Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.

Purpose: Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones.

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool.

Development and Effectiveness Verification of Metaverse Cognitive Therapy Contents for MCI Patients.

It is very important to prevent dementia by intervening in advance in the stage of mild cognitive impairment, which is the pre-stage of dementia. Recently, cognitive therapy research using metaverse has been on the rise. We propose a way to utilize metaverse cognitive therapy content as a non-drug treatment method of mild cognitive impairment patients.

Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features.

Methods for detecting emotions that employ many modalities at the same time have been found to be more accurate and resilient than those that rely on a single sense. This is due to the fact that sentiments may be conveyed in a wide range of modalities, each of which offers a different and complementary window into the thoughts and emotions of the speaker. In this way, a more complete picture of a person's emotional state may emerge through the fusion and analysis of data from several modalities.

Transfer Learning for Effective Urolithiasis Detection.

Purpose: Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology.

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm.

In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set.

An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach.

With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires.

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations.

Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization.

E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset's sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them.

Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm.

Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker's features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main steps: acoustic processing, feature extraction, and classification/recognition. The purpose of feature extraction is to illustrate a speech signal using a predetermined number of signal components.

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People.

Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible.

A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis.

Purpose: This paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them.

Methods: This paper proposes the optimal ureter stone detection model using various AI technologies.

Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images.

Among researchers using traditional and new machine learning and deep learning techniques, 2D medical image segmentation models are popular. Additionally, 3D volumetric data recently became more accessible, as a result of the high number of studies conducted in recent years regarding the creation of 3D volumes. Using these 3D data, researchers have begun conducting research on creating 3D segmentation models, such as brain tumor segmentation and classification.

Design and Development of a Battery State of Health Estimation Model for Efficient Battery Monitoring Systems.

An uninterruptible power supply (UPS) is a device that can continuously supply power for a certain period when a power outage occurs. UPS devices are used by national institutions, hospitals, and servers, and are located in numerous public places that require continuous power. However, maintaining such devices in good condition requires periodic maintenance at specific time points.

Architecting Intelligent Smart Serious Games for Healthcare Applications: A Technical Perspective.

The immune system of human beings plays a pivotal role in guarding against different types of diseases. During the COVID-19 pandemic, people with weak immune systems were more likely to die. Regular physical activities and healthy food intake can significantly improve the immune system; however, people with a sedentary lifestyle and a busy job schedule find it challenging and tedious to maintain regularity.

An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems.

Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning.

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in colonoscopy videos help endoscopists to identify colorectal disease more easily, making it a promising method to prevent colon cancer. In this study, we developed a fully automated pixel-wise polyp segmentation model named A-DenseUNet.

Characteristics of Facial Asymmetry in Congenital Superior Oblique Palsy according to Trochlear Nerve Absence.

Methods: A total of 287 consecutive patients diagnosed with congenital SOP and 82 control subjects were included. Congenital SOP patients were grouped according to the presence (present group) or absence (absent group) of the trochlear nerve using thin-section high-resolution MRI of cranial nerves. We developed a computer-aided detection (CAD) system that could automatically analyze objective indices of facial asymmetry using frontal face photographs.

Edge-based effective active appearance model for real-time wrinkle detection.

Background: Recently, the field of face and facial features has been progressively studied. The features of facial expression have gained increasing attention for related applications. The wrinkle is the most representative feature, and its research and applications have been topics of high interest.

The Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System.

In upcoming fourth industrial revolution era, it is inevitable to address smart healthcare as not only scientist but also clinician. We have the task to plan and realize this through human imagination, creativity, and applicability for the clarification of the direction of the development and utilization of this technology. One thing that is clear is that it is important to understand what information is needed, how to interpret it, what will be the outcomes, and how to respond in artificial intelligence and Internet of Things era.

Virtual Reality and Simulation for Progressive Treatments in Urology.

In urology technologies and surgical practices are constantly evolving and virtual reality (VR) simulation has become a significant supplement to existing urology methods in the training curricula of urologists. However, new developments in urology also require training and simulation for a wider application. In order to achieve this VR and simulation could play a central role.

Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band.

Purpose: Though it is very important obtaining exact data about patients' voiding patterns for managing voiding dysfunction, actual practice is very difficult and cumbersome. In this study, data about urination time and interval measured by smart band device on patients' wrist were collected and analyzed to resolve the clinical arguments about the efficacy of voiding diary. By developing a smart band based algorithm for recognition of complex and serial pattern of motion, this study aimed to explore the feasibility of measurement the urination time and intervals for voiding dysfunction management.

Development of Personalized Urination Recognition Technology Using Smart Bands.

Purpose: This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems.

Methods: This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture.