On the public citation graph

Corn Disease Classification Using Convolutional Neural Network with Weighted Loss

https://doi.org/10.2139/ssrn.4678951
0 signed · 34 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-3-031-37303-9_14openalex-extracted
Advanced MRI Segmentation Algorithm for the Detection of Brain Tumor Using U-Net Architecture with Transfer Learning EfficientNet-B7
inferredcites →https://doi.org/10.1007/978-3-319-97982-3_16openalex-extracted
A Study on CNN Transfer Learning for Image Classification
inferredcites →https://doi.org/10.1007/s00521-022-07246-wopenalex-extracted
On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves
inferredcites →https://doi.org/10.1007/s10916-019-1475-2openalex-extracted
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-extracted
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s41348-020-00368-0openalex-extracted
Plant disease detection using computational intelligence and image processing
inferredcites →https://doi.org/10.1007/s42979-020-0114-9openalex-extracted
Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks
inferredcites →https://doi.org/10.1016/j.compag.2020.105456openalex-extracted
An optimized dense convolutional neural network model for disease recognition and classification in corn leaf
inferredcites →https://doi.org/10.1016/j.inpa.2016.10.005openalex-extracted
Detection of plant leaf diseases using image segmentation and soft computing techniques
inferredcites →https://doi.org/10.1016/j.media.2020.101692openalex-extracted
Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy
inferredcites →https://doi.org/10.1016/j.procs.2023.01.149openalex-extracted
Convolutional Neural Network Based Maize Plant Disease Identification
inferredcites →https://doi.org/10.1038/s41598-020-59108-xopenalex-extracted
Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM
inferredcites →https://doi.org/10.1088/1742-6596/2406/1/012023openalex-extracted
Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods
inferredcites →https://doi.org/10.1109/access.2020.2976045openalex-extracted
Enhanced Skin Condition Prediction Through Machine Learning Using Dynamic Training and Testing Augmentation
inferredcites →https://doi.org/10.1109/access.2021.3058773openalex-extracted
Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN
inferredcites →https://doi.org/10.1109/confluence.2019.8776937openalex-extracted
Segmentation Techniques for Breast Cancer Imaging Modalities-A Review
inferredcites →https://doi.org/10.1109/elconrus51938.2021.9396706openalex-extracted
Remote Sensing Data Classification Using A Hybrid Pre-Trained VGG16 CNN- SVM Classifier
inferredcites →https://doi.org/10.1109/icoei48184.2020.9142871openalex-extracted
A CNN Approach for Corn Leaves Disease Detection to support Digital Agricultural System
inferredcites →https://doi.org/10.1109/icts.2019.8850946openalex-extracted
Classification of Tobacco Leaf Pests Using VGG16 Transfer Learning
inferredcites →https://doi.org/10.1109/itnec48623.2020.9084689openalex-extracted
Study on Corn Disease Identification Based on PCA and SVM
inferredcites →https://doi.org/10.1155/2021/4428964openalex-extracted
A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects
inferredcites →https://doi.org/10.11591/ijict.v12i2.pp115-126openalex-extracted
Analyzing performance of deep learning models under the presence of distortions in identifying plant leaf disease
inferredcites →https://doi.org/10.24002/jbi.v13i1.5022openalex-extracted
Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN)
inferredcites →https://doi.org/10.24843/spektrum.2021.v08.i02.p3openalex-extracted
RANCANG BANGUN APLIKASI IDENTIFIKASI PENYAKIT TANAMAN PEPAYA CALIFORNIA BERBASIS ANDROID MENGGUNAKAN METODE CNN MODEL ARSITEKTUR SQUEEZENET
inferredcites →https://doi.org/10.31227/osf.io/dguhbopenalex-extracted
SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA TANAMAN JAGUNG DENGAN METODE BAYES
inferredcites →https://doi.org/10.31294/p.v22i2.8907openalex-extracted
Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN)
inferredcites →https://doi.org/10.3389/fpls.2022.808380openalex-extracted
Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network
inferredcites →https://doi.org/10.3389/fpls.2022.864486openalex-extracted
Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention
inferredcites →https://doi.org/10.3390/electronics10050529openalex-extracted
Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain
inferredcites →https://doi.org/10.3390/plants12132433openalex-extracted
MTDL-EPDCLD: A Multi-Task Deep-Learning-Based System for Enhanced Precision Detection and Diagnosis of Corn Leaf Diseases
inferredcites →https://doi.org/10.3390/rs13214218openalex-extracted
High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
inferredcites →https://doi.org/10.34248/bsengineering.1322907openalex-extracted
Corn Disease Detection Using Transfer Learning
inferredcites →https://doi.org/10.46808/informa.v7i2.199openalex-extracted
Klasifikasi Penyakit Pada Daun Jagung Menggunakan Convolutional Neural Network
inferredcites →https://doi.org/10.7150/jca.28769openalex-extracted
Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study
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.