Abstract. This study investigates the application of convolutional neural networks (CNNs) to the task of classifying male and female eyes. Using a dataset of eye images, the research explores the potential of deep learning to accurately distinguish between the genders based solely on eye features. The proposed CNN model achieved 94% accuracy on the training set and 91% on the validation set. The study addresses the challenges and limitations in feature extraction from eye images and compares the proposed model with (...) traditional machine learning approaches. The results demonstrate the model's robustness, providing significant insights into gender recognition through partial facial analysis. (shrink)

This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.

Abstract. This study explores the use of convolutional neural networks (CNNs) for classifying different pineapple varieties, specifically pineapples and mini pineapples. By using a dataset of pineapple images, the research demonstrates the effectiveness of a pre-trained VGG16-based CNN model in accurately classifying these fruit categories. The model achieved over 99% accuracy on both the training and validation sets. The performance of the CNN was compared to traditional machine learning algorithms to highlight the advantages of deep learning in image classification tasks. (...) The results underscore the model’s ability to generalize well to the classification task, offering insights into feature extraction from complex image datasets. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. Furthermore, the (...) paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies. (shrink)

West Nile virus (WNV) is a mosquito-borne flavivirus that was first identified in 1937 in the West Nile district of Uganda. The virus is now widely distributed throughout the world and is considered a significant public health concern. WNV is primarily transmitted to humans through the bite of infected mosquitoes, with birds serving as the primary reservoir host. Most people infected with WNV will not experience any symptoms, but approximately 1 in 5 will develop a fever, and a smaller percentage (...) may develop more severe symptoms such as meningitis or encephalitis. There is no specific treatment for WNV infection, and prevention efforts focus on mosquito control measures and personal protective measures to avoid mosquito bites. While WNV is not typically considered a major threat to human health, outbreaks have occurred sporadically in various parts of the world, and ongoing surveillance and research are necessary to better understand and control the virus. Objectives: This paper will solve the problems of treatment of West Nile virus (WNV) through correct diagnosis and treatment. Methods: In this research, we provide an expert system for the diagnosis of West Nile virus (WNV) which will help doctors to explore everything related to the problems of West Nile virus (WNV). We look forward to providing simplified answers to West Nile virus (WNV). (shrink)

Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes (...) rule-based techniques to analyze symptoms associated with mango pests. By inputting observed symptoms, such as leaf spots, fruit damage, leaf mild and stem cankers the system can accurately identify the specific pest species affecting the mango tree. For each pest, the paper discusses their specific symptoms and Favorable conditions that affect the mango plant. By understanding the identification and symptoms of these pests, mango growers and agricultural professionals can implement appropriate pest management strategies to mitigate the economic losses caused by these insects. The research presented in this paper aims to contribute to the body of knowledge on mango pest management, facilitating sustainable mango production and ensuring the availability of high-quality mangoes in global markets. (shrink)