Synchronizing smart city nodes using Skew Integrated Timestamp (SIT).

Time synchronization among smart city nodes is critical for proper functioning and coordinating various smart city systems and applications. It ensures that different devices and systems in the smart city network are synchronized and all the data generated by these devices is consistent and accurate. Synchronization methods in smart cities use multiple timestamp exchanges for time skew correction.

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data.

Humans are still better than ChatGPT: Case of the IEEEXtreme competition.

Since the release of ChatGPT, numerous studies have highlighted the remarkable performance of ChatGPT, which often rivals or even surpasses human capabilities in various tasks and domains. However, this paper presents a contrasting perspective by demonstrating an instance where human performance excels in typical tasks suited for ChatGPT, specifically in the domain of computer programming. We utilize the IEEExtreme Challenge competition as a benchmark-a prestigious, annual international programming contest encompassing a wide range of problems with different complexities.

Ensemble learning for multi-class COVID-19 detection from big data.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19.

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks.

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes.

Probing the Use of Silane-Grafted Fumed Silica Nanoparticles to Produce Stable Transformer Oil-Based Nanofluids.

In this experimental investigation, hydrophobic silane-grafted fumed nano-silica was employed in transformer oil to formulate nanofluids (NFs). A cold-air atmosphere-pressure plasma reactor working on the principle of dielectric barrier discharge was designed and utilized to functionalize the surface of these nanoparticles. A field emission scanning electron microscope (FE-SEM) coupled with energy-dispersive X-ray (EDX) module and Fourier transform infrared (FTIR) spectroscopy were used to scan surface features of new and plasma-treated nanoparticles.

Comparative study with practical validation of photovoltaic monocrystalline module for single and double diode models.

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module.

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost.

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things.

Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination.