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JOURNAL ARTICLE
Hanzhuo Tan, Chunpu Xu, Jing Li, Yuqun Zhang, Zeyang Fang, Zeyu Chen, Baohua Lai
Natural language understanding (NLU) is integral to various social media applications. However, the existing NLU models rely heavily on context for semantic learning, resulting in compromised performance when faced with short and noisy social media content. To address this issue, we leverage in-context learning (ICL), wherein language models learn to make inferences by conditioning on a handful of demonstrations to enrich the context and propose a novel hashtag-driven ICL (HICL) framework. Concretely, we pretrain a model, which employs #hashtags (user-annotated topic labels) to drive BERT-based pretraining through contrastive learning...
April 15, 2024: IEEE Transactions on Neural Networks and Learning Systems
#22
JOURNAL ARTICLE
Yiming Fei, Jiangang Li, Yanan Li
In radial basis function neural network (RBFNN)-based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this article proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism...
April 15, 2024: IEEE Transactions on Neural Networks and Learning Systems
#23
JOURNAL ARTICLE
Jun Hu, Xiao-Dong Zhao, Yu-Lin Zhou, Ning Dong, Meng-Fei Ma, Yu-Hao Chen, Zheng-Cheng Sheng, Jie Dong, Can-Qin He, Song Xu
OBJECTIVE: To compare the diagnostic efficacy of AI-guided mpMRI-TRUS fusion assisted transperineal systematic biopsy, targeted biopsy and combined biopsy in the diagnosis of PCa, and to evaluate the clinical application value of combined biopsy. METHODS: From April 2022, the general personal information and clinical data of patients with suspicious prostate lesions (PI-RADS≥3) detected by 3.0T mpMRI were collected, then underwent AI-guided mpMRI-TRUS fusion-assisted transperineal prostate biopsy...
August 2023: Zhonghua Nan Ke Xue, National Journal of Andrology
#24
JOURNAL ARTICLE
Qianwen Li, Zhi Yang, Kaili Chen, Ming Zhao, Hai Long, Yueming Deng, Haoran Hu, Chen Jia, Meiyu Wu, Zhidan Zhao, Huan Zhu, Suqing Zhou, Mingming Zhao, Pengpeng Cao, Shengnan Zhou, Yang Song, Guishao Tang, Juan Liu, Jiao Jiang, Wei Liao, Wenhui Zhou, Bingyi Yang, Feng Xiong, Suhan Zhang, Xiaofei Gao, Yiqun Jiang, Wei Zhang, Bo Zhang, Yan-Ling He, Liwei Ran, Chunlei Zhang, Wenting Wu, Quzong Suolang, Hanhuan Luo, Xiaojing Kang, Caoying Wu, Hongzhong Jin, Lei Chen, Qing Guo, Guangji Gui, Shanshan Li, Henan Si, Shuping Guo, Hong-Ye Liu, Xiguang Liu, Guo-Zhang Ma, Danqi Deng, Limei Yuan, Jianyun Lu, Jinrong Zeng, Xian Jiang, Xiaoyan Lyu, Liuqing Chen, Bin Hu, Juan Tao, Yuhao Liu, Gang Wang, Guannan Zhu, Zhirong Yao, Qianyue Xu, Bin Yang, Yu Wang, Yan Ding, Xianxu Yang, Hu Kai, Haijing Wu, Qianjin Lu
BACKGROUND: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. OBJECTIVES: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. METHODS: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects...
April 15, 2024: Journal of the European Academy of Dermatology and Venereology: JEADV
#25
JOURNAL ARTICLE
Bo Zhang, Qigui Xiao, Jingtao Gu, Weifan Zhang, Huapeng Lu, Jiaoqiong Zhang, Lan Lang, Yan Sun, Qingyong Ma, Liang Han
AIM: To investigate the relationship among emotional intelligence (EI), resilience and academic procrastination (AP), and provide suggestions for the development of targeted intervention strategies and lowering of AP level of nursing undergraduates. DESIGN: A cross-sectional study. METHODS: Three provincial universities offering nursing courses in China were investigated in this study. A convenience sample of 256 nursing undergraduates from May 2021 to September 2021 were recruited, with a response rate of 91...
April 2024: Nursing Open
#26
JOURNAL ARTICLE
Arman Toofani, Sepehr H Eraghi, Ali Basti, Hamed Rajabi
Presenting a novel framework for sustainable and regenerative design and development is a fundamental future need. Here we argue that a new framework, referred to as complexity biomechanics, which can be used for holistic analysis and understanding of natural mechanical systems, is key to fulfilling this need. We also present a roadmap for the design and development of intelligent and complex engineering materials, mechanisms, structures, systems, and processes capable of automatic adaptation and self-organization in response to ever-changing environments...
April 15, 2024: Interface Focus
#27
JOURNAL ARTICLE
Shaoxu Wu, Yun Wang, Guibin Hong, Yun Luo, Zhen Lin, Runnan Shen, Hong Zeng, Abai Xu, Peng Wu, Mingzhao Xiao, Xiaoyang Li, Peng Rao, Qishen Yang, Zhengyuan Feng, Quanhao He, Fan Jiang, Ye Xie, Chengxiao Liao, Xiaowei Huang, Rui Chen, Tianxin Lin
BACKGROUND: The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. METHODS: In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation...
May 2024: EClinicalMedicine
#28
REVIEW
T Hutsul, M Khobzei, V Tkach, O Krulikovskyi, O Moisiuk, V Ivashko, A Samila
The history of the use of mines dates back almost two centuries. The geography of their use and the associated social harm have made them, without exaggeration, a global problem. At the same time, searches were underway for safe methods of their neutralization using various technical means. In so doing, until now, none of the existing methods provides a 100% guarantee of cleaning the territory, which determines the purpose of finding innovative methods and the possibility of combining them with existing ones...
April 15, 2024: Heliyon
#29
JOURNAL ARTICLE
Anichur Rahman, Tanoy Debnath, Dipanjali Kundu, Md Saikat Islam Khan, Airin Afroj Aishi, Sadia Sazzad, Mohammad Sayduzzaman, Shahab S Band
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines...
2024: AIMS Public Health
#30
JOURNAL ARTICLE
Aijuan Jiang, Jiajie Li, Lujie Wang, Wenshu Zha, Yixuan Lin, Jindong Zhao, Zhaohui Fang, Guoming Shen
BACKGROUND: Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM...
May 2024: Diabetes/metabolism Research and Reviews
#31
JOURNAL ARTICLE
Pei-Yi Wong, Huey-Jen Su, Shih-Chun Candice Lung, Wan-Yu Liu, Hsiao-Ting Tseng, Gary Adamkiewicz, Chih-Da Wu
PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a...
April 12, 2024: Environmental Pollution
#32
JOURNAL ARTICLE
Danning Wu, Jiaqi Qiang, Weixin Hong, Hanze Du, Hongbo Yang, Huijuan Zhu, Hui Pan, Zhen Shen, Shi Chen
AIM: To build a facial image database and to explore the diagnostic efficacy and influencing factors of the artificial intelligence-based facial recognition (AI-FR) system for multiple endocrine and metabolic syndromes. METHODS: Individuals with multiple endocrine and metabolic syndromes and healthy controls were included from public literature and databases. In this facial image database, facial images and clinical data were collected for each participant and dFRI (disease facial recognition intensity) was calculated to quantify facial complexity of each syndrome...
April 3, 2024: Diabetes & Metabolic Syndrome
#33
JOURNAL ARTICLE
Franciscus Hendericus Aäron Bakker, Joris V de Nijs, Tim J M Jaspers, Peter H N de With, Alexander J W Beulens, Henk van der Poel, Fons van der Sommen, Willem M Brinkman
Objective To construct a Convolutional Neural Network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical towards optimal outcomes...
April 13, 2024: Journal of Endourology
#34
JOURNAL ARTICLE
Stefanie Bette, Luca Canalini, Laura-Marie Feitelson, Piotr Woźnicki, Franka Risch, Adrian Huber, Josua A Decker, Kartikay Tehlan, Judith Becker, Claudia Wollny, Christian Scheurig-Münkler, Thomas Wendler, Florian Schwarz, Thomas Kroencke
In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity...
March 28, 2024: Diagnostics
#35
REVIEW
Eleni Kokkotou, Maximilian Anagnostakis, Georgios Evangelou, Nikolaos K Syrigos, Ioannis Gkiozos
Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. The distinction between RWD and data derived from randomized clinical trials lies in the method of data collection, as RWD by definition are obtained at the point of care...
April 4, 2024: Cancers
#36
JOURNAL ARTICLE
Md Jasim Uddin, Jordan Sherrell, Anahita Emami, Meysam Khaleghian
Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm...
April 8, 2024: Sensors
#37
JOURNAL ARTICLE
Amira J Zaylaa, Sylva Kourtian
Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images...
April 5, 2024: Sensors
#38
JOURNAL ARTICLE
Fei-Lung Huang, Kai-Ying Chen, Wei-Hao Su
Smart city is an area where the Internet of things is used effectively with sensors. The data used by smart city can be collected through the cameras, sensors etc. Intelligent video surveillance (IVS) systems integrate multiple networked cameras for automatic surveillance purposes. Such systems can analyze and monitor video data and perform automatic functions required by users. This study performed main path analysis (MPA) to explore the development trends of IVS research. First, relevant articles were retrieved from the Web of Science database...
March 31, 2024: Sensors
#39
JOURNAL ARTICLE
Bruno Gomes, Christophe Soares, José Manuel Torres, Karim Karmali, Salim Karmali, Rui S Moreira, Pedro Sobral
In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal...
March 31, 2024: Sensors
#40
JOURNAL ARTICLE
Petros Zervoudakis, Nikolaos Karamolegkos, Eleftheria Plevridi, Pavlos Charalampidis, Alexandros Fragkiadakis
The technology landscape has been dynamically reshaped by the rapid growth of the Internet of Things, introducing an era where everyday objects, equipped with smart sensors and connectivity, seamlessly interact to create intelligent ecosystems. IoT devices are highly heterogeneous in terms of software and hardware, and many of them are severely constrained. This heterogeneity and potentially constrained nature creates new challenges in terms of security, privacy, and data management. This work proposes a Monitoring-as-a-Service platform for both monitoring and management purposes, offering a comprehensive solution for collecting, storing, and processing monitoring data from heterogeneous IoT networks for the support of diverse IoT-based applications...
March 29, 2024: Sensors
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