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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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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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Optimal Resource Allocation in Fog Computing for Healthcare Applications

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computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Lychee Leaf Disease Detection by Vision Transformer and computer Vision 3

Lychee Leaf Disease Detection by Vision Transformer and Comp...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Prince, Nayem Uddin Al Mamun, Md. Abdullah Shagar, Md. Tanvir Miah Emon, Md. Rezaul Karim Sajib, Md. Sahadat Hossen Varendra University Department of Computer Science and Engineering Rajshahi Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh Washington University of Science and Technology United States

ISBN: (纸本)9798350354348

Northern Bangladesh is home to the most lychee growing, which has a major economic impact. Lychee output and quality are reduced by various leaf and fruit diseases. Deep learning is used to construct a disease detection system for lychee diseases to detect them early and accurately. We correctly identified 6,000 photos of healthy and unhealthy lychee foliage and fruits. Six deep learning algorithms - VGG16, CustomCNN, MobileNet, InceptionV3, ResNet50, and Vision Transformer - classified the photos. Normalization and augmentation improved model resilience during data preparation. F1-score, recall, accuracy, and precision were utilized to train and evaluate the models. Vision Transformer (ViT) scores 99.91% in accuracy, recall, and F1-score, outperforming the other models. This achievement shows that ViT can better recognize complex lychee disease traits. The ViT algorithm for disease detection can help Bangladeshi farmers identify illnesses quickly and accurately. Reduced lychee loss and improved fruit quality and attractiveness should boost lychee sales in domestic and international markets. © 2024 IEEE.

关键词： Fruits

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Effective skin cancer prediction using hybrid Xception and XGBoost approach tuned by honey badger optimization

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Multimedia Tools and Applications 2024年第36期83卷 83889-83916页

作者： Naveena, T. Jerine, S. Department of Computer Science Noorul Islam Centre for Higher Education Tamil Nadu Kumaracoil629180 India Department of Software Engineering Noorul Islam Centre for Higher Education Tamil Nadu Kumaracoil629180 India

One of the several malignancies that can afflict humans and spread the fastest is skin cancer. The most and most serious kind of skin cancer, melanoma typically starts out on the skin's surface before spreading deeper into the skin layers. But melanoma patients have a 96% survival rate with straightforward and affordable treatments if they are discovered at an early stage. Expert dermatologists, equipment, and biopsies are employed in the traditional technique of melanoma diagnosis. Deep learning has shown to offer cutting edge solutions for skin cancer identification at an early stage with high accuracy, helping dermatologists prevent the costly diagnosis and assisting patients. Hybrid Xception-XGBoost classifier tuned by HBA is developed in this paper for effective skin cancer prediction. Raw skin image is given as an input for the proposed method. Dingo Optimized Texture based Histogram Equalization strategy is employed to enhance the raw input image. Then pre-processed image is split into different regions using U-net based segmentation approach. This segmented image is then given as an input for the Hybrid Xception-XGBoost approach which was tuned by Honey Badger Optimization for final prediction of skin cancer. In this classifier, the fully connected layers of Xception network is replayed with XGBoost classifier. HBA is utilized to choose best tunable variables of XGBoost. The evaluation outcomes show that the proposed approach achieves 98% accuracy, 97% precision and 113 s of training time. Thus, proposed hybrid classifier is the best choice for predicting skin cancer effectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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