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8th IET Smart Cities Symposium (SCS 2024)

作者： Yaser Alkurdi Qasem Abu Al-Haija Mustafa Al Fayoumi Department of Cybersecurity Data Science King Hussein School of Computing Sciences Prince Sumaya University for Technology PO Box 1438 Amman 11941 Jordan Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid 22110 Jordan

ISBN: (数字)9781837243105

Powerful new computers based on the principles of quantum mechanics could crack the codes that safeguard our information today. The National Institute of Standards and Technology is running a competition to find more secure encryption methods to address this future challenge. CRYSTALS-Kyber is a messaging scramble Algorithm built with a complex structure called a lattice. This design is considered secure against powerful quantum computers because there is no known shortcut for them to break the code efficiently, unlike some other classical encryption Algorithms. In this paper, we introduce primitive information about quantum characteristics, computation, and main gate circuits, as well as the quantum theories for the design of post-quantum cryptosystems. Then, we listed the main key players in the quantum field and their achievements. We also compared the programming platforms used in developing programs in a quantum environment. Finally, we focused on cryptography applications and showed some of the mathematics background required to build knowledge for post-quantum cryptography algorithms, especially the CRYSTALS-Kyber algorithms and its core block Number Theoretic Transformation (NTT).

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Design and Promotion of Cost-Effective IOT-Based Heart Rate Monitoring

Design and Promotion of Cost-Effective IOT-Based Heart Rate ...

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2022 International Conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Moshayedi, Ata Jahangir Kolahdooz, Amin Roy, Atanu Shuvam Rostami, Seyyed Ali Latifi Xie, Xiaoyun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Faculty of Technology School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou China

ISBN: (纸本)9781510656673

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate. © 2022 SPIE.

关键词： Cost effectiveness

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Ontology-Based data Federation 35

Ontology-Based Data Federation

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35th International Workshop on Description Logics, DL 2022

作者： Gu, Zhenzhen Lanti, Davide Mosca, Alessandro Xiao, Guohui Xiong, Jing Calvanese, Diego KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano Bolzano39100 Italy Faculty of Social Sciences University of Bergen Bergen Norway Ontopic S.R.L. Bolzano39100 Italy Department of Computing Science Umeå University Umeå901 87 Sweden

We formally introduce ontology-based data federation (OBDF), to denote a framework combining ontology-based data access (OBDA) with a data federation layer, which virtually exposes multiple heterogeneous sources as a single relational database. In this setting, the SQL queries generated by the OBDA component by translating user SPARQL queries are further transformed by the data federation layer so as to be efficiently executed over the data sources. The structure of these SQL queries directly affects their execution time in the data federation layer and their optimization is crucial for performance. We propose here novel optimizations specific for OBDF, which are based on "hints" about existing data redundancies in the sources, empty join operations, and the need for materialized views. Such hints can be systematically inferred by analyzing the OBDA mappings and ontology and exploited to simplify the query structure. We also carry out an experimental evaluation in which we show the effectiveness of our optimizations. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Enhancing data Quality in Image Pre-processing: A Case Study on Plant Disease Classification

Enhancing Data Quality in Image Pre-processing: A Case Study...

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Technological Engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： Ge Ye Ong Nurzulaikha Abdullah Fakhitah Ridzuan Wan Azani Mustafa Laith H. Alzubaidi School of Engineering and Technology INTI International College Penang Penang Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan Kota Bharu Malaysia Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Arau Perlis Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

data quality plays a vital role in image pre-processing for machine learning applications. The effectiveness of building accurate and reliable models lies in the high-quality of data. Prioritizing data quality in the initial stages of the image pre-processing pipeline lays a strong foundation for subsequent machine learning stages. Therefore, this research aims to identify the appropriate steps for image pre-processing and compare the performance of machine learning model based on different pre-processing approaches. In this study, we utilized a plant diseases dataset sourced from Kaggle, comprising approximately 87,000 RGB images of both healthy and diseased crop leaves, categorized into 38 distinct classes. To significantly enhance the image quality, we implemented a range of transformation techniques, including resizing, normalization, data augmentation, and cropping. The analysis clearly indicates that implementing comprehensive pre-processing techniques enhances data quality and improves the machine learning model’s classification performance.

关键词： Image quality Plant diseases Analytical models data integrity Computational modeling Pipelines Machine learning

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Enhancing Spam Email Classification using Multilayer Perceptron: Performance Analysis and Comparative Evaluation

Enhancing Spam Email Classification using Multilayer Percept...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Ghizlane Khababa Nor Hazlyna Harun Sadik Bessou Chu Sin Ning Ooi Poa Ning Department of Computer Sciences Faculty of Sciences Sétif 1 University Sétif Algeria Data Science Research Lab (DSRL) School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia

With the rapid increase in spam email volume and the associated productivity and security risks, effective spam email classification mechanisms are crucial in ensuring smooth communication systems. After several studies on spam email classification, the outcome demonstrated that Multilayer Perceptron (MLP) outperforms SVM and Decision Trees, making it the chosen model for further research in this paper, as it presented a study on spam email classification using a multilayer perceptron (MLP) model. The proposed MLP-based approach demonstrates strong performance in distinguishing between legitimate and spam emails. A comprehensive evaluation is conducted using a dataset of 5,172 emails, resulting in an accuracy rate of 94.40% on the testing set. The analysis of the results reveals interesting patterns and trends in the data, providing valuable insights into the effectiveness of the MLP model. Furthermore, a comparison with some state-of-the-art approaches showcases the superiority of the MLP model in terms of accuracy, F1 score, precision, recall, and MSE. The results of this study aid in the creation of reliable and precise spam email classification systems, increasing productivity and defending communication channels against spam threats.

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CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning

arXiv

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arXiv 2022年

作者： Jin, Di Wang, Luzhi Zheng, Yizhen Li, Xiang Jiang, Fei Lin, Wei Pan, Shirui College of Intelligence and Computing Tianjin University Tianjin China Department of Data Science and AI Faculty of IT Monash University Australia Meituan Beijing China

Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score. In addition, existing unsupervised graph similarity learning methods are mainly clustering-based, which ignores the valuable information embodied in graph pairs. To this end, we propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning in order to calculate the similarity between any two input graph objects. Specifically, we generate two augmented views for each graph in a pair respectively. Then, we employ two strategies, namely cross-view interaction and crossgraph interaction, for effective node representation learning. The former is resorted to strengthen the consistency of node representations in two views. The latter is utilized to identify node differences between different graphs. Finally, we transform node representations into graph-level representations via pooling operations for graph similarity computation. We have evaluated CGMN on eight real-world datasets, and the experiment results show that the proposed new approach is superior to the state-ofthe-art methods in graph similarity learning downstream tasks. © 2022, CC BY.

关键词： Collaborative filtering

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A Bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN) 2nd

A Bio-Inspired Metaheuristics Algorithm for Reduction of Cov...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ajibade, Samuel-Soma M. Abdul Majeed, Anwar P. P. Oyebode, Oluwadare Joshua Jasser, Muhammed Basheer Ajayi, Babatunde Adedotun Alebiosu, David Olayemi Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Civil and Environmental Engineering Afe Babalola University Ekiti Ado Ekiti Nigeria Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

While Next Generation Wireless Networks are widely used, controlling them can be challenging regardless of whether a network is fully connected, there may be coverage gaps. In this study, we provide a technique for placing nodes in a wireless network while making sure that there is a minimum coverage hole in the network coverage area. We use the firefly algorithm to close the coverage gap in order to increase connectivity. The Firefly algorithm provides a novel solution for overcoming the drawbacks of traditional overburden hole detection techniques, such as downtime, low accuracy, and high operating costs, by installing sensor nodes in the appropriate locations and scheduling those network devices. The algorithm looks for the best locations where the NGWN sensor may be placed inside the designated zone in order to reduce the coverage hole area. The algorithm will treat randomly generated solutions as fireflies, and it will determine how bright they are based on how well they perform in terms of the goal function. Simulation results show that the proposed method decreases the covering hole area by 96%. The provided approach was suitable for NGWN since it reduced computation time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Operating costs

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Vehicle Check-In data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network 19

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yuwen Zhang, Jie Dou, Ruihan Zhou, Xiaokang Xu, Xiaolong Wang, Shoujin Qi, Lianyong College of Computer Science and Technology Qingdao China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology China Shanghai Meteorological Information and Technology Support Center China University of Waterloo Faculty of Mathematics Waterloo Canada Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Riken Japan Nanjing University of Information Science and Technology School of Computer and Software China Macquarie University School of Computing Australia Qufu Normal University School of Computer Science China

ISBN: (纸本)9798350346558

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations. © 2022 IEEE.

关键词： Singular value decomposition

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Automatic Section Classification in Spanish Clinical Narratives Using Chunked Named Entity Recognition

Automatic Section Classification in Spanish Clinical Narrati...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Carvallo, Andrés Rojas, Matías Muñoz-Castro, Carlos Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Santiago Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Santiago Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile Institute for Mathematical Computing Pontificia Universidad Católica de Chile Santiago Chile Center for Mathematical Modeling Santiago Chile Millenium Institute Foundational Research on Data Santiago Chile

The extraction and classification of important information from Spanish Electronic Clinical Narratives (ECNs) can be challenging due to the complexity of the clinical text and the limited availability of labeled data. In this paper, we introduce a chunked Named Entity Recognition model designed to parse and classify sections of ECNs into predefined categories. The model aims to improve section identification and classification accuracy within ECNs in the context of the IberLEF ClinAIS Task. Our system achieves a promising performance, obtaining a weighted B2 score of.6958, demonstrating its capability to accurately distinguish borders and boundaries between sections. The paper concludes with a comprehensive analysis of the results, discussing potential implications and suggesting directions for further improvements in clinical text analysis. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Classification (of information)

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Using data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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