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4th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2021

作者： Gao, Lei Guan, Ling Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

ISBN: (纸本)9781665418652

In this paper, a novel correntropy analysis (CORA) method is proposed for multi-view feature representation. By joint utilization the correntropy and nonlinear kernel transformation tools, the presented CORA method is able to measure the localized similarity between two random variables and further reveal the intrinsic relation between them effectively, leading to a high quality feature representation. Unlike many existing techniques for feature representation such as canonical correlation analysis (CCA) and kernel CCA (KCCA), CORA indicates and explores the mutual relation of two random variables according to the probability density. In addition, different from the kernel entropy component analysis (KECA) method revealing the structural information only from a single data space, CORA is able to explore the mutual structural information between two data spaces jointly instead. The effectiveness of the proposed method is evaluated through experiments on audio emotion recognition and face recognition examples. Comparisons are conducted on the statistics machine learning (SML) and deep neural network (DNN) based algorithms. The results show that the proposed CORA method outperforms other methods. © 2021 IEEE.

关键词： Face recognition

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AI-Enhanced 7-Point Checklist for Melanoma Detection Using Clinical Knowledge Graphs and Data-Driven Quantification

arXiv

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arXiv 2024年

作者： Wang, Yuheng Yu, Tianze Cai, Jiayue Kalia, Sunil Lui, Harvey Wang, Z. Jane Lee, Tim K. The School of Biomedical Engineering Department of Electrical and Computer Engineering Department of Dermatology and Skin Science University of British Columbia VancouverBC Canada The School of Biomedical Engineering Health Science Center Shenzhen University Guangdong China

The 7-point checklist (7PCL) is widely used in dermoscopy to identify malignant melanoma lesions needing urgent medical attention. It assigns point values to seven attributes: major attributes are worth two points each, and minor ones are worth one point each. A total score of three or higher prompts further evaluation, often including a biopsy. However, a significant limitation of current methods is the uniform weighting of attributes, which leads to imprecision and neglects their interconnections. Previous deep learning studies have treated the prediction of each attribute with the same importance as predicting melanoma, which fails to recognize the clinical significance of the attributes for melanoma. To address these limitations, we introduce a novel diagnostic method that integrates two innovative elements: a Clinical Knowledge-Based Topological Graph (CKTG) and a Gradient Diagnostic Strategy with Data-Driven Weighting Standards (GD-DDW). The CKTG integrates 7PCL attributes with diagnostic information, revealing both internal and external associations. By employing adaptive receptive domains and weighted edges, we establish connections among melanoma’s relevant features. Concurrently, GD-DDW emulates dermatologists’ diagnostic processes, who first observe the visual characteristics associated with melanoma and then make predictions. Our model uses two imaging modalities for the same lesion, ensuring comprehensive feature acquisition. Our method shows outstanding performance in predicting malignant melanoma and its features, achieving an average AUC value of 85%. This was validated on the EDRA dataset, the largest publicly available dataset for the 7-point checklist algorithm. Specifically, the integrated weighting system can provide clinicians with valuable data-driven benchmarks for their evaluations. © 2024, CC BY.

关键词： Forecasting

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Malicious Network Traffic Detection in IoT Environments Using A Multi-level Neural Network 19

Malicious Network Traffic Detection in IoT Environments Usin...

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19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

作者： Li, Menglu Achiluzzi, Eleonora Al Georgy, Md Fahd Kashef, Rasha Ryerson University Electrical Computer and Biomedical Engineering Department TorontoON Canada

ISBN: (纸本)9781665421744

The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies, and data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge that the development of IoT is facing is the existence of malicious botnet attacks. Recently, research on botnet traffic detection has become popular. However, most state-of-the-art detection techniques focus on one specific type of device in IoT or one particular botnet attack type. Therefore, we propose a neural network-based algorithm, 2-FFNN, which can detect malicious traffic in the IoT environment and be deployed generally without restricting device or attack types. The proposed model consists of two levels of the Feed Forward Neural Network framework to identify some hard-to-detect botnet attacks. Experimental analysis has shown that the 2-FFNN outperforms the baseline FFNN and some state-of-the-art methods based on the detection accuracy and ROC score. © 2021 IEEE.

关键词： Anomaly detection

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LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution

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computers in Biology and Medicine 2024年 173卷 108303-108303页

作者： Din, Sadia Mourad, Omar Serpedin, Erchin Electrical and Computer Engineering Program Texas A&M University Doha Qatar Weill Cornell Medicine Doha Qatar Department of Electrical and Computer Engineering Texas A&M University College StationTX United States

The rising occurrence and notable public health consequences of skin cancer, especially of the most challenging form known as melanoma, have created an urgent demand for more advanced approaches to disease management. The integration of modern computer vision methods into clinical procedures offers the potential for enhancing the detection of skin cancer. The UNet model has gained prominence as a valuable tool for this objective, continuously evolving to tackle the difficulties associated with the inherent diversity of dermatological images. These challenges stem from diverse medical origins and are further complicated by variations in lighting, patient characteristics, and hair density. In this work, we present an innovative end-to-end trainable network crafted for the segmentation of skin cancer. This network comprises an encoder–decoder architecture, a novel feature extraction block, and a densely connected multi-rate Atrous convolution block. We evaluated the performance of the proposed lightweight skin cancer segmentation network (LSCS-Net) on three widely used benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, and ISIC 2018. The generalization capabilities of LSCS-Net are testified by the excellent performance on breast cancer and thyroid nodule segmentation datasets. The empirical findings confirm that LSCS-net attains state-of-the-art results, as demonstrated by a significantly elevated Jaccard index. © 2024 The Author(s)

关键词： Convolution

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Hotel Information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Bioengineering approaches for patient-specific analysis of placenta structure and function

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Placenta 2025年 166卷 154-163页

作者： Scott, Adrienne K. Fodera, Daniella M. Yang, Patrick Arter, Abigail Hines, Amelia M. Kolluru, Samyuktha S. Zambuto, Samantha G. Myers, Kristin M. Kamilov, Ulugbek S. Odibo, Anthony O. Oyen, Michelle L. Department of Biomedical Engineering Washington University in St. Louis St. Louis MO United States Center for Women's Health Engineering Washington University in St. Louis St. Louis MO United States Center for Regenerative Medicine Washington University in St. Louis St. Louis MO United States Department of Biomedical Engineering Columbia University New York NY United States Department of Obstetrics and Gynecology Washington University School of Medicine in St. Louis St. Louis MO United States Department of Mechanical Engineering Columbia University New York NY United States Department of Computer Science & Engineering Washington University in St. Louis St. Louis MO United States Department of Electrical & Systems Engineering Washington University in St. Louis St. Louis MO United States

The leading cause of perinatal mortality is fetal growth restriction (FGR), defined as in utero fetal growth below the 10th percentile. Insufficient exchange of oxygen and nutrients at the maternal-fetal interface is associated with FGR. This transport occurs through the vasculature of the placenta, particularly in the terminal villi, where the vascular membranes have a large surface area and are the thinnest. Altered structure of the placenta villi is thought to contribute to decreased oxygen exchange efficiency, however, understanding how the three-dimensional microstructure and properties decrease this efficiency remains a challenge. Here, a novel, multiscale workflow is presented to quantify patient-specific biophysical properties, 3D structural features, and blood flow of the villous tissue. Namely, nanoindentation, optical coherence tomography, and ultrasound imaging were employed to measure the time-dependent material properties of placenta tissue, the 3D structure of villous tissue, and blood flow through the villi to characterize the microvasculature of the placenta at increasing length scales. Quantifying the biophysical properties, the 3D architecture, and blood flow in the villous tissue can be used to infer changes in maternal-fetal oxygen transport at the villous membrane. Overall, this multiscale understanding will advance knowledge of how microvascular changes in the placenta ultimately lead to FGR, opening opportunities for diagnosis and intervention. © 2024 The Authors

关键词： Doppler ultrasound Fetal growth restriction Nanoindentation Optical coherence tomography Placenta Villi

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SELF-SCHEDULING PROBLEM OF A RENEWABLE COMMUNITY AGGREGATOR TRADING IN ENERGY AND RESERVE MARKETS 12

SELF-SCHEDULING PROBLEM OF A RENEWABLE COMMUNITY AGGREGATOR ...

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12th International Conference on Renewable Power Generation, RPG 2023

作者： Wang, Jian Ilea, Valentin Bovo, Cristian Xie, Ning Wang, Yong Department of Electrical Engineering Shanghai Jiao Tong University Dongchuan Road 800 Shanghai200240 China Department of Energy Politecnico di Milano Via La Masa 34 Milan20156 Italy Department of Electrical Computer and Biomedical Engineering Università degli Studi di Pavia S.da Nuova 65 Pavia27100 Italy

In this paper, a renewable community aggregator (RCA) performs joint energy and reserve schedule considering its participation in both energy and reserve markets. RCA consists of gas turbine, electricity storage, and prosumers with flexible electricity demands and controllable roof-top PV panels. To provide a reasonable schedule and represent the contribution of each device and prosumer inside RCA, this paper models the reserve providing ability of all devices and prosumers in detail with their own technical constraints. Two numerical cases are set to analyse the relationship between energy schedule and reserve schedule, and to calculate the commercial profit of providing reserve services. © The Institution of engineering & Technology 2023.

关键词： Power markets

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Wearable sensing smart solutions for workers' remote control in health-risk activities 8

Wearable sensing smart solutions for workers' remote control...

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8th International Conference on Control, Instrumentation and Automation, ICCIA 2022

作者： Visconti, Paolo De Fazio, Roberto Velazquez, Ramiro Al-Naami, Bassam Ghavifekr, Amir Aminzadeh University of Salento Department of Innovation Engineering Lecce Italy Universidad Panamericana Facultad de Ingeniería Aguascalientes Mexico The Hashemite University Faculty of Engineering Department of Biomedical Engineering Zarqa Jordan University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9781665495691

This paper presents a smart garment to monitor the environmental parameters and user's vital signs;the wearable application is meant to monitor workers' conditions in harsh workplaces. The smart jacket integrates a multisource harvesting section for gathering energy from sources associated with the human body (light, heat, movements), guaranteeing the energy autonomy of the sensing unit. This last relies on a custom microcontroller board to acquire and process biophysical and environmental data from several sensors distributed on the garment. Also, it wirelessly sends the acquired data towards the IBM Cloud platform, allowing remote monitoring of worker's conditions, real-time anomalies detection, and decentralized storing of acquired data. The tests indicated that the harvesting section could provide up to 217 mW mean power, fully covering the energy requirements of the sensing unit in all tested scenarios. However, the 380 mAh LiPo battery, charged by the harvesting system, allows the device about 16 days lifetime when no further energy contribution is available. © 2022 IEEE.

关键词： Microcontrollers

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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A Small, Multiband SRR-loaded Folded Monopole for GPS-L1 and 5G Applications

A Small, Multiband SRR-loaded Folded Monopole for GPS-L1 and...

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2022 Advances in Science and engineering Technology International Conferences, ASET 2022

作者： Alja'afreh, Saqer S. Alshamaieleh, Malaak H. Altakhaineh, Amjaad Almajali, E'qab R. Yousaf, Jawad Mutah University Dept of Electrical Engineering Alkarak Jordan University of Sharjah Electrical Engineering Department Sharjah United Arab Emirates Abu Dhabi University Dept of Electrical Computer Biomedical Eng Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665418010

This article proposes a new small antenna for dual-band wireless GPS L1 and 5G applications. The antenna is a simple dual-branch printed patch monopole antenna. The maximum dimension equals to one radian-length in-terms of the longest operational wavelength. Split Ring Resonator (SRR) is used for providing extra loading on antenna, and thus makes the design very compact. The simulation results of the reflection coefficient and the far-field radiation patterns are present. They show that the proposed antenna represents an antenna solution for both GPS band (1.565-1.585 GHz) and 5G bands between 3.3 GHz to 5.3 GHz. Further simulation and experimental results will be presented in the near future. © 2022 IEEE.

关键词： Monopole antennas

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