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Purpose: The aim of this study is to evaluate of the predictive ability of goodwill and other intangible assets on forecasting corporate profitability. Subsequently, this study compares the efficiency of deep learning model to that of other machine learning models such as random forest (RF) and support vector machine (SVM) as well as traditional statistical methods such as the linear regression model. Design/methodology/approach: Studies confirm that goodwill and intangibles are valuable assets that give companies a competitive advantage to increase profitability and shareholders' returns. Thus, by using as sample Greek-listed financial data, this study investigates whether or not the inclusion of goodwill and intangible assets as input variables in this modified deep learning models contribute to the corporate profitability prediction accuracy. Subsequently, this study compares the modified long-short-term model with other machine learning models such as SVMs and RF as well as the traditional panel regression model. Findings: The findings of this paper confirm that goodwill and intangible assets clearly improve the performance of a deep learning corporate profitability prediction model. Furthermore, this study provides evidence that the modified long short-term memory model outperforms other machine learning models such as SVMs and RF , as well as traditional statistical panel regression model, in predicting corporate profitability. Research limitations/implications: Limitation of this study includes the relatively small amount of data available. Furthermore, the aim is to challenge the authors' modified long short-term memory by using listed corporate data of Greece, a code-law country that suffered severely during the recent fiscal crisis. However, this study proposes that future research may apply deep learning corporate profitability models on a bigger pool of data such as STOXX Europe 600 companies. Practical implications: Subsequently, the authors believe that their paper is of interest to different professional groups, such as financial analysts and banks, which the authors' paper can support in their corporate profitability evaluation procedure. Furthermore, as well as shareholders are concerned, this paper could be of benefit in forecasting management's potential to create future returns. Finally, management may incorporate this model in the evaluation process of potential acquisitions of other companies. Originality/value: The contributions of this work can be summarized in the following aspects. This study provides evidence that by including goodwill and other intangible assets in the authors' input portfolio, prediction errors represented by root mean squared error are reduced. A modified long short-term memory model is proposed to predict the numerical value of the profitability (or the profitability ratio) in contrast to other studies which deal with trend predictions, i.e. the binomial output result of positive or negative earnings. Finally, posing an extra challenge to the authors' deep learning model, the authors' used financial statements according to International Financial Reporting Standard data of listed companies in Greece, a code-law country that suffered during the recent fiscal debt crisis, heavily influenced by tax legislation and characterized by its lower investors' protection compared to common-law countries. [ABSTRACT FROM AUTHOR]

Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results. [ABSTRACT FROM AUTHOR]

High-quality academic publishing is built on rigorous peer review. I Algorithms i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

Thanks to the efforts of our reviewers in 2022, the median time to first decision was 17 days and the median time to publication was 37 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Mathematics i : those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). I Mathematics i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]