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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Lai, Wen-Hsing Ma, Chia-Hao Wang, Siou-Lin National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350318241

This paper achieves the conversion of monaural audio into spatially immersive stereo audio through the use of binaural speech synthesis and Pyroomacoustics. It integrates dynamic spatial perception with the acoustic response of architectural spaces to recreate immersive audio experiences in desired environments like concert halls. The system encompasses diverse options for motion trajectory generation and architectural space modeling within its interface, allowing users to customize and merge various environmental spatial effects and surround modes, delivering an unparalleled auditory encounter. © 2024 IEEE.

关键词： Speech synthesis

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A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

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A Comprehensive Survey on Automotive Hacking 1

A Comprehensive Survey on Automotive Hacking

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Maodah, Khawla A. Alhomdy, Sharaf A. Thabit, Fursan Dept. Inforamtion Technology Sana’a University Sana’a Yemen Dept. Computer Engineering Ege University İzmir Turkey

ISBN: (纸本)9798331533557

Using cutting-edge technologies in automobiles has increased concerns about car hacking. Network connections like Bluetooth, Wi-Fi, and 4G LTE have expanded the attack surface. The weaknesses in hardware and software may allow unauthorized access to private systems, endangering drivers and passengers. Hackers exploit the Controller Area Network (CAN) bus to manipulate car functionality, potentially causing accidents. Vehicle manufacturers and cybersecurity experts are implementing stronger security measures to mitigate this threat. Comprehensive security risk assessments are conducted throughout a vehicle's lifespan, involving identifying assets, monitoring security threats, and analyzing concerns. Car hacking highlights the need for modern cybersecurity safeguards. This paper briefly overviews the rise in cyber threats against smart technologies, such as attacks connected to smart vehicles. It discusses major developments in connected and autonomous car technology, the challenges posed by growing cyber threats, and how important automotive cybersecurity and IT are to preserving these breakthroughs. © 2024 IEEE.

关键词： Cyber attacks

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IoT: Communication protocols and security threats

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 1-13页

作者： Gerodimos, Apostolos Maglaras, Leandros Ferrag, Mohamed Amine Ayres, Nick Kantzavelou, Ioanna School of Computer Science and Informatics University of Thessaly Lamia Greece School of Computing at Edinburgh Napier University Edinburgh United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering Dept.of Informatics and Computer Engineering University of West Attica Athens Greece

In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and general implementation problems, presenting several sectors that can benefit the most from IoT development. Discussion over the findings of this review reveals open issues and challenges and specifies the next steps required to expand and support IoT systems in a secure framework. © 2023 The Authors

关键词： Network architecture

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Vision Transformer-based Automatic Detection of COVID-19 in Chest X-ray Images 2

Vision Transformer-based Automatic Detection of COVID-19 in ...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yurdakul, Mustafa Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept. Kirikkale Turkey Selçuk University Technology Faculty Computer Engineering Dept. Konya Turkey

ISBN: (纸本)9798350373363

The COVID-19 virus, which first emerged in the city of Wuhan in China, rapidly spread across the globe due to its high contagiousness. Detecting the virus early is crucial to stop its spread and to provide timely treatment to affected individuals. Chest X-ray (CXR) images are a quick, cost- effective, and non-invasive method commonly used for the diagnosis of COVID-19. CXR images are manually inspected by experts for diagnosis. However manually detection is not only time-consuming but also prone to errors due to human fatigue. For these reasons, there is an urgent need for a system that can detect COVID-19 from CXR images. In this study, the Vision Transformer (ViT) model was used to classify Normal, Pneumonia, and COVID-19 from CXR images. Experimental results show that the Vision Transformer (ViT) possesses a robust and high generalization capability, with an accuracy rate of 97%, indicating its significant potential in medical image analysis. © 2023 IEEE.

关键词： COVID-19

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Employing Convolutional Neural Network for Real-time Detection and Localization of Physical Contact in a Redundant Manipulator Body 12

Employing Convolutional Neural Network for Real-time Detecti...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Jafari, Amirhossein Tafti, Ali Abolhasani Karami, Abbas Isfahan University of Technology Dept. of Electrical and Computer Engineering Isfahan Iran Isfahan University of Technology Dept. of Mechanical Engineering Isfahan Iran Shiraz University of Technology Dept. of Mechanical Engineering Shiraz Iran

ISBN: (纸本)9798331529734

In the developing field of human-robot collaboration, robots increasingly share their workspace with humans, which becomes necessary to achieve seamless and safe interactions for manual guidance of robots in physical cooperation scenarios. The present study proposes a new method based on a convolutional neural network (CNN) for safe human-robot cooperation in the presence of intentional physical interactions, which is able to detect the occurrence and localization of contact on the robot arm in real-time. In this approach, contact detection and its localization are done only using the online external torque vector obtained by the residual-based observer of the robot, which eliminates any need to use additional force and vision sensors. Unlike traditional methods that depend on predefined thresholds or model-based techniques, our presented architecture with one-dimensional CNN can automatically learn the patterns from collected raw torque data and, by analyzing them, can properly predict the contact position. The performance of the proposed approach during manipulation has been validated through multiple online experiments in static and dynamic states on a KUKA LBR iiwa robot. © 2024 IEEE.

关键词： Redundant manipulators

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Multi-label Bengali Abusive Comments Classification using Problem Transformation Method 20

Multi-label Bengali Abusive Comments Classification using Pr...

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20th International Conference on Electrical engineering, Computing Science and Automatic Control, CCE 2023

作者： Khan, Tahsin Tasnia Hassan, Abid Ahamed, Md Faysal Islam, Samiul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh North South University Dept. of Electrical & Computer Engineering Dhaka Bangladesh International Standard University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306767

Sentiment analysis on the Bengali language is limited to binary and multi-class classifications. This paper focuses on the multi-label classification of the Bengali abusive social media comments dataset having 10220 rows of different social media negative comments using the problem transformation method. The texts are categorized into five labels: toxic, threat, obscene, insult, and racism. Three different multi-label approaches: Binary Relevance, Label Powerset, and Classifier Chain combined with three popular machine learning algorithms: Multinomial Naive Bayes, Random Forest, and Logistic Regression are implemented in this research. This study evaluates the performance of these models by applying them to various classification scenarios, considering five-label, four-label, three-label, and two-label classifications independently. Our research outcomes are evaluated with some performance indicators: Accuracy, Precision, Recall, F1-Scores, Confusion matrix, Macro-average, and Hamming score. The combination of Label Powerset and Logistic Regression outperformed the other two classifier-model combinations because of their quick adaptation to the dataset with an accuracy of 88.07% for five label classifications, 90.12% for four label classifications, 90.56% considering three label classifications and 92.67% for two label classification individually. With 88.07% accuracy, the Label Powerset with Logistic Regression approach can be applied to classify five types of sentiment in a single text. © 2023 IEEE.

关键词： Classification (of information)

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The Low-Complexity Timing Synchronization Circuits for IEEE 802.15.4a/z IR-UWB Systems 6

The Low-Complexity Timing Synchronization Circuits for IEEE ...

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

作者： Wang, Kuan-Wei Shen, Chung-An Huang, Chin-Ya National Taiwan University of Science and Technology Electronic and Computer Engineering Dept. Taipei Taiwan

ISBN: (纸本)9798350378511

This paper presents the design and implementation of a low-complexity timing synchronization circuit for the IEEE 802.15.4a/z IR-UWB wireless communication standard. The circuit structure of a low-complexity correlator for complex ternary values is designed. Moreover, a two-stage synchronization approach is utilized for a more accurate sample alignment. Therefore, a balance between the performance of synchronization and hardware complexity is achieved. The proposed synchronization architecture is designed and implemented based on the Xilinx ZC706 FPGA platform. The experimental results show that the proposed design saves 74% of buffer and 87.4% of multiplier utilization. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Software Effort Estimation using ANN (Back Propagation) 7

Software Effort Estimation using ANN (Back Propagation)

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ravi Kumar, B.N. Suresh, Yeresime Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (纸本)9781665464086

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. This model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results. © 2023 IEEE.

关键词： Neural networks

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Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity 9

Early Discovery of Emerging Entities in Persian Twitter with...

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9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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