Machine Learning

Machine learning (ML) is a key area of artificial intelligence (AI) that helps computers learn from data and get better at tasks over time, without needing to be directly programmed. By recognizing patterns in data, ML algorithms can make predictions and decisions that are useful in many fields, from healthcare to finance and e-commerce. Whether it’s improving customer service or helping businesses make smarter decisions, machine learning is changing the way we interact with technology. Keep up with the latest in machine learning by following our blog for updates and insights.

Pixel-Level Concrete Crack Quantification: A Breakthrough in Structural Health Monitoring

Pixel-Level Concrete Crack Quantification: A Breakthrough in Structural Health Monitoring

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Concrete cracks are more than just surface imperfections—they’re early warning signs of structural degradation that can compromise the safety and longevity of buildings, bridges, roads, and other critical infrastructure. Traditional inspection methods often rely on manual assessments, which are time-consuming, subjective, and prone to human error. However, recent advancements in computer vision and deep learning […]

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RetiGen: Revolutionizing Retinal Diagnostics with Domain Generalization and Test-Time Adaptation

RetiGen: Revolutionizing Retinal Diagnostics with Domain Generalization and Test-Time Adaptation

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Introduction: Bridging the Gap in AI-Powered Retinal Diagnostics Artificial intelligence (AI) has made remarkable strides in medical imaging, particularly in ophthalmology. Deep learning models now assist clinicians in diagnosing conditions like diabetic retinopathy (DR), age-related macular degeneration, and glaucoma using color fundus photographs. However, a persistent challenge remains: domain shift—the performance drop when models trained

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the Improved Pelican Optimization Algorithm (IPOA)—a novel metaheuristic approach designed specifically for stochastic OPF (S-OPF) problems under uncertainty.

An Improved Pelican Optimization Algorithm for Solving Stochastic Optimal Power Flow

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As the world transitions toward clean and sustainable energy, power systems are increasingly integrating renewable energy resources (RERs) such as solar photovoltaic (PV) and wind power. While these sources reduce carbon emissions and operational costs, their inherent uncertainty and intermittency pose significant challenges to maintaining grid stability and efficiency. One of the most critical tools

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Diagram showing modified final splits (MDFS) in a classification tree for improved subpopulation targeting in policy decisions.

Modifying Final Splits of Classification Trees (MDFS) for Subpopulation Targeting

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In the rapidly evolving field of machine learning for public policy, precision and fairness in decision-making are paramount. One of the most widely used tools—classification trees—has long been a cornerstone for identifying high-risk or high-need subpopulations. However, traditional methods like CART (Classification and Regression Trees) often fall short when the goal is not just prediction,

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Diagram of Hybrid Deep Learning Model

Building Electrical Consumption Forecasting with Hybrid Deep Learning | Smart Energy Management

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As global energy demand continues to rise due to rapid urbanization and technological advancements, building electrical consumption forecasting has become a critical component of modern energy management systems. With buildings accounting for nearly 40% of total global energy use, accurate prediction of electricity demand is essential for optimizing energy efficiency, reducing operational costs, and supporting

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Diagram of Multi-Teacher Knowledge Distillation with Reinforcement Learning (MTKD-RL)

Multi-Teacher Knowledge Distillation with Reinforcement Learning (MTKD-RL) – A Breakthrough in Visual Recognition

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In the rapidly evolving field of deep learning, knowledge distillation (KD) has emerged as a powerful technique for transferring knowledge from large, complex models—referred to as teachers—to smaller, more efficient models known as students. This process enables lightweight neural networks to achieve high accuracy, making them ideal for deployment on edge devices and real-time applications.

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Discrete Migratory Bird Optimizer with Transfer Learning Aided Multi-Retinal Disease Detection

Discrete Migratory Bird Optimizer with Deep Transfer Learning for Multi-Retinal Disease Detection

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Retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are leading causes of irreversible vision loss worldwide. Early detection is critical to preventing permanent blindness, yet manual diagnosis remains time-consuming and subjective. Recent advances in artificial intelligence have paved the way for automated, high-accuracy diagnostic systems. Among them, a groundbreaking approach—Discrete

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PSO-optimized fractional order CNNs revolutionize breast cancer detection with 99.35% accuracy, superior sensitivity, and robust image analysi

PSO-Optimized Fractional Order CNNs for Enhanced Breast Cancer Detection

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Early Detection, Smarter AI: How PSO-Optimized Fractional Order CNNs Are Transforming Breast Cancer Diagnosis Every year, millions of women face the daunting challenge of a breast cancer diagnosis. Despite advances in medical imaging, traditional mammography still struggles with high false-positive and false-negative rates, especially in patients with dense breast tissue. These limitations can lead to

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Visual representation of AMGF-GNN framework for tumor grading using multi-graph fusion in histopathology.

AMGF-GNN: Adaptive Multi-Graph Fusion for Tumor Grading in Pathology Images

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In the rapidly evolving field of computational pathology, accurate tumor grading in pathology images remains a cornerstone for effective cancer diagnosis and treatment planning. With the advent of artificial intelligence (AI), researchers are increasingly turning to advanced deep learning models to automate and enhance this critical process. Among the latest breakthroughs, the Adaptive Multi-Graph Fusion-based

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Anchor-Based Knowledge Distillation (AKD), a breakthrough in trustworthy AI for efficient model compression.

Anchor-Based Knowledge Distillation: A Trustworthy AI Approach for Efficient Model Compression

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In the rapidly evolving field of artificial intelligence (AI), knowledge distillation (KD) has emerged as a cornerstone technique for compressing powerful, resource-intensive neural networks into smaller, more efficient models suitable for deployment on mobile and edge devices. However, traditional KD methods often fall short in capturing the full richness of a teacher model’s knowledge, especially

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