Lung cancer remains one of the most lethal cancers globally. Its early detection is vital to improving survival rates. In this work, we propose a hybrid computer-aided diagnosis (CAD) pipeline for lung cancer classification using Computed Tomography (CT) scan images. The proposed CAD pipeline integrates ten image preprocessing techniques and ten pretrained deep learning models for feature extraction including convolutional neural networks and transformer-based architectures, and four classical machine learning classifiers. Unlike traditional end-to-end deep learning systems, our approach decouples feature extraction from classification, enhancing interpretability and reducing the risk of overfitting. A total of 400 model configurations were evaluated to identify the optimal combination. The proposed approach was evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative dataset, which comprises 1018 thoracic CT scans annotated by four thoracic radiologists. For the classification task, the dataset included a total of 6568 images labeled as malignant and 4849 images labeled as benign. Experimental results show that the best performing pipeline, combining Contrast Limited Adaptive Histogram Equalization, Swin Transformer feature extraction, and eXtreme Gradient Boosting, achieved an accuracy of 95.8%.

Purpose: (1) developing a dynamic and progressive software defect prediction model to successfully manage novel and huge amounts of software defect data and lessen the computational time. (2) to avoid the great diminish of static batch learning algorithms efficiency once the amount of data achieves a certain level.Design/methodology/approach: This study explores the proficiency of the incremental classification based approach to elaborate anincremental software defect prediction system which helps recognizing and treating real-time software data streams.Findings: The proposed method, as demonstrated by experimental results, is clearly competitive with the relevant two-class classifiers currently in use for software defect diagnosis. Detailed experimental findings clearly demonstrated the performance and efficiency of the suggested software defect detection approach: Incremental Discriminant-based Support Vector Machine (IDSVM) to differentiate between defective and non-defective objects.Originality/value: To the best of our knowledge, this is the first a real-time prediction method that investigates incremental classification in software defect prediction research

Millions of anti-personnel landmines have been left in the ground during war conflicts in many countries, which causes many security, humanitarian and economic problems. It is therefore necessary to develop an automatic and efficient technique for landmines detection and localization, in order to clear the existing minefields. Ground-penetrating radar (GPR) is a geophysical method that is able to detect any buried object in the soil, but it suffers from high false alarm rate. Several landmine detection and localization systems have been developed, using GPR data and based on competitive classification methods, such as the One-class Support Vector Machine (OSVM), which handles the common problem of unbalanced data, in a proper manner. Nevertheless, in the landmine detection problem, we assume that the target class is composed of several subclasses related to metallic contents, which are not considered by related classification methods, including OSVM. For this reason, in this paper, we propose a subclass landmine detection and localization system based on a novel variation of the OSVM that takes into account the existence of subclasses in the landmine target class in order to jointly minimize the within and between subclass dispersion and to estimate the optimal decision function. Our proposed landmine detection and localization method has been evaluated and compared to other methods based on relevant one-class classifiers, on several real-world datasets extracted from the well-known MACADAM database. Experimental results have shown clearly that our proposed method is competitive with the existing relevant one-class classifiers, in landmine detection.