Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Africa wildlife prediction based on custom convolutional neural network

Domaine:

environment and energy

Type de record:

paper
Créateur:
Kez
Éditeur:
EWA
Hôte:
In domains like psychology, computer science, and artificial intelligence, facial expression recognition has major ramifications. This paper suggests classifying input animal photos using convolutional neural network (CNN). The CNN layer’s kernel size has been modified to (2,2), (3,3), (4,4). The primary objective is to examine how the model responds to changes in the CNN layer’s kernel size and operation. African Wildlife, a data set of four African species, was utilized for the studies. There are 1508 different themes in this collection, divided into 4 groups with 377 photographs each. Different numbers of test photos and training images were used to determine overall performances. The custom CNN model with a kernel size of (3,3) achieved an accuracy of 57.57% on the dataset. According to the experimental findings, having a kernel that is either too large or too tiny may negatively impact the model and result in undesirable poor accuracy. This study could provide suggestions for predicting animals based on the development of convolutional neural networks.

Visit

doi.org

Tasks

computer visionimage classification

Similaires

Convolutional Neural Network Based Maize Plant Disease IdentificationFracture Detection In X-rays Using Custom Convolutional Neural Network (CNN) And Transfer Learning ModelsConvolutional neural network-based deep learning model for air quality prediction in October city of EgyptImage-Based Poultry Disease Detection Using Deep Convolutional Neural NetworkAn image-based convolutional neural network system for road defects detectionSentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network

Convolutional Neural Network Based Maize Plant Disease Identification

Fracture Detection In X-rays Using Custom Convolutional Neural Network (CNN) And Transfer Learning Models

Bone fractures present a major global health challenge, often resulting in pain, reduced mobility, a

Convolutional neural network-based deep learning model for air quality prediction in October city of Egypt

Purpose Modern human society has continuous advancements that have a negative impact on the qualit

Image-Based Poultry Disease Detection Using Deep Convolutional Neural Network

Image-Based Poultry Disease Detection Using Deep Convolutional Neural Network

Poster presented at the Deep Learning Indaba 2022 by Hope Mbelwa

An image-based convolutional neural network system for road defects detection

An application of convolutional neural network (CNN) technique for road surface defects detection is

Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network

In order to maximize the applicability of sentiment analysis results, it is necessary to not only cl