deep-learning

Scientific Reports, Published online: 11 September 2026; doi:10.1038/s41598-026-71035-x An attention-guided dual-branch global–local deep learning framework for multiclass surface defect classification in steel billets

Mathematicians now have a formidable tool for breaking open challenging problems, but working out how best to use it isn't going to be easy

Accurate and efficient automated analysis of Optical Coherence Tomography (OCT) images is critical for large-scale retinal disease screening. However, current deep learning models often fail to simultaneously achieve high classification accuracy, practical computational feasibility, and interpretability. To deal with these problems, this paper presents a deep learning framework on YOLOv11 for eig…

Originally published on tamiz.pro . The chasm between a convincing Jupyter notebook demo and a production-grade AI system is not merely one of scale; it is one of engineering discipline. When you first integrate a Large Language Model (LLM) or any generative AI into your application, the initial results are often miraculous: the model understands context, generates fluent text, and solves the spe…

In this article, you will learn how to fine-tune an agentic AI system holistically, covering all four critical dials: training data, parameter-efficient fine-tuning, runtime hyperparameters,...

The reliability of subseasonal-to-seasonal climate forecasts is vital for agriculture, where farmers and agribusinesses depend on accurate predictions to time planting, irrigation, and harvests, especially in regions vulnerable to drought or shifting

Ensuring the safety and dependability of power batteries has become a major concern due to the rapid expansion of electric vehicles (EVs), and fault detection has emerged as an essential approach for guaranteeing system stability. This study develops a Deep Learning (DL)-based defect prediction technique that combines an optimization algorithm and a Context-Aware Recurrent Neural Network with Ada…

Lithium-ion batteries are utilized in electric vehicles (EVs), and accurate predictions of battery state of charge (SOC), state of health (SOH), and remaining useful life (RUL) are needed to ensure reliability, security, and durability of these batteries. This study provides a hybrid deep learning (DL) system, termed as Neural Basis Expansion Variational Ladder Transformer (NBE-VLT), integrating …

Every time I want to build a desktop utility in 2026, the modern web ecosystem gives me the same repetitive answer: "Just bundle it with Electron!" Don't get me wrong: Electron is an impressive engineering feat. But packaging an entire Chromium browser and Node.js runtime just to display a local database and fire off a few API calls means: 160 MB to 220 MB download installers. 500 MB to 700 MB of…

IntroductionRapid detection of war-induced damage is vital for humanitarian response and post-war reconstruction, yet large-scale, well-annotated benchmarks for war-damage change detection are still lacking.MethodsWe present a large-scale, high-resolution bi-temporal optical remote sensing benchmark for war-damage change detection, named WD-CD. WD-CD contains 9,687 bi-temporal image pairs of 512 …

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See how AdaCore-sponsored students built an open-source NPU, then used it to run a 14-stage CNN on RISC-V.

Scientific Reports, Published online: 10 September 2026; doi:10.1038/s41598-026-68884-x A hybrid deep and handcrafted feature learning approach for imbalanced wafer map defect classification