Abstract: With the widespread use of Internet services, the risk of cyber attacks has increased significantly. Existing anomaly-based network intrusion detection systems suffer from slow processing ...
Abstract: Vitamin deficiency is a widespread global health issue that affects millions, often leading to severe physiological and dermatological complications. Early detection is essential for timely ...
Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive ...
Abstract: Identification of PCB board defects early in the manufacturing process is crucial, as PCB quality control plays an important role in the electronics manufacturing industry. Defective PCBs ...
Abstract: To improve the precision of CT lung nodule detection, this paper presents a parallel fusion model based on CNN and Transformer network, which integrates features of the two networks to fully ...
Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
Abstract: Small uncrewed autonmous vehicles (UAVs) equipped with deep learning models are increasingly used to detect small objects both on the ground and in aerial environments. Since small objects ...
Abstract: This research suggests a strong framework for automated malaria detection using a Convolutional Neural Network (CNN) model. The dataset, sourced from Kaggle, consists of 27,558 ...
Abstract: Image steganography conceals secret data within a cover image to generate a new image (stego image) in a manner that makes the secret data undetectable. The main problem in image ...
Abstract: This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for ...
Abstract: It has never been easy to identify plant diseases accurately and quickly. A significant amount of food grains is lost by farmers each year as a result of the absence of automated tools that ...
Abstract: Semantic segmentation of remote sensing imagery has achieved pixel-level precision in land cover classification through deep learning and computer vision technologies, providing automated ...