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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Zhang, Shenghan Li, Haoxuan Tang, Ruixiang Ding, Sirui Rasmy, Laila Zhi, Degui Zou, Na Hu, Xia Xi'an Jiaotong-Liverpool University Department of Computer Science Xi'An China Wuhan University Department of Computer Science Wuhan China Rice University Department of Computer Science Houston United States Texas A&m University Department of Computer Science and Engineering College Station United States UTHealth Houston School of Biomedical Informatics Houston United States Texas A&m Univerisity Department of Engineering Technology & Industrial Distribution College Station United States

ISBN: (纸本)9798350302639

Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from the heterogeneous EHR data remains a challenge. In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. Firstly, we employ multiple deep neural networks to learn reliable representations from the sparse structured EHR data and redundant clinical notes. A multi-modal model then aligns multi-modal features onto the same latent space to predict phenotypes. Secondly, we leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions. We chose seven diseases to evaluate the phenotyping performance of the proposed framework. Experimental results show that using multi-modal data significantly improves phenotype prediction in all diseases, the proposed ensemble learning framework can further boost the performance. © 2023 IEEE.

关键词： Forecasting

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis

Improved Accuracy of Decision Tree with XGBoost Technique in...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Nanda Safrina Maya Silvi Lydia Benny Benyamin Nasution Master of Informatics Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy.

关键词： Accuracy Prevention and mitigation Training data Skin Decision trees Medical diagnosis Expert systems Diseases Testing Python

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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"Impressively Scary:' Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications 25

"Impressively Scary:' Exploring User Perceptions and Reactio...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： West, Jack Cagiltay, Bengisu Zhang, Shirley Li, Jingjie Fawaz, Kassem Banerjee, Suman Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States School of Informatics University of Edinburgh Edinburgh United Kingdom Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI United States

ISBN: (纸本)9798400713941

Machine learning models deployed locally on social media applications are used for features, such as face filters which read faces in-real time, and they expose sensitive attributes to the apps. However, the deployment of machine learning models, e.g., when, where, and how they are used, in social media applications is opaque to users. We aim to address this inconsistency and investigate how social media user perceptions and behaviors change once exposed to these models. We conducted user studies (N=21) and found that participants were unaware to both what the models output and when the models were used in Instagram and TikTok, two major social media platforms. In response to being exposed to the models' functionality, we observed long term behavior changes in 8 participants. Our analysis uncovers the challenges and opportunities in providing transparency for machine learning models that interact with local user data. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

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Improved Facial Expression Recognition Through Occluded Optical Flow Reconstruction Using Deep Convolutional Generative Adversarial Network

Improved Facial Expression Recognition Through Occluded Opti...

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International Conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Abdelaali KEMMOU Adil EL MAKRANI Ikram ELAZAMI Fouad LEHLOU Moulay Hafid AABIDI Department of Informatics Laboratory of Research in Informatics FS UIT Kenitra Morocco Department of Computer and Mathematical Engineering Higher School of Technology USMS Khenifra Morocco

ISBN: (数字)9798350377866

ISBN: (纸本)9798350377873

Facial expression recognition (FER) plays a crucial role across a spectrum of fields including healthcare, road safety, and marketing, where real-time feedback on user emotions is invaluable. While significant progress has been made in controlled environments such as well-lit, frontal, and unobstructed settings facial expression recognition still pose challenges in natural, unconstrained environments. The presence of occlusions presents a challenging hurdle as they obscure crucial information within facial images. To address this challenge, various approaches have been proposed. These solutions generally fall into two broad categories: those that concentrate on discerning visible regions of the face, and those that aim to reconstruct obscured facial features. In this study, we introduce a controlled Deep Convolutional Generative Adversarial Network (DCGAN)-based solution tailored for reconstructing information obscured by facial occlusions. We demonstrate the effectiveness of this architecture in mitigating the impact of occlusions for improved FER performance.

关键词： Face recognition Wireless networks Video sequences computer architecture Generative adversarial networks Road safety Real-time systems

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Selected Mapping Without Side Information for PAPR Reduction in Modified Walsh-Hadamard Code Division Multiplexing

Selected Mapping Without Side Information for PAPR Reduction...

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International Conference on Advanced Technologies for Communications, ATC

作者： Daihi Maruyama Toshiharu Kojima Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Modified Walsh-Hadamard code division multiplexing (MWHCDM) is effective for helicopter satellite communications, where the rotor blades block the transmission channel pe-riodically. This paper addresses reducing peak-to-average power ratio (PAPR) for MWHCDM, applying a selected mapping (SLM) method without side information, proposed for orthogonal frequecy division multiplexing (OFDM) previously, to MWHCDM. The SLM method is especially suitable for the primary modu-lation of quaternary phase-shift keying (QPSK), although the primary modulation of MWHCDM is binary phase-shift keying (BPSK). A novel multiplexing processing based on the structure of MWHCDM signals resolves this mismatch. In addition, introducing amplitude information to the evaluation function employed for blind estimation of phase sequence enhances the estimation performance. computer simulation shows that the proposed SLM method achieves excellent PAPR reduction and phase sequence estimation performance, causing no degradation in bit error rate (BER) performance.

关键词： Degradation Code division multiplexing computer simulation Bit error rate Estimation Rotors Peak to average power ratio Binary phase shift keying Signal resolution Satellite communications

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Deep-Source EEG Generation with Deep Learning from Different Configurations of Surface EEG

Deep-Source EEG Generation with Deep Learning from Different...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Zou, Jiadao Zhang, Qingxue Luddy School of Informatics Computing and Engineering Indiana University United States Department of Computer Science Indiana University Luddy School of Informatics Computing and Engineering Indianapolis United States Department of Biomedical Engineering Indiana University Luddy School of Informatics Computing and Engineering Indianapolis United States

ISBN: (纸本)9798331521165

Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology. © 2025 IEEE.

关键词： Deep learning

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Network Analysis for Projects with High Risk Levels in Uncertain Environments

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computers, Materials & Continua 2022年第1期70卷 1281-1296页

作者： Mohamed Abdel-Basset Asmaa Atef Mohamed Abouhawwash Yunyoung Nam Nabil M.AbdelAziz Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

The critical path method is one of the oldest and most important techniques used for planning and scheduling *** main objective of project management science is to determine the critical path through a network representation of *** critical path through a network can be determined by many algorithms and is useful for managing,monitoring,and controlling the time and cost of an entire *** essential problem in this case is that activity durations are uncertain;time presents considerable uncertainty because the time of an activity is not always easily or accurately *** issue increases the need to use neutrosophic theory to solve the critical path ***-world problems are characterized by a lack of precision,consistency,and *** concept of neutrosophic sets has been introduced as a generalization of fuzzy,intuitionistic fuzzy,and crisp sets to overcome the ambiguity surrounding real-world ***-,falsity-,and indeterminacy-membership functions are used to express neutrosophic *** study was performed to examine a neutrosophic event-oriented algorithm for determining the critical path in activity-on-arc *** activity time estimates are presented as trapezoidal neutrosophic numbers,and score and accuracy functions are used to obtain a crisp model of the *** appropriate numerical example is then used to explain the proposed method.

关键词： Project management project life cycle project planning project scheduling CPM neutrosophic set theory trapezoidal neutrosophic number

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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OnlineProver: Experience with a Visualisation Tool for Teaching Formal Proofs 13

OnlineProver: Experience with a Visualisation Tool for Teach...

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13th International Workshop on Theorem Proving Components for Educational Software, ThEdu 2024

作者： Perháč, Ján Novotný, Samuel Chodarev, Sergej Kristensen, Joachim Tilsted Tveito, Lars Shturmov, Oleks Thomsen, Michael Kirkedal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia Department of Informatics University of Oslo Oslo Norway Department of Computer Science University of Copenhagen Copenhagen Denmark

OnlineProver is an interactive proof assistant tailored for the educational setting. Its main features include a user-friendly interface for editing and checking proofs. The user interface provides feedback directly within the derivation, based on error messages from a proof-checking web service. A basic philosophy of the tool is that it should aid the student while still ensuring that the students construct the proofs as if they were working on paper. We gathered feedback on the tool through a questionnaire, and we conducted an intervention to assess its effectiveness for students in a classroom setting, alongside an evaluation of technical aspects. The initial intervention showed that students were satisfied with using OnlineProver as part of their coursework, providing initial confirmation of the learning approach behind it. This gives clear directions for future developments, with the potential to find and evaluate how OnlineProver can improve the teaching of natural deduction. © J. Perháč et al.

关键词： Teaching

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