On the public citation graph

An n-Sigmoid Activation Function to Improve the Squeeze-and-Excitation for 2D and 3D Deep Networks

https://doi.org/10.3390/electronics12040911

0 signed · 65 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-46723-8_49openalex-snapshot

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

inferredcites →https://doi.org/10.1007/s00521-020-05008-0openalex-snapshot

Deep network in network

inferredcites →https://doi.org/10.1007/s10489-021-02247-zopenalex-snapshot

PSigmoid: Improving squeeze-and-excitation block with parametric sigmoid

inferredcites →https://doi.org/10.1007/s42979-021-00815-1openalex-snapshot

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

inferredcites →https://doi.org/10.1016/j.neucom.2018.01.084openalex-snapshot

Improving deep neural network with Multiple Parametric Exponential Linear Units

inferredcites →https://doi.org/10.1016/j.procs.2019.05.053openalex-snapshot

System Architecting Approach for Designing Deep Learning Models

inferredcites →https://doi.org/10.1038/s41598-021-96723-8openalex-snapshot

Universal activation function for machine learning

inferredcites →https://doi.org/10.1049/cvi2.12020openalex-snapshot

TanhExp: A smooth activation function with high convergence speed for lightweight neural networks

inferredcites →https://doi.org/10.1049/el.2020.0854openalex-snapshot

Accuracy controlled iterative method for efficient sigmoid function approximation

inferredcites →https://doi.org/10.1049/iet-ipr.2019.1462openalex-snapshot

FuSENet: fused squeeze‐and‐excitation network for spectral‐spatial hyperspectral image classification

inferredcites →https://doi.org/10.1109/access.2019.2916582openalex-snapshot

Optimal Pricing and Pre-Sale Policies for Perishable Product in an EOQ Inventory Model

inferredcites →https://doi.org/10.1109/ccwc47524.2020.9031119openalex-snapshot

Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot

Xception: Deep Learning with Depthwise Separable Convolutions

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-snapshot

Aggregated Residual Transformations for Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00716openalex-snapshot

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

inferredcites →https://doi.org/10.1109/cvpr.2018.00745openalex-snapshot

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.1109/icmla.2017.00038openalex-snapshot

Parametric Exponential Linear Unit for Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.23919/acc53348.2022.9867181openalex-snapshot

Learning Monotone Dynamics by Neural Networks

inferredcites →https://doi.org/10.33564/ijeast.2020.v04i12.054openalex-snapshot

ACTIVATION FUNCTIONS IN NEURAL NETWORKS

inferredcites →https://doi.org/10.3390/app122312478openalex-snapshot

FNNS: An Effective Feedforward Neural Network Scheme with Random Weights for Processing Large-Scale Datasets

inferredcites →https://doi.org/10.3390/e21070627openalex-snapshot

Smooth Function Approximation by Deep Neural Networks with General Activation Functions

inferredcites →https://doi.org/10.3390/electronics11213570openalex-snapshot

LRSE-Net: Lightweight Residual Squeeze-and-Excitation Network for Stenosis Detection in X-ray Coronary Angiography

inferredcites →https://doi.org/10.3390/healthcare11020213openalex-snapshot

Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification

inferredcites →https://doi.org/10.3390/rs14133184openalex-snapshot

A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification

inferredcites →https://doi.org/10.3390/sym14051027openalex-snapshot

An Efficient Asymmetric Nonlinear Activation Function for Deep Neural Networks

inferredcites →https://doi.org/10.3390/w12051500openalex-snapshot

Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

inferredcites →https://doi.org/10.3934/mbe.2023059openalex-snapshot

Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.5244/c.30.87openalex-snapshot

Wide Residual Networks

inferredcites →https://doi.org/10.9790/4200-0702012633openalex-snapshot

Performance Analysis of the Sigmoid and Fibonacci Activation Functions in NGA Architecture for a Generalized Independent Component Analysis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4776611openalex-extracted

A Deep Convolutional Neural Network for Blind Element Error Correction of Spatial Heterodyne Spectrometer Using Line Selective Convolutional Blocks

inferred← citeshttps://doi.org/10.2139/ssrn.4890315openalex-extracted

Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and Resnet50

inferred← citeshttps://doi.org/10.2139/ssrn.5022207openalex-extracted

From Discounts to Delivery: Decoding Customer Care Interactions in Warehousing

inferred← citeshttps://doi.org/10.1007/978-981-96-9585-0_7openalex-snapshot

Implementation of Tanh Function Based on DNA Strand Displacement

inferred← citeshttps://doi.org/10.1007/s10554-026-03684-yopenalex-snapshot

MyoClass: A modular multimodal auto-classification system for myocardial tissue characterization

inferred← citeshttps://doi.org/10.1007/s11760-025-03971-6openalex-snapshot

Linguistic neural networks for optimizing S-box selection in image encryption

inferred← citeshttps://doi.org/10.1007/s11760-025-04443-7openalex-snapshot

Modified convolutional architecture based anomaly detection for video surveillance with mask R-CNN segmentation model for object detection

inferred← citeshttps://doi.org/10.1007/s11760-025-04697-1openalex-snapshot

Analyzing the traffic light control systems based on novel fuzzy neural network under triangular fuzzy numbers

inferred← citeshttps://doi.org/10.1007/s11816-024-00944-yopenalex-snapshot

Spinach leaf disease identification based on deep learning techniques

inferred← citeshttps://doi.org/10.1007/s12145-025-01991-xopenalex-snapshot

Smoke characterization for incipient wildfire detection from Sentinel-2 sensor based on sigmoid activation function and momentum gradient descent optimizer

inferred← citeshttps://doi.org/10.1007/s42044-025-00242-yopenalex-snapshot

From image classification to segmentation: a comprehensive empirical review

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2026.111384openalex-snapshot

Modified LinkNet and improved RCNN-based object detection for copy-move forgery detection

inferred← citeshttps://doi.org/10.1016/j.dsp.2025.105579openalex-snapshot

AdLU: Adaptive double parametric activation functions

inferred← citeshttps://doi.org/10.1016/j.jqsrt.2024.109199openalex-snapshot

A deep convolutional neural network for blind element error correction of spatial heterodyne spectrometer using line selective convolutional blocks

inferred← citeshttps://doi.org/10.1016/j.knee.2026.104361openalex-snapshot

An improved activation function for the recognition of knee osteoarthritis severity

inferred← citeshttps://doi.org/10.1021/acsomega.3c04535openalex-snapshot

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network

inferred← citeshttps://doi.org/10.1080/01969722.2025.2459959openalex-snapshot

Heart Disease Detection and Prognosis Using IoT-Based ECG Sensor Data with Hybrid Deep Learning Architecture and Optimal Resource Allocation

inferred← citeshttps://doi.org/10.1109/access.2023.3339884openalex-snapshot

Classifying the Vermicompost Production Stages Using Thermal Camera Data

inferred← citeshttps://doi.org/10.1109/atc63255.2024.10908160openalex-snapshot

Hardware Implementation and Optimization of the Sigmoid Activation Function in CNNs Using Taylor Series Approximation

inferred← citeshttps://doi.org/10.1109/ricai64321.2024.10911106openalex-snapshot

A Spectral Clustering Algorithm for Similar Matrix Reconstruction based on Adaptive Neighborhood Standard Deviation

inferred← citeshttps://doi.org/10.1177/15330338251414224openalex-snapshot

EPIDSeg-Net: A Multi-Modal Fusion Framework Based on DRR Guidance in Radiotherapy is Used for Precise Segmentation of MV-EPID Lung Targets

inferred← citeshttps://doi.org/10.12913/22998624/172771openalex-snapshot

A Comparative Analysis of Image Segmentation Using Classical and Deep Learning Approach

inferred← citeshttps://doi.org/10.1364/ao.506920openalex-snapshot

Regularized cost function in wavefront shaping for advancing the contrast of structured light

inferred← citeshttps://doi.org/10.32604/cmc.2025.059966openalex-snapshot

A Latency-Efficient Integration of Channel Attention for ConvNets

inferred← citeshttps://doi.org/10.3389/fpls.2025.1521508openalex-snapshot

Sustainable deep vision systems for date fruit quality assessment using attention-enhanced deep learning models

inferred← citeshttps://doi.org/10.3390/electronics15163720openalex-snapshot

Lightweight Redesign of Long-Used Operators in Vision Backbones for Efficient Visual Recognition

inferred← citeshttps://doi.org/10.3390/engproc2026150058openalex-snapshot

Forecasting Customer Complaints in the Mobile Telecommunication Sector Using Supervised Machine Learning

inferred← citeshttps://doi.org/10.3390/fractalfract7110791openalex-snapshot

Difference Equations and Julia Sets of Several Functions for Degenerate q-Sigmoid Polynomials

inferred← citeshttps://doi.org/10.3390/jimaging11020029openalex-snapshot

Remote Sensing Target Tracking Method Based on Super-Resolution Reconstruction and Hybrid Networks

inferred← citeshttps://doi.org/10.3390/s24227215openalex-snapshot

Three-Dimensional Instance Segmentation Using the Generalized Hough Transform and the Adaptive n-Shifted Shuffle Attention

inferred← citeshttps://doi.org/10.36680/j.itcon.2025.041openalex-snapshot

An Innovative Software Development Methodology for Deep Learning-Driven Visual Computing in Built Environment Applications

inferred← citeshttps://doi.org/10.54097/hset.v69i.12536openalex-snapshot

Study on Intercalated Melt-blown Nonwovens Based on Product Performance Control Mechanism

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.