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AI and Explainable AI in Visual Quality Assurance: A Comprehensive Survey in Manufacturing

The paper published in the journal Electronics explores the crucial role of Artificial Intelligence (AI) and Explainable AI (XAI) in Visual Quality Assurance (VQA) within manufacturing. While AI-based Visual Quality Control (VQC) systems are prevalent in defect detection, the study advocates for broader applications of VQA practices and increased utilization of XAI to enhance transparency and interpretability, ultimately improving decision-making and quality assurance in the industry.

10 Nov 2023

IoT-Integrated Drone Cybersecurity: Using DL and ML for Intrusion Detection

This paper explores the integration of IoT with drone technology to enhance data communication and security across various industries, including agriculture and smart cities. The study focuses on the use of machine learning and deep learning techniques to detect cyberattacks within drone networks and presents a comprehensive framework for intrusion detection.

2 Nov 2023

Predicting Future AI Research Directions with AI and Network Features

Researchers introduced the Science4Cast benchmark to forecast future AI research, emphasizing the importance of network features for precise predictions. This approach offers a promising tool to accelerate scientific progress in artificial intelligence.

19 Oct 2023

Advancing Railway Safety: Predicting Critical Speeds with AI Models

This research paper introduces innovative machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to assess critical speeds on railway tracks, especially those on soft soils. The study's dataset, created through advanced numerical methods and validated experiments, supports the development of predictive models for assessing critical speeds in various track sections.

17 Oct 2023

Comparing AI and Lexicon-Based Approaches for Short Text Classification in Social Sciences

A recent study delves into the automated classification of short texts from social media, crucial for social science research. The research compares lexicon-based and supervised machine learning approaches, highlighting the significance of traditional ML algorithms in short text classification and their efficiency compared to deep neural architectures, especially in cases with limited data resources.

3 Oct 2023

AI Revolutionizes Otitis Media Diagnosis: A Game-Changer for Primary Care

Researchers conduct a systematic review of AI techniques in otitis media diagnosis using medical images. Their findings reveal that AI significantly enhances diagnostic accuracy, particularly in primary care and telemedicine, with an average accuracy of 86.5%, surpassing the 70% accuracy of human specialists.

11 Sep 2023

AI Revolutionizes Ocular Imaging to Tackle SANS in Space Exploration

This review explores how Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and Supervised Learning, revolutionizes ocular imaging in space, offering new insights into Spaceflight Associated Neuro-Ocular Syndrome (SANS), a condition affecting astronauts' eyes during long-duration space missions.

1 Aug 2023

Unveiling High-Risk Scenarios: Deep Embedded Clustering for Autonomous Vehicle Testing

This study presents a novel approach to identifying typical car-to-powered two-wheelers (PTWs) crash scenarios for autonomous vehicle (AV) safety testing. By utilizing stacked autoencoder methods to extract embedded features from high-dimensional crash data, followed by k-means clustering, six high-risk scenarios are identified. Unlike previous research, this method eliminates manual selection of clustering variables and provides a more detailed scenario description, resulting in more robust and effective AV testing scenarios.

26 Jul 2023