Prevalence and Risk Factors of Pressure Ulcers in Bedbound Diabetic vs. Non-diabetic Patients in a Lower-Middle-Income Country.

Introduction Pressure ulcers, also known as bedsores, are a significant concern for bedridden individuals, presenting both physical and socioeconomic challenges. Factors such as prolonged immobility, chronic medical conditions, and poor nutrition contribute to their development. Despite extensive research in some regions, studies comparing diabetic and non-diabetic populations remain limited, particularly in low-income settings.

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, such approaches do not deliver required levels of speech intelligibility in everyday noisy environments. Intelligibility-oriented (I-O) loss functions have recently been developed to train DL approaches for robust speech enhancement.

Ensemble learning-based IDS for sensors telemetry data in IoT networks.

The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data.

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning.

A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion.

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning.

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user.

Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics.

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e.