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Structural Investigation and Enriched Catalysis of Cu-Complex-Encapsulated Microporous Catalyst with Pragmatic Modeling for Prediction of Activity by Using Machine Learning.

Silicoaluminophosphates (SAPOs) are structurally diverse materials widely used in separation, catalysis, and environmental applications. In this study, a simple post-synthetic method is used to create a hybrid porous material by immobilizing a copper(II) complex onto base-functionalized SAPO molecular sieves. The copper complex, synthesized using 2,9-dimethyl-1,10-phenanthroline and copper nitrate...

DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing

Hyperdimensional Computing (HDC) is emerging as a promising approach for edge AI, offering a balance between accuracy and efficiency. However, current HDC-based applications often rely on high-precision models and/or encoding matrices to achieve competitive performance, which imposes significant computational and memory demands, especially for ultra-low power devices. While recent efforts use te...

Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments.

Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in ...

Development and Validation of Machine Learning Algorithms for Predicting Prolonged Postoperative Opioid Use in Spinal Metastatic Disease.

Introduction: Operative management of spinal metastatic disease is largely for symptom palliation and revolves around the expectation that postoperati...

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.

An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This...

The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023.

Today, drug discovery and development is one of the fields where Artificial Intelligence (AI) is used extensively. Therefore, this study aims to syste...

Prediction via Shapley Value Regression

Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley v...

GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance

In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharma...

With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations

Group Recommender Systems (GRS) employing social choice-based aggregation strategies have previously been explored in terms of perceived consensus, ...

X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains

Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research con...

PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework

Federated Learning (FL) enables collaborative model training while preserving data privacy, but its classical cryptographic underpinnings are vulner...

Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs

The remarkable advancements in Large Language Models (LLMs) have revolutionized the content generation process in social media, offering significant...

Efficient Vision-based Vehicle Speed Estimation

This paper presents a computationally efficient method for vehicle speed estimation from traffic camera footage. Building upon previous work that ut...

The effects of the post-delay epochs on working memory error reduction.

Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects ...

Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of po...

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device ...

An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.

Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has ...

In defence of post-hoc explanations in medical AI

Since the early days of the Explainable AI movement, post-hoc explanations have been praised for their potential to improve user understanding, prom...

WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the...

Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation

Detecting small drones, often indistinguishable from birds, is crucial for modern surveillance. This work introduces a drone detection methodology b...