Machine learning
Researchers from Karabuk University have developed a machine learning model that can accurately determine sex from computed tomography (CT) images of the skull. The study analyzed CT images of 200 individuals, aged 19-65 years, and used various machine learning algorithms to estimate sex based on morphometric measurements from the facial
Machine learning
Researchers at Veer Surendra Sai University of Technology have published a new report on the use of artificial intelligence in improving short-term load forecasting (STLF) in contemporary power networks. The study examined the effectiveness of particle swarm optimization (PSO), enhanced particle swarm optimization (EPSO), and artificial neural network (ANN) methods
Machine learning
Researchers from Technical University Valencia (TU Valencia) have made a significant breakthrough in using Artificial Intelligence (AI) to optimize patient care in emergency departments. During Seasonal Respiratory Diseases (SRDs), emergency departments often experience nursing shortages, leading to increased patient waiting times, overcrowding, and high infection rates. The study proposes merging
Machine learning
Researchers have developed a new tool, called the Spike-Wave discharge Artificial Neural Network (SWAN), to detect and classify spike-wave discharges in absence epilepsy. This automated tool uses spectrograms to evaluate complex spatiotemporal patterns in electroencephalographic (EEG) recordings. SWAN has been shown to achieve high precision (0.96) and sensitivity (0.
Machine learning
A team of researchers at Michael Okpara University of Agriculture has made a groundbreaking contribution to sustainability research by developing a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete. This innovative study utilizes six advanced metaheuristic optimization algorithms to evaluate the compressive strength, tensile strength,
Machine learning
Cureus, a medical publishing platform, is pushing back against the use of its open-access content by artificial intelligence systems. In an effort to preserve the intellectual integrity of its articles, Cureus has updated its terms of use to prohibit the use of its site content for AI training, data mining,
Machine learning
The global data center GPU market is anticipated to grow significantly, driven by the rapid adoption of artificial intelligence (AI) and machine learning (ML) across industries. Enterprises are utilizing GPUs for deep learning, large language model training, and advanced data analytics, which require powerful parallel processing. The rise of generative
Machine learning
The AI Shopping Assistant market was valued at USD 3.65 billion in 2024 and is projected to reach USD 24.90 billion by 2032, registering a strong Compound Annual Growth Rate (CAGR) of 27.22 percent during 2025-2032, according to a report by SNS Insider. Market growth is driven
Machine learning
Research published in the journal Discover Artificial Intelligence highlights the application of long short-term memory (LSTM) networks and simulated annealing algorithms in smart rural construction and college students' skill entrepreneurship. The study explores the potential of these algorithms in predicting key indicators for crop growth and agricultural market fluctuations,
Machine learning
A new research study by the Department of Computer Science presents fresh data on artificial intelligence, with a focus on developing a predictive model for STEM students using advanced ensemble machine learning algorithms. The study, which gathered secondary data from three Ethiopian universities, employed techniques such as Random Forest, CatBoost,
Machine learning
A new research study conducted by the Department of Computer Science has unveiled a revolutionary chatbot-based recommendation system that utilizes generative AI and prompt engineering techniques to provide personalized and context-aware educational resources. The system, named Generative Artificial Intelligence in Ubiquitous Learning, has demonstrated significant improvements in delivering adaptive and
Machine learning
A new machine learning-driven framework has been introduced, integrating Natural Language Processing (NLP) with a Support Vector Classifier (SVC) to improve email threat detection. The proposed model utilizes feature extraction techniques to capture linguistic patterns indicative of malicious emails. The framework was trained and evaluated on a benchmark dataset, achieving
