environmental aspects

There is a recognized need for developing renewable energies and in this regard, gasification process. As a result, much research is being conducted on gasification of different materials and particularly, plastic waste. Although several studies have investigated different types of models to predict gasification performance, there are few studies to predict polyethylene waste performance in gasification using multilayer perceptron (MLP) machine learning algorithms and interpreting them using multi-criteria decision-making methods. The main aims of this study are to develop MLP artificial neural networks and regression models to predict polyethylene gasification performance with high accuracy and to compare the accuracy of the models developed to determine the best one, moreover, to rank different tests using the TOPSIS method to determine the best and worst trials. One of the finding of this research is an MLP model developed and tuned according to hyperparameters with an accuracy of 99.93% with 0.190 of root mean square error, 0.003 of mean absolute error, and 0.058 of mean absolute percentage error. The research results represent a further step towards developing machine learning algorithms instead of classic regression models in polyethylene gasification. One regression model is needed for each output variable; however, this problem is solved by artificial neural networks by developing only one model. Moreover, the results showed that the using of TOPSIS and machine learning methods can be an efficient method for ranking and predicting polyethylene gasification properties.

Multiplexed Surface Protein Detection and Cancer Classification Using Gap-Enhanced Magnetic-Plasmonic Core-Shell Raman Nanotags and Machine Learning Algorithm.

Cancer is the second leading cause of death attributed to disease worldwide. Current standard detection methods often rely on a single cancer marker, which can lead to inaccurate results, including false negatives, and an inability to detect multiple cancers simultaneously. Here, we developed a multiplex method that can effectively detect and classify surface proteins associated with three distinct types of breast cancer by utilizing gap-enhanced Raman scattering nanotags and machine learning algorithm. We synthesized anisotropic magnetic core-gold shell gap-enhanced Raman nanotags incorporating three different Raman reporters. These multicolor Raman nanotags were employed to distinguish specific surface protein markers in breast cancer cells. The acquired signals were deconvoluted and analyzed using classical least-squares regression to generate a surface protein profile and characterize the breast cancer cells. Furthermore, computational data obtained via finite-difference time-domain and discrete dipole approximation showed the amplification of the electric fields within the gap region due to plasmonic coupling between the two gold layers. Finally, a random forest classifier achieved an impressive classification and profiling accuracy of 93.9%, enabling effective distinguishing between the three different types of breast cancer cell lines in a mixed solution. With the combination of immunomagnetic multiplex target specificity and separation, gap-enhancement Raman nanotags, and machine learning, our method provides an accurate and integrated platform to profile and classify different cancer cells, giving implications for identification of the origin of circulating tumor cells in the blood system.

Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort.

Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank. This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development. Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99. ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and inflammatory biomarkers. These abnormalities were also observed in T2D and CAD patients. Risk scores showed great predictive performance in predicting CAD.

PENGARUH BEBAN OPERASIONAL/PENDAPATAN OPERASIONAL (BOPO) TERHADAP KINERJA KEUANGAN PADA PT BANK RAKYAT INDONESIA TBK (PERIODE 2017-2022)

This study aims to determine the partial and simultaneous effect of Operational Expenses/Operating Income (BOPO) on Financial Performance at PT Bank Rakyat Indonesia Tbk (2017-2022 Period). This study uses secondary data. The population in this study is the Financial Statements of Bank Rakyat Indonesia which are registered with BRI. The technique used to analyze the data used is the classical assumption test and simple linear regression test. The results of this study indicate that partially BOPO has a significant effect on ROA. The results of the t test were obtained at -6.985. The t-test significance value was obtained at 0.002. A significance value of less than 0.05 indicates that BOPO has a significant effect on ROA. Meanwhile simultaneously BOPO has an effect on ROA. The results of the F test were 48.787. The F value of the significance of the test was obtained at 0.002. A significance value greater than 0.05 indicates that BOPO has a significant effect on ROA.

Quantile set-regression with application in precision medicine

Quantile regression is commonly used in statistical analysis. It is more general than the ordinary regression. However in some practical investigations, the interest is the conditional quantile of the response given the covariates belonging to some set. For example in subgroup analysis, the goal is to classify the patients into two subgroups, one with whom benefit the most from the treatment and the other not so beneficial. This is equivalent to finding an optimal set in the covariate space, such that a patient is favored for the treatment if his/her covariates fall in this set, and otherwise not. Motivated by this practical problem we extend our recent work on set-regression to quantile set regression, defined as the conditional quantile of the response given the covariates belonging to a given set. This extends the notion of the classical quantile regression and is particularly suitable for precision medicine and other applications. The method is easy to use and in principle it works for sets of any shapes, but our current codes only work for certain particular shaped sets, the balls and rectangles. Simulation studies are conducted to evaluate the performance of the proposed method, which show superior promising results of the proposed method. Then the method is applied to analyze an AIDS Clinical Trials Group data.

Extreme Fuzzy Broad Learning System: Algorithm, Frequency Principle, and Applications in Classification and Regression.

As an effective alternative to deep neural networks, broad learning system (BLS) has attracted more attention due to its efficient and outstanding performance and shorter training process in classification and regression tasks. Nevertheless, the performance of BLS will not continue to increase, but even decrease, as the number of nodes reaches the saturation point and continues to increase. In addition, the previous research on neural networks usually ignored the reason for the good generalization of neural networks. To solve these problems, this article first proposes the Extreme Fuzzy BLS (E-FBLS), a novel cascaded fuzzy BLS, in which multiple fuzzy BLS blocks are grouped or cascaded together. Moreover, the original data is input to each FBLS block rather than the previous blocks. In addition, we use residual learning to illustrate the effectiveness of E-FBLS. From the frequency domain perspective, we also discover the existence of the frequency principle in E-FBLS, which can provide good interpretability for the generalization of the neural network. Experimental results on classical classification and regression datasets show that the accuracy of the proposed E-FBLS is superior to traditional BLS in handling classification and regression tasks. The accuracy improves when the number of blocks increases to some extent. Moreover, we verify the frequency principle of E-FBLS that E-FBLS can obtain the low-frequency components quickly, while the high-frequency components are gradually adjusted as the number of FBLS blocks increases.

Navigating Economic Transformations: Accounting for Paradigm Shifts in Post Covid-19 in Listed Companies in NSE, Kenya

The global landscape of accounting practices has witnessed unprecedented shifts in the aftermath of the Covid-19 pandemic, necessitating a comprehensive exploration of the ensuing paradigm shifts in the listed companies at the NSE in Kenya. However, the paradigm shifts have emerged in the approaches of listed companies at the NSE in Kenya in terms of sustainability, governance and technological adoption during transformative period. Further, the business need to navigate the economic transformations triggered by the covid-19 pandemic. The purpose of this research was to examine the Navigating Economic Transformations: Accounting for Paradigm shifts in Post covid-19 in listed companies in NSE, Kenya. Specifically the study aims to assess the impact of post-covid-19 economic shifts on financial reporting practices in NSE-listed companies. The study was based on Adaptive Resilience Theory. In order to meet the objectives of this study, an explanatory research design was used utilizing a quantitative approach. This study is astudy that uses secondary data. The target population of the study comprised listed companies on the NSE. Random sampling of NSE-listed companies to ensure representative insights. Audited annual reports for the five year period from 2019 to 2023 were used. Data reliability was ensured by use of audited reports. Documentary evidence was employed by collecting the relevant information from the annual reports by use of document schedule. The panel data fixed effect estimation model was applied for the data analysis. The collected data was analyzed using descriptive statistics and classical linear regression modelling. This research aims to contribute valuable insights and fostering a more robust understanding of the intersection between accounting, economic shifts, and corporate strategies in the post-covid-19 era for NSE-listed companies in Kenya

КЛАССИФИКАЦИЯ СОСТОЯНИЯ РАСТЕНИЙ СРЕДСТВАМИ ТЕКСТУРНОГО ВЕЙВЛЕТ-АНАЛИЗА И МАШИННОГО ОБУЧЕНИЯ

The article demonstrates a method that combines the wavelet transform and machine learning methods to classify plants health using colored digital images. The input data for classifi cation is comprised of a built vector of Haralick texture features. The software was developed via the Python programming language to classify digital images with the multilevel discrete Daubechies wavelet transform and methods of classifi cation for machine learning, particularly classic logistic regression and perceptron. The effi ciency of this method for solving the problem of multiclass image classifi cation is demonstrated. The study concludes and assesses the prospects of the method.