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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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FLoomChecker: Repelling Free-riders in Federated Learning via Training Integrity Verification 30

FLoomChecker: Repelling Free-riders in Federated Learning vi...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Liang, Guanghao Chang, Shan Dai, Minghui Zhu, Hongzi Donghua University School of Computer Science and Technology Shanghai China Shanghai Jiaotong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798331515966

Federated learning is a mechanism that allows participating clients to train locally with their own data in order to receive rewards, thus avoiding the transfer of data to a central server and protecting users' privacy. However, some 'lazy' clients may adopt the strategy of fabricating false model local updates in an attempt to 'free-riding' without actually contributing real data or consuming local computational resources. To address this issue, we propose FLoomChecker, an integrity detection scheme for federated learning training models. The scheme combines the techniques of trusted execution environments and Bloom filters to efficiently identify clients that do not train honestly by committing and proving. We conducted experimental evaluations of FLoomChecker, examining three main aspects: query time, build time, and memory footprint in trusted execution environment (TEE). The experimental results demonstrate the effectiveness of our scheme, and its performance improves as the number of local training rounds increases. © 2024 IEEE.

关键词： Federated learning

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Development of a hybrid stacked machine learning model for predicting compressive strength of high-performance concrete

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Asian Journal of Civil engineering 2023年第8期24卷 2985-3000页

作者： Tipu, Rupesh Kumar Suman Batra, Vandna Department of Civil Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram 122103 India Department of Computer Science and Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram 122103 India

This paper presents a state-of-the-art hybrid stacked machine learning (ML) model for predicting the compressive strength of high-performance concrete (HPC). The proposed model combines the strengths of five individual ML models, namely support vector machine (SVR), decision tree (DT), random forest (RF), gradient boosting (GB), and extreme gradient boost (XGBoost), and uses particle swarm optimization (PSO) meta-heuristic algorithm to optimize the hyperparameters. Linear regression is used to ensemble the predictions of all base learners to improve overall prediction accuracy. A k-fold cross-validation technique with tenfold is employed to prevent overfitting on the train set of the final stacked model. In addition, several interaction features are created to improve the accuracy of ML models. All the data samples are scaled before training models. The model was trained and evaluated on an experimental dataset from the literatures study and achieved an accuracy of 97.1% in terms of the determination coefficient (R ²) and a root mean squared error (RMSE) of 3.8 MPa. The feature importance analysis showed that the water–cement ratio, age, and superplasticizer are the most significant factors affecting the compressive strength of HPC. The proposed model provides a more accurate and efficient way of predicting the compressive strength of HPC, thereby improving its mix design, and enhancing its mechanical properties. It reduces the dependency on laboratory destructive and uneconomical tests, which can save time, resources, and costs in the construction industry. The model can be used by engineers and construction professionals to make informed decisions on the appropriate combination of concrete constituents to achieve the desired strength and durability of HPC. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Compressive strength Cross-validation Hyperparameter Machine learning Particle swarm optimization Support vector machine

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Emotion estimation from nose feature using pyramid structure

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Multimedia Tools and Applications 2023年第27期82卷 42569-42591页

作者： Shaila, S.G. Vadivel, A. Avani, Suma Department of CSE Dayananda Sagar University Bangalore India Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru Campus Bengaluru India

The facial expression and human emotions are considered as important components for building various real-time applications such as human expression and emotion recognition systems. Various parts of the human face contribute to recognizing expression. The contribution of action units on the nose is considered important. In this paper, input images are converted into HSV color space for better representation. The nose area is localized and the boundary is drawn by segmentation process using Fuzzy C-means Clustering (FCM). The segmented nose on the human face is modelled using a pyramid/tetrahedron structure and it is superimposed on the reference face. The feature points are identified on the pyramid model, where the Action Units (AUs) falling on the tetrahedron are identified. These points are validated with the theoretical properties of the tetrahedron so that the constructed feature vector is robust. The degree of deformation at various points is constructed as the feature vector. The feature vector is extracted for all the database images, say JAFE and CK++ datasets, and the feature database is created and stored separately. The feature data sets are used for training and thus, they are n-fold cross-validated to avoid over and under fitting. Given an input image for estimating the expression and emotion, the feature vector of the input image is compared with the feature vector of deformed images stored in the database. We have used Support Vector Machine (SVM), and Multilayer Perceptron (MLP) and Random Forest classifier to classify the expression and derive emotion. The JAFE and CK++ datasets are used for experimental analysis. It is found that the Nose feature using pyramid/tetrahedron structure is giving good results. Most of the time the classification accuracy is more than 95%. The experimental results are compared with some of the well-known approaches and the proposed tetrahedron model has performed well with classification accuracy more than 95%. © 2023,

关键词： Support vector machines

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Automating Object Identification from Hyper Spectral Data with Markov Random Fields

Automating Object Identification from Hyper Spectral Data wi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Chetan Devrani, Rekha Aadil, Mir Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Hyper Spectral statistics (HSD) is a type of fact acquired from optical sensors, which include satellites, scanners, and microscopes that captures and measures various wavelengths of energy. Object identification from HSD is problematic because of the high spectral variability in pixels of the image and the great variety of product classes usually present. Markov Random Fields (MRF) is a graphical model that may be used to version spatial dependencies among neighboring pixels which can assist in decorating the object identification process. This summary explores the feasibility of automating item identity from HSD through MRF. We introduce the idea of a priori information, which includes labels assigned to distinct spectral signatures within the image and a classification system that uses MRF as a priori information to enhance object identity accuracy. We describe and demonstrate the exact rules for this challenge via simulation. The consequences of the take look advocate that utilizing the MRF-based category gadget to improve item identification accuracy from HSD is possible. © 2024 IEEE.

关键词： classification consequences Hyper Spectral microscopes neighboring

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Investigation of LSSVM and RBFNN-based Techniques for Intrusion Detection Systems for IoT Networks

Investigation of LSSVM and RBFNN-based Techniques for Intrus...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Jayapal, Medida Subhashree, K. Ashwini, B. Satheesh Kumar, D. Dimri, Sushil Chandra Nishant, Neerav Department of Cse Telangana Hyderabad India Karp Agam College of Engineering Department of Computer Science and Engineering Coimbatore India Knowledge Institute of Technology Department of Cse Tamilnadu Salem India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Engineering Babu Banarasi das University Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350300857

As more and more devices become internet-enabled, it's no longer possible to imagine modern life without them. In particular, Internet of Things (IoT) devices have become widespread and indispensable. But there are issues that appear to be worsening and for which there are currently no good solutions. The safety of Internet of Things (IoT) devices and other connected gadgets is a growing concern. There has been a lot of development in the area of safeguarding IoT networks, but there is always room for improvement. The proposed methodology consists of three stages: preprocessing the data, selecting the features to use, and training the model. In data preprocessing, normalization and standardization techniques are utilized. The use of particle swarm optimization helps improve feature selection. Models are trained with SVM-RBFNN after information gain is used to choose relevant features. The proposed method achieves better results than both SVM and LSSVM, two widely usedalternatives. © 2023 IEEE.

关键词： Internet of things

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Advancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA 25

Advancing AI-Powered Medical Image Synthesis: Insights from ...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Peter, Ojonugwa Oluwafemi Ejiga Rahman, Md Mahmudur Khalifa, Fahmi Department of Computer Science SCMNS School Morgan State University BaltimoreMD21251 United States Electrical & Computer Engineering Dept. School of Engineering Morgan State University BaltimoreMD21251 United States

The MEDVQA-GI challenge addresses the integration of AI-driven text-to-image generative models in medical diagnostics, aiming to enhance diagnostic capabilities through synthetic image generation. Existing methods primarily focus on static image analysis and lack the dynamic generation of medical imagery from textual descriptions. This study intends to partially close this gap by introducing a novel approach based on fine-tuned generative models to generate dynamic, scalable, and precise images from textual descriptions. Particularly, our system integrates fine-tuned Stable Diffusion and DreamBooth models, as well as Low-Rank Adaptation (LORA), to generate high-fidelity medical images. The problem is around two sub-tasks namely: image synthesis (IS) and optimal prompt production (OPG). The former creates medical images via verbal prompts, whereas the latter provides prompts that produce high-quality images in specified categories. The study emphasizes the limitations of traditional medical image generation methods, such as hand sketching, constrained datasets, static procedures, and generic models. Our evaluation measures showed that Stable Diffusion surpasses CLIP and DreamBooth + LORA in terms of producing high-quality, diversified images. Specifically, Stable Diffusion had the lowest Fréchet Inception Distance (FID) scores (0.099 for single center, 0.064 for multi-center, and 0.067 for combined), indicating higher image quality. Furthermore, it had the highest average Inception Score (2.327 across all datasets), indicating exceptional diversity and quality. This advances the field of AI-powered medical diagnosis. Future research will concentrate on model refining, dataset augmentation, and ethical considerations for efficiently implementing these advances into clinical practice. © 2024 Copyright for this paper by its authors.

关键词： Diagnosis

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A Peer-to-Peer Trading Mechanism Considering Preferences: A Two-Stage Game Theory Approach 6

A Peer-to-Peer Trading Mechanism Considering Preferences: A ...

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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Wang, Qian Liu, Yitong Xie, Yi Liu, Xing Cheng University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9798350353563

This paper presents a peer-to-peer (P2P) energy trading mechanism based on two-stage game theory. It classifies prosumers as buyers and sellers, according to the net energy during the trading period. The interaction between buyers and sellers is modeled as Stackelberg game, and the interaction between buyers is modeled as mixed-strategy game. By solving the models, the optimal strategies for prosumers are obtained. To adapt to the diverse preferences of individual prosumers, a trading mechanism is designed. The simulation results show that the mechanism can not only effectively improve the economic benefits of the system, but also satisfy the individual preferences of the participants, and realize efficient and personalized energy trading. © 2024 IEEE.

关键词： Marketplaces

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Prompt-augmented Feature with Cross-domain Contrastive Learning for Efficient Multi-domain Sentiment Analysis

Prompt-augmented Feature with Cross-domain Contrastive Learn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Rui Yang, Tao Liu, Cheng Jiang, Dazhi Wong, Hau-San Wu, Si Department of Computer Science Shantou University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Engineering South China University of Technology China

ISBN: (纸本)9798350368741

Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more serious in multi-domain scenarios. In this paper, we propose to extract multi-layer features from the PrLM for efficient training since the training process is independent to its large backbone. Meanwhile, compared with the conventional feature extraction, we leverage prompts to induce PrLM for generating sentiment-aware features which lead to significant improvement on the sentiment analysis. In addition, most previous methods adopted a domain alignment paradigm for multi-domain learning, which becomes cumbersome when the number of domains is large. Therefore, we propose a novel prompt-augmented cross-domain contrastive learning for generalizable performance, which clusters samples with the same label under different prompts or domains. Our method is evaluated on two public multi-domain sentiment analysis benchmarks, which significantly outperforms recent state-of-the-art methods. Extensive ablation studies also verify the effectiveness of each proposed component. © 2025 IEEE.

关键词： multi-domain learning pre-trained language model prompt augmentation Sentiment analysis sentiment-aware feature

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A4RSIC: Attention-Augmented Adversarial Autoencoder for Remote Sensing Image Compression

A4RSIC: Attention-Augmented Adversarial Autoencoder for Remo...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Sivarajan, Saran Byju, Akshara Preethy School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Kerala Amritapuri India

ISBN: (纸本)9798331507541

Reconstruction of images from the recent deep learning based models for image compression tasks does not necessarily corroborate with human perception and lose relevant inherent structural details. To address this, in this research work, a novel deep attention-augmented adversarial autoencoder based lossy remote sensing image compression (A4RSIC) approach is proposed that achieves superior compression ratio compared to the state-of-the-art methods. The A4RSIC model embeds an encoder-generator duo to generate a compact latent vector representation of the input image, which is then used to reconstruct the original image with minimal loss. The proposed model utilizes a discriminator, trained to differentiate between real and A4RSIC generated images using an adversarial approach, ensuring the fidelity of the reconstructed images. Furthermore, the model integrates a convolutional block attention module (CBAM) at relevant strategic points within the encoder and generator to enhance the salient features by leveraging spatial and channel-wise attention mechanism. This improves the quality of feature representation and compression performance. Experimental results demonstrate the efficacy of the proposed method compared to JPEG as well as other state-of-the-art methods. © 2025 IEEE.

关键词： Image compression

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