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Corrections to “OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations”

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IEEE Access 2025年 13卷 30833-30833页

作者： Ghulam Mustafa Naveed Ahmad Jhamat Zeeshan Arshad Nadia Yousaf Md. Nazmul Abdal Mohammed Maray Dokhyl Alqahtani Mohamad Amir Merhabi Muhammad Abdul Aziz Touseef Ahmad Department of Information Technology University of the Punjab Gujranwala Campus Gujranwala Pakistan Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh College of Computer Science and Information Systems King Khalid University Abha Saudi Arabia Electrical Engineering Department Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Management Marketing and Entrepreneurship Track American University of Beirut Beirut Lebanon Greenwich Business School University of Greenwich London U.K.

Presents corrections to the paper, Corrections to “OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations”.

关键词： Ontologies Electronic commerce

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Hesitant bipolar fuzzy set-based decision system for electric vehicle charging station location planning

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Thilagasree, Chakkarapani Sumathi Jayakumar, Thippan Ramya, Lakshmanaraj Suvitha, Krishnan Pamucar, Dragan Pedrycz, Witold Kureethara, Joseph Varghese Department of Mathematics Sri Ramakrishna Mission Vidyalaya College of Arts and Science Coimbatore India Department of Mathematics Bharathiar University Coimbatore641 046 India Centre for Nonlinear Systems Chennai Institute of Technology Tamilnadu Chennai India Department of Applied Mathematical Science College of Science and Technology Korea University Sejong Korea Republic of Yuan Ze University College of Engineering Department of Industrial Engineering and Managemente Taoyuan City Taiwan Industrial Engineering Department Faculty of Engineering and Natural Sciences İstinye University İstanbul Turkey Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Bangalore India

The selection of electric vehicle (EV) charging station locations is a critical challenge that significantly affects the growth and acceptance of the EV industry. As EVs offer a sustainable solution to fossil fuel depletion and environmental pollution, identifying optimal charging station sites involves dealing with uncertain, inconsistent, and conflicting criteria. To address these challenges, this paper presents an innovative decision-making framework based on Hesitant Bipolar-Valued Fuzzy Sets (HBVFSs), which account for both positive and negative hesitant membership values to better model uncertainty in expert judgments. A novel hybrid Multi-Criteria Decision-Making (MCDM) technique is proposed, combining the Step-wise Weight Assessment Ratio Analysis (SWARA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods to determine robust criteria weights within the HBVFS environment. The Preference Ranking Organization METHod for Enrichment Evaluation II (PROMETHEE-II) is employed for the final site ranking. This integrated approach enables a more comprehensive and reliable evaluation of potential locations by incorporating both qualitative and quantitative factors. The proposed methodology has practical applications in real-world infrastructure planning and supports more resilient decision-making in sustainable transportation networks. The results demonstrate the model's effectiveness and adaptability in addressing the site selection problem under uncertainty. © 2025 Elsevier Ltd

关键词： Hybrid vehicles

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Vector Doppler imaging of small vessels using directionally filtered Power Doppler images

Vector Doppler imaging of small vessels using directionally ...

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IEEE International Ultrasonics Symposium

作者： Bastian Generowicz Luuk Verhoef Frits Mastik Stephanie Dijkhuizen Nikki van Dorp Jason Voorneveld Johannes Bosch Karishma Kumar Geert Leus Chris de Zeeuw Sebastiaan Koekkoek Pieter Kruizinga Erasmus Medical Center Rotterdam the Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft the Netherlands Netherlands Institute of Neuroscience Royal Dutch Academy for Arts and Sciences Amsterdam the Netherlands

ISBN: (数字)9781728154480

ISBN: (纸本)9781728154497

Power Doppler (PD) imaging has become a staple in high frame rate ultrasound imaging due to its ability to image small vessels and slow-moving flows, such as in the case of imaging blood flow in the brain. Alternatively, color Doppler (CD) can be used to determine the one-dimensional directional information of the blood scatterers. This can help determine if the flow is arterial or venous, or distinguish between adjacent flows that have an opposite direction. Current methods for estimating 2D blood velocity vectors rely mostly on trigonometric solutions using synthetic apertures or, large plane-wave angles in transmission and sub-apertures in receive to be able to resolve the 2D vector. Relative to PD or CD techniques, these methods are more computationally expensive and have not been successfully used to image blood flow direction within micrometer sized vasculature. In this paper, we propose to use the orientations of the vessels derived from a directional spatial filter in combination with the CD signal to enhance the PD images with directional information. This approach was tested on simulated data as well as on a 2D image containing brain vasculature of a mouse.

关键词： Ultrasonic imaging Two dimensional displays Imaging Information filters Doppler effect Blood Blood flow

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1113-1120页

作者： Fangfang Zhao Cuiqiao Chen Wei He Shuzhi Sam Ge School of Computer Science and Engineering and Center for RoboticsUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation and Electrical Engineering University of Science and Technology of BeijingBeijing 100083China Social Robotics Laboratory Interactive Digital Media Instituteand Department of Electrical and Computer EngineeringNational University of SingaporeSingapore 117576Singapore Singapore 117576 Singapore

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

关键词： Celestial navigation integrated navigation multiple model adaptive estimation unscented Kalman filter （MMAEUKF） strap-down inertial navigation

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Hydrometeorological Network of Namal Valley, Pakistan

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Scientific data 2025年第1期12卷 979页

作者： Muhammad Sheraz Talha Manzoor Malik Jahan Khan Usman Ali Hamza Tariq Shabeh Ul Hasson Centre for Water Informatics and Technology Lahore University of Management Sciences (LUMS) Lahore Pakistan. Department of Computer Science Lahore University of Management Sciences (LUMS) Lahore Pakistan. School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan. Faculty of Computing Engineering and Media De Montfort University Leicester LE1 9BH UK. HAREME Lab Institute of Geography CEN Universität Hamburg Bundesstrasse 55 Hamburg 20146 Germany. shabeh.hasson@uni-hamburg.de.

This article describes data collected from a sensor network deployed in the catchment of The Namal Lake, situated in Mianwali district, Pakistan. The lake serves as a reservoir for the Namal Dam. The sensor network consists of 14 unique observation points in a watershed spanning a 425 sq km area. The data includes measurements of precipitation at a resolution of 0.246 mm at all sensor locations and measurements of water level at a 1 mm resolution at 7 locations (including the lake itself). The first sensor installation was made in November 2020 with the last one deployed in July 2022. The associated time series for both variables is generated at a 10-minute sampling frequency. Multiple aggregations of the dataset have been made available at hourly, daily, and monthly timescales. The data can be used for calibrating hydrological models for various applications such as flood prediction, watershed management, and decision-making for reservoir operation. The dataset also contributes to understanding the hydrometeorological processes in similar small catchments at the Monsoon Margin.

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Relational deep feature learning for heterogeneous face recognition

arXiv

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arXiv 2020年

作者： Cho, MyeongAh Kim, Taeoh Kim, Ig-Jae Lee, Kyungjae Lee, Sangyoun School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Center for Imaging Media Research Korea Institute of Science and Technology Seoul Korea Republic of Department of Computer Science Yongin University Yongin Korea Republic of

Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the lack of databases, HFR methods usually exploit the features pre-trained on the large-scale visual database. However, this can cause performance degradations because of the large domain discrepancy between modalities. Therefore, it is necessary to reduce the domain gap from the pre-trained features. From this motivation, we propose a graph-structured module called Relational Graph Module (RGM) that focuses on the high-level relational information between facial components. Since pretrained features contains low-level information, using relation of features rather than feature itself can reduce the low-level information in each domain. For example, when matching between the VIS and non-VIS identity, it is more useful to consider the part relations, distances, or ratio (high-level information) than texture, color or shape (low-level information). Regardless of domain, our RGM embeds spatially correlated feature vectors into graph node vectors and performs relation modeling between them. Relation propagation from the graph helps to focus on the interdependent relational features. In addition, we propose a Node Attention Unit (NAU) that performs node-wise recalibration to concentrate on the more informative nodes arising from the relation-based propagation. Furthermore, we suggest a novel conditional-margin loss function (C-softmax) for efficient projection learning of the embedding vector in HFR. The proposed method outperforms other state-of-the-art methods on five different HFR databases. Furthermore, we show performance improvement on three different backbones since our module can be plugged into any pre-trained face recognition backbone to help overcome the limitations of a small HFR database. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Correction: Fog computing network security based on resources management

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EURASIP Journal on Wireless Communications and Networking 2023年第1期2023卷 1-1页

作者： Daoud, Wided Ben Othmen, Salwa Hamdi, Monia Khdhir, Radhia Hamam, Habib NTS’Com Research Unit ENET’Com University of Sfax Sfax Tunisia Department of Computers and Information Technologies College of Sciences and Arts Turaif Northern Border University Arar Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science College of Science and Arts in Qurayyat Jouf University Al-Jouf Saudi Arabia Faculty of Engineering Uni de Moncton Moncton Canada International Institute of Technology and Management Libreville Gabon Spectrum of Knowledge Production & Skills Development Sfax Tunisia Department of Electrical and Electronic Engineering Science School of Electrical Engineering University of Johannesburg Johannesburg South Africa

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A robotic interface for the administration of language, literacy, and speech pathology assessments for children 8

A robotic interface for the administration of language, lite...

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8th ISCA Workshop on Speech and Language technology in Education, SLaTE 19

作者： Yeung, Gary Bailey, Alison L. Afshan, Amber Tinkler, Morgan Pérez, Marlen Q. Martin, Alejandra Pogossian, Anahit A. Spaulding, Samuel Park, Hae Won Muco, Manushaqe Alwan, Abeer Breazeal, Cynthia Dept. of Electrical and Computer Engineering University of California Los Angeles United States Dept. of Education University of California Los Angeles United States Media Lab Massachusetts Institute of Technology Cambridge United States

A preliminary implementation of a robotic interface for the administration of language, literacy, and speech pathology assessments for children is presented. This robot assessment protocol will be used for several ongoing studies to improve the performance of educational robots for children. The robot used is JIBO, a personal assistant-style robot capable of expressing itself with its poseable body. JIBO's implementation is intended for children as young as 4 years old. JIBO is designed to have friendly interactions with young children while administering assessments such as the evaluation of pronunciation, alphabetic knowledge, and explanatory discourse. Additionally, this implementation is currently being used to collect a speech database of such assessments being administered to children. © SLaTE 2019. All rights reserved.

关键词： Pathology

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Algorithms for computing in fog systems: Principles, algorithms, and challenges

arXiv

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arXiv 2020年

作者： Soni, Nikheel Malekian, Reza Bogatinoska, Dijana Capeska Amazon Web Services Cape Town8001 South Africa Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria0002 South Africa Department of Computer Science and Media Technology Malmö University Malmö20506 Sweden University of Information Science and Technology "St. Paul the Apostle" Ohrid Macedonia

Fog computing is an architecture that is used to distribute resources such as computing, storage, and memory closer to end-user to improve applications and service deployment. The idea behind fog computing is to improve cloud computing and IoT infrastructures by reducing compute power, network bandwidth, and latency as well as storage requirements. This paper presents an overview of what fog computing is, related concepts, algorithms that are present to improve fog computing infrastructure as well as challenges that exist. This paper shows that there is a great advantage of using fog computing to support cloud and IoT systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Fog computing

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