American scientific publishing group

Cybersecurity is facing serious problems with the proliferation of malware in the internet world. The ever-changing nature of malicious software makes it difficult for traditional detection technologies to keep up. In order to make malware detection systems more accurate and resilient, this study investigates how to apply ensemble machine learning techniques. Using meta-learning frameworks like stacking and boosting in conjunction with various base models like logistic regression, Gaussian Naïve Bayes, and random forest allows the suggested method to make the most of each model's strengths while reducing their shortcomings. By utilizing a large dataset that includes both malicious and benign samples, the stacking algorithm surpassed the rivals in the prediction process, with a recall, precision, and f1-score of 100 after using the encoding method to convert the dataset from a numerical to a categorical format.

Using Artificial Intelligence (AI) in farming has become a game-changing idea, especially when it comes to controlling pests. The goal of this study is to improve pest control methods by using AI systems to make economic threshold (ET) predictions more accurate and useful. AI methods, like machine learning (ML) and deep learning (DL), can be used to analyze very large datasets that come from agroecosystems. These types of data include things like how insect pest populations change over time, food return information, information about the environment, and old ways of controlling pests. AI can find patterns and connections in this huge amount of data by using both controlled and unsupervised learning methods. This lets us make more accurate guesses about pest attacks and the economic levels that go with them. Multidisciplinary knowledge from entomology, agronomy, and data science are used in this study to create AI-driven models that can predict ET. The models are based on a lot of past data that includes things like temperature data, soil conditions, and insect pest behavior. This lets them respond to changes in their surroundings. The data show that AI models are much better at detecting ET than traditional methods. This gives farmers immediate information that can help them make smart decisions about how to control pests.