Forest fires are a serious ecological concern, and smoke is an early warning indicator. Early smoke images barely capture a tiny portion of the total smoke. Because of the irregular nature of smoke’s dispersion and the dynamic nature of the surrounding environment, smoke identification is complicated by minor pixel-based traits. This study presents a new framework that decreases the sensitivity of various YOLO detection models. Additionally, we compare the detection performance and speed of different YOLO models such as YOLOv3, YOLOv5, and YOLOv7 with prior ones such as Fast R-CNN and Faster R-CNN. Moreover, we follow the use of a collected dataset that describes three distinct detection areas, namely close, medium, and far distance, to identify the detection model’s ability to recognize smoke targets correctly. Our model outperforms the gold-standard detection method on a multi-oriented dataset for detecting forest smoke by an mAP accuracy of 96.8% at an IoU of 0.5 using YOLOv5x. Additionally, the findings of the study show an extensive improvement in detection accuracy using several data-augmentation techniques. Moreover, YOLOv7 outperforms YOLOv3 with an mAP accuracy of 95%, compared to 94.8% using an SGD optimizer. Extensive research shows that the suggested method achieves significantly better results than the most advanced object-detection algorithms when used on smoke datasets from wildfires, while maintaining a satisfactory performance level in challenging environmental conditions.

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

Liver diseases are among the most common diseases worldwide. Because of the high incidence and high mortality rate, these diseases diagnoses are vital. Several elements harm the liver. For instance, obesity, undiagnosed hepatitis infection, and alcohol abuse. This causes abnormal nerve function, bloody coughing or vomiting, insufficient kidney function, hepatic failure, jaundice, and liver encephalopathy.. The diagnosis of this disease is very expensive and complex. Therefore, this work aims to assess the performance of various machine learning algorithms at decreasing the cost of predictive diagnoses of chronic liver disease. In this study, five machine learning algorithms were employed: Logistic Regression, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Artificial Neural Network (ANN) algorithm. In this work, we examined the effects of the increased prediction accuracy of Generative Adversarial Networks (GANs) and the synthetic minority oversampling technique (SMOTE). Generative opponents’ networks (GANs) are a mechanism to produce artificial data with a distribution close to real data distribution. This is achieved by training two different networks: the generator, which seeks to produce new and real samples, and the discriminator, which classifies the augmented samples using supervised classifications. Statistics show that the use of increased data slightly improves the performance of the classifier.