Adaptive congestion control in IoT networks: Leveraging one-way delay for enhanced performance.

With the exploding number of IoT devices generating vast data volumes, there is a growing risk of significant performance degradation without efficient congestion management. To tackle this challenge, efficient regulation and supervision are essential for managing congestion in IoT networks. This research work introduces a One-Way-Delay (OWD)-based congestion control (CC) method that estimates data transmission delays of the communication path and adjusts network traffic accordingly.

Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams.

is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope.

Applying Dynamic Systems to Social Media by Using Controlling Stability.

This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When , where and are the state vectors of the drive and response systems, respectively, and Wan ( = ∓1)), the two systems' hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach.

A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization.

In this study, a novel 7D hyperchaotic model is constructed from the 6D Lorenz model via the nonlinear feedback control technique. The proposed model has an only unstable origin point. Thus, it is categorized as a model with self-excited attractors.

Secure Complex Systems: A Dynamic Model in the Synchronization.

Chaotic systems are one of the most significant systems of the technological period because their qualities must be updated on a regular basis in order for the speed of security and information transfer to rise, as well as the system's stability. The purpose of this research is to look at the special features of the nine-dimensional, difficult, and highly nonlinear hyperchaotic model, with a particular focus on synchronization. Furthermore, several criteria for such models have been examined; Hamiltonian, synchronizing, Lyapunov expansions, and stability are some of the terms used.

The Commitment of Nineveh Governorate Residents to the Precautionary Measures against Global 2019 Pandemic and Dermatological Affection of Precautions.

The world has changed dramatically since the novel pandemic pours in all aspects of life, including economic, health, and social life. The first case was initially observed in the Wuhan province of China; fast spread occurs around the world. Until now, there is no proven effective treatment for it.

Network Management System for IoT Based on Dynamic Systems.

The Internet of Things (IoT) has the potential to transform the public sector by combining the leading technical and business trends of mobility, automation, and data analysis to dramatically alter the way public bodies collect data and information. Embedded sensors, actuators, and other devices that capture and transmit information about network activity in real-time are used in the Internet of Things to connect networks of physical objects. The design of a network management system for an IoT network is presented in this paper, which uses the edge computing model.

Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches.

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example).

Neutrosophic sets in determining Corona virus.

An attempt that is made here is to apply neutrosophic sets to a medical data. By means of extended Hausdorff minimum distance we find out the core symptoms of the patients. From the minimum distance or the core symptoms we can get a clue for the type of disease affecting the patient.