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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Nagarajan, S. Ramprasath, M. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu India

ISBN: (纸本)9798350305708

Medical imaging is capturing pictures of bodily components for diagnostic or research reasons. Because of advancements in image-handling techniques, which include picture recognition, examination, and upgrading, clinical imaging is expanding swiftly. Picture division is a vital subject in picture handling and PC vision with applications, for example, scene grasping, clinical picture examination, mechanical discernment, video observation, expanded reality, and picture pressure, among numerous others. Numerous picture segmentation methods have been created and are available in the literature. There has been a lot of work done to develop techniques for segmenting pictures using deep-learning models since they have proved effective in various vision applications. Deep learning has turned into a functioning exploration point in the field of clinical picture examination. Compared to the laborious and lengthy conventional technique, deep learning (DL) automatic detection algorithms can speed up diagnostics, increase test accuracy, lower expenses, and lessen the workload on the radiologist. Kidney segmentation is an integral part of any non-intrusive computer-assisted diagnostic (CAD) system for rapidly recognising severe renal repudiation because of the intensity of inhomogeneity brought on by errors in the image capture process, efficient kidney segmentation is challenging in evaluating and therapeutic planning of kidney-related disorders. This article addresses recent developments in DL-based kidney tumor segmentation systems. We highlight their fundamental components and numerous approaches as we discuss the main medical image kinds, segmentation methods, and evaluation standards for segmentation outcomes in kidney tumor++ segmentation. © 2023 IEEE.

关键词： Automatic Detection algorithms Computer-Assisted diagnostic system Deep Learning Kidney tumor segmentation Vision applications

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

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Mathematical Biosciences and Engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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Computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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GAM-GCN: Decomposing Graph Convolutional Networks via Generalized Additive Models 15

GAM-GCN: Decomposing Graph Convolutional Networks via Genera...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Liu, Chen Yu, Lequan Han, Kai Yin, Guosheng The University of Hong Kong School of Computing and Data Science Hong Kong Hong Kong

ISBN: (纸本)9798331508821

The vast adoption of graph neural networks (GNNs) in broad applications calls for versatile interpretability tools so that a better understanding of the GNNs' intrinsic structures can be gained. We propose an interpretable GNN framework to decompose the prediction into the additive combination of node features' main effects and the contributions of edges. The key component of our framework is the generalized additive model with the graph convolutional network (GAM-GCN) that allows for global node feature interpretations. The inherent interpretability of GAM and the expressive power of GCN are preserved and naturally connected. Further, the effects of neighboring edges are measured by edge perturbation and surrogate linear modeling, and the most important subgraph can be selected. We evaluate the proposed approach using extensive experiments and show that it is a useful tool for interpreting GNNs in the attempt to unlock the black box. © 2024 IEEE.

关键词： Graph neural networks

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Predictive Stress Management for Working Women in Educational Institutions using Federated Learning and Wearable Technology 8

Predictive Stress Management for Working Women in Educationa...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Devi, D. Sudha Sujithra, M. Velvadivu, P. Kavya, V.V. Jhunandhini, S. Data Science Coimbatore Institute of Technology Department of Computing Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

The proposed methodology uses Artificial Neural Networks (ANN) to predict stress levels of women working in educational institutions by analyzing physiological and demographic data collected through wearable technology. TensorFlow Federated framework is used here to ensure the data privacy by enabling secure and decentralized training of the proposed model. The approach integrates various steps, including data preprocessing, feature selection, and model training with federated learning. Based on predicted stress levels, the platform provides personalized recommendations to manage stress and improve mental well-being, while keeping user data private. The process involves collecting data from surveys and wearable devices, optimizing the model through techniques like hyperparameter tuning and regularization, and deploying the system via a web interface for real-time stress management. © 2024 IEEE.

关键词： Adversarial machine learning

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Traffic Sign Detection Using YOLOv8 2

Traffic Sign Detection Using YOLOv8

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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MMD GRAPH KERNEL: EFFECTIVE METRIC LEARNING FOR GRAPHS VIA MAXIMUM MEAN DISCREPANCY 12

MMD GRAPH KERNEL: EFFECTIVE METRIC LEARNING FOR GRAPHS VIA M...

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12th International Conference on Learning Representations, ICLR 2024

作者： Sun, Yan Fan, Jicong School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computing National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China

This paper focuses on graph metric learning. First, we present a class of maximum mean discrepancy (MMD) based graph kernels, called MMD-GK. These kernels are computed by applying MMD to the node representations of two graphs with message-passing propagation. Secondly, we provide a class of deep MMD-GKs that are able to learn graph kernels and implicit graph features adaptively in an unsupervised manner. Thirdly, we propose a class of supervised deep MMD-GKs that are able to utilize label information of graphs and hence yield more discriminative metrics. Besides the algorithms, we provide theoretical analysis for the proposed methods. The proposed methods are evaluated in comparison to many baselines such as graph kernels and graph neural networks in the tasks of graph clustering and graph classification. The numerical results demonstrate the effectiveness and superiority of our methods. Our code is available at https://***/yan-sun-x/MMD-Graph-Kernel. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Message passing

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Offline Behavior Distillation 38

Offline Behavior Distillation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lei, Shiye Zhang, Sen Tao, Dacheng School of Computer Science The University of Sydney Australia College of Computing & Data Science Nanyang Technological University Singapore

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity O(1/(1 − γ)2). We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity O(1/(1 − γ)). Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. The code is available at https://***/LeavesLei/OBD. © 2024 Neural information processing systems foundation. All rights reserved.

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Polycystic Ovary Syndrome Prediction through CNN based Image Analysis: A Deep Learning Based Approach 8

Polycystic Ovary Syndrome Prediction through CNN based Image...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthik, Yazhini Sruthi, R. Sujithra, M. Coimbatore Institute of Technology Department of Computing - Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women during their reproductive years. It is characterized by irregular menstrual cycles, excessive hair growth, and the presence of cysts in the ovaries. The diagnosis of PCOS typically involves a combination of clinical evaluations and laboratory tests. The paper proposes a novel approach using Convolutional Neural Networks (CNNs) for predicting PCOS through the analysis of ultrasound images. The dataset comprises images obtained from ovarian ultrasound scans of both PCOS-positive and PCOS-negative individuals. These images undergo preprocessing and augmentation techniques such as rescaling, shearing, zooming, flipping, and rotation to enhance the diversity of the training data. Multiple CNN-based models with varying architectures are constructed, altering parameters such as the number of convolutional and pooling layers, activation functions, and other hyperparameters. The models are trained and validated using a split of the dataset into training and validation sets, with metrics like loss and accuracy monitored during training to assess model performance. Upon completion of training, the models are tested using unseen data to evaluate their predictive capabilities. The results demonstrate that deep learning models trained on ultrasound images can accurately predict the presence or absence of PCOS. Such models hold promise in aiding healthcare professionals with early diagnosis and management of PCOS, ultimately improving patient care and outcomes. © 2024 IEEE.

关键词： Ultrasonic imaging

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