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15th International Conference on Information technology and Electrical Engineering, ICITEE 2023

作者： Bussabong, Naphat Anusas-Amornkul, Tanapat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350304466

A keystroke dynamics is a simple example of a biometric authentication by using a typing rhythm on a keyboard without any special device. Previous research used Euclidean distance to distinguish between an authentic user and an imposter [1]. However, the Euclidean distance has a flaw in that a distance could be the same even though the keystroke rhythms are different. Therefore, this paper proposed to enhance the keystroke dynamics authentication by using a keystroke vector and calculating a standard deviation for each key to give different weights such that the variations of each keystroke is taking into account. A dataset is similar in [1] but some data is different. The performance was measured using Equal Error Rate (EER) and the result showed that the EER was 3.33%, compared to 4% in [1]. © 2023 IEEE.

关键词： Authentication

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in Engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Multiscale characteristics of sentence length variability in Hopscotch by Julio Cortázar 38

Multiscale characteristics of sentence length variability in...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Dec, Jakub Dolina, Michal Drozdz, Stanislaw Kwapien, Jaroslaw Stanisz, Tomasz Faculty of Computer Science and Telecommunications Cracow University of Technology Poland Institute of Nuclear Physics Polish Academy of Sciences Poland

Sentence length variability (SLV) is a crucial aspect of writing that can significantly impact the overall effectiveness, expressiveness and readability of the written work. In the communication presented here, the dynamics of SLV are subjected to quantitative analysis with an attempt to identify potential multiscale patterns and their multifractal characteristics using the Multifractal Detrended Fluctuation Analysis (MFDFA). This study examines the outstanding and unique work of world literature, such as Hopscotch by Julio Cortázar. The present analysis includes the original version of this work as well as translations into English and Polish. Clear evidence of rich multifractality in the dynamics of SLV in the original arrangement of chapters as well as in numerous permutations of them is obtained. This result quantitatively confirms the widespread belief that Hopscotch is a work with a non-linear narrative. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Complexity Hopscotch Large language models Multifractality Natural language

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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An inorganic liquid crystalline dispersion with 2D ferroelectric moieties

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National science Review 2024年第5期11卷 254-263页

作者： Ziyang Huang Zehao Zhang Rongjie Zhang Baofu Ding Liu Yang Keyou Wu Youan Xu Gaokuo Zhong Chuanlai Ren Jiarong Liu Yugan Hao Menghao Wu Teng Ma Bilu Liu Shenzhen Graphene Centre Shenzhen Key Laboratory of Advanced Layered Materials for Value-added ApplicationsTsinghua-Berkeley Shenzhen Institute and Institute of Materials ResearchTsinghua Shenzhen International Graduate School Tsinghua University Institute of Technology for Carbon Neutrality Faculty of Materials Science and Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences School of Physics and Institute for Quantum Science and Engineering School of Chemistry and Institute of Theoretical ChemistryHuazhong University of Science and Technology Xi'an Research Institute of High Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Department of Applied Physics Hong Kong Polytechnic University

Electro-optical effect-based liquid crystal devices have been extensively used in optical modulation techniques, in which the Kerr coefficient reflects the sensitivity of the liquid crystals and determines the strength of the device's operational electric field. The Peterlin–Stuart theory and the O'Konski model jointly indicate that a giant Kerr coefficient could be obtained in a material with both a large geometrical anisotropy and an intrinsic polarization, but such a material is not yet reported. Here we reveal a ferroelectric effect in a monolayer two-dimensional mineral vermiculite. A large geometrical anisotropy factor and a large inherent electric dipole together raise the record value of Kerr coefficient by an order of magnitude, till 3.0 × 10-4mV-2. This finding enables an ultra-low operational electric field of10～2–10～4V m-1and the fabrication of electro-optical devices with an inch-level electrode separation, which has not previously been practical. Because of its high ultraviolet stability（decay <1% under ultraviolet exposure for 1000 hours）, large-scale production, and energy efficiency, prototypical displayable billboards have been fabricated for outdoor interactive scenes. This work provides new insights for both liquid crystal optics and two-dimensional ferroelectrics.

关键词： 2D materials layered minerals vermiculite ferroelectric inorganic liquid crystal Kerr coefficient

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ma, Xitong Kuang, Yuansen Yuan, Lin Tian, Cheng Zeng, Yijie Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation;2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily;3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models. © 2024 IEEE.

关键词： Radiology

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LSTM Deep Learning Method for Radiation Short and Long-Term Prediction 7th

LSTM Deep Learning Method for Radiation Short and Long-Term ...

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7th International Conference on Smart City Applications, SCA 2022

作者： Khala, Mohamed Abouzid, Houda Teidj, Sara Eloutassi, Omar Messaoudi, Choukri Optoelectronics and Applied Energy Techniques Faculty of Science and Technology Moulay Ismail University of Meknes Meknes Morocco Computer Science and New Technology Trends Abdelmalek Essaadi University Tetouan Morocco Department of Physics Faculty of Science and Technology Moulay Ismail University of Meknes Meknes Morocco

ISBN: (纸本)9783031268519

The renewable energy market, like conventional energy, is often unstable. Solar energy in particular presents disturbances linked to the spatiotemporal fluctuations that solar radiation undergoes. To remedy this problem, it is essential to predict its fluctuations that have exhausting consequences for integration into the electricity network and reduce the dependence on fossil fuels essential for socio-economic development. The main objective of this study is to propose a prognostication model based on artificial intelligence to predict with great accuracy the fluctuations of global solar radiation (GSR). Indeed, we adopted a deep dynamic recurrent memory artificial neural network (ANN) model which can manipulate the dynamics of atmospheric factors and is capable to save the history of these characteristics. For this purpose, we developed two long-short term memory (LSTM) models for short and long term forecasting of global solar radiation time series. The first model is a monovariate LSTM, the second one is a multivariate LSTM, after selecting the input variables (predictors) by correlation study, the models are trained by dividing the data set into two subsections, the first subsection is used for model training and the second subsection is reserved for testing. The indices and results established in this work demonstrate the robustness attributed to our method which can provide power grid managers at Er-Rachidia, MOROCCO (Latitude: 31°55′53″N;Longitude: 4°25 ′35″ W;Altitude: 1039 m) credible predictions to ensure better supervision of the energy system and to adapt solar energy to the electricity production system, especially multivariate LSTM model which proved more accuracy compared to monovariate LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt Engineering

An Entity Relation Extraction Framework Based on Large Langu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Geng, Haibin Shi, Chenglong Jiang, Xuesong Kong, Zan Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shi, Chenglong Geng, Haibin Jiang, Wenfeng Li, Jinbao Wu, Chao Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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