Lightweight convolutional neural network (CNN) model for obesity early detection using thermal images.

Objective: The presence of a lightweight convolutional neural network (CNN) model with a high-accuracy rate and low complexity can be useful in building an early obesity detection system, especially on mobile-based applications. The previous works of the CNN model for obesity detection were focused on the accuracy performances without considering the complexity size. In this study, we aim to build a new lightweight CNN model that can accurately classify normal and obese thermograms with low complexity sizes.

Effects of mobile phone electromagnetic radiation on thyroid glands and hormones in brain: An analysis of thyroid function, reactive oxygen species, and monocarboxylate transporter 8.

The aim of this study was to investigate the effects of mobile phone electromagnetic radiation (MP-EMR) on the thyroid glands and hormones in brain in term of thyroid function, reactive oxygen species (ROS), and monocarboxylate transporter 8 (MCT8) concentration. Forty rats were divided into different groups: control (without EMR exposure), EMR1 (120-min/day exposure), EMR2 (150-min), and EMR3 (180-min). The levels of serum thyroid stimulating hormone (TSH), thyroxine (T), and malondialdehyde (MDA) and brain and MCT8 were measured using enzyme-linked immunosorbent assay.

Knowledge management strategy for managing disaster and the COVID-19 pandemic in Indonesia: SWOT analysis based on the analytic network process.

Indonesia has significant expertise in disaster management due to its disaster geography. Collective expertise and knowledge are valuable resources for lowering disaster risk and enhancing disaster resilience. Additionally, in the current pandemic situation, a clearer understanding of COVID-19 is growing, which could make a difference in how effectively we respond to this and future pandemics.

BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network.

The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its implementation framework. BreaCNet consists of an effective segmentation algorithm for breast thermograms and a classifier based on the mobile CNN model.

Knowledge creation elements for enhancing community resilience towards disaster: A Delphi study.

Knowledge capacity plays a vital role in building community resilience to disasters. However, the problem is that there is no resilience framework that integrates the knowledge creation process. This article introduces a new framework for increasing community resilience based on knowledge creation theory (KCT).

Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition.

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition.

DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents.

Analysis of degraded ancient documents is challenging due to the severity and combination of degradation present in a single image. Ancient documents also suffer from additional noise during the digitalization process, particularly when digitalization is done using low-specification devices and/or under poor illumination conditions. The noises over the degraded ancient documents certainly cause a troublesome document analysis.

Cross-spectral iris recognition using phase-based matching and homomorphic filtering.

In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance.

Effective and fast binarization method for combined degradation on ancient documents.

Document image binarization is a challenging task because of combined degradation in a document. In this study, a new binarization method is proposed for binarizing an ancient document with combined degradation. The proposed method comprises the following four stages: histogram analysis, contrast enhancement, local adaptive thresholding, and artifact removal.

A secure online image trading system for untrusted cloud environments.

In conventional image trading systems, images are usually stored unprotected on a server, rendering them vulnerable to untrusted server providers and malicious intruders. This paper proposes a conceptual image trading framework that enables secure storage and retrieval over Internet services. The process involves three parties: an image publisher, a server provider, and an image buyer.