RESUMEN
The study of natural disasters is a crucial field that involves analyzing the occurrence, impact, and aftermath of various natural hazards that can cause significant harm to communities and the environment. Efficient waste management and environmental protection require proper classification of waste. Analyzing natural disasters and categorizing waste can be a time-consuming task, and conventional methods often struggle with it. However, a new approach called Visual Geometry Group with Federated Learning (VGG-FL) has been introduced to address these challenges. This methodology uses the golden search optimization (GSO) algorithm for feature selection and leverages VGG with federated learning for feature extraction and classification. To test the effectiveness of this method, a disaster image dataset was used to train the VGG-FL model. The results showed that the VGG-FL model attained exceptional accuracy in discerning and categorizing various disaster scenarios. The waste classification dataset simultaneously trains the VGG-FL model to categorize waste based on its characteristics and potential hazards. To measure the model's performance, several evaluation metrics such as accuracy, specificity, precision, F1-score, and recall are utilized to assess the effectiveness of the proposed VGG-FL method. These results are then compared with existing methodologies. The VGG-FL method performs exceptionally well, achieving 98.52% accuracy, 97.48% precision, 97.83% recall, 97.58% F1-score, and 97.12% specificity. These experimental findings demonstrate the efficacy of the VGG-FL method in analyzing natural disasters and classifying waste materials.
Asunto(s)
Algoritmos , Desastres Naturales , Aprendizaje Automático , Administración de Residuos/métodos , Monitoreo del Ambiente/métodosRESUMEN
Being an important source of berberine, Berberis chitria Buch.-Ham. ex Lindl. (Berberidaceae) has high demand in pharmaceutical industries. Its populations are diminishing due to overexploitation, habitat loss, slow-growing nature, and climate change. It is important to develop propagation protocols to sustain its natural populations and ensure its survival in the future. Fertilizers play an essential role in the yield and productivity of different crops. Among others, urea is the most abundantly used fertilizer in crops. Its effects on the yield and survival of medicinal plants are poorly studied. However, it is known that applying urea for a long time affects the soil negatively. Due to these negative effects, alternative fertilizers such as graphene-based metal composite (GMC) are being tested for their efficiency. In the present study, for the first time, we tested the effects of urea and GMC on the germination and performance of B. chitria. GMC showed maximum germination at 30 ppm (75%) and urea at 15 ppm (79%). Findings reveal non-significant effects of GMC and urea on germination and performance of B. chitria, suggesting the use of GMC as an alternative fertilizer.
RESUMEN
Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K-means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.