All Articles in march, 2025

This paper shows the developments and directions in feature processing. We begin by revisiting conventional feature processing methods, then focus on deep feature extraction techniques and the application of feature processing. The article also analyzes the current research challenges and outlines future development directions, providing valuable insights in related fields.

The integration of deep learning and genetic analysis has transformed the assessment of elite sports performance, particularly in competitive swimming. This study examines the fusion of deep learning techniques with DNA markers, physiological biometrics, and performance analytics to enhance the prediction and optimization of swimmer performance. A structured dataset comprising genetic sequences, physiological parameters, and biomechanical attributes was utilized to train a neural network model capable of categorizing swimmers based on genetic predisposition and athletic potential. The model achieved high classification accuracy, demonstrating a strong link between genetic markers, physiological traits, and competitive swimming outcomes. The findings emphasize the potential of AI-driven analytics in talent identification, customized training adaptations, and injury prevention. Furthermore, the study highlights the effectiveness of deep learning in analyzing complex genomic and physiological data to generate meaningful insights for performance enhancement. While the results validate the feasibility of using genetic and AI-based models for performance prediction, further studies are needed to broaden dataset diversity, integrate epigenetic influences, and test the model across varied athlete populations. This research contributes to the expanding field of AI-driven sports science and provides a solid foundation for incorporating genomics with deep learning to enhance elite athletic performance.

Cardiovascular Diseases (CVDs) remain a major global health concern, necessitating accurate and comprehensive diagnostic techniques. Traditional medical imaging modalities, such as CT angiography, PET, MRI, and ultrasound, provide crucial but limited information when used independently. Image fusion techniques integrate complementary modalities, enhance visualization, and improve diagnostic accuracy. This paper presents a theoretical study of advanced image fusion methods applied to cardiovascular imaging. We explore wavelet-based, Principal Component Analysis (PCA), and deep learning-driven fusion models, emphasizing their theoretical underpinnings, mathematical formulation, and potential clinical applications. The proposed framework enables improved coronary artery visualization, cardiac function assessment, and real-time hemodynamic analysis, offering a non-invasive and highly effective approach to cardiovascular diagnostics.MSC Codes: 68U10,94A08,92C55,65T60,62H25,68T07.

The global menace of cancer requires supplementary treatments beyond standard medical approaches for effective medical intervention. The Ketogenic Diet (KD) composed of high fats combined with moderate proteins and low carbohydrates has become popular as a metabolic therapy for cancer. The anti-cancer mechanism of KD works through metabolic stress induction in cancer cells, reduced insulin and IGF-1 signaling pathways, improved mitochondrial function, inflammation, and immune regulation. Standard cancer treatments receive enhanced outcomes through KD synergistic action which simultaneously decreases treatment-related side effects. To achieve optimized treatment outcomes in cancer, ketogenic diet practitioners need to use personalized nutritional planning in combination with metabolic tracking and exogenous ketone supplements. It is essential to find solutions for diet adherence issues and nutrient deficiencies because they determine KD’s effectiveness as a cancer treatment. The fight against cancer needs sustained and multipronged clinical research and validation to establish the proper implementation of this method.