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检索条件"机构=Informatics and Computer Engineering"

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Detecting Human Walking Direction Using Wi-Fi Signals 1

引用

International Conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Ali, Hanan Awad Hassan Seytnazarov, Shinnazar Faculty of Computer Science and Engineering Innopolis University Innopolis420500 Russia Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (数字)9789819720040

ISBN: (纸本)9789819720033

Using wireless signals, it is possible to map some patterns in the received signal to human activities in the vicinity of the wireless receiver. One of the potential applications of this is to detect the direction of human movement in a user device-free manner. To achieve it, we propose to use the channel state information (CSI) of received Wi-Fi signals and process the raw CSI using calibration, Hampel filter, and discrete wavelet transform to minimize the noise and retrieve the useful features of phase and amplitude components of the CSI. Then machine learning algorithms are applied to processed CSI to classify the direction of human walk. Our experimental study with off-the-shelf commodity Wi-Fi hardware and diverse users showed that the proposed system can produce 92.9%, 95.1%, and 89% accuracy for data from two different environments, combined data, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Atomic Lifecycle for Cyber Threat Intelligence 17

Atomic Lifecycle for Cyber Threat Intelligence

引用

17th International Conference on Information Security and Cryptology, ISCTurkiye 2024

作者： Arikan, Suleyman Muhammed Kocak, Aynur Alkan, Mustafa Computer Forensics Institute of Informatics Gazi University Ankara Turkey Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

The utilisation of cyber threat intelligence provides a multitude of advantages in the assurance of security. A growing body of research is being published on the subject of cyber threat intelligence. However, the precise sequence of steps involved in the cyber threat intelligence life cycle remains unclear. The life cycle presented in each study is not consistent. Moreover, the transition from data to intelligence throughout the life cycle is not sufficiently delineated. The intelligence produced in many studies can be of different types. In our study, all existing lifecycles were examined and their naming approaches were analysed. The objective was to present a cyber threat intelligence life cycle that is as atomic as possible, with each step clearly expressed. This should facilitate a more rigorous comparison of studies in the existing literature on cyber threat intelligence. Furthermore, the life cycle can be utilised in new cyber threat intelligence studies. Researchers who aspire to implement best practices in their study are able to run more effective processes by following the life cycle. © 2024 IEEE.

关键词： Phishing

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Non-stationary Domain Generalization: Theory and Algorithm 40

Non-stationary Domain Generalization: Theory and Algorithm

引用

40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Pham, Thai-Hoang Zhang, Xueru Zhang, Ping Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains. However, in many applications, domains may evolve along a specific direction (e.g., time, space). Without accounting for such non-stationary patterns, models trained with existing methods may fail to generalize on OOD data. In this paper, we study domain generalization in non-stationary environment. We first examine the impact of environmental non-stationarity on model performance and establish the theoretical upper bounds for the model error at target domains. Then, we propose a novel algorithm based on adaptive invariant representation learning, which leverages the non-stationary pattern to train a model that attains good performance on target domains. Experiments on both synthetic and real data validate the proposed algorithm. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Adversarial machine learning

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Driver drowsiness detection using Convolutional Neural Networks-inspired features and Principal component analysis with K-Nearest Neighbors 1

Driver drowsiness detection using Convolutional Neural Netwo...

引用

1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Panganai, Marvelous Alexander Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Drowsiness and fatigue are the major reasons for triggering serious and severe road crashes in Zimbabwe and the whole world at large. The developments in technology in recent years brought support and backing to drivers using intelligent automobile systems. Several research studies used datasets of a single driver for training and testing. Some as well used mainly day time images for training and testing the models. Therefore, fatigue and drowsiness is a key possible field of study to prevent numerous number of sleep induced road crashes. In this paper, two methods of feature extraction were proposed which are the MLP (Multilayer - Perceptron), the CNN (Convolutional Neural Network). The PCA (Principal Component Analysis) method was used for dimensionality reduction. Based on these methods, five classifiers where used to detect drowsiness on the driver. The five classifiers used where the LDA, XGBoost, LR, Decision Tree and the K-Nearest neighbors. Experiments were done in order to examine the capacity and usefulness of the approaches contrasted with other techniques. Experimental outcomes demonstrate that the feature extraction technique of CNN provided high accuracy on the five classifiers. The KNN was the average best classifier with a 100% accuracy. Experimental results indicated that the PCA improved the classifiers. This study delivers important and significant answers in practice to curb motor vehicle crashes due to drowsiness. © 2022 IEEE.

关键词： Feature extraction

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Balinese Dance Attributes Utilizing Augmented Reality with Marker Based Tracking Method 1

Balinese Dance Attributes Utilizing Augmented Reality with M...

引用

1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Ariana, Anak Agung Gede Bagus Wiguna, I Komang Arya Ganda Indrawan, I Putu Yoga Iswara, Agus Ari Permana, I Putu Hendika Indraswari D, Ni Luh Komang Ratna Institut Bisnis Dan Teknologi Indonesia Computer Systems Engineering Bali Denpasar Indonesia Institut Bisnis Dan Teknologi Indonesia Informatics Engineering Bali Denpasar Indonesia

ISBN: (纸本)9798350365191

This study presents the design, implementation, and testing of an application for introducing Balinese dance attributes using Augmented Reality (AR) with a Marker Based Tracking method. The application aims to enhance user knowledge about the meanings and attributes of Balinese dances, specifically Tari Pendet, Tari Legong, and Tari Tedung Sari. The research began with data collection, system analysis, system design, creation of 3D objects, user interface implementation, and blackbox testing. The marker-based tracking method allows users to view 3D objects by scanning markers provided within the application. The blackbox testing results confirmed that the application functions as expected. Furthermore, user experience and satisfaction were evaluated using a User Experience Questionnaire (UEQ) with 35 respondents. The UEQ results indicated positive evaluations, with scores of 1.890 for Attractiveness, 1.380 for Perspicuity, 1.450 for Efficiency, 1.735 for Dependability, 1.535 for Stimulation, and 1.440 for Novelty. These findings demonstrate that the application is effective in providing cultural education and promoting the preservation of Balinese dance traditions through interactive and engaging technology. © 2024 IEEE.

关键词： Black-box testing

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Insurance Fraud Detection using Machine Learning 1

Insurance Fraud Detection using Machine Learning

引用

1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Tongesai, MacHinya Mbizo, Godfrey Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Many insurance companies today deal with the issue of fraudulent insurance claims, which results in significant yearly financial loss. Since the losses are covered by raising policyholders' premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. Therefore, using machine learning and the XGBoost method, we construct an automated fraud detection application framework in this study. Accurately identifying fraud claims in a shorter amount of time is the goal. Data analysis is utilized throughout the process to validate, sanitize, and extract the pertinent data. As a result, the insurance firm can retain its reputation outside by employing this structure and has a reliable relationship with clients that they can share. © 2022 IEEE.

关键词： Insurance

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Automatic detection of Covid-19 based on lung CT images using Deep Convolutional Neural Networks (CNN) 1

Automatic detection of Covid-19 based on lung CT images usin...

引用

1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Mahachi, Shawn Zvarevashe, Kudakwashe Nyandoro, Leslie Kudzai University of Zimbabwe Faculty of Computer Engineering Informatics and Communications Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

In recent years, the COVID-19 pandemic has spread all over the world. Due to its rapid transmission, techniques that automatically detect COVID-19 infections and distinguish it from other forms of pneumonia are crucial. The scientific community has embarked on finding solutions to quick detection of COVID-19 through implementation of deep learning(DL) techniques that can diagnose COVID-19 using computed tomography (CT) lung scans. The use of CT images has been widely accepted in medical imaging and it is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. Also, most developed DL models developed have been end-to-end from feature extraction to categorization of the COVID19 infected images. The proposed model results showed high accuracy rates on both training and testing of the model in COVID-19 classification. A customised ResNet-50 architecture has the best results in classifying the images and achieved state of art accuracy of 97% on training and testing using the COVID dataset with 200 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of normal and infected individuals. The model can help in effective early screening of COVID-19 cases hence reducing the burden on healthcare systems. © 2022 IEEE.

关键词： COVID-19

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Smart Victims Detection in Natural Disaster using Deep Learning 24

Smart Victims Detection in Natural Disaster using Deep Learn...

引用

24th International Electronics Symposium, IES 2022

作者： Adamu Islam Mashuri, M. Hadi, Moch Zen Samsono Sudibyo, Rahardhita Widyatra Kristalina, Prima Pratiarso, Aries Department of Informatics and Computer Engineering Electronic Engineering Jawa Timur Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Nowadays, artificial intelligence has been widely implemented in agriculture, animal husbandry, education, and many more. The most widely developed model at this time is the Deep Learning model. To get maximum results, the best Deep Learning architecture is needed. In this paper, we will compare methods for detecting victims of natural disasters based on the Deep Learning model. The models that we will compare are the You Only Look Once (YOLO) algorithm, namely YOLOv5, and Convolutional Neural Network (CNN) with the MobileNet model and the VGG-16 model. We train each algorithm through a natural disaster victim training data set and analyze performance to determine what model is the most optimal. To identify the types of victims of natural disasters. From our results, the MobileNet model is superior to other models with 98% accuracy. © 2022 IEEE.

关键词： Agriculture

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Late Breaking Results: Language-level QoR modeling for High-Level Synthesis 24

Late Breaking Results: Language-level QoR modeling for High-...

引用

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Masouros, Dimosthenis Ferikoglou, Aggelos Zervakis, Georgios Xydis, Sotirios Soudris, Dimitrios Microprocessors and Digital Systems Laboratory Ece National Technical University of Athens Greece Department of Computer Engineering and Informatics University of Patras Greece

ISBN: (纸本)9798400706011

This paper proposes a language-level modeling approach for HighLevel Synthesis based on the state-of-the-art Transformer architecture. Our approach estimates the performance and required resources of HLS applications directly from the source code when different synthesis directives, in terms of HLS #pragmas, are applied. Results show that the proposed architecture achieves 96.02% accuracy for predicting the feasibility class of applications and an average of 0.95 and 0.91 R2 scores for predicting the actual performance and required resources, respectively. © 2024 Copyright held by the owner/author(s).

关键词： High level synthesis

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study 10

Data Driven Methods to Predict Vertical Profiles of the Rail...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Handoko, Yunendar Aryo Purwadinata, Awan Hermawan Saputro, Dimas Wisnu Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Smart City and Community Innovation Center Bandung Indonesia Bandung Institute of Technology Faculty of Mechanical and Aerospace Engineering Bandung Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia Bandung State of Polytechnic Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350339543

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains). © 2023 IEEE.

关键词： Forecasting

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