Landscape Image Classification Using Convolutional Neural Network on Intel Image Classification Datase

Enhancing image classification accuracy with AL-CNN: a hybrid of AlexNet and LeNet architectures

Image classification is a key application of computer vision with direct relevance to medical diagnostics, autonomous vehicles, and remote sensing. This paper discusses the use of an adaptive learning convolutional neural network (AL-CNN) for image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation. The AL-CNN architecture integrates convolutional, pooling, and fully connected layers. The model was systematically trained on a subset of the dataset and subsequently tested on an independent validation subset to evaluate its efficiency and generalization capability. In addition, optimization techniques such as data augmentation, dropout, and advanced activation functions were employed to further enhance model performance. The results, based on accuracy metrics, indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification. This study demonstrates the potential of the AL-CNN approach to address various complexities in image classification, thereby enabling further innovation in this domain.

A Comparative Study of Deep Learning Architectures for Image Classification

Image classification is a major area in computer vision, driven by rapid advances in deep learning. Over the last decade, convolutional neural networks (CNNs) and their variants have achieved high performance in applications such as medical diagnosis, autonomous driving, industrial inspection, remote sensing, and biometrics. However, choosing the right model remains challenging due to trade-offs between accuracy, computational cost, efficiency, and robustness. This paper presents a comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures. Using a common evaluation framework and standard datasets, the study analyzes performance based on key design factors such as depth, width, receptive field, skip connections, and normalization. Theoretical concepts like convolution operations, residual learning, and optimization are also discussed.The results show that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency. Lightweight models perform well in resource-limited and real-time environments. Overall, the study offers practical guidance for selecting suitable architectures and highlights future research areas such as neural architecture search, self-supervised learning, and efficient model deployment.

Website-Based Implementation of Convolutional Neural Networks for Real Face and Deepfake Image Detection

The development of artificial intelligence increases the risk of facial image manipulation through deepfake technology that is difficult to distinguish visually. This study aims to implement a Convolutional Neural Network (CNN) based on EfficientNetB0 to classify real and deepfake static facial images and integrate the model into a website application. The study follows the CRISP-DM framework using the FaceForencis++ extracted frames dataset from Kaggle, consisting of 60,000 images with 30,000 real images and 30,000 fake images. The data were divided into 70:15:15 ratios for training, validation, and testing, then processed through 224 x 224 pixel resizing, data augmentation, and facial area cropping using OpenCV Haar Cascade during inference. The model achieved 82.50% validation accuracy and 0.3874 validation loss at the 10th epoch. Decision threshold optimization at 0.78 produced 80.99% accuracy, 80.98% macro F1-score, and 78.56% recall for the fake class on the test data. The model was deployed in a Streamlit application, allowing users to upload facial images and receive detection results directly. The results indicate that the combination of EfficientNetB0 and threshold optimization can support preliminary verification of digital facial image authenticity.

Comparative Analysis of Custom and Pre-Trained Convolutional Neural Networks (CNNs) for Object Recognition on the Cifar-10 Dataset

Convolutional Neural Networks (CNNs) have significantly changed image classification over the years by allowing computers to learn features directly from raw pixel data. However, deciding between building a customised model and using a pre-trained one can be a difficult task, especially when working with small datasets. In this study, we compare a custom CNN with three pre-trained models—VGG16, ResNet50, and MobileNetV2—on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories. We measured model performance using accuracy, precision, recall, F1-score, and training time. The results show that pre-trained models performed much better than the customised model. ResNet50 had the highest accuracy at 92.4%. However, MobileNetV2 gave the best mix of speed (1,800 seconds to train) and accuracy (90.2%). The custom CNN reached 82.3% accuracy, used less memory, and did not need image resizing. These results offer clear benchmarks for choosing models in the face of limited resources. They also demonstrate that transfer learning can achieve strong performance, while showing that custom CNNs remain useful for learning and simple tasks.

Application of CNN algorithm with enhanced pooling module and activation function in image recognition

Detection of AI-Generated Images Using Pretrained CNN Feature Extraction and Support Vector Machine Classification

Because Generative AI, we now see highly realistic synthetic images that are difficult to distinguish from the real thing. This technology raises concerns about misinformation, the trustworthiness of digital content, and how people can misuse synthetic media. Therefore, finding an accurate and efficient way to detect AI-Generated images is a significant research challenge. In this research, we explore a hybrid image classification setup: we combine CNN-based feature extraction (using a pre-trained network) with SVM classification to determine which images are AI-Generated and which are real. For feature extraction, we use MobileNetV2, pre-trained on ImageNet, as the fixed extractor. Then, to classify these features, we use SVM with an RBF kernel. This experiment uses the Kaggle dataset AI Generated Images vs real Images, with a total of 975 images divided into training and testing sets with a stratified 80:20 split. Our model performs quite well: 84.1% accuracy, 81.8% precision, 91.7% recall, and an F1 score of 86.5%. The confusion matrix shows that the model is able to detect most of the AI-generated images, demonstrating its sensitivity to synthetic content. Overall, our CNN–SVM setup offers an effective and efficient alternative to full end-to-end deep learning methods for detecting these images. This holds promise for applications such as digital content verification, combating disinformation, and automated authenticity checking.