Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Leveraging Artificial Intelligent For Optimized Crop Production: An Ann-Based Approach, Yahya Layth Khaleel, Fadya A. Habeeb, Mustafa Abdulfattah Habeeb, Fatimah N. Ameen
Leveraging Artificial Intelligent For Optimized Crop Production: An Ann-Based Approach, Yahya Layth Khaleel, Fadya A. Habeeb, Mustafa Abdulfattah Habeeb, Fatimah N. Ameen
Mesopotamian Journal of Computer Science
To incite modern day crop production and ensure sustainability, exact crop recommendations are key to the process. This study pays significant attention to the need for the use of big data tools in studies involving comprehensive data sets that contain information on soil and other environmental characteristics. The set of data used in this research includes Nitrogen, Phosphorus, and Potassium content coordinated with Temperature, Humidity, pH Value, and Rainfall. Knowing these factors is to make a favorable decision about improving agricultural products yield, availability and management of the resources, as well as general well-being of the crops. Specialized advisory on …
Enhancing Security And Performance In Vehicular Adhoc Networks: A Machine Learning Approach To Combat Adversarial Attacks, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Ahmed Raheem Abdulnabi
Enhancing Security And Performance In Vehicular Adhoc Networks: A Machine Learning Approach To Combat Adversarial Attacks, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Ahmed Raheem Abdulnabi
Mesopotamian Journal of Computer Science
Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive distillation for improving model resilience to adversarial perturbations. From the results, it is clear that models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing the potential of …