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Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes. [ABSTRACT FROM AUTHOR]

Technological advancements have significantly progressed and innovated across various industries. However, these advancements have also led to an increase in cyberattacks using malware. Researchers have developed diverse techniques to detect and classify malware, including a visualization-based approach that converts suspicious files into color or grayscale images, eliminating the need for domain-specific expertise. Nonetheless, converting files to color and grayscale images may result in the loss of texture details due to noise, adversely affecting the performance of machine learning models. The aim of this study is to present to assess the texture features and noise contributions of the red, green, and blue channels in color images. We propose a novel method to enhance model performance in terms of accuracy, precision, recall, f1-score, memory utilization, and computing cost during testing and training. This study introduces an approach involves separating color channels into individual red, green, and blue datasets and using various Discrete Wavelet Transform levels to reduce dimensions and remove noise. The extracted texture characteristics are then used as input for machine learning classifiers for training and prediction. Through comprehensive evaluation, we compare the performance of grayscale images with that of the red, green, and blue datasets. The results show that grayscale images significantly lose critical textural artifacts and perform worse than the color channels. Notably, employing extra tree classifiers yielded the best results, achieving an accuracy of 98.37%, precision of 98.64%, recall of 97.60%, and an f1-score of 98.04% with the red channel of color dataset. [ABSTRACT FROM AUTHOR]