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arXiv 2023年

作者： Lew, Hah Min Kim, Jae Seong Lee, Moon Hwan Park, Jaegeun Youn, Sangyeon Kim, Hee Man Kim, Jihun Hwang, Jae Youn Department of Electrical Engineering and Computer Science DGIST Daegu42988 Korea Republic of Deep Learning Research Team KLleon R&D Center Seoul04637 Korea Republic of Department of Information and Communication Engineering DGIST Daegu42988 Korea Republic of Software Team ALPINION MEDICAL SYSTEMS Anyang-si14117 Korea Republic of Department of Health Promotion Center Yonsei University College of Medicine Gangnam Severance Hospital Seoul06273 Korea Republic of Department of Electrical Engineering and Computer Science The Interdisciplinary Studies of Artificial Intelligence DGIST Daegu42988 Korea Republic of

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues. Copyright © 2023, The Authors. All rights reserved.

关键词： Phantoms

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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An Interactive Automation for Human Biliary Tree Diagnosis Using computer Vision

arXiv

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arXiv 2022年

作者： AL-Oudat, Mohammad Alomari, Saleh Qattous, Hazem Azzeh, Mohammad AL-Munaizel, Tariq Department of Computer Science Applied Science Private University Amman Jordan Department of Science and Information Technology Jadara University Irbid Jordan Department of Software Engineering Princess Sumaya University for Technology Jordan Department of Hepatobiliary and Liver transplant King Hussein Medical Center Amman Jordan

The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image's region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients' condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature. Copyright © 2022, The Authors. All rights reserved.

关键词： Diagnosis

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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software Testing Issues Improvement in Quality Assurance

Software Testing Issues Improvement in Quality Assurance

引用

Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Mohammed Salahat Raed A. Said Khalid Hamid Usama Haseeb Elsaid Abdel Maguid Abdel Ghani Abedallah Abualkishik Muhammad Waseem Iqbal Mohammad Inairat College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Faculty of Management Canadian University Dubai Dubai UAE Department of Computer Science Superior University Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Software Engineering Superior University Lahore Pakistan Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above.

关键词： software testing Quality assurance Philosophical considerations Process control Documentation Production software

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SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data

引用

ACM/IMS Transactions on Data science 2021年第4期2卷 1–24页

作者： Xia, Feng Guo, Teng Bai, Xiaomei Shatte, Adrian Liu, Zitao Tang, Jiliang School of Engineering It and Physical Sciences Federation University Australia Ballarat3353 Australia School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China Tal Education Group Beijing100080 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who may be less cognitively and emotionally mature. In addition to academic performance, employers' unconscious biases are a potential obstacle to graduating students in becoming employed. Thus, it is necessary to understand the nature of such unconscious biases to assist students at an early stage with personalized intervention. In this paper, we analyze the existing bias in college graduate employment through a large-scale education dataset and develop a framework called SUMMER (biaS-aware gradUate eMployMEnt pRediction) to predict students' employment status and employment preference while considering biases. The framework consists of four major components. Firstly, we resolve the heterogeneity of student courses by embedding academic performance into a unified space. Next, we apply a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to overcome the label imbalance problem of employment data. Thirdly, we adopt a temporal convolutional network to comprehensively capture sequential information of academic performance across semesters. Finally, we design a bias-based regularization to smooth the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. © 2022 Association for Computing Machinery.

关键词： Forecasting

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Spatial Constraint for Efficient Semi-Supervised Video Object Segmentation

SSRN

引用

SSRN 2023年

作者： Chen, Yadang Ji, Chuanjun Yang, Zhi-Xin Wu, Enhua Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China Beijing100190 China

Semi-supervised video object segmentation is the process of tracking and segmenting objects in a video sequence based on annotated masks for one or more frames. Recently, memory-based methods have attracted a significant amount of attention due to their strong performance. Having too much redundant information stored in memory, however, makes such methods inefficient and inaccurate. Moreover, a global matching strategy is usually used for memory reading, so these methods are susceptible to interference from semantically similar objects and are prone to incorrect segmentation. We propose a spatial constraint network to overcome these problems. In particular, we introduce a time-varying sensor and a dynamic feature memory to adaptively store pixel information to facilitate the modeling of the target object, which greatly reduces information redundancy in the memory without missing critical information. Furthermore, we propose an efficient memory reader that is less computationally intensive and has a smaller footprint. More importantly, we introduce a spatial constraint module to learn spatial consistency to obtain more precise segmentation;the target and distractors can be identified by the learned spatial response. The experimental results indicate that our method is competitive with state-of-the-art methods on several benchmark datasets. Our method also achieves an approximately 30 FPS inference speed, which is close to the requirement for real-time systems. © 2023, The Authors. All rights reserved.

关键词： Real time systems

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

引用

IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Keywords-driven web APIs group recommendation for automatic app service creation process

Keywords-driven web APIs group recommendation for automatic ...

引用

作者： Gong, Wenwen Lv, Chao Duan, Yucong Liu, Zengguang Khosravi, Mohammad R. Qi, Lianyong Dou, Wanchun College of Information and Electrical Engineering China Agricultural University Beijing China China Telecom Smart Home Competence Center Nanjing China E-Surfing Smart Home Technology Co. Ltd Nanjing China School of Computer Science and Cyberspace Security Hainan University Haikou China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Engineering Persian Gulf University Bushehr Iran Telecommunications Group Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran School of Information Science and Engineering Qufu Normal University Rizhao China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

With the ever-increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision-makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords-driven web APIs recommendation approach called keywords-driven and compatibility-aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real-world web APIs dataset, that is, PW dataset crawled from ***. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work. © 2020 John Wiley & Sons Ltd

关键词： Application programs

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Automatic Diagnosis of Early Pregnancy Fetal Nasal Bone Development Based on Complex Mid-Sagittal Section Ultrasound Imaging

SSRN

引用

SSRN 2024年

作者： Chen, Xi Xu, Xiaoyu Tong, Lyuyang Zhao, Huangxuan Du, Bo School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan430072 China The Obstetrical Department Wuhan University People’s Hospital East Branch Wuhan430072 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430072 China

Early prenatal screening of fetal nasal bone (FNB) development is crucial for detecting chromosomal abnormalities. Existing deep learning approaches primarily focus on detection rather than diagnosis of FNB. This paper introduces an early prenatal FNB development automated diagnostic system (FNB-ADS), which employs a cascaded hierarchical filtering method to reduce noise interference in mid-sagittal plane ultrasound images. Specifically, the system employs YOLOv8 for precise FNB localization, segments the nasal bone, tip, and prenasal skin using a specially designed lightweight segmentation network, and diagnoses developmental abnormalities using Resnet34 classification methods. Furthermore, this paper has collected and publicly released the FNB-UDV dataset, which includes a detection subset and a video subset. The detection subset contains 1,007 two-dimensional ultrasound images, while the video subset comprises 12 ultrasound videos. Upon a comprehensive evaluation, the diagnostic accuracy of FNB-ADS reaches 92.37% with a processing time of 0.14 seconds per image, and the video diagnostic accuracy is 98.69% with a per-frame inference speed of 0.37 seconds in the FNB-UDV dataset. Representing the first deep-learning approach tailored specifically for early pregnancy FNB ultrasound video diagnosis, FNB-ADS significantly enhances the standardization of diagnostic procedures and reduces the dependence on subjective clinical assessments. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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