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arXiv 2023年

作者： Wang, Ziliang Zhang, Xiaohong Li, Ze Shi Huang, Sheng Yan, Meng Ministry of Education School of Computer Science Peking University Beijing China The Department of Computer Science The University of Victoria Canada Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on dataset D1 and 8.32% on dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments. © 2023, CC0.

关键词： Prediction models

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A Unified Framework for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images 10th

A Unified Framework for Preoperative Early Recurrence Predic...

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10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Ouyang, Shuyi Xu, Yingying Wang, Weibin Li, Yinhao Wang, Fang Chen, Qingqing Lin, Lanfen Chen, Yen-Wei Hu, Hongjie College of Computer Science and Technology Zhejiang University Hangzhou310000 China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China College of Information Science and Engineering Ritsumeikan University Shiga Kusatsu525-8577 Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou310000 China

ISBN: (纸本)9789811934391

Early recurrence (ER) after surgery is one of the main causes of death in Hepatocellular Carcinoma (HCC). Making an effective prediction of the risk of ER is important in developing treatment strategies. The existing methods generally use radiomic features or simple deep learning features, which have insufficient feature representation capabilities. Here, we present a deep learning-based strategy for obtaining enriched multi-phase fusion features that can effectively represent feature information. We present an effective preoperative prediction model that uses multi-phase CT scans as input. Our proposed framework significantly utilizes and enhances multi-phase contextual information by effectively performing multi-phase feature fusion. We also present a novel backbone network with attention mechanisms called Inv-ResNet, that can extract more discriminative features from each CT phase using ResNet block and involution operator. Further, we design a Multi-Phase Feature Fusion Module that allows for global-Squeeze and phase-Excitation of important feature weights across all CT phases to selectively fuse features. In comparison to existing methods of preoperative prediction of ER of HCC, our method achieves 72.70% accuracy and a value of 0.7478 area under the ROC curve (AUC), which is the highest accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computerized tomography

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Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Prachi Arihant Chougule Sarumathi S Ravi Kant Raj Kumar Gupta U. Pushpa Latha Anvesh Perada Department of Electrical Engineering DKTE Society's Tetile and Engineering Institute Ichalkaranji India Department of Computer Science and Engineering HKBK College of Engineering Bangalore India Department of Computer and Data Sciences YSAICDS Shoolini University Solan India Department of Physics Sardar Vallabhbhai Patel College Bhabua VKSU Ara India Department of English Malla Reddy Engineering College (Autonomous) Hyderabad India Department of Electrical and Computer Engineering Drexel University Philadelphia United States of America

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models.

关键词： Training Cloud computing Accuracy Bidirectional long short term memory Medical services Predictive models Feature extraction Internet of Things Security Secure storage

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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A Pilot Study on Video-based Eye Movement Assessment of the NeuroEye Examination

A Pilot Study on Video-based Eye Movement Assessment of the ...

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2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Hassan, Mohamed Abul Yin, Xuwang Zhuang, Yan Aldridge, Chad M. McMurry, Timothy Southerland, Andrew M. Rohde, Gustavo K. Imaging and Data Science Lab Department of Biomedical Engineering University of Virginia VA22903 United States Imaging and Data Science Lab Department of Electrical and Computer Engineering University of Virginia VA22903 United States The Department of Neurology University of Virginia VA22903 United States

ISBN: (纸本)9781665403580

The ability to perform automated neurological assessment could enhance diagnosis and treatment in the pre-hospital setting such as during telemedicine or emergency medical services (EMS) encounters. In this pilot study we present the preliminary analysis of a video-based eye movement assessment workflow (NeuroGaze) for neurological eye examinations. Our workflow uses a pupil detector to detect the center of the pupil for both eyes for a given video and determine the conjugacy of eye movement. The study evaluated the proposed workflow on 18 healthy volunteers while performing a digitally adapted neurological eye examination (NeuroEye) to assess conjugacy of gaze in all cardinal positions. The NeuroGaze performance was compared to the COTS eye tracker. The video-based eye movement assessment workflow reported an average Pearson correlation coefficient of 0.84, compared to the COTS eye tracker with an average of 0.95. The preliminary analysis indicates that NeuroGaze can be used to determine the individuals ability to perform conjugate eye movements. Future study will assess the performance of NeuroGaze in patients with pathological eye movements. © 2021 IEEE

关键词： Eye movements

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Unsupervised Manifold Linearizing and Clustering

Unsupervised Manifold Linearizing and Clustering

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International Conference on computer Vision (ICCV)

作者： Tianjiao Ding Shengbang Tong Kwan Ho Ryan Chan Xili Dai Yi Ma Benjamin D. Haeffele Mathematical Institute for Data Science Johns Hopkins University USA Department of Electrical Engineering and Computer Sciences University of California Berkeley USA The Hong Kong University of Science and Technology (Guangzhou) PRC

We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data. 4

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ML Based Phenotype Analysis Using Differential Gene Expression data in Schizophrenia

ML Based Phenotype Analysis Using Differential Gene Expressi...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Aadhithyan D Shabarirajan KJ Varun Maniappan Shivanesh B Divya S Praghaadeesh R Kalpana Raja I R Oviya Department of Computer Science and Engineering (Artificial Intelligence) Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384635

In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. The dataset contains information on cis eQTNs, gene orientation, probe information, gene expression profiles, and SNP information. When first investigating well-known machine learning methods, a hybrid model combining Random Forests and XGBoost is suggested, however it only offers a little increase. Increases in predicted accuracy of more than 99.12% are made possible by the H2O AutoML package. The results demonstrate the capability of ML-based analysis to unravel the underlying biological mechanisms of schizophrenia and contribute to the design of individualised therapeutic approaches. The paper represents the capability of machine learning to comprehend the relationship between gene expression patterns and complicated illnesses like schizophrenia.

关键词： Water Training Accuracy Phenotypes Mental disorders Predictive models data models

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Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks

arXiv

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arXiv 2024年

作者： Lee, Hunmin Seong, Hongju Kim, Wonbin Kwon, Hyeokchan Seo, Daehee Department of Computer Science and Engineering University of Minnesota Minneapolis55455 United States Department of Computer Education Sunchon National University Suncheon57922 Korea Republic of Department of Artificial Intelligence and Data Engineering Sangmyung University Seoul03016 Korea Republic of Daejeon34129 Korea Republic of

Automatic modulation classification (AMC) serves a vital role in ensuring efficient and reliable communication services within distributed wireless networks. Recent developments have seen a surge in interest in deep neural network (DNN)based AMC models, with Federated Learning (FL) emerging as a promising framework. Despite these advancements, the presence of various noises within the signal exerts significant challenges while optimizing models to capture salient features. Furthermore, existing FL-based AMC models commonly rely on linear aggregation strategies, which face notable difficulties in integrating locally fine-tuned parameters within practical non-IID (Independent and Identically Distributed) environments, thereby hindering optimal learning convergence. To address these challenges, we propose FedVaccine, a novel FL model aimed at improving generalizability across signals with varying noise levels by deliberately introducing a balanced level of noise. This is accomplished through our proposed harmonic noise resilience approach, which identifies an optimal noise tolerance for DNN models, thereby regulating the training process and mitigating overfitting. Additionally, FedVaccine overcomes the limitations of existing FL-based AMC models’ linear aggregation by employing a split-learning strategy using structural clustering topology and local queue data structure, enabling adaptive and cumulative updates to local models. Our experimental results, including IID and non-IID datasets as well as ablation studies, confirm FedVaccine’s robust performance and superiority over existing FL-based AMC approaches across different noise levels. These findings highlight FedVaccine’s potential to enhance the reliability and performance of AMC systems in practical wireless network environments. © 2024, CC BY.

关键词： Signal to noise ratio

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Hiding Scrambling Text Message in Speech Signal Based on Lightwight Hyperchaotic Map and Conditional Lsb Mechanisms

SSRN

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SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

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Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model

Impact of Fuzziness for Skin Lesion Classification with Tran...

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International Conference on Computing, Electronics & Communications engineering (iCCECE)

作者： Israt Yasmin Suriya Sultana Syeda Jobaida Begum Muhammed J. A. Patwary Tarik Adnan Almohamad Iftekhar Salam Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Electrical-Electronics Engineering Karabuk University Karabuk Türkiye School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

Skin lesion is one of the most commonly encountered illnesses that need to be detected and treated at an early stage. Numerous Convolutional Neural Network (CNN) classifiers were utilized to identify skin conditions using visual features. When it comes to computational effectiveness and accuracy, the recently released Vision Transformers (ViT) have been shown to outperform CNNs. Since the ViTs perform well in classifying images, we use ViT models in our approach. This research proposes a fuzziness-based transformer model to classify images. The divide-and-conquer technique is employed to examine the effectiveness of fuzzy logic and classifying images more accurately. To increase the effectiveness of image classification, the testing dataset has been divided into three groups, including low, medium, and high-fuzzy instances, based on the degree of fuzziness for each sample. For better classification of images, we propose a straightforward mechanism that consists of a fuzzy divide-and-conquer strategy with ViT. Experiments are carried out to compare the model’s performance with and without using this proposed mechanism. We noticed that retraining classifiers with low fuzzy samples can enhance image classifier performance. The classification performance on the PAD-UFES-20 dataset using this strategy revealed substantial improvements in the classifier’s performance.

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