International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence

Towards Privacy-Preserving and Explainable CNN-Based Image Classification

Deng Mile, Abdul Salam Shah — Fri, 08 May 2026 00:00:00 +0000

The paper introduces a two-axis extension of the standard Convolutional Neural Network (CNN) image classification studies; the technical exploration is based on an experimental baseline of Fashion-MNIST and generalizes its results to two frontier research directions of pressing societal importance Federated Learning (FL) to train distributed models privately and Explainable Artificial Intelligence (XAI) to make transparent and clinically interpretable decisions. It is a three-block sequential CNN (with 32, 64, and 64 filters Convolutional layers, max-pooling, dense classification and softmax output) that is trained on the 15,000 sample Fashion-MNIST test data with Adam optimizer categorical cross-entropy in 15 epochs (resulting in 89.57 percent accuracy and macro-averaged F1-score of 0.90). This performance profile per-class, i.e., high F1 on morphologically distinct classes (Trouser: 0.98; Bag: 0.98; Sandal: 0.96) and significantly lower performance on visually confusable classes (Shirt: 0.71) is systematically studied to incentivise a Federated Learning architecture that can quickly train CNNs on decentralised and non-IID data partitions without access to raw training examples and a Gradient-weighted Class Activation Mapping (Grad-CAM) XAI addition that can make Combined, these extensions map out a technically sound research path to deploy privacy-conscious, transparent CNN classifiers in high-stakes areas of application such as clinical diagnostic imaging, federated retail AI, and image pathology across institutions.

Multi-Class Image Classification on Fashion-MNIST Using a Custom CNN

Abdulrahman Nasser Abdullah Algharem, Abdul Salam Shah — Fri, 24 Apr 2026 00:00:00 +0000

This paper presents a custom lightweight Convolutional Neural Network (CNN) designed from scratch for multi class classification on Fashion-MNIST. The architecture employs progressive convolutional blocks (32/64/128 filters), batch normalization for stable training, max/global pooling for dimensionality reduction, dropout for regularization, and data augmentation to enhance generalization. Implemented in TensorFlow/Keras and trained over 100 epochs with Adam optimization, the model totals just 111,370 parameters.

Convolutional Neural Networks for Multi-Class Image Classification

Andrew Leewei Hobbs, Abdul Salam Shah — Thu, 30 Apr 2026 00:00:00 +0000

The given work is a design, implementation and evaluation of a tailored Convolutional Neural Network (CNN) that can be trained to provide multi-class image classification in ten different clothing categories based on the Fashion-MNIST benchmark data set (Xiao, Rasul, and Vollgraf, 2017). The data sets include 70,000 28x28 grayscale 28x28 pixel images (60,000 training data and 10,000 test data). In the proposed CNN architecture, three convolutional-pooling blocks each containing a filter depth of 32, 64 and 128 are used, then finally a fully connected classifier with a softmax output layer to generate class probabilities in the ten categories. The data preprocessing steps involved pixel value rescaling in [0, 1] range, reshaping tensors to meet the Conv2D layer requirements, stratified train/validation/test splitting, and sparse integer label encoding with the sparse categorical cross-entropy loss. The Adam optimizer (Kingma and Ba, 2015) was used, with validation-based callbacks, i.e. Early Stopping and ReduceLROnPlateau, to reduce overfitting and guarantee generalizable behavior. After testing on the held-out test set, the final model had test accuracy of about 92% and test loss of about 23 percent indicating high generalization to unknown data. The confusion analysis has shown that classification errors were clustered across the visually similar category of upper-body garments, in particular, shirt, T-shirt, coat, and pullover, which is also very common in the Fashion-MNIST literature and is also primarily due to the low pixel density of the dataset (Xiao et al., 2017). This conclusion is supported by the fact that a small, purposely designed CNN architecture is an effective and computationally efficient solution to this benchmark classification problem, and that the performance gaps still exist largely due to natural limits of the data sets, and not due to architecture weaknesses.

Image Classification Using Convolutional Neural Networks

Farzana Binti Abdul Aziz, Abdul Salam Shah — Sun, 26 Apr 2026 00:00:00 +0000

The given paper explores the issue of Convolutional Neural Network (CNN)-based image classification on the Fashion-MNIST dataset and elaborates on the findings in relation to the impacts on smart retail artificial intelligence and the process of its implementation into practice through transfer learning and edge computing. It is a custom three-block sequential CNN, which consists of progressive convolutional layers consisting of 32, 64, and 128 filters, max-pooling, fully connected dense layer of 128 neurons, and a 10-class softmax output, trained, and evaluated on 10 epochs with the Adam optimizer and sparse categorical cross-entropy loss in TensorFlow/Keras. This model attains an approximate test accuracy of 88.8 per-class F1-scores are high in categories that are morphologically distinct, like Trouser, Bag, and Sandal (F1[?] 0.97) and lowly with those that are closely similar in appearance like Shirt and T-shirt/top by the greyscale as they may be inter-classes. These empirical results are conceptually mapped into real-world implementation scenarios: automated retailing, e-commerce product tagging with AI, virtual try-on, and fashion recognition on the edge. The article also discusses transfer learning models, specifically MobileNetV2 and EfficientNetB3, as computationally efficient frameworks to be used in computing resource-limited deployment settings, comparing their parameter efficiency and inference rate with the specialized architecture. The business environment of AI-in-fashion is the global market, which is currently USD 2.23 billion with a compound annual growth rate of 39 percent (SmartDev, 2025) and is expected to increase to USD 60 billion in 2034.

5G-Enabled Drone System

Jianfeng Su , Jian-Zhou Lu — Mon, 27 Apr 2026 00:00:00 +0000

This study provides an evidence-based decision framework and a quantitative feasibility assessment of a 5G-based drone system in precision agriculture. Uplink transmission times under 4G and several 5G scenarios were simulated using a data-driven model based on actual drone image transmissions (0.574 GB per mission). The simulation showed a more than 90% increase in performance using 5G, which successfully addresses the 4G transmission constraint by reducing the average transmission delay (τ1) from 1,205 seconds (20.1 minutes) under 4G to between 61 and 96 seconds (1-1.6 minutes) under 5G. However, a rural edge coverage stress test (100 simulations) showed a high standard deviation in τ1 (approximately 43 seconds), which resulted in a long tail in the latency distribution, quantifying the unreliability of the network as a primary business risk. A technical artefact, the MNDVI Heatmap algorithm, validated the system’s ability to extract valuable information from low-cost RGB sensors. To address the high capital costs, a conditional adoption approach using a Hybrid Network Architecture (5G as a service for bandwidth, a mesh network for C2 reliability) and a Drone as a Service (DaaS) business model is proposed.

CNN-Based Image Classification on the Fashion-MNIST Dataset

Chia Chen Yee, Abdul Salam Shah — Sat, 02 May 2026 00:00:00 +0000

The paper is an empirical study on the use of convolutional neural network (CNN)-based multi-class image classification on the Fashion-MNIST benchmark dataset. Fashion-MNIST, consisting of 70,000 grayscale images of shoes and clothing of ten classes, was chosen as a more difficult follow-up to the canonical MNIST digit recognition benchmark, with significantly higher levels of intra-class and inter-class visual similarity to real world fashion recognition problems. A sequential CNN model was created and trained with TensorFlow and Keras and featured stacked convolutional layers with ReLU activation, max-pooling to perform spatial downsampling, dropout regularization to reduce overfitting, and a fully connected softmax output layer to estimate the probability of classes. The objective function was sparse categorical cross-entropy and the Adam optimizer was used to train the model. The held-out test partition offers empirical assessment with an ultimate classification error of 88.85, which is remarkable generalization and no major indication of overfitting. The high discriminative results of the morphologically distinctive categories, such as trousers, bags, and footwear, and the systematic inter-class confusion observed within the upper-body garment category, specifically the confusion between shirts, T-shirts, and pullovers, indicate that the results are per-class, meaning that the results are influenced by morphologically distinctive categories rather than by arbitrary combinations of such categories. Such results are placed in the context of the larger deep learning literature on fashion image recognition, and the future research directions such as data augmentation, more complex architectures, and attention-based methods are discussed.

Convolutional Neural Networks for Image Classification

Gam Chui Ern, Abdul Salam Shah — Mon, 04 May 2026 00:00:00 +0000

In this paper, an experimental design, implementation and evaluation of a regularized Convolutional Neural Network (CNN) to classify multi-label images on the Fashion-MNIST benchmark are presented and extended to a new and urgent research area: a application of CNN-based image classification to sustainable fashion and textile circular economy systems. The architecture used by the custom CNN consists of three convolutional blocks with increasing filter depth (32, 64, 128 channels), batch normalization, data augmentation (rotation, shift, zoom), dropout regularization, early stopping, and 256-unit dense classification head with categorical cross-entropy loss and Adam as the optimizer and up to 20 epochs. The model attains a test accuracy of about 90% on 10,000-sample Fashion-MNIST test set, and a Macro-average F1-score of very high discriminative power on morphologically distinct categories (Trouser, Bag, Sandal: F1[?]0.97) and poorer on the visually similar garments (Shirt: F1[?]0.74). These findings are put in perspective of the fashion sustainability crisis on the global scale: according to the estimates provided by the United Nations Environment Programme (UNEP), 92 million tonnes of textiles are dumped into landfill each year, which makes up to 8 per cent of the total global greenhouse emissions. CNN-based visual classification The identical convolutional feature extractor, which is proven on Fashion-MNIST, is already used in intelligent textile sorting systems with 93% classification accuracy in 2025 pilot line, and in post-consumer fabric recycling pipelines to sort textiles into recycling streams at 95 percent accuracy at a rate of one item/second (Tsai and Yuan, 2025). The paper builds a technical roadmap of extending the Fashion-MNIST CNN engineering capabilities to the use in the circular economy, recognizes four AI-enabled pillars of the circle economy that are supported by the literature, and suggests specific future research directions on the cross-section of computer vision, sustainability, and regulatory compliance.