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Facial Surface Anthropometric Features and Measurements With an Emphasis on Rhinoplasty (vol 42, pg 133, 2022)

AESTHETIC SURGERY JOURNAL 2022年第2期42卷 230-230页

作者： Celikoyar, M. Mazhar Perez, Michael F. Akbas, M. Ilhan Topsakal, Oguzhan Department of Otolaryngology Istanbul Florence Nightingale Hospital Istanbul Turkey Computer Science Department Florida Polytechnic University Lakeland FL USA Electrical Computer Software and Systems Engineering Department Embry-Riddle Aeronautical University Daytona Beach FL USA

Background Facial features and measurements are utilized to analyze patients’ faces for various reasons, including surgical planning, scientific communications, patient-surgeon communications, and post-surgery evaluations. Objectives There are numerous descriptions regarding these features and measurements scattered throughout the literature, and the authors did not encounter a current compilation of these parameters in the medical literature. Methods A narrative literature review of the published medical literature for facial measurements used for facial analysis in rhinoplasty was conducted through the electronic databases MEDLINE/PubMed and Google Scholar, along with a citation search. Results A total of 61 facial features were identified: 45 points (25 bilateral, 20 unilateral), 5 lines (3 bilateral, 2 unilateral), 8 planes, and 3 areas. A total of 122 measurements were identified: 48 distances (6 bilateral, 42 unilateral), 57 angles (13 bilateral, 44 unilateral), and 17 ratios. Supplemental figures were created to depict all features and measurements utilizing a frontal, lateral, or basal view of the face. Conclusions This paper provides the most comprehensive and current compilation of facial measurements to date. The authors believe this compilation will guide further developments (methodologies and software tools) for analyzing nasal structures and assessing the objective outcomes of facial surgeries, in particular rhinoplasty. Moreover, it will improve communication as a reference for facial measurements of facial surface anthropometry, in particular rhinoplasty.

关键词： anthropometry face rhinoplasty software surgical procedures operative nose facial features

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Towards Digital Twining of Lithium-ion Battery Management systems: An Extended Kalman Filter for State-of-Charge Estimation in Cloud-platform

Towards Digital Twining of Lithium-ion Battery Management Sy...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Karnehm, Dominic Anekal, Latha Samanta, Akash Neve, Antje Williamson, Sheldon Bundeswehr University Munich Institute of Intelligent Distributed Systems Neubiberg Germany Ontario Tech University Electrical Computer and Software Engineering ONL1G 0C5 Canada

ISBN: (纸本)9798350316445

In practice, extended Kalman filter (EKF) is one of the most popular lithium-ion battery (LIB) state of charge (SOC) estimation algorithms in electric vehicles onboard battery management system (BMS). With the increasing necessity of off-loading the computational burden and data storage capacity of onboard BMS, the application of battery digital twin and cloud-BMS are getting popularity. Therefore, to assess the performance of EKF in terms of accuracy, the computational time, and overall request and execution time, an EKF-based SOC estimation method is developed in the cloud platform of Amazon Web Services (AWS). For a specific battery test data set, the cloud-based EKF demonstrated a mean absolute error of 0.00226 and a request time of 0.028s that are highly satisfactory towards developing a digital twin enabled cloud-based BMS. © 2023 IEEE.

关键词： cloud Computing computational cost electric vehicles intelligent state estimation transportation electrification

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Left and Right Lung-Specific Method of Segmentation in Chest Radiographs

Left and Right Lung-Specific Method of Segmentation in Chest...

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Applications of Machine Learning 2024

作者： Marsh, John J. Narayanan, Barath Narayanan Hardie, Russell C. Electrical and Computer Engineering Department University of Dayton 300 College Park DaytonOH45469 United States Sensor and Software Systems Division University of Dayton Research Institute 300 College Park DaytonOH45469 United States

ISBN: (纸本)9781510679368

Lung segmentation supports essential functionality within the realm of computer-aided detection and diagnosis using chest radiographs. In this research, we present a novel bifurcation approach of segmenting the lungs by separating them down the spinal column and having separate networks for the right and the left lungs respectively. Results from the right lung and left lung networks are then merged to form the overall lung. We utilize DeepLabV3+ network with ResNet50 as the backbone for both left and right lung networks. Results are presented for publicly available datasets such as Shenzhen Dataset and Japanese Society of Radiological Technology (JSRT) dataset. Our proposed bifurcation approach achieved an overall accuracy of 98.8% and an IoU (Intersection over Union) of 0.977 for a set of 100 cases in Shenzhen dataset. We conducted an additional robustness study of this method by training and testing on an independent dataset utilizing a hold-out methodology. We utilize a private dataset for the training and testing occurs on an independent JSRT dataset comprising 140 cases and our algorithm achieved an overall IoU of 0.945 thereby demonstrating its efficacy against other whole lung models and setting a new benchmark for future research works. © 2024 SPIE.

关键词： Image segmentation

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A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detection in Water Distribution Plant

A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detecti...

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2023 Innovations in Intelligent systems and Applications Conference, ASYU 2023

作者： Sahin, Ali Kivanc Cavdar, Bora Dogan, Ramazan Ozgur Ayas, Selen Ozgenc, Busra Ayas, Mustafa Sinasi Erzurum Technical University Electrical and Electronics Engineering Erzurum Turkey Karadeniz Technical University Energy Systems Engineering Trabzon Turkey Gümüshane University Software Engineering Gümüşhane Turkey Karadeniz Technical University Computer Engineering Trabzon Turkey Karadeniz Technical University Electrical and Electronics Engineering Trabzon Turkey

ISBN: (纸本)9798350306590

To reduce potential threats to public health and economic losses, water distribution systems, which are critical parts of industrial control systems, require accurate and effective anomaly detection. The successful incorporation of machine learning models into anomaly detection procedures has been made possible by recent developments in computer science. In this research, a hybrid CNN-LSTM framework for anomaly detection in water distribution systems is presented using Python programming language in the JupyterLab environment. The performance of the proposed CNN-LSTM network model is carefully evaluated on the publicly available WADI dataset, which contains several types of attacks for a water distribution testbed, using a statistical window-based anomaly detection approach. The experimental results show acceptable precision, recall and F1 scores of 0.8619, 0.7041, and 0.775, respectively, demonstrating the efficiency of the framework in detecting anomalies in the water distribution system. © 2023 IEEE.

关键词： Anomaly detection

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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ML-Based Service Type Priority Decision Method Using Ambient Information for 5GB

ML-Based Service Type Priority Decision Method Using Ambient...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Altunan, Umut Sazak, Halenur Yazar, Ahmet Eskisehir Osmangazi University Department of Electrical and Electronics Engineering Eskisehir Turkey Eskisehir Osmangazi University Department of Computer Engineering Eskisehir Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Eskisehir Osmangazi University Department of Software Engineering Eskisehir Turkey

ISBN: (纸本)9798350302523

5G communications systems offer several types of service groups include enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). The ability to meet various mobile communications requirements of different users is ensured by these service groups. In this paper, a novel service type priority decision method is proposed for 5G and beyond (5GB) systems to determine the most needed service group under a specific region. This approach is useful for the resource allocation planning and optimization through a coverage region. The proposed method is based on the machine learning (ML) usage with several ambient information. Instance-based and model-based techniques are compared for ML. Also, the ensemble methods are tested on the generated synthetic dataset. © 2023 IEEE.

关键词： Machine learning

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DDoS Detection Using Active and Idle Features of Revised CICFlowMeter and Statistical Approaches 4

DDoS Detection Using Active and Idle Features of Revised CIC...

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4th International Conference on Advanced Science and engineering, ICOASE 2022

作者： Ali, Basheer Husham Sulaiman, Nasri Al-Haddad, S.A.R. Atan, Rodziah Hassan, Siti Lailatul Mohd Department of Electrical and Electronic Engineering Serdang Malaysia Faculty of Engineering Department of Electrical and Electronic Engineering Serdang Malaysia Department of Computer and Communication Systems Engineering Serdang Malaysia Software Eng. and Information Systems Dept Serdang Malaysia School of Electrical Engineering Universiti Teknologi Mara Shah Alam Malaysia

ISBN: (纸本)9781665472227

Distributed Denial of services (DDoS) attack is one of the most dangerous attacks that targeted servers. The main consequence of this attack is to prevent users from getting their legitimate services by bringing down targeted victim. CICFlowMeter tool generates bi-directional flows from packets. Each flow generates 83 of different features. The research focuses on 8 features which are active min (f1), active mean (f2), active max (f3), active std (f4), idle min (f5), idle mean (f6), idle max (f7), and idle std (f8). CICFlowMeter tool has several problems that affected on the detection accuracy of DDoS attacks. The idle and active based feature of Shannon entropy and sequential probability ratio test (SE-SPRT) approach was implemented in this research. The problems of original CICFlowMeter were presented, and the differences between original and revised version of CICFlowMeter tool were explored. The DARPA database and confusion matrix were used to evaluate the detection technique and present the comparison between two versions of CICFlowMeter. The detection method detected neptune and smurf attacks and had higher accuracy, f1-score, sensitivity, specificity, and precision when revised version of CICFlowMeter used to generate flows. However, the detection method failed to detect neptune attack and had higher miss-rate, lower accuracy, lower f1-score, and lower specificity, and lower precision when original version used in generating flows. © 2022 IEEE.

关键词： Denial-of-service attack

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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Day-ahead PV Output Power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Hokmabad, Hossein Nourollahi Husev, Oleksandr Vinnikov, Dmitri Belikov, Juri Petlenkov, Eduard Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9798350396782

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R2 score compared to standalone LSTM models. © 2023 IEEE.

关键词： boosting ensemble method LightGBM LSTM numerical weather prediction Solar power forecasting

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