Three-stage ensemble of image net pre-trained networks for pneumonia detection

Focusing on detection of pneumenia disease in the Chest X-Ray images, this paper proposes a three-stage ensemble methodology utilizing multiple pre-trained Convolutional Neural Networks (CNNs). In the first-stage ensemble, k subsets of training data are firstly randomly generated, each of which is then used to retrain a pre-trained CNN to produce k CNN models for the ensemble in the first stage. In the second-stage ensemble, multiple ensemble CNN models based on multiple pre-trained CNNs are integrated to reduce variance and improve the performance of the prediction. The third-stage ensemble is based on image augmentation, i.e., the original set of images are augmented to generate a few sets of additional images, after which each set of images are input to the ensemble models from the first two stages, and the outputs based multiple sets of images are then integrated. In integrating outputs in each stage, four ensemble techniques are introduced including averaging, feed forward neural network-based, decision tree-based, and majority voting. Thorough experiments were conducted on Chest X-Ray images from a Kaggle challenge, and the results showed the effectiveness of the proposed three-stage ensemble method in detecting pneumonia disease in the images

A New Credit Scoring Model For Vehicle Leasing Company

Usaha kecil dan menengah menjadi salah satu bisnis yang terdampak akibat penyebaran virus corona. Situasi pandemi di Indonesia menyebabkan penderitaan besar pada perusahaan-perusahaan ini. Untuk mencegah kerugian di masa pandemi saat ini. PT XYZ memutuskan untuk membuat model penilaian kredit untuk memprediksi risiko dari calon pelanggan mereka. Model akan terdiri dari dua jenis. Yang pertama adalah penilaian atau kartu skor sistem pakar. Data yang diperoleh dari sistem pakar nantinya akan dimasukkan ke dalam machine learning menggunakan metode statistik untuk mendapatkan model credit scoring. Kerangka kerja CRISP-DM akan digunakan untuk memandu proses pembuatan untuk memastikan keluaran model yang andal

Adaptive One-Dimensional Convolutional Neural Network for Tabular Data

This study introduces an innovative approach for tackling the credit risk prediction problem using an Adaptive One-Dimensional Convolutional Neural Network (1D CNN). The proposed methodology is designed for one-dimensional data, such as tabular data, through a combination of feed-forward and back-propagation phases. During the feed-forward phase, neuron outputs are computed by applying convolution operations to previous layer outputs, along with bias terms and activation functions. The subsequent back-propagation phase updates weights and biases to minimize prediction errors. A custom weight initialization algorithm tailored to Leaky ReLU activation is employed to enhance model adaptability. The core of the proposed algorithm lies in its ability to process each training data sample across layers, optimizing weights and biases to achieve accurate predictions. Comprehensive evaluations are conducted on various machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, ensemble methods, and neural networks. The proposed Adaptive 1D CNN emerges as the top performer, consistently surpassing other methods in precision, recall, F1-score, and accuracy. This success is attributed to its specialized weight initialization, effective back-propagation, and integration of 1D convolutional layers

Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México

Markovian Projection of Mexican Corporate Credit Ratings by 2020 and 2021The objective of this work is to predict the probability of migration between corporate ratings in Mexico, during the period 2018-2021.  The Markovian process methodology is applied in discrete time. Empirical evidence shows that corporations' credit ratings have a declining but stable trend in the short term. For the long term, no analyses are applied due to the intrinsic limitations of the model in predicting the probability of transition to long timeframes. Important contribution of this research to the financial literature lies in the application of the Markovian transition to an entire corporate sector to analyze changes in credit ratings, in an environment of uncertainty.  The results allow to conclude the relevance of the model to predict the stochastic phenomenon of credit ratings, considering the data characteristics and memory loss. Based on the results obtained it is recommended that companies deepen their economic diversification and maintain a disciplined management of their operations.      El objetivo del presente trabajo es predecir la probabilidad de migración entre las calificaciones corporativas en México, durante el período 2018-2021.  Se aplica la metodología  de procesos Markovianos a tiempo discreto. La evidencia empírica comprueba que las calificaciones de crédito de las corporaciones  presentan una tendencia decreciente pero estable en el corto plazo. A largo plazo no se aplican análisis debido a las  limitaciones intrínsecas del modelo en cuanto a la predicción de la probabilidad de transición a plazos largos.  Importante contribución de la presente investigación a la literatura financiera radica en la aplicación de la transición Markoviana a todo un sector corporativo para analizar  cambios en las calificaciones crediticias, en un entorno de incertidumbre.  Los resultados permiten concluir la relevancia del modelo para predecir el fenómeno estocástico de las calificaciones crediticias, debido a las características de los datos y la pérdida de memoria. A partir de los resultados obtenidos se recomienda que las empresas profundicen su  diversificación económica y mantengan una gestión disciplinada de sus operaciones

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic