A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

/pdf/a-comprehensive-review-of-machine-learning-used-to-combat-7pltsvdf.pdf

A Comprehensive Review of Machine Learning Used to Combat COVID-19

The main objective of this research is to evaluate the performance of machine learning algorithms in the field of credit card fraud detection and then compare them according to various performance metrics. Seven different supervised classification algorithms including Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbors and Support Vector Machine were used. The performance of these algorithms was measured through a comprehensive evaluation of metrics including Accuracy, Precision, Recall, F-Score, AUC and AUPRC values. Furthermore, ROC curves and confusion matrices were used to evaluate these algorithms. The data preparation phase is critical in this study. The data imbalance problem arises as an unequal distribution between fraudulent and non-fraudulent transactions. Addressing this imbalance is imperative for successful model training and subsequent reliable results. Various techniques, such as Scaling and Distribution, Random Under-Sampling, Dimensionality Reduction, and Clustering, are employed to ensure an accurate evaluation of model performance and its ability to generalize effectively. As a result, the "Random Forest" and "K-Nearest Neighbors" algorithms exhibit the highest performance levels in this research with 97% accuracy rates. This study contributes significantly to the ongoing fight against financial fraud and provides valuable guidance for future research efforts. 

pdf/comparative-analysis-of-machine-learning-techniques-for-1ponu6zq3x.pdf

Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets

Towards data and analytics driven B2B-banking for green finance: A cross-selling use case study

When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step.This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar.The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the testing phase of Coefficient of Determination (R = 0.9005), Mean Absolute Percentage Error (MAPE = 8.40%), and Relative Root Mean Square Error (rRMSE = 12.56%).Nevertheless, the ANFIS model outperformed the GA-ANN model in forecasting daily GSR, with the best values of indicators when testing the model being R = 0.9374, MAPE = 7.78%, and rRMSE = 10.54%.Generally, we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved, and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes. 

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Loans are one of the main profit sources in banking system. Banks try to select reliable customers and offer them personal loans, but customers can sometimes reject bank loan offers. Prediction of this problem is an extra work for banks, but if they can predict which customers will accept personal loan offers, they can make a better profit. Therefore, at this point, the aim of this study is to predict acceptance of the bank loan offers using the Support Vector Machine (SVM) algorithm. In this context, SVM was used to predict results with four kernels of SVM, with a grid search algorithm for better prediction and cross validation for much more reliable results. Research findings show that the best results were obtained with a poly kernel as 97.2% accuracy and the lowest success rate with a sigmoid kernel as 83.3% accuracy. Some precision and recall values are lower than normal ones, like 0.108 and 0.008 due to unbalanced dataset, like for 1 true value, there are 9 negative values (9.6% true value). This study recommends the use of SVC in banking system while predicting acceptance of bank loan offers.

/pdf/predicting-acceptance-of-the-bank-loan-offers-by-using-2ppq5elw.pdf

Predicting acceptance of the bank loan offers by using support vector machines

Bankacılık, müşterilerle sık sık iletişime girilmesi gereken bir sektördür. Bankalar müşterilerine, onların durumlarına uygun bir kredi vermek istediğinde müşteriyi telefonla ararlar. Çoğu zaman müşteri, teklif edilen krediyi reddeder, bu da müşteriyle iletişime geçen personelin zamanından büyük bir kayıptır. Bu çalışmada, banka müşterilerinin verilerinin bulunduğu veri seti ele alınarak ve çeşitli makine öğrenmesi sınıflama modelleri kullanılarak müşterinin kredi alıp almayacağı tahmin edilmiştir. Elde edilen çalışma sonuçlarına göre, makine öğrenmesi yöntemleri ile müşterinin kredi alma eğilim tahmini başarılı bir şekilde gerçekleşmiştir. Çalışma sonucunda K-Best uygulanan modellerin arasında doğruluk değeri en yüksek olan sınıflandırıcı modelinin %98,86 ile Rastgele Orman algoritması olduğu, özellik seçimi yapılmadan eğitilen modellerin arasında en yüksek olan modelin %93,66 ile Rastgele Orman algoritması olduğu, cross-validation ve grid search ile eğitilen modellerin arasında ise en yüksek değerin %98,6 ile Rastgele Orman algoritmasında olduğu görülmüştür.

A Comparative Analysis of Bank Customers' Loan Propensity Using Machine Learning Methods

Pneumonia is among the common symptoms of the virus that causes COVID‐19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x‐ray (CXR) is a fast, low‐cost, and practical method widely used in this field. The fact that different pathogens other than COVID‐19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID‐19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID‐19, and other viral‐induced pneumonia. In the classifications performed with five different state‐of‐art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/coin.12526

A deep learning‐based approach for diagnosing COVID‐19 on chest x‐ray images, and a test study with clinical experts

Heart diseases are common worldwide and cause one-third of global deaths. The difficulty in distinguishing the symptoms of heart disease and the fact that most heart patients are not aware of the symptoms until the moment of crisis make the diagnosis of the disease difficult. Machine learning, an artificial intelligence discipline, provides experts with successful decision support solutions in diagnosing new cases based on known data. In this study, classifications were made using various machine learning techniques for the early diagnosis of heart diseases. The study was carried out on the UCI heart disease dataset, which is widely used in the literature. In order to increase the classification success, resampling techniques were used to ensure the class balance of the dataset. For each of 8 different machine learning techniques, namely Naive Bayes, Decision Trees, Support Vector Machine, K Nearest Neighbor, Logistic Regression, Random Forest, AdaBoost, and CatBoost, in addition to no-sampling classification, 8 different methods from oversampling and undersampling techniques were used to make a total of 72 classification processes were carried out. The result of each classification process is reported with 5 different parameters: accuracy, precision, recall, F1 score, and AUC. The highest accuracy value was obtained as 98.46% in the classification using Random Forest and InstanceHardnessThreshold undersampling technique. It was observed that the measurements obtained were higher than the results obtained in similar studies conducted in the literature in recent years.

A Comparative Study of Heart Disease Diagnosis using Various Classifiers and Resampling Techniques

Solar energy systems which is one of renewable energy sources takes more interest and gains prevalence day by day. As in other many renewable energy sources, a significant problem in solar energy systems is the unstability of the energy that the system will provide. Prediction of the energy to be obtained is very important in this respect. In this study, solar radiation is predicted using meteorological data taken from the General Directorate of Meteorology for Isparta. For predictions, the random forest (RF), KNN (k-Nearest Neighbor), ANN (Artificial Neural Networks) and Deep Learning (DL) methods are used. In addition, the results of dummy variable usage for time data are examined with these different methods. According to the findings obtained, it is seen that the dummy variable usage increases performance for ANN and DL methods but decreases performance for random forest and KNN methods. Best results are obtained for the prediction of the solar radiation with ANN and DL.

/pdf/predicting-solar-radiation-based-on-meteorological-data-3hbbxg72.pdf

Predicting Solar Radiation Based on Meteorological Data Using Machine Learning Techniques: A Case Study of Isparta

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