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6th International Conference on computer Information science and Application Technology, CISAT 2023

作者： Chen, Mengze Chen, Zhili Abba, Adamu Abubakar School of Computer Science and Engineering Shenyang Jianzhu University China

ISBN: (纸本)9781510668546

To reduce the dependence of lesion detection algorithms on annotated data and fully leverage the advantages of medical image big data from healthy people, this paper proposes an unsupervised breast lesion detection model based on a parallel convolutional neural network. It treats breast lesion detection in mammograms as a one-class classification problem. The network comprises two parallel convolutional neural network branches. One branch utilizes normal mammographic images as training samples to extract the deep features of normal images and considers the compactness measure of features as the training loss. Simultaneously, to prevent the lack of inter-class discrimination in features extracted from the first branch's sample learning, the other branch introduces a subset of the ImageNet dataset as training samples and employs the descriptive measure of features as the training loss. This ensures that the extracted features not only satisfy intra-class similarity but also maintain inter-class discrimination. ResNet50 is chosen by comparison as the backbone network for the parallel branch and further improved. Experiments are conducted using the INbreast and BCDR datasets, and the results indicate that the proposed unsupervised lesion detection method could achieve comparable performance with the supervised methods without requiring any lesion annotation data. © 2023 SPIE.

关键词： Convolution

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Bimodal Biometric Human Recognition System Using Deep Learning Algorithm 2

Bimodal Biometric Human Recognition System Using Deep Learni...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Vasavi, J. Chidambaranathan, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505745

Biometric recognition system combining two or more traits is a method of identifying human using deep learning techniques. Protection is the primary issue for humans nowadays. Biometric human identification system using single trait may not be accurate due to inconsistencies in the captured data, making system more vulnerable to spoofing or hacking and also sensitive to environmental conditions. Recently, Multimodal biometric techniques help mitigate these issues by providing high security and more accuracy. This research work combines two traits such as iris and palm print to encounter the growing demand for high precision in today's biometric system known as Bimodal Biometric system. Bimodal systems are working to extend the high performance which will not be possible using single trait. This paper reviews the background of iris and palm print, then apply preprocessing techniques to remove noise and classification using Convolutional Neural Network (CNN) for human identification. © 2025 IEEE.

关键词： Convolutional neural networks

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An AC Contactor Remaining Useful Life Prediction Method based on Degradation Event Analysis 6

An AC Contactor Remaining Useful Life Prediction Method base...

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Ren, Yiran Lyu, Jianhua Jebali, Mejed Zhang, Baili Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350316155

Predicting the remaining useful life of equipment is crucial to ensure safe operations and is widely used in various industries. Data-driven methods for remaining useful life prediction are efficient and scalable, and have garnered significant attention from researchers. However, existing studies often lack comprehensive analysis of equipment degradation data, resulting in low accuracy and reliance on large sample sizes. AC contactors are extensively used in power systems and industrial settings, but their complex structure and operation make traditional maintenance methods impractical. To address these challenges, we propose a novel method for predicting the remaining useful life of AC contactor equipment. Our approach estimates remaining useful life by assessing the impact of degradation events on equipment's life loss value. By constructing a degradation index, extracting relevant degradation events, matching them to reference events, and estimating remaining useful life, we accurately predict the remaining life of AC contactors. Extensive comparative experiments demonstrate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Forecasting

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ML-based Vehicle Accident Severity Prediction Framework for Traffic Management Systems 2

ML-based Vehicle Accident Severity Prediction Framework for ...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Patel, Kavya Gondalia, Unnati Kakkar, Riya Gupta, Rajesh Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Sr University School of Computer Science and Ai Telangana Warangal India

ISBN: (纸本)9798350387933

The present research examines the current issues arises with the growing usage of personal vehicles in the transportation industry. This adoption is due to the technical advancements in the automobile sector. Although personal automobiles have many benefits, such as convenience and safety features, the ensuing increase in traffic congestion has become a serious problem and is contributing to an increase in traffic accidents. In today's dynamic transportation scene, striking a balance between the advantages and problems provided by this paradigm change is essential. We suggest an all-encompassing paradigm for accident severity prediction in response to these difficulties. The proposed framework makes use of cutting-edge sensor technologies and machine learning methods including KNearest Neighbours, Random Forest, XGBoost, and Bagging. This all-encompassing strategy seeks to improve overall transportation efficiency, optimise traffic management, and proactively predict the severity of accidents. The goal of this research is to improve road safety and accident management by providing useful information to emergency services, law enforcement, and decisionmakers. © 2024 IEEE.

关键词： Motor transportation

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A Novel Transformer-Based Pipeline for Lung Cytopathological Whole Slide Image Classification 48

A Novel Transformer-Based Pipeline for Lung Cytopathological...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Gaojie Liu, Qing Liu, Haotian Liang, Yixiong Central South University School of Computer Science and Engineering China

ISBN: (纸本)9781728163277

We propose a novel three-stage Transformer-based methodology for entire cytopathological whole slide image (WSI) classification. The key idea is to leverage Transformer to extract the fine-grained lesion-level features and then progressively aggregate them into intermediate-grained patch-level features and coarse-grained WSI-level features for classification. Specifically, we first extract multi-scale lesion features from each patch image via Transformer-based lesion detection, and then adaptively aggregate the extracted lesion features into the corresponding patch feature with an MLP-Mixer. Finally, we select the most representative patch features and feed them into the Vision Transformer (ViT) for the final WSI-level classification. We collect a dataset consisting of 961 lung cytopathological WSIs of pleural effusions cytology specimens and conduct extensive experiments on it. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art (SOTA) methods for cytopathological WSI classification. © 2023 IEEE.

关键词： Cytopathology Lesion Detection Lung Cancer Transfomer WSI classification

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Early Renaissance Art Generation Using Deep Convolutional Generative Adversarial Networks 9

Early Renaissance Art Generation Using Deep Convolutional Ge...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Sanjay, T. Gladwin, J. Sreedevi, A.G. Deepan, M. Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (纸本)9798350397376

Recent developments in generative art technology have demonstrated the efficacy of neural networks trained on publicly available samples in creating lifelike visuals. However, there are situations where there aren't many training samples to choose from. It's safe to say that you won't see another picture quite like an early Renaissance masterpiece again. In this paper, a pipeline consisting of Deep Convolutional Generative Adversarial Network (DCGAN) preceeded by an augmentation setup is proposed for the automatic generation of early Renaissance-style paintings. Using a 100-dimensional noise vector as input, a neural network with deep convolutional layers is employed as the generator model. The discriminator is a deep convolutional neural network-based classifier with two output classes - authentic and counterfeit. The proposed model uses an open-source dataset of Early Renaissance artwork hand-picked from the WikiArt database. Since original early renaissance paintings are so hard to come by, a transformation process including polarization, inversion, elastic augmentation, solarization, and gaussian noise is employed to generate more samples. The significance of this research is rising high over time due to the scarcity of data and the difficulty in recreating intricate brush strokes in generative models. The investigation is motivated by an interest in Chinese landscape paintings [1] as a source of generative inquiry. © 2023 IEEE.

关键词： Convolution

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AERO: Audio Super Resolution in the Spectral Domain 48

AERO: Audio Super Resolution in the Spectral Domain

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Mandel, Moshe Tal, Or Adi, Yossi The Hebrew University of Jerusalem School of Computer Science and Engineering Israel

ISBN: (纸本)9781728163277

We present AERO, a audio super-resolution model that processes speech and music signals in the spectral domain. AERO is based on an encoder-decoder architecture with UNet like skip connections. We optimize the model using both time and frequency domain loss functions. Specifically, we consider a set of reconstruction losses together with perceptual ones in the form of adversarial and feature discriminator loss functions. To better handle phase information the proposed method operates over the complex-valued spectrogram using two separate channels. Unlike prior work which mainly considers low and high frequency concatenation for audio super-resolution, the proposed method directly predicts the full frequency range. We demonstrate high performance across a wide range of sample rates considering both speech and music. AERO outperforms the evaluated baselines considering Log-Spectral Distance, ViSQOL, and the subjective MUSHRA test. Audio samples and code are available Blue here. © 2023 IEEE.

关键词： audio super-resolution bandwidth extension speech synthesis

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit In Kim, Dong Jamalipour, Abbas Ben Letaief, Khaled Tafazolli, Rahim University of Electronic Science and Technology of China National Key Laboratory on Wireless Communications Chengdu610054 China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Nanyang Technological University College of Computing and Data Science Jurong West Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon03063 Korea Republic of University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute for Communication Systems School of Computer Science and Electronic Engineering University of Surrey GuildfordGU2 7XH United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Mitigate the Man-in-the-Middle attack by the use of Machine Learning Techniques: A Review 6

Mitigate the Man-in-the-Middle attack by the use of Machine ...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： da Conceicao Mahangue, Edson Bernardo Sharma, Vivek Gupta, Keshav Department of Computer Science & Engineering School of Engineering and Technology Sharda University UP Greater Noida India

ISBN: (纸本)9798350374629

The wide use of Internet brought many challenges were security has become a need to protect information and resources against attacks such as Man-in-the-Middle (MITM). This type of attack aims to eavesdrop a communication between two or more legitimate entities without there awareness. Machine Learning (ML) algorithms combined with traditional approaches such Intrusion Detection System (IDS) and Firewalls are often adopted as solution to security concerns that this type of attack brings, were the accuracy of proposed schemes has shown how secure these approaches can be. The traditional approaches without an combined intelligent technique are normally deceived by the new type of unknown MITM attack due this the combination of intelligent computing techniques such ML can bring accuracy that achieve in the best scenario 98% of accuracy being possible to detect even unknown MITM attacks compared to other methodologies. Even though many studies performed in this attack, it continues bringing security concerns due the various reasons such vulnerabilities in communication channels that are continually exploited. ©2024 IEEE.

关键词： Machine learning

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Simultaneous Bulk-and Surface-initiated Living Polymerization Studied with a Heterogeneous Stochastic Reaction Model

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Chinese Journal of Polymer science 2024年第3期42卷 364-372,I0008页

作者： Jia-Shu Ma Zhi-Ning Huang Jia-Hao Li Bang-Ping Jiang Yan-Da Liao Shi-Chen Ji Xing-Can Shen State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources(Ministry of Education of China)School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China School of Computer Science and Engineering&School of Software Guangxi Normal UniversityGuilin 541004China

To better characterize the properties of surface-initiated polymers, simultaneous bulk-and surface-initiated polymerizations are usually carried out by assuming that the properties of the surface-initiated polymers resemble those of the bulk-initiated polymers. Through a Monte Carlo simulation using a heterogeneous stochastic reaction model, it was discovered that the bulk-initiated polymers exhibit a higher molecular weight and a lower dispersity than the corresponding surface-initiated polymers, which indicates that the equivalent assumption is invalid. Furthermore, the molecular weight distributions of the two types of polymers are also different, suggesting different polymerization mechanisms. The results can be simply explained by the heterogeneous distributions of reactants in the system. This study is helpful to better understand surface-initiated polymerization.

关键词： Surface-initiated polymerization Polymer brush Stochastic reaction model Heterogeneous polymerization Simultaneous polymerization

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