Results 41 to 50 of about 60,322 (264)

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers

2025 IEEE Symposium on Computers and Communications (ISCC)
Accepted at IEEE ISCC ...
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino   +3 more
openaire   +2 more sources

DL-HEED: A Deep Learning Approach to Energy-Efficient Clustering in Heterogeneous Wireless Sensor Networks

Applied Sciences
Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial automation, and smart cities. The Hybrid Energy-Efficient Distributed (HEED) protocol is a popular clustering algorithm designed to prolong network lifetime by ...
Abdulla Juwaied, Lidia Jackowska-Strumillo   +1 more
doaj   +1 more source

An Accelerated Edge Cloud System for Energy Data Stream Processing Based on Adaptive Incremental Deep Learning Scheme

IEEE Access, 2020
As smart metering technology evolves, power suppliers can make low-cost, low-risk estimation of customer-side power consumption by analyzing energy demand data collected in real-time.
Seong-Hwan Kim, Changha Lee, Chan-Hyun Youn   +2 more
doaj   +1 more source

Autophagy and mitophagy in pancreatic β‐cell homeostasis and their involvement in diabetes pathophysiology

FEBS Letters, EarlyView.
This review focuses on the role of autophagy and mitophagy in maintaining pancreatic β‐cell function and homeostasis. We discuss how genetic defects affecting these pathways contribute to the development of type 1, type 2, monogenic, and gestational diabetes. We further explore their potential as therapeutic targets. Created in BioRender.
Yunkyeong Lee, Mingming Tong, Anna L. Gloyn   +2 more
wiley   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Twofold Sparsity: Joint Bit- and Network-Level Sparsity for Energy-Efficient Deep Neural Network Using RRAM Based Compute-In-Memory

IEEE Access
On-device intelligence and AI-powered edge devices require compressed deep learning algorithm and energy efficient hardware. Compute-in-memory (CIM) architecture is a more suitable candidate than traditional Complementary Metal-Oxide-Semiconductor (CMOS)
Foroozan Karimzadeh, Arijit Raychowdhury
doaj   +1 more source

From energy provision to protein synthesis: Tunnelling nanotubes as mediators of intercellular metabolic cooperation in cancer

FEBS Open Bio, EarlyView.
The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley   +1 more source

Internet of things and deep learning-enhanced monitoring for energy efficiency in older buildings

Case Studies in Thermal Engineering
Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older buildings contribute greatly to energy waste since they typically lack new energy-efficient technology.
M. Arun, Gokul Gopan, Savithiri Vembu, Dilber Uzun Ozsahin, Hijaz Ahmad, Maged F. Alotaibi   +5 more
doaj   +1 more source

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Energies, 2019
Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in ...
Yihang Du, Ying Xu, Lei Xue, Lijia Wang, Fan Zhang   +4 more
doaj   +1 more source

Self‐Regulated Learning Meets AI: Reinterpreting Self‐Regulation, Co‐Regulation, and Socially Shared Regulation in Human–AI Interaction

New Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability.
Moon‐Heum Cho, Jerusalem Merkebu
wiley   +1 more source