The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.

The rise of low-cost drones, or Unmanned Aerial Vehicles (UAVs), has transformed industries like agriculture, disaster response, and infrastructure inspection. However, this increase poses considerable issues for safety, security, and airspace management. To address these challenges, radiofrequency (RF)-based approaches are emerging as potential options for real-time UAV identification and classification since they are non-intrusive and compatible with existing systems. This research offers a unique Artificial Neural Network (ANN) model for UAV classification that includes efficient preprocessing and feature extraction approaches. The suggested architecture reduces feature dimensionality using Principal Component Analysis (PCA) and improves classification performance with statistical and frequency-domain characteristics like Root Mean Square (RMS) and Zero-Crossing Rate (ZCR). Experimental evaluations of the DroneRF dataset show that the proposed ANN is superior, with classification accuracies of 99.46%, 98.52%, and 83.40% for 2-class, 4-class, and 10-class tasks, respectively. This work is a big step toward robust, real-time drone detection and classification systems.

Ad hoc protocols like AODV, DSDV, and OLSR have been studied in healthcare, but their application to CGM networks with real-time integrated Machine Learning (ML) presents unique challenges due to distinct traffic patterns and node configurations. This paper explores how CGM, ML, and communication protocols jointly enable continuous monitoring and timely intervention, where transmission reliability directly affects ML performance in anomaly detection. Using NS3, we evaluate AODV, DSDV, and OLSR based on throughput, delay, and packet delivery ratio (PDR) under varying network sizes and data rates. Results show AODV offers minimal delay and packet loss, enhancing ML accuracy, while DSDV and OLSR perform better in dense networks. We also identify challenges related to scalability and data security, highlighting directions for future optimization of CGM-ML systems through hybrid and energyefficient protocols.

Heart disease continues to be the foremost cause of mortality across both developed and developing nations, posing a critical public health challenge. This life-threatening condition often necessitates urgent hospitalization and rapid diagnosis in emergency departments to avert fatal outcomes. Such outcomes can arise from a diverse range of complications associated with heart disease, including cardiogenic shock, pulmonary edema, myocardial rupture, the progression of congestive heart failure, thromboembolism, asystole, and ventricular fibrillation. Although numerous studies have focused on predicting heart disease, relatively few have addressed the complexity of multi-label classification for this issue. In this paper, we propose an innovative framework that integrates a K-Nearest Neighbors-based imputation technique with a Gradient Boosting algorithm to classify multiple heart disease complications. The experimental findings reveal that our approach achieves superior accuracy compared to existing methods. Additionally, we present a comprehensive comparative analysis of the proposed data pre-processing methodology by applying various machine learning algorithms. Moreover, we provide a detailed comparison between our multi-label classification strategy and previously established approaches. A diverse set of evaluation metrics, including accuracy, precision, recall, F1-score, and Hamming loss, is utilized to demonstrate the robustness and effectiveness of the proposed framework.