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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tariq, Hussain Ahmed Fengli, Zhang Salim Shakib, Muhamud Md Javed, Iram School of Information and Software Engineering University of Electronic Science and Technology of China China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331519254

In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNetV3 is developed for higher classification accuracy at the cost of slightly lower computational efficiency and is therefore suitable for clinical use. The proposed model overcomes the class imbalance problem and can make the model robust to the variations of MRI scans included in the extensive data augmentation methods, such as random rotations, flips. The proposed model trained and evaluated using a publicly available MRI dataset. It achieved a high Area Under the Curve (AUC) value of 0.99 for both the Precision-Recall and Receiver Operating Characteristic (ROC) curves. The results show that MedNetV3 is effective in correctly distinguishing brain tumors and therefore has the potential to be used in medical image classification. © 2024 IEEE.

关键词： Deep learning

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From Faces to Traffic Lights: A Multi-Scale Approach for Emotional State Representation 25

From Faces to Traffic Lights: A Multi-Scale Approach for Emo...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Nguyen, Minh Yan, Wei Qi Auckland University of Technology Department Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350330014

This paper presents an innovative Smart Traffic Light System designed to assess Emotional Expression in enclosed settings such as corporate offices and educational facilities. The system combines artificial intelligence, super-resolution algorithms, and facial recognition to provide both real-time and longitudinal insights into the collective emotional atmosphere. Utilizing a three-tier light indicator, the system visualizes emotional trends in the short-term (immediate), medium-term (daily or weekly), and long-term (semi-annual or annual) contexts, employing a color-coded scheme to denote positive or negative emotional states. This augments situational awareness and contributes to the emotional well-being of individuals in these settings. The underlying technology leverages closed-circuit television (CCTV) footage and introduces a state-of-the-art temporal expression recognition mechanism to capture a broad spectrum of human emotions. A notable innovation is the incorporation of facial super-resolution techniques to enhance the quality of low-resolution facial data, thereby boosting the system's overall accuracy and robustness. This enhancement is accomplished while maintaining adherence to ethical standards for data collection and usage. This system has implications that reach far beyond simple emotional recognition. It serves as a cornerstone for the development of emotionally intelligent smart cities. Providing both immediate feedback and historical data on community emotional states, it enables more informed decision-making, streamlined resource allocation, and ultimately, an improvement in the overall quality of life. This research contributes to the evolving discussion on how smart cities can foster communities that are not just more efficient, but also more emotionally resilient. © 2023 IEEE.

关键词： Smart city

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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Body Sensor-Based Multimodal Nurse Stress Detection Using Machine Learning 16

Body Sensor-Based Multimodal Nurse Stress Detection Using Ma...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mathur, Abhinav Shikha Sethia, Divyashikha Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9798350383119

Stress is a significant concern in the work-place, as it contributes to 80% of workplace injuries, and medical professionals are particularly susceptible, especially during emergencies such as the Covid-19 outbreak. Nurses play a crucial role in providing care in hospitals. Stress prediction for nurses in a hospital environment is essential to work optimally without suffering from the chronic effects of stress. This paper aims to investigate the application of body sensors and machine learning algorithms in monitoring physiological indicators to detect stress levels in nurses. It uses a multimodal sensor dataset of Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Inter-Beat Interval (IBI) signals from 15 nurses. The study trains and evaluates several classification models separately for each physiological signal and the multimodal data. Performance evaluation reveals that the decision tree outperforms other models with a weighted F1 score of 0.99 for both 2-level and 3-level multimodal stress classification. Furthermore, the IBI-derived Heart Rate Variability (HRV) features is more promising than other physiological signals, with a weighted F1 score of 0.84 and 0.79 for 2-level and 3-level stress classification, respectively. The findings show that multimodal data can help reliably detect stress for nurses and ensure stress-free working in hospital environments to reduce human errors. © 2024 IEEE.

关键词： Body sensors Heart Rate Variability Machine Learning Multimodal Stress Detection

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Increasing e-Health systems security and availability by using noSQL databases 23

Increasing e-Health systems security and availability by usi...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Contasel, Cristian Palacean, Alexandru-Viorel Turcanu, Dinu Stoica, Victor-Valentin National University of Science and Technology POLITEHNICA Bucharest Department of Computer Science and Engineering Bucharest Romania Technical University of Moldova Department of Software Engineering and Automatics Chişinǎu Moldova

ISBN: (纸本)9798331540388

As e-Health systems become more widely used starting with COVID-19 pandemic, the amount of data they collect increases significantly. The volume, diversity, and unpredictability of patient data necessitate distinct storage types for long-Term preservation. As a result, one of the primary problems with e-Health systems is making patient data accessible and combined into different styles so that AI algorithms can analyze it. This paper presents an improved persistence system that utilizes NoSQL databases such as Couchbase. The aim of this system is to improve overall system performance while reducing security risks associated with data availability, confidentiality, and integrity. © 2024 IEEE.

关键词： Electronic health record

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SkeMo: Sketch Modeling for Real-Time Model Component Generation 26

SkeMo: Sketch Modeling for Real-Time Model Component Generat...

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26th ACM/IEEE International Conference on Model Driven engineering Languages and Systems, MODELS 2023

作者： Chapai, Alisha Sharma Rapos, Eric J. Miami University Department of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350324808

software modeling is a powerful tool in the design and implementation of high-quality software systems. Models can be used from high-level design to formal code generation, with various applications in between. Often, software models are initially created informally, by sketching on a whiteboard or paper during the early design phase of the system, and eventually converted into formal models using advanced modeling tools. The formalization of sketches into actual model components can be time-consuming, error-prone, and laborious. To address these shortcomings, we present SkeMo, an environment for real-Time model component generation from sketch-based inputs. We curated a sketch dataset of 3000 images of various class diagram components and implemented a powerful Convolution Neural Network to classify input sketches as model components. We integrated our sketch classifier into an existing web-based model editor and added a touch interface to support sketch-based modeling. We evaluated the SkeMo environment in two ways: Through ten-fold cross-validation of the image classifier and collection of metrics and feedback from a 20-participant user study. Based on our results, sketch-based modeling demonstrates significant promise as an intuitive interface that is both easy to use and allows for faster model creation among most users. © 2023 IEEE.

关键词： Deep neural networks

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dataset Generation and Automated Code Production 8

AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dat...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： El-Gnainy, Nada Shanouda, Mira Essam, Ahmed Ibrahim, Anas Abdallah William, John Elsharkawy, Mariam Moustafa, Passant Ahmed Al Ansary, Mohamed Mahmoud, Hossam Moro, Ahmed Salama, Cherif Siemens Digital Industries Software Cairo Egypt The American University in Cairo Department of Computer Science and Engineering Egypt Ain Shams University Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350362138

This research presents a novel approach to stream-line the configuration of AUTOSAR (Automotive Open System Architecture) modules using Artificial Intelligence (AI)-based tools. Traditional methods of generating AUTOSAR-compliant ARXML files are time-consuming and require meticulous man-ual input, posing significant challenges for developers. In this study, an AI-driven methodology is introduced to address these challenges. By mapping English descriptions to YAML through a Python script and subsequently converting YAML to Autosar XML (ARXML), this approach conserves tokens and significantly accelerates the AI training process. The AI model, trained on a dataset of 12,000 samples and tested on 3,000 unseen inputs, achieved a 100% accuracy rate in converting descriptions. Additionally, a comprehensive configurator tool and a code generator were developed to facilitate seamless integration and deployment of the generated configurations. The proposed solution not only reduces manual effort but also increases reliability in AUTOSAR configuration, offering a practical tool for developers in the automotive software industry. © 2024 IEEE.

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