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ICCK Publications

Total Publications: 2
Open Access | Research Article | 21 January 2026
Embedded Electronic IoT System for Poultry Health Monitoring and AI-Powered Disease Detection from Feces
ICCK Journal of Image Analysis and Processing | Volume 2, Issue 1: 1-16, 2026 | DOI: 10.62762/JIAP.2025.569459
Abstract
Poultry farming plays a vital role in global food production, requiring efficient management to ensure productivity and animal welfare. Traditional methods, largely based on manual monitoring, are often inefficient, error-prone, and costly. With the rise of Internet of Things (IoT) technologies, intelligent systems now enable remote monitoring and management of environmental conditions, farm operations, and disease prevention. Platforms such as ThingSpeak allow for real-time data collection, processing, and visualization, offering a cost-effective solution for poultry farm management. By integrating sensors to measure temperature, humidity, air quality, and feeding, and by leveraging ThingSp... More >

Graphical Abstract
Embedded Electronic IoT System for Poultry Health Monitoring and AI-Powered Disease Detection from Feces
Open Access | Research Article | 08 November 2025
Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification
ICCK Journal of Image Analysis and Processing | Volume 1, Issue 4: 162-171, 2025 | DOI: 10.62762/JIAP.2025.421429
Abstract
Breast cancer remains one of the most significant health challenges, being the second leading cause of death among women worldwide. Early and accurate diagnosis is critical to improving treatment outcomes and increasing survival rates. In this study, we present an innovative application of the WRN-28-2 model, a deep convolutional neural network pre-trained on ImageNet, for the classification of histopathological breast cancer images from the BreakHis dataset. By leveraging transfer learning, the model was fine-tuned to differentiate between benign and malignant cases, achieving a remarkable classification accuracy of 99.16% on the test set. Moreover, the model outperformed existing state-of-... More >

Graphical Abstract
Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification