-
121
Evaluating machine learning technologies for food computing from a data set perspective
Published 2023“…Food computing benefits from technologies based on modern machine learning techniques, including deep learning, deep convolutional neural networks, and transfer learning. …”
-
122
A systematic review and meta-analysis on the impact of early vs. delayed pharmacological thromboprophylaxis in patients with traumatic brain injury
Published 2024“…Our findings indicated that early prophylaxis significantly reduced the incidence of VTE, deep vein thrombosis (DVT), pulmonary embolism (PE), and overall mortality when compared to late administration. …”
-
123
A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory
Published 2025“…<p dir="ltr">Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. …”
-
124
Food fraud detection using explainable artificial intelligence
Published 2023“…<div><p>Recently, the global food supply chain has become increasingly complex, and its scalability has grown. …”
-
125
-
126
TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images
Published 2024“…Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. …”
-
127
-
128
The global, regional, and national burden of stomach cancer in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease study 2017
Published 2020“…The highest age-standardised incidence rates in 2017 were seen in the high-income Asia Pacific (29·5, 28·2–31·0 per 100 000 population) and east Asia (28·6, 27·3–30·0 per 100 000 population) regions, with nearly half of the global incident cases occurring in China. …”
-
129
-
130
Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review
Published 2023“…It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field.…”
-
131
Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures
Published 2023“…To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. …”
-
132
-
133
-
134
Identifying Regional Trends in Avatar Customization
Published 2019“…We find that avatar customization correlates with increased social activity, and we are able to identify distinct visual trends among the U.S.…”
-
135
Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS
Published 2025“…To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). …”
Get full text
article -
136
YOLO-DefXpert: An Advanced Defect Detection on PCB Surfaces Using Improved YOLOv11 Algorithm
Published 2025“…Compared to the standard YOLOv11 model, the proposed YOLO-DefXpert attained an improvement of 9.3% and 13.2% in mAP50 and mAP95, an 11.25% increase in frames per second, and a 69.85MB decrease in model size. …”
-
137
YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images
Published 2025“…It has recently become increasingly popular to apply deep learning algorithms to the identification of ships in SAR images. …”
-
138
Hyperspectral-physiological based predictive model for transpiration in greenhouses under CO<sub>2</sub> enrichment
Published 2023“…Three machine learning models were investigated for transpiration modelling and prediction: deep neural networks (DNN), extreme gradient boosting (XGBoost), and support vector machine regression (SVR). …”
-
139
-
140
Efficient Detection of Hepatic Steatosis in Ultrasound Images Using Convolutional Neural Networks: A Comparative Study
Published 2023“…Problem Statement: This study aims to evaluate deep learning methods for binary classification of hepatic steatosis using ultrasound images. …”