Paper Title

A MATHEMATICAL PERSPECTIVE ON DYNAMICAL CHARACTERISTICS OF BLOOD FLOW IN HUMAN ARTERIES

Abstract

The effects of an overlapping stenosis on blood flow characteristics in an artery have been studied. Blood has been represented by a couple stress fluid is observed that the resistance to the flow, pressure drop and wall shear stress increases with height and length of the stenosis. And it is noticed that the resistance to the flow and pressure drop decreases with couple stress fluid parameters. The effect of different physical characteristics on liquid impedance to flowing, velocity, and surface shearing stress are studied. Herschel–Bulkley equation has been taken to represent the non-Newtonian character of blood. The problem is investigated by a combined use of analytical and numerical techniques. The shape, length and height of the stenosis in an artery under various flow conditions play a pivotal role. Research has been carried out by many researchers on the nature of geometry of the stenosis in the artery, nature of the blood and its flow. Finite difference scheme has been used to solve the unsteady nonlinear Navier–Stokes equations in cylindrical coordinate’s system governing flow assuming axial symmetry under laminar flow condition so that the problem effectively becomes two-dimensional. The relations for surface shearing stress, pressure drop, flow resistance, and velocity are calculated analytically by applying a mild stenosis approximation.
KEYWORDS : Blood flow, flow resistance, Herschel–Bulkley, flowing, velocity, two-dimensional.

Paper Title

A HYBRID MACHINE LEARNING APPROACH FOR BONE FRACTURE DETECTION IN RADIOGRAPHIC IMAGES

Abstract

Accurate detection and classification of bone fractures from radiographic (X-ray) images is essential for clinical diagnosis, emergency treatment planning, orthopedic workflows, and automated medical decision support systems. Manual diagnosis is often challenging due to overlapping anatomical structures, subtle fracture lines, variable image quality, and inter observer differences among radiologists. Traditional machine-learning approaches rely on handcrafted features, which often fail to generalize across diverse fracture types and imaging conditions. This survey paper reviews existing fracture detection methodologies and evaluates deep-learning-based classification models. Special emphasis is given to the model, a hybrid transfer learning architecture that integrates Dense Net’s feature reuse with Inception’s multi-scale pattern extraction. Its architecture, workflow, accuracy, and clinical applicability are thoroughly discussed. Bone fractures are among the most common global health problems and require rapid and accurate diagnosis to prevent complications and reduce healthcare costs. Traditional diagnostic methods rely on the manual examination of X-ray images by experienced radiologists, a process that is time-consuming and prone to human error, especially in emergency departments with high patient volumes. This study proposes a lightweight hybrid approach that integrates deep learning–based feature extraction with classical machine learning algorithms for automatic bone fracture detection. These findings confirm that combining deep feature extraction with ensemble learning can effectively support automated diagnostic systems for bone fracture detection, enabling healthcare professionals to make faster and more reliable decisions.
KEYWORDS : Bone fracture detection, Medical imaging, Radiographs, Transfer learning, hybrid transfer learning

Paper Title

A MACHINE LEARNING FRAMEWORK FOR PROACTIVE NETWORK DIAGNOSIS AND OPTIMIZATION

Abstract

Advances in machine learning and parallel computing underpin new powerful tools that have the potential to tackle these complex challenges. In this article, we develop a general machine learning-based framework that leverages artificial intelligence to forecast future traffic demands and characterize traffic features. This makes it possible to exploit such traffic insights to improve the performance of critical network control mechanisms, such as load balancing, routing, and scheduling. In contrast to prior works that design problem-specific machine learning algorithms, our generic approach can be applied to different network functions, allowing reuse of existing control mechanisms with minimal modifications. We explain how our framework can orchestrate ML to improve two different network mechanisms. The exponential growth of data-intensive applications and heterogeneous network architectures has increased the demand for intelligent systems capable of predicting network performance and optimizing data flow in real time. Traditional static models are inadequate for addressing dynamic network conditions, latency variations, and bandwidth fluctuations. This paper presents a comprehensive review of existing methodologies and proposes a conceptual machine learning framework for predictive network performance and data flow optimization. The framework integrates supervised and reinforcement learning models with deep neural architectures to forecast traffic patterns, congestion probabilities, and throughput variations. defined networking (SDN), and edge intelligence for adaptive traffic routing and self-healing network behaviors.
KEYWORDS : Machine learning framework, Predictive Analytics, Network Performance Optimization, Software-Defined Networking

Paper Title

APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CLINICAL OUTCOMES FROM HEALTHCARE DATA

Abstract

Machine learning (ML) has emerged as a transformative force in healthcare, enabling more accurate and timely prediction of patient outcomes by analyzing complex and heterogeneous datasets. By leveraging diverse data sources, ML algorithms uncover subtle patterns and risk factors often missed by traditional statistical methods. This paper comprehensively explores the pivotal role of machine learning in healthcare outcome prediction, emphasizing a patient-centric approach that integrates individual patient characteristics and preferences to tailor care effectively. discussion spans the variety of ML techniques employed, the types of clinical and non-clinical data utilized, and specific applications across medical specialties that enhance clinical decision-making and enable proactive interventions. It also critically addresses challenges such as data privacy, model transparency, bias, and integration hurdles in clinical workflows. Finally, the paper outlines future directions where advancements in explainable AI, federated learning, and patient engagement can further refine predictive models to improve healthcare delivery and outcomes. The integration of machine learning (ML) into healthcare has revolutionized the prediction of patient outcomes, offering improved accuracy, speed, and personalization in clinical decision making. ML algorithms can analyze large volumes of patient data, including electronic health records (EHRs), genetic profiles, imaging data, and clinical notes, to detect patterns that might escape traditional statistical methods.
KEYWORDS : Machine Learning, Healthcare Outcomes, Predictive Models, Patient-Centric Care, Clinical Decision Support