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IOT Based Smart Parking System Using Ensemble Learning

Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Usability Assessment of OpenEMR: Enhancing Healthcare Interactions 68th

Usability Assessment of OpenEMR: Enhancing Healthcare Intera...

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68th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2024

作者： Mahmoud, Abdelrahman Saleem, Jason J. University of Louisville LouisvilleKY United States Department of Computer Science and Engineering Alamein International University Matrouh New Alamein City Egypt Department of Computer Science and Engineering University of Louisville KY United States Center for Human Systems Engineering University of Louisville KY United States

Electronic medical record (EMR) systems are pivotal in modern healthcare for digitizing patient records and improving clinical workflows. However, persistent usability challenges hinder their effective adoption and user satisfaction. This study employs a comprehensive user-centered design approach to identify and address usability issues in one such EHR, an open-source system named openEMR. Our main objective was to assess openEMR’s usability through several usability metrics. In addition to a summative usability test with 10 participants, we employed usability inspection (i.e., a heuristic evaluation) to generate additional findings using multiple methods. The results from the summative usability test provide a comprehensive perspective on openEMR’s performance. In addition to task-specific observations, participants offered insights into openEMR’s overall usability through a debrief interview. Through quantitative surveys, usability testing, qualitative interviews, and a heuristic evaluation, this study identified critical usability issues, proposes targeted solutions, and discusses implications for enhancing EMR systems in healthcare settings. © 2024 Human Factors and Ergonomics Society.

关键词： Electronic health record

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Status Quo of Hate Content Research using Bibliometric Analysis

Status Quo of Hate Content Research using Bibliometric Analy...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Chetty, Naganna Alathur, Sreejith Pai, Vinayak NMAM Institute of Technology Nitte University Department of Information Science & Engineering Mangaluru India Indian Institute of Management Department of Information Systems Kozhikode India Manipal Institute of Technology MAHE Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798331542344

Expression of hate content through social media is prevalent and a global issue. The objective of this paper is to conduct analysis on hate content research and suggest policies to combat online hate content. The Scopus database containing published articles is queried with a set of keywords and filtering techniques to obtain relevant documents on hate content research. Different bibliometric analysis approaches such as bibliometric coupling, co-occurrence of keywords and citation analysis of documents are applied on selected dataset. Bibliometric analysis resulted with major sources of hate content research, most associated keywords with hate content and top cited articles. From the year 2015 onwards, there is a rapid increase in hate content research. Currently, different social media and its content are the most associated keywords with hate content. © 2024 IEEE.

关键词： bibliometric analysis bibliometric coupling governance hate content Social media violent

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A Study of Cyber Bullying Classification Using Social Media and Texual Analysis Based on Machine Learning Approches 14

A Study of Cyber Bullying Classification Using Social Media ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman Aronno, Md. Shafiur Zumma, Md.Thoufiq Prodhan, Rashed Zohora, Fatema Tuz Sakib, Nazmus Tahmiduzzaman, K.B.M. Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Wilmington University Department of Information Systems Technologies DE United States Bangladesh University of Business &technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

In today's world, cyberbullying is a problem that is becoming more and more common, especially among teenagers and young people. The prevalence of social networking sites and other digital communication tools has made it simpler for offenders to harass their victims in secret and without repercussions. Natural language processing (NLP) methods have been used in recent years to categorize instances of cyberbullying and assist identify them. The language used in online conversations is examined using these approaches to look for trends and signs of cyberbullying behavior. The purpose of this study is to investigate how well NLP approaches can be used to recognize and categorize cyberbullying behavior. To provide a thorough knowledge of the many types of cyberbullying, the study will use a variety of data sources, including social media posts, chat logs, and other online conversations. Overall, this research will further our knowledge of the intricate nature of cyberbullying and shed light on the potential applications of NLP approaches to lessen its negative impacts. © 2023 IEEE.

关键词： Digital communication systems

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Autoencoder-Enhanced Roommate Recommendation System 6

Autoencoder-Enhanced Roommate Recommendation System

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Zahran, Khaled Amir, Abdallah Elbanhawy, Magy Ghanem, Ingy El-Ghamry, Amir Fouad, Khaled M. Moawad, Ibrahim F. New Mansoura University Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science Mansoura University University of Hertfordshire Hosted by Global Academic Foundation Faculty of Computer and Information Sciences Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Saudi Arabia

ISBN: (纸本)9798350378511

This proposed system is designed for creating a new way of giving personalized recommendations by focusing on people's behaviors and preferences. The system uses traditional machine learning algorithms integrating it with deep learning techniques to get the most out of data and suggest recommendations which are designed according to each individual's unique preferences. A deep learning autoencoder is used to learn a lower-dimensional representation of the data, with an assurance on feature extraction and reconstruction accuracy. The encoded features are passed to be clustered using KMeans, with the effectiveness of clustering estimated through internal validation metrics such as silhouette score, Calinski-Harabasz index, and Davies-Bouldin index. Also, t-Distributed Stochastic Neighbor Embedding (t-SNE) is utilized for visualizing clustered data in a simple manner. Additionally, a silhouette plot is given to provide a visual representation of the silhouette scores across clusters, highlighting the degree of cohesion within clusters and separation between them. Finally, using cosine similarity that identifies similar users within the same cluster. © 2024 IEEE.

关键词： Contrastive Learning

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Orbiting Optimization Model for Tracking Voltage Security Region Boundary in Bulk Power Grids

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CSEE Journal of Power and Energy systems 2022年第2期8卷 476-487页

作者： Xue Li Tao Jiang Linquan Bai Xiao Kou Fangxing Li Houhe Chen Guoqing Li the Department of Electrical Engineering Northeast Electric Power UniversityJilin 132012China the Department of Systems Engineering and Engineering Management University of North Carolina at CharlotteCharlotteNC28223 USA the Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996 USA

A voltage security region(VSR)is a powerful tool for monitoring the voltage security in bulk power grids with high penetration of *** can prevent cascading failures in wind power integration areas caused by serious over or low voltage *** bottlenecks of a VSR for practical applications are computational efficiency and *** bridge these gaps,a general optimization model for tracking a voltage security region boundary(VSRB)in bulk power grids is developed in this paper in accordance with the topological characteristics of the ***,the initial VSRB point on the VSRB is examined with the traditional OPF by using the base case parameters as initial ***,the rest of the VSRB points on the VSRB are tracked one after another,with the proposed optimization model,by using the parameters of the tracked VSRB point as the initial value to explore its adjacent VSRB *** proposed approach can significantly improve the computational efficiency of the VSRB tracking over the existing algorithms,and case studies,in the WECC 9-bus and the Polish 2736-bus test systems,demonstrate the high accuracy and efficiency of the proposed approach on exploring the VSRB.

关键词： Optimal power flow(OPF) stress direction voltage security critical point voltage security region voltage security region boundary

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FUNCTIONAL ANALYSIS OF CYBER-PHYSICAL systems FOR CONFIDENTIALITY QUANTUM RESILIENCE

FUNCTIONAL ANALYSIS OF CYBER-PHYSICAL SYSTEMS FOR CONFIDENTI...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Van Bossuyt, Douglas L. Hale, Britta Naval Postgraduate School Department of Systems Engineering MontereyCA United States Naval Postgraduate School Department of Computer Science MontereyCA United States

ISBN: (纸本)9780791888353

The pending development of a cryptographically relevant quantum computer (CRQC) puts cryptographic security relying on public key cryptography at risk. Such cryptography can be found throughout systems today, including cyber-physical systems, which raises a question on when to transition security to quantum resistant alternatives, i.e., post-quantum cryptography. Criticality of transition timeline and planning can be non-trivial for system managers and the cybersecurity risk of transition delay is often opaque. This paper investigates functional system modeling techniques for planning and risk mitigation against quantum threats to confidentiality in cyber-physical systems. The research in this paper applies systems engineering and design engineering tools such as Functional Modeling in the form of Functional Failure Identification and Propagation (FFIP) and Flow State Logic (FSL) to analyze flow paths and provide quantum vulnerability metrics for cyber-physical systems. Using this insight, this work further provides guidance on how such metrics can be used to plan system transition for quantum resistance. Timeline upgrade prioritization of functions and their component solutions to prepare for the threat of a CRQC is possible with the method proposed in this paper. A simplified case study of a polar research facility is presented to demonstrate the method. This paper introduces the concept of Quantum Vulnerability Information Timeline (QVIT) in the context of system modeling and demonstrates within the case study how QVIT values can be calculated for various system components to estimate preparation timelines against quantum threats. Copyright © 2024 by The United States Government.

关键词： Quantum computers

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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive Learning

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EEG Signal-Based Autism Spectrum Disorder Detection Through Normalized Mutual Information and Convolutional Neural Network 2nd

EEG Signal-Based Autism Spectrum Disorder Detection Through ...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Peya, Zahrul Jannat Maria, Mahfuza Akter Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9789819989362

Autism Spectrum Disorder (ASD) is a diverse neurological problem with several contributing factors involving both genetic and environmental variables. The diagnosis of ASD based on neural activity analysis of various signals from the brain, especially electroencephalography (EEG), has drawn attention. A technique for capturing an electrogram of the brain's spontaneous electrical activity is called EEG which is simple to use and non-intrusive. The aim of this study is to classify people with ASD and controls using the most efficient band by examining different sub-bands (such as alpha, beta, and gamma) of the EEG data. Normalized Mutual Information (NMI) is used to create Connectivity Feature Maps (CFMs). Classification is performed through Convolutional Neural Network (CNN). Gamma band is found effective for ASD detection with an accuracy of 96%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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