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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Periya Nayaki, A. VishwaShree, S.V. Mithra, N. Priyadharshini, C. Thanabal, M.S. Penyameen, K. Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

Heart disease remains a leading global cause of mortality, emphasizing the need for innovative diagnostic solutions. Traditional Clinical Decision Support Systems (CDSS) often struggle with limited datasets and imbalances, impacting prediction accuracy. This study proposes a novel approach by integrating Conditional Generative Adversarial Networks (CGANs) into a CDSS framework, enhancing its ability to generate high-quality synthetic data. The augmented dataset is utilized to train a Gradient Boosting model capable of identifying subtle patterns in patient data. This methodology significantly improves prediction precision and addresses limitations of traditional techniques. The results demonstrate the model's superiority inaccurately identifying heart disease risks, making it a valuable tool for early diagnosis and clinical decision-making. By combining advanced machine learning techniques with domain- specific innovations, this research sets a new benchmark in the application of AI for healthcare. © 2025 IEEE.

关键词： Generative adversarial networks

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TurfIt: An Instant Turf Booking Application

TurfIt: An Instant Turf Booking Application

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Rajeswari, A.M. Tamilselvan, V. Abishek, M.R.P. Dept. of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331512965

As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectiveness in scheduling, and over-bookings, leading to frustrations and operational problems. This paper focuses on those problems by the presentation of an online turf booking application to streamline the booking process. The solution offers real-time updates, automatic payment, and safe transactions based on QR code. The more user-friendly the application is, the more efficient it will be. Firebase would be used for real-time data synchronization and the responsiveness of React interface ensures the smooth and conflict-free bookings. This solution would lead to increasing customer satisfaction as well as increasing the booking rates and effective utilization of sports facilities. © 2025 IEEE.

关键词： Customer satisfaction

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Cow face recognition based on Transformer group 4

Cow face recognition based on Transformer group

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Li, Xujian Liu, Yunhao College of Computer Science and Engineering Shandong University of Science and Technology Shandong China

ISBN: (纸本)9781510682528

This paper found that in existing cattle face recognition algorithms, convolutional neural networks (CNN) are more commonly used. The processing speed and performance of CNNs in processing large data are often limited by the receptive field and the number of network layers. At the same time, the visual transformer has achieved great success due to its high performance and excellent ability to process large data. Therefore, this paper borrows the traditional cattle face recognition process, using the DETR algorithm for object detection and the Swin-transformer algorithm for image classification, thereby improving the running efficiency and enhancing the ability to process large data. Comprehensive experiments show that the algorithm in this paper is superior to existing traditional models, achieving a good balance between speed and accuracy. Compared with traditional algorithms, this algorithm utilizes the emerging Transformer group, and not only can effectively identify the unique identity of cattle's front face, but also has good effect on identifying the unique identity of cattle's side face and rear face due to using the Transformer group to train and test a large amount of data. © 2024 SPIE.

关键词： Convolutional neural networks

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DGEAHorNet: High-order Spatial Interaction Network with Dual Cross Global Efficient Attention for Skin Lesion Segmentation

DGEAHorNet: High-order Spatial Interaction Network with Dual...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Haixin Peng, Yanjun Chen, Xue Chen, Zhenxiang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China

ISBN: (纸本)9798350386226

Skin lesion segmentation is a complex and severe project, which aims to accurately segment abnormal regions in skin lesion images. However, obtaining accurate segmentation results is difficult because of the great uncertainty in the shape, location, and scale of the target region. To address these challenges, we propose a higher-order spatial interaction framework with dual cross global efficient attention (DGEAHorNet), which employs a neural network architecture based on recursive gate convolution to adequately extract multi-scale contextual information from images. Specifically, a Dual Cross-Attentions (DCA) is added to the skip connection that can effectively blend multi-stage encoder features and narrow the semantic gap. In the bottleneck stage, global channel spatial attention module (GCSAM) is used to extract image global information. To obtain better feature representation, we feed the output from the GCSAM into the multi-branch dense layer (SENetV2) for excitation. Furthermore, we adopt Depthwise Over-parameterized Convolutional Layer (DO-Conv) in order to replace the common convolutional layer in the input and output part of our network, then add Efficient Attention (EA) to diminish computational complexity and enhance our model's performance. For evaluating the effectiveness of our proposed DGEAHorNet, we conduct comprehensive experiments on three publicly-available skin datasets, and achieving 0.9320, 0.9337 and 0.9474 in Dice similarity coefficient on ISIC2018, ISIC2017, and PH2 severally. Our proposed method outperforms other state-of-the-art methods. The code is available at https://***/penghaixin/mymodel. © 2024 IEEE.

关键词： Deep learning

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Distributed and Coordinated Multi-Agent Learning for Resource Allocation in Vehicular Networks 21

Distributed and Coordinated Multi-Agent Learning for Resourc...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Jiang, Rui Zhang, Fuxin Wang, Guangping College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350365221

In cellular-based vehicular communication networks, a V2V link shares the same spectrum resources with a V2I link, resulting in mutual interference and adversely affecting vehicular communication performance. The existing resource allocation strategies in cellular-based vehicular communication networks rely on individual links to adjust network parameters, leading to unfair resource allocation. To resolve this problem, we propose a joint spectrum sharing and power control strategy based on deep reinforcement learning is proposed. Firstly, a coordination graph is designed to describe the dependencies among V2V links. Then, a graph attention mechanism is proposed to extract the relevant information from neighboring V2V links. Finally, based on the multi-agent deep deterministic policy gradient method, a coordination learning algorithm for wireless resource allocation in cellular-based vehicular networks is established. Experimental results show that the proposed method can improve the transmission rate of V2I links and the successful transmission rate of V2V links. © 2024 IEEE.

关键词： Deep reinforcement learning

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Dynamic Path-Sensitive Memory Dependence Prediction 9

Dynamic Path-Sensitive Memory Dependence Prediction

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Liu, Weiding Li, Lunbo College of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331509453

Memory dependence prediction is essential in superscalar out-of-order processors as it improves instruction-level parallelism efficiency by predicting dependencies between memory access instructions. However, existing state-of-the-art memory dependence predictors lack the ability to effectively utilize the dynamic execution information of conflicting instructions, leading to an excessive number of false positives and subsequently hindering overall performance. This work proposes Dynamic Path-Sensitive Memory Dependence Prediction (DPMDP), a novel dynamic path-sensitive memory dependence prediction method that dynamically selects the history length for each conflicting load instruction when multiple paths exist. Our key observation is that for load-store pairs with an initial occurrence of a violation if the conflicting store is waiting for an address translation that causes an execution delay, it does not create a dependency in the predictor initially and only creates it upon subsequent occurrences effectively. This observation is proven by running the SPEC CPU 2006 suite on the clock-accurate simulator GEM5. Through cycle-accurate simulation of the SPEC CPU 2006 suite, we show that DPMDP achieved an average performance improvement of 1.08% compared to Store Set and 13.06% compared to PHAST. © 2024 IEEE.

关键词： Parallel processing systems

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Introduction

Signals and Communication Technology

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Signals and Communication technology 2024年 Part F1492卷 3-17页

作者： Tian, Yu-Chu Gao, Jing School of Computer Science Queensland University of Technology BrisbaneQLD Australia College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

The main theme of this book is network analysis and architecture design for network planning. While there are various types of networks, this book specifically focuses on computer networks for data communication. Therefore, it uses the term networks to specifically refer to computer networks unless specified explicitly otherwise. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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CMCD-Net:Unsupervised domain adaptation with Contrastive learning for cross-modality and cross-disease brain lesion segmentation

CMCD-Net:Unsupervised domain adaptation with Contrastive lea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Xue Peng, Yanjun Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China

ISBN: (纸本)9798350386226

Unsupervised domain adaptation (UDA), as a robust transfer learning strategy that utilizes source domain richly labeled data to solve the target domain unlabeled semantic segmentation, has great potential to tackle the problem of data scarcity and domain shift. However, in the medical domain, most existing methods are limited to image-level constrained alignment by image-to-image translation, and few can simultaneously address the dual domain shift of both modality and lesion discrepancies. The motivation of this paper is to explore the potential of UDA for addressing cross-modal and cross-lesion brain lesion segmentation without generative training. The specific novelty of our proposal mainly includes: (1) proposing a UDA technology for cross-modality and cross-disease brain imaging segmentation, which are more general and urgent in clinical practice;(2) exploring the potential of Fourier-based bi-directional frequency domain adaptation to alleviate imaging protocol discrepancies as an alternative to burdensome generative training;(3) proposing a self-training strategy with category-level prototype contrastive learning to exploit cross-domain similar semantic features. The proposed CMCD-Net exhibited encouraging adaptation performance in two segmentation tasks: multi-modality MRI gliomas, multi-modality MRI glioma to meningioma. In addition, massive ablation experiments validated the effectiveness of each component in our proposal. The code is publicly available at https://***/Snow1949/C2_MUDA. © 2024 IEEE.

关键词： Semantic Segmentation

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