Scopus Research — Najwan Thair Ali

Fake News is one of the most widespread phenomenon with significant consequences on our daily life, particularly in the political realm. Due to the increasing use of the internet and social media, it is now much simpler to propagate false information. Therefore, the identification of elusive news is a significant issue that must be addressed, mostly due to obstacles such as the limited number of benchmark datasets and the volume of news produced per second. This study suggested using comparative data analysis based on random forest machine learning algorithm to identify bogus 4news. In this study the size of the whole dataset is 20.761 fake news record, whereas the size of it is 4.345 records. The first step in the data preparation process is to remove any unnecessary special characters, numbers, English letters, and whitespace. Before implementing the proposed classification algorithms, the most prevalent feature extraction approach (TF-IDF) is used. The data indicate that the highest level of accuracy attained was 88.24%. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

The motivation behind this study stems from identifying contemporary challenges associated with prosecuting electronic financial crimes. Highlights ongoing efforts to identify and address credit card fraud and fraud as there are many credit card fraud issues in the financial industry. Traditional methods are no longer able to keep up with modern methods of tracking the behavior of credit card users and detecting suspicious cases. Artificial intelligence technology offers promising solutions to quickly detect and prevent future fraud by credit card users. Datasets used to detect financial anomalies are affected by imbalances in financial transactions, and this study aims to address the imbalance of financial fraud datasets using adversarial algorithm techniques and compare them with the most commonly used methods in the scientific literature.The results showed that the function of the adversarial algorithm is consistent in several ways, including allowing researchers and interested parties to determine data growth rates, which helps bring the dataset closer to real-time data from financial markets and banks. This study proposes a hybrid machine learning model consisting of three machine learning algorithms: decision trees, logistic regression, and Naive Bayes algorithm, and calculates performance metrics such as accuracy, specificity, precision, and F1 score. Experimental results reveal varying degrees of accuracy in fraud detection. Model testing using the SMOTE method recorded an accuracy of 98.1% and an F-score of 98.3%. On the other hand, the oversampling and under sampling test methods showed similar performance, with the two methods recording an accuracy of 94.3 and 95.3 and an F-score of 94.7 and 95.1, respectively. Finally, the GAN method excelled, receiving a test score and accuracy of 99.9%, as well as exceptional precision, recall, and F1 score. As a result, we conclude that the GAN method is able to balance the data set, which in turn is reflected in the performance of the model in training and the accuracy of predictions when tested. Historical transaction analysis identifies behavioral patterns and adapts to evolving fraud techniques. This approach enhances transaction security and protects against potential financial losses due to fraud. This contribution allows financial institutions and companies to proactively combat fraudulent activities. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

Lung cancer is a leading cause of mortality among all cancer-related illnesses. The primary method of diagnosis is conducting a scan examination of the patient's lungs. The scanning analysis can encompass X-ray, CT scan, or MRI techniques. The automated categorization of lung cancer poses a formidable challenge, primarily because of the diverse imaging techniques employed to capture images of a patient's lungs. Image processing and machine learning methodologies have demonstrated significant promise in the identification and categorization of lung cancer. We present a very efficient model in this study that accurately detects lung cancer and categorizes it as either benign or malignant. The initial phase involves the execution of many procedures to carry out the picture preprocessing process. During the second stage, the image undergoes Wavelet Transform to divide it into three levels. This division allows for the extraction of distinct properties from each level. The third step involves employing an auto-encoder technique to effectively decrease dimensions and eliminate noise, while also identifying any anomalies within the recovered features. The MLP algorithm was employed in the final section. The suggested method underwent testing on a total of 9541 photos, which were categorized into two distinct types: benign, consisting of 4044 images, and malignant, consisting of 5497 images. The proposed approach attained a remarkable accuracy rate of 100%. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).