The continuous threat of malware against digital systems exists because its attack methods develop rapidly, reducing the effectiveness of traditional detection systems. Current static and dynamic analysis methods for malware detection face challenges with scalability and robustness when handling lar...

Fire detection in smart cities requires intelligent visual recognition systems capable of distinguishing fire from visually similar phenomena while maintaining real-time performance under diverse environmental conditions. Existing deep learning approaches employ attention mechanisms that aggregate s...

In response to the challenges of insufficient accuracy in face detection and missed small targets under low-light conditions, this paper proposes a detection scheme that combines image preprocessing and detection model optimization. Firstly, Zero-DCE low-light enhancement is introduced to adaptively...

Maize productivity in India, a major global producer, is severely threatened by leaf diseases. Accurate identification of Common Rust (CR), Northern Corn Leaf Blight (NCLB), and Gray Leaf Spot (GLS) remains challenging with traditional methods. This study evaluated traditional and ensemble-based cla...

At present, the aging process of China’s population is accelerating very rapidly. As the third pillar of the pension security system, the private pension scheme has faced challenges such as low participation rates since its pilot launch in November 2022. Through analyzing its development history and...

Inflation targeting has become a cornerstone framework for contemporary monetary policy governance, yet its effectiveness varies significantly across countries. This study employs panel data from five countries—New Zealand, Canada, South Korea, Poland, and South Africa—over the period 2010 to 2020 t...

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Accurate diagnosis from histopathological biopsy samples is essential, yet manual examination is time-consuming and subject to inter-observer variability, particularly given the shortage of trained pathol...

The escalating global prevalence of diabetes renders effective screening for Diabetic Retinopathy (DR) indispensable to prevent irreversible vision loss. Although deep learning models, particularly Convolutional Neural Networks (CNNs), attain diagnostic accuracy comparable to that of human experts,...

In this paper, we study the problem of predefined-time synchronization for distinct-order fractional delayed Cohen-Grossberg neural networks. Fractional-order models are known for their ability to capture memory effects and complex dynamics more accurately than classical integer-order systems. In pa...

Ultrasound (US) imaging is widely employed in medical diagnostics due to its non-invasive nature and real-time imaging ability. The existence of mixed noise, consisting of Gaussian and speckle noise, significantly impairs image quality, hindering accurate diagnosis. This study introduces an advanced...