Utility-Driven End-to-End Network Slicing for Diverse IoT Users in MEC: A Multi-Agent Deep Reinforcement Learning Approach.

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates.

Cyberterrorism as a global threat: a review on repercussions and countermeasures.

An act of cyberterrorism involves using the internet and other forms of information and communication technology to threaten or cause bodily harm to gain political or ideological power through threat or intimidation. Data theft, data manipulation, and disruption of essential services are all forms of cyberattacks. As digital infrastructure becomes more critical and entry barriers for malicious actors decrease, cyberterrorism has become a growing concern.

Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches.

The Internet has become a vital source of knowledge and communication in recent times. Continuous technological advancements have changed the way businesses operate, and everyone today lives in the digital world of engineering. Because of the Internet of Things (IoT) and its applications, people's impressions of the information revolution have improved.

An Urdu speech for emotion recognition.

Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis.

The association between late preterm birth and cardiometabolic conditions across the life course: A systematic review and meta-analysis.

Background: The effect of being born late preterm (34-36 weeks gestation) on cardiometabolic outcomes across the life course is unclear.

Objectives: To systematically review the association between being born late preterm (spontaneous or indicated), compared to the term and cardiometabolic outcomes in children and adults.

Data Sources: EMBASE(Ovid), MEDLINE(Ovid), CINAHL.

Impact of disasters, including pandemics, on cardiometabolic outcomes across the life-course: a systematic review.

Background: Disasters are events that disrupt the daily functioning of a community or society, and may increase long-term risk of adverse cardiometabolic outcomes, including cardiovascular disease, obesity and diabetes. The objective of this study was to conduct a systematic review to determine the impact of disasters, including pandemics, on cardiometabolic outcomes across the life-course.

Design: A systematic search was conducted in May 2020 using two electronic databases, EMBASE and Medline.

GenInfoGuard--a robust and distortion-free watermarking technique for genetic data.

Genetic data, in digital format, is used in different biological phenomena such as DNA translation, mRNA transcription and protein synthesis. The accuracy of these biological phenomena depend on genetic codes and all subsequent processes. To computerize the biological procedures, different domain experts are provided with the authorized access of the genetic codes; as a consequence, the ownership protection of such data is inevitable.