AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A structured literature review of CNN-based food image classification studies published between 2020 and 2025 identifies persistent gaps in statistical validation, class imbalance treatment, explainability, reproducibility, public code availability and deployment-oriented evaluation.
Purpose - This study analyzes the classification performance and computational efficiency of five pretrained Convolutional Neural Network (CNN) architectures for identifying South Kalimantan traditional food images as an expanded benchmark. Design/methods/approach - The models were trained on a curated traditional food...
This review systematically compares CNNs, RNNs/LSTMs, Transformers, GNNs, GANs, and hybrid architectures, as well as transfer learning, self-supervised learning, contrastive learning, few-shot learning, lightweight networks, edge computing, and multimodal fusion.
Digital technologies, including the Internet of Things (IoT) and deep learning, are increasingly propelling smart agriculture. A vital aspect is the automated classification of objects for crop assessment and quality control. This study addresses the practical challenge of limited labeled data by investigating the effi...
The freshness of meat products is important for food safety and consumer health. Manual inspection methods are subjective and hard to scale, which leads to the need for automated vision-based solutions. This study compares six pretrained convolutional neural network architectures which are MobileNetV2, MobileNetV3, Eff...
The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to...
Banana ripeness classification is an important task in agriculture and food processing that supports automatic and objective sorting processes. Advances in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated high capability in image classification tasks. This research aims to analyze and...
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