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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rahul Gupta, Meenu Kumar, Rakesh Obaid, Ahmed J. Chandigarh University Dept. of CSE Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq National University of Science and Technology College of Techincal Engineering Dhi Qar Iraq

ISBN: (纸本)9798350356816

This study presents a CNN architecture aimed at increasing the accuracy of skin lesion detection by incorporating advanced techniques such as batch normalization data augmentation, dropout layers and data balancing techniques. Through experiments conducted on the ISIC dataset, the effectiveness of these modifications is demonstrated. The model, despite its simplicity, exhibits significant potential for enhancement through additional training epochs. Remarkably, achieving an accuracy rate exceeding 90%, outperforming other models with data balancing, augmentation, and dropout layer defeating overfitting. This achievement highlights the model's applicability in various settings, including real-time medical diagnostics. However, the research also emphasizes the importance of further development and testing. Suggesting future research avenues, including the exploration of transfer learning, evaluation with larger datasets, and expansion into more medical diagnostic areas. The paper concludes by advocating for continued research efforts to ensure the reliability and effectiveness of these techniques in clinical environments, pointing to their promising future in medical diagnostics. © 2024 IEEE.

关键词： Dermatology

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks 9

Identify Initial Nodes for Spreader in A New Diffusion Model...

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9th International Conference on Web Research, ICWR 2023

作者： Khosravi, Ehsan Yazdanifar, Fatemeh Salimi, Nikoo Beni, Hamid Ahmadi Sharif University of Technology Dept. of Computer Engineering Tehran Iran Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Engineering and Naval Architecture University of Genoa Dept. of the Electrical Electronics Electronics and Telecommunication Genoa Italy Azarbaijan Shahid Madani University Dept. of Software Engineering Tabriz Iran

ISBN: (纸本)9798350399691

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time. © 2023 IEEE.

关键词： Topology

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Empathy-Aware Behavior Trees for Social Care Decision Systems

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Procedia computer science 2024年 239卷 1727-1735页

作者： Pierangelo Dell’Acqua Stefania Costantini Abeer Dyoub Giovanni De Gasperis Andrea Monaldini Andrea Rafanelli Dept. of Science and Technology Linköping University Norrköping 60351 Sweden Dept. of Information Science and Engineering and Mathematics University of Aquila L’Aquila 67100 Italy Dept. of Computer Science University of Pisa Pisa 56100 Italy

There is growing attention on the importance of building intelligent systems where humans and Artificial Intelligence-based systems (AIs) form teams exploiting the potentially synergistic relationships between humans and automation. In the last decade, the computational modeling of empathy has gained increasing attention. Empowering interactive agents with empathic capabilities leads, on the human’s side, to more trust, increases engagement, and thus interaction length, helps cope with stress. These findings suggest that agents endowed with empathy may enhance social interaction in educational applications, artificial companions, medical assistants, and gaming applications. This article focuses on modeling the empathic behavior of virtual agents interacting with humans. We propose a formal model that enables virtual agents to exhibit empathic, emotional behavior. Specifically, we extend the modeling of empathy via behavior trees with a new type of node allowing the specification of various kinds of empathy. Using the proposed extension, we show how different agents’ reactive behavior can be modeled.

关键词： Behavior Trees Affective Computing Decision Making

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FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Classification

FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Cl...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Lu, Cheng-Han Hung, Shih-Hao National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9781665480451

Mainstream deep learning algorithms are designed for typical images sizing from 224 × 224 to 600 × 600 pixels, which are relatively small compared to the whole-slide images (WSI) with billions of pixels, causing existing methods to be inefficient due to computational and memory constraints. Furthermore, most WSIs are annotated with slide-level labels, as patch-level annotations are time-consuming and have to be conducted by experienced pathologists. At the same time, the regions of interest (ROIs), e.g., where cancer, tumor, and bacteria reside, would occupy relatively small areas. Thus, we propose Flexible and Efficient Zoom-In (FEZ) as a method to precisely locate ROIs to accelerate ultra-large image classification. FEZ initially examines a low-resolution WSI with a policy network (PN) to select the potentially critical ROIs and zooms in the selected regions using medium-resolution images to select finer-grain ROIs with another PN. Eventually, FEZ loads the high-resolution patches and classifies them with a value network (VN) to make a prediction. Since FEZ only loads a few selected patches instead of the high-resolution WSI, it dramatically reduces the image loading time and the computation time needed for analyzing the patches. Experimental results show that FEZ is capable of delivering similar accuracy with higher speed than a recently proposed whole-slide training method (WST) and existing sampling methods such as Multiple Instance Learning (MIL) and CLAM. FEZ is 42× and 22× faster than MIL on the Camelyon16 and TCGA Lung Cancer datasets, respectively. With FEZ, we can reduce the classification time for ultra-large images from minutes to seconds to support interactive applications. © 2022 IEEE.

关键词： Image classification

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A multi-point capacitive sensing smart clothing applied to posture detection

A multi-point capacitive sensing smart clothing applied to p...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Liao, Kuo-Cheng Wu, Ze-We Chen, Shih-Hung Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a system of multi-point capacitive sensing smart garment designed for posture detection. This smart garment utilized multiple capacitive sensors placed at different positions to monitor changes in body posture movements. These sensors were capable of detecting subtle variations between the garment and the wearer's body and translating this data into understandable information. By analyzing the collected data, this smart garment can provide real-Time monitoring of the user's posture, such as standing, sitting, or lying down. © 2024 IEEE.

关键词： garment multi-point capacitive sensing posture respiration

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Car Price Prediction using Machine Learning 9

Car Price Prediction using Machine Learning

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Ahmad, Muhammad Farooq, Muhammad Ali Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Zunnurain Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Predicting car prices involves determining a vehicle's market worth based on various attributes like brand, model, year of manufacture, mileage, and overall state. This prediction holds significant value for the auto industry, aiding both prospective buyers and sellers in understanding pricing and making educated decisions regarding vehicle transactions. Machine learning techniques, trained on historical datasets sourced from online automotive marketplaces, dealerships, and auction platforms, are adept.at forecasting the prices of both new and pre-owned cars, using their specifications. Noteworthy machine learning models applied include Linear Regression, AdaBoostRegressor, Lasso Regression, and Ridge Regression. Integrating more diverse data points, such as customer reviews, prevailing market dynamics, and regional factors, can further refine the accuracy of these prediction models. In recent trials with the provided dataset, the Lasso Regression and Ridge Regression models stood out, delivering an impressive accuracy rate nearing 90%. © 2024 IEEE.

关键词： Forecasting

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An Improved Dual Authentication Techniques for Secure Data Transmission in VANETs

An Improved Dual Authentication Techniques for Secure Data T...

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2023 International Conference on information Technology and Computing, ICITCOM 2023

作者： Cahyadi, Eko Fajar Khair, Fauza Amri Ginting, Jafaruddin Gusti Ariyadi, Tamsir Hwang, Min-Shiang Institut Teknologi Telkom Purwokerto Dept. of Telecommunication Engineering Purwokerto53147 Indonesia Universitas Bina Darma Dept. of Computer Engineering Palembang30266 Indonesia Asia University China Medical University Dept. of Computer Science & Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350359633

Research related to user authentication techniques in vehicular ad hoc networks (VANETs) has received significant attention in the last decade. However, its security and privacy still become a central issue that is widely open to discussion. In 2016, Vijayakumar et al. published dual authentication and key management techniques for secure data transmission in VANETs. The scheme aims to provide a high level of security on the vehicle side to prevent unauthorized vehicles from entering the VANETs effectively. It relies on the fingerprint and hashes code HC for the authentication process. The authors claimed that the mechanism could withstand the replaying attack by appending it with an updated timestamp and the packet's transmission. However, this paper shows that their scheme suffered a denial of service (DoS) attack due to misplaced timestamps. So, we propose an improvement of their scheme to withstand the attacks. © 2023 IEEE.

关键词： Authentication

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning 16

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Shrestha, Sujan Taniguchi, Yoji Tanaka, Tetsuo Graduate school of Engineering Kanagawa Institute of Tehnology Course of Information and computer sciences Atsugi Japan Daiichi Institute of Technology Artificial Intelligence and Data Science Department of Information Tokyo Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan

ISBN: (纸本)9798350377903

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence;however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Collision Avoidance for AGV Navigation Using the Time Elastic Band Algorithm

Collision Avoidance for AGV Navigation Using the Time Elasti...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Pei-En Lin, Cheng-Hung He, Jyun-Hong Chiang, Hsin-Han National Taiwan Normal University Dept. Electrical Engineering Taipei Taiwan National Taipei University of Technology Dept. Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology Dept. Vehicle Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

This paper presents a design and implementation of an automated guided vehicle (AGV) that addresses the dynamic obstacle avoidance problem in navigation. We employ the TEB path planner, which is based on laser range sensors, as the algorithm for dynamic obstacle avoidance. In the development process, the Stage simulation is previously conducted to obtain suitable parameters. The environment used in the Stage simulation is a flat map of the real vehicle verification site, and the robot specifications and sensors used are the same as those of the actual vehicle. Finally, we apply the parameters obtained from the simulation to the real AGV for validation. © 2023 IEEE.

关键词： Navigation

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A smart clothing sensing system for long-term ECG Monitoring

A smart clothing sensing system for long-term ECG Monitoring

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Chen, Shih-Hung Liao, Kuo-Cheng Wu, Ze-We Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study integrated smart garment, data acquisition device (DAD), iPad and remote server to develop an ECG measurement system for long-term healthcare monitoring in daily life. This system allowed the subjects to measure in a comfortable state, and their movements are not restricted. The experimental results showed that good ECG signals can be acquired from sleeping, sitting, standing and walking. Therefore, the system can not only be applied to daily long-term monitoring, but also has the opportunity for clinical application. © 2024 IEEE.

关键词： data acquisition device ECG iPad long-term remote server smart garment

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