Traffic classification in SDN-based IoT network using two-level fused network with self-adaptive manta ray foraging.

The rapid expansion of IoT networks, combined with the flexibility of Software-Defined Networking (SDN), has significantly increased the complexity of traffic management, requiring accurate classification to ensure optimal quality of service (QoS). Existing traffic classification techniques often rely on manual feature selection, limiting adaptability and efficiency in dynamic environments. This paper presents a novel traffic classification framework for SDN-based IoT networks, introducing a Two-Level Fused Network integrated with a self-adaptive Manta Ray Foraging Optimization (SMRFO) algorithm.

Predictive analytics technique based on hybrid sampling to manage unbalanced data in smart cities.

A smart city is deemed smart enough because it has the capability to make decisions on its own. Artificial intelligence needs a lot of data from the physical world to make correct decisions. IoT sensor devices collect data from the surroundings, which is further used for predictive analytics.

DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge.

Association between Alexithymia and Depression among King Khalid University Medical Students: An Analytical Cross-Sectional Study.

Alexithymia is a condition in which a person is unable to explain his/her emotions, bodily sensations, or discuss sentiments. This study aims to determine the prevalence of alexithymia and its relationships with socio-demographics and depression among medical students. A cross-sectional survey was conducted among medical students at King Khalid University (KKU), Saudi Arabia.

Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment.

The fog layer provides substantial benefits in cloud-based IoT applications because it can serve as an aggregation layer and it moves the computation resources nearer to the IoT devices; however, it is important to ensure adequate performance is achieved in such applications, as the devices usually communicate frequently and authenticate with the cloud. This can cause performance and availability issues, which can be dangerous in critical applications such as in the healthcare sector. In this paper, we analyze the efficacy of the fog layer in different architectures in a real-world environment by examining performance metrics for the cloud and fog layers using different numbers of IoT devices.