AIMC Topic: Neural Networks, Computer

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Mele's Digital Zygote: Developer Responsibility for Neural Networks.

Science and engineering ethics
Should developers be held responsible for the predictions of their neural networks-and if not, does that introduce a responsibility gap? The claim that neural networks introduce a responsibility gap has seen significant pushback, with philosophers ar...

TLMACEA: design of a transfer learning model for correlative analysis of auscultation and clinical parameters via explainable AI-based recommender.

Biomedical physics & engineering express
Auscultations are commonly used to analyze lung conditions through signal processing and classification techniques. However, the efficiency of these models is often limited by factors like signal quality, sensor performance, and dataset size. Current...

D2FLS-Net: Dual-Stage DEM-guided Fusion Transformer for landslide segmentation.

PloS one
Landslide segmentation from remote sensing imagery is crucial for rapid disaster assessment and risk mitigation. Owing to the pronounced heterogeneity of landslide scales and the subtle visual contrast between some landslide bodies and their backgrou...

Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm for Mobile Edge Computing Networks (EHRL).

PloS one
Mobile Edge Computing (MEC) is a computational paradigm that brings resources closer to the network edge to provide fast and efficient computing services for Mobile Devices (MDs). However, MDs are often constrained by limited energy and computational...

SCADET: A detection framework for AI-generated artwork integrating dynamic frequency attention and contrastive spectral analysis.

PloS one
With the rapid development of generative AI technology, AI-generated images pose significant challenges for authenticity verification and originality validation. This paper proposes SCADET, a novel detection framework that integrates Dynamic Frequenc...

AI-driven neural time series network forecasting and cost analysis for dye removal prediction in packed bed adsorption using ultrasonic biomass composites for sustainable wastewater management.

Environmental research
The study investigates the application of Artificial Intelligence (AI) driven neural network time series (NNTS) model for the forecasting prediction of dye removal using ultrasonic activated mixed biomass. Surface and functional characterization of u...

A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen-π Interactions by Training Neural Networks.

Journal of chemical information and modeling
Noncovalent interactions, such as halogen bonds (XB), play a crucial role in molecular recognition and drug design, yet halogen···π contacts remain comparatively underexplored. Here, we report a proof-of-concept QM-AI approach that integrates high-le...

Mobile phone-based plasmodium parasites stage detection from Giemsa stained blood smear by convolutional neural networks.

Parasitology research
Plasmodium vivax is a malaria parasite with a broad geographic distribution worldwide. The unique biological characteristics of P. vivax, such as early gametocytogenesis and its latent hypnozoite stage, make it more difficult to control compared to P...

NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.

Scientific reports
Brain age prediction based on anatomical MRI scans, as an essentially new measure in neuroimaging and aging research, provides a crucial marker for the early diagnosis of neurodegenerative diseases, cognitive health appraisal, and biological age pred...

MoleculeFormer is a GCN-transformer architecture for molecular property prediction.

Communications biology
Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological a...