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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises. [ABSTRACT FROM AUTHOR]

Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its' indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models. [ABSTRACT FROM AUTHOR]

Forest management in Lithuania is focused on sustainable development, balancing economic, environmental, and social objectives. Lithuanian forests, covering approximately one-third of the country's land area, are a critical resource for biodiversity, timber production, carbon sequestration, and recreation. To better understand how forests develop over time and how various factors influence their growth, it is important to identify tree growth functions. These functions are mathematical models that describe the relationship between forest growth and other variables such as age, environmental conditions, and management practices. Additionally, AI plays a crucial role in forest planning and management due to its ability to process large datasets, enhance decision-making, and improve sustainability practices. AI and machine learning (ML) are extensively used in forestry, as these methods enable the analysis of large datasets, including climate data, soil conditions, and historical forest management records, to optimise forest operations. For example, AI algorithms can help plan sustainable logging activities by predicting tree growth patterns and yield, while ML models assess the impact of different forest management practices on biodiversity and ecosystem health. AI-driven decision support systems can also assist in the realtime monitoring and management of forest resources, ensuring efficient and sustainable practices. This research aims to develop tree growth functions for Lithuanian forests and extend the GAYA stand simulator to new contexts, adapting it to Lithuanian conditions and reflecting the country's unique compositions of species and ecological conditions. We are integrating these growth functions into the GAYA framework to ensure accurate projections for Lithuanian forest ecosystems. Moreover, through a literature review, we have identified ways to improve forest planning using AI methods. Initial results show that estimating tree volume growth requires accounting for various forest parameters such as volume, height, age, diameter, and information about cutting activities. By applying ML models, forest planning can be further improved by integrating diverse datasets and evaluating multiple forest management scenarios. [ABSTRACT FROM AUTHOR]

Arun Kumar Mishra wrote that every movement of goods from one point to the next must have the attached documents. According to Statista, logistics industry worldwide grows 0.5 trillion dollars each year, meaning more transportation is required the more documents needs to be processed. To efficiently manage huge volumes of documents and automate a decision-making process a classification system is required. This research focuses on logistics documents classification utilizing deep learning and machine learning algorithms. For this study, 50 GB of unlabeled data were presented, and the initial experiments were conducted using 5078 manually selected documents. Manually selected documents were assigned to 4 commonly used logistics document categories: CMRs, invoices, receipts, and others. The dataset was split into train and test sets, where 80% or 4058 of the documents were designated for training and 20% or 1014 of the documents for testing. Five main preprocessing steps were applied: convertion from PDF to JPG, resizing, deskewing, tint and noise removal. Two main methodologies were applied, application of neural networks and traditional machine learning classification techniques. Both approaches utilized pretrained backbone models on ImageNet. For neural networks we used Efficient-Net80, VGG16, MobileNet, ResNet50, DenseNet and InceptionV3. The neural network with the ResNet50 backbone outperformed other models achieving 0.9582 accuracy, 0.9593 precision, 0.9582 recall and 0.9585 F1 score. Rest models showed comparable results in performance evaluation: EfficientNet80 achieved 0.9467, VGG16 0.9176, MobileNet 0.9307, DenseNet 0.9387 and InceptionV3 0.9387 F1 scores. In addition, traditional machine learning classifiers, including Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and XGBoost (XGB), were trained using features extracted from the ResNet50 backbone. The best-performing machine learning model was the Support Vector Classifier, achieving an accuracy of 0.9471, 0.9470 precision, 0.9471 recall and 0.9466 F1 score, while the XGBoost classifier, Random Forest, and KNearest Neighbors classifiers achieved F1 scores of 0.9440, 0.9455, and 0.9281, respectively. The research showed that the most promising solution for logistics document classification is the ResNet50 model and that it could be implemented in logistic environments to automate document separation. Future research will focus on dataset expansion utilizing pretrained ResNet50 model to label the remaining unused documents and further fine-tune models to enhance model F1 score, minimizing the need for human intervention in document classification. [ABSTRACT FROM AUTHOR]

The study presents a systematic review of 232 studies on various aspects of the use of artificial intelligence methods for identification of financial distress (such as bankruptcy or insolvency). We follow the guidelines of the PRISMA methodology for performing the systematic reviews. The study discusses bankruptcy-related financial datasets, data imbalance, feature dimensionality reduction in financial datasets, financial distress prediction, data pre-processing issues, non-financial indicators, frequently used machine-learning methods, performance evolution metrics, and other related issues of machine-learning-based workflows. The study findings revealed the necessity of data balancing, dimensionality reduction techniques in data preprocessing, and allow researchers to identify new research directions that have not been analyzed yet. [ABSTRACT FROM AUTHOR]

This research describes the problem of classification method selection for credit scoring. Credit risk is one of main risks of financial institutions. That's why researchers and analysts are constantly looking for better credit scoring model creation. Nowadays, a number of different features, such as information about the clients: his habits, previous credits, bank accounts' information, insurance and other, can be included in credit scoring models. In recent years, deep learning become one of the most popular machine learning methods due to its performance. One of deep learning methods is convolution neural network (ConvNet), which learns all aspects directly from the data, by creating network layers, which in abstract level reveals the main features of the category. This method is suitable to accommodate large number of features and have good accuracy results, what's why such companies as: Google, Facebook, Microsoft, IBM is using it for different classification tasks. Three real-world data sets from UCI repository are obtained in the experiments for credit scoring problems. Different ConvNet architectures were created random for each data set, based on the logic of the wrapper method (to optimize the accuracy). These results are compered in three ways: with each other; with conventional machine learning methods; with the works of other authors. The experimental outcomes reveal the further development of the ConvNet is expedient in a larger number of features and data sets, either in the combinations of different voting of classifications algorithms (including the ConvNet method). [ABSTRACT FROM AUTHOR]

We discuss an experiment on automatic identification of bi-gram multiword expressions (MWE) in parallel Latvian and Lithuanian corpora. Raw corpora, lexical association measures (LAMs) and supervised machine learning (ML) are used due to the scarceness and quality of lexical resources (e.g., POS-tagger, parser) and tools. Combining LAMs with ML works well for other languages, our experiments show that it perform well for Lithuanian and Latvian as well. We analyse and discuss frequency thresholds in terms of potential MWE and LAMs values. Finally, combining LAMs with ML we have achieved 98,8% precision and 57,5% recall for Latvian and 96,9% precision and 61,8% recall for Lithuanian. [ABSTRACT FROM AUTHOR]