We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.
Weeds pose the most persistent and costly threat to crop production in Canada, driving widespread herbicide use and accelerating the rise of herbicide-resistant species ...
Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...
Insurance companies aren't experimenting with AI. They're deploying it at scale across three critical functions that directly ...
Ethical disclosures and Gaussian Splatting are on the wane, while the sheer volume of submitted papers represents a new ...
Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
The online information landscape, driven in large part by social media, rewards engagement and is curated by classification ...
Multimodal Learning, Deep Learning, Financial Statement Analysis, LSTM, FinBERT, Financial Text Mining, Automated Interpretation, Financial Analytics Share and Cite: Wandwi, G. and Mbekomize, C. (2025 ...
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How brain-inspired algorithms could drive down AI energy costs
In a study published in Frontiers in Science, scientists from Purdue University and the Georgia Institute of Technology ...
Fraud detection is defined by a structural imbalance that has long challenged data-driven systems. Fraudulent transactions typically account for a fraction of a percent of total transaction volume, ...
The study points up interpretability as a critical barrier to trust and adoption. Many AI-based cybersecurity tools function ...
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