Machine learning
A recent study employing machine learning techniques has analyzed hourly electricity consumption for 125 households participating in the People Demand Response (DR) program, a Peak Time Rebate (PTR) initiative in Korea, and found varying effects of DR interventions across different household clusters. The research also discovered learning effects over time
Machine learning
A systematic literature review has analyzed and categorized existing resource allocation techniques in cloud computing environments, focusing on optimization strategies, heuristic algorithms, and machine learning-based approaches. The study, conducted by researchers at Maharshi Dayanand University, examined 100 research articles published between 2014 and 2025, identifying strengths and limitations of different
Machine learning
Research conducted by Qiqi Gao and Tuo Hua from the Wuxi Institute of Technology has led to breakthroughs in artificial intelligence, particularly in image restoration accuracy. The study introduced a hybrid genetic algorithm that utilizes an elite opposition-based learning strategy and the firefly algorithm to optimize the initial weight and
Machine learning
Researchers at the University of Hong Kong have developed an explainable machine learning framework to understand the relationship between urban morphology and building energy consumption in Dongguan, China. The study integrates large-scale smart meter records with 3D building footprints to construct high-resolution datasets, demonstrating superior predictive performance over comparative methods.
Machine learning
A massive outage at Amazon Web Services (AWS) affected numerous high-profile apps, including those of HMRC, Halifax, and Lloyds, causing a spike in reported outages. This incident highlights the consequences of relying on shared digital infrastructure, where a single failure can cascade into a broader disruption. Meanwhile, Wikipedia has experienced
Machine learning
The inventors Dinh, Hung, Linsey, David J., and Mohanty, Bijan Kumar have developed a cloud instance prediction platform that utilizes machine learning algorithms to predict the configuration of a cloud instance based on the features of an application. The platform aims to simplify the process of selecting the optimal virtual
Machine learning
The global Quantum Warfare Market is on the cusp of significant growth, driven by national security needs, technological convergence, and strategic competition among powerful countries. Quantum warfare, an emerging technology that combines quantum physics and military applications, is becoming increasingly important for national security and defense posture. The market size
Machine learning
Research conducted at the Cochin University of Science and Technology has developed a novel model for vehicle ad-hoc network security, leveraging machine learning and quantum cryptography to ensure secure communication between vehicles. This breakthrough addresses the persistent challenge of secure communication during vehicle-to-vehicle interactions. The proposed model, named Light Gradient-Boosting
Machine learning
Researchers at Galgotias University have unveiled a novel Quantum-Inspired Hybrid Reinforcement Learning and Multi-Objective Optimization Framework (QHRMOF) designed to optimize task scheduling, dynamic load balancing, and server consolidation while minimizing power consumption and enhancing system performance. The framework integrates quantum-inspired evolutionary algorithms, hybrid deep reinforcement learning, and multi-objective optimization techniques
Precision medicine
Researchers at Yuncheng Central Hospital affiliated to Shanxi Medical University have developed a novel prognostic signature for lower-grade gliomas (LGGs) using machine learning and multi-omics data. The study, published in Discover Oncology, identifies cell adhesion molecules (CAMs) as crucial regulators of tumor biology and immune remodeling in LGGs. Key Takeaways:
Machine learning
Research published by Universite Clermont Auvergne, in collaboration with other institutions, presents a novel approach to energy-efficient information processing. By developing a recurrent hierarchical spiking neural network called Predictive Coding Light (PCL), the team has proposed a new method for unsupervised representation learning. PCL differs from previous predictive coding approaches
Machine learning
A team of researchers at the University of Ulsan in South Korea has made a breakthrough in simulating high-temperature proton exchange membrane fuel cells (PEMFCs) using a novel deep learning approach. The study aimed to address the high computational cost and data generation challenges in PEMFC simulations by introducing a
