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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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SCAF-DG: A Multi-site Medical Image Denoising with A Domain-Generalized Spatial-Channel Attention Fusion 6

SCAF-DG: A Multi-site Medical Image Denoising with A Domain-...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Klugah-Brown, Benjamin Tettey, Obed Nartey Aggrey, Esther Stacy E.B. Agyemang, Isaac Osei Adjei-Mensah, Isaac Yang, Yang University of Electronic Science Technology of China School of Information and Software Engineering Chengdu China University of Electronic Science Technology of China School of Life Science and Technology Chengdu China University of Electronic Science Technology of China School of Information and Communication Engineering Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (纸本)9798350325485

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. these features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-To-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-the-Art denoising models on multi-site medical images. © 2023 IEEE.

关键词： Image denoising

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Towards Interpreting the Behavior of Large Language models on software engineering Tasks

Towards Interpreting the Behavior of Large Language Models o...

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international conference on software engineering - Companion, ICSE, Companion

作者： Atish Kumar Dipongkor Kevin Moran University of Central Florida Orlando Florida USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Large Language models (LLMs) have ushered in a significant break-through within the field of Natural Language Processing. Building upon this achievement, analogous language models have been developed specifically for code-related tasks, commonly referred to as Large Language models for Code (LLMsC). Notable examples of LLMsC include CodeBERT, UnixCoder, CoPilot, among others. these models have demonstrated exceptional performance across various software engineering (SE) tasks, encompassing code summarization, test case generation, natural language to code conversion, bug triaging, malware detection, program repair, and more. Despite the promising results achieved by LLMsC in SE tasks, there remains fundamental questions regarding their decision-making processes. Understanding these model decision mechanisms is crucial for further enhancing the performance of LLMsC. In pursuit of this objective, my PhD dissertation aims to pioneer novel methodologies for interpreting and comprehending the behavior of LLMsC.

关键词： Codes Decision making Computer bugs Medical services predictive models Natural language processing Trajectory

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Personal Credit Risk Assessment Based on Swarm Intelligence and Random Forest

Personal Credit Risk Assessment Based on Swarm Intelligence ...

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Hanyin Zhang Facheng Dai Xiangguang Dai Washington University in St. Louis Chongqing Three Gorges University

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Personal credit risk assessment plays a critical role in the issuance of credit loans by banks and other financial institutions, fundamentally serving as a predictive problem. However, traditional methods often only achieve local optimal solutions. this paper introduces an innovative method for personal credit risk assessment by integrating swarm intelligence and random forest techniques, aiming to find the global optimal solution. the practicality and efficiency of this algorithm have been demonstrated through experimental validation on the German credit database.

关键词： Support vector machines Adaptation models Accuracy predictive models Prediction algorithms Classification algorithms Risk management Particle swarm optimization Random forests Optimization

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Unmasking the Artist: Discriminating Human-Drawn And AI-Generated Human Face Art through Facial Feature Analysis 6

Unmasking The Artist: Discriminating Human-Drawn And AI-Gene...

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6th international conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Nguyen, Minh-Quang Ho, Khanh-Duy Nguyen, Hoang-Minh Tu, Canh-Minh Tran, Minh-Triet Do, Trong-Le University of Science VNU-HCM Faculty of Information Technology Ho Chi Minh City Viet Nam University of Science VNU-HCM Software Engineering Laboratory Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

In the 1990s, the world witnessed a revolutionary breakthrough in the realm of AI-generated art, where its applications surpassed mere visual effects. An ever-increasing number of AI-generating applications emerged, posing a critical challenge for many art contest organizations. these organizations stipulate that the artwork presented must result from human creativity without any help from AI-generated applications. this dilemma accentuates the urgency of drawing a clear distinction between AI-generated and human-made artwork, a task of paramount importance. After observing multiple works of art from AI and humans, the authors notice that the Gradient-Based feature can be utilized to differentiate images generated from different sources of generators. therefore, the authors decided to create the dataset by collecting paintings from a state-of-the-art AI-generating art model, using algorithms for extracting features with multiple models for detecting AI-generated images and evaluating their performance. Solving this problem can help many art contest organizations as they require artists to create artwork rather than AI. © 2023 IEEE.

关键词： Computer vision

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ATTENTIV: Instrumented Peripheral Catheter for the Detection of Catheter Dislodgement in IV Infiltration 44

ATTENTIV: Instrumented Peripheral Catheter for the Detection...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Bo, Jessica Y. Ta, Kevin Nishida, Rio Yeh, Gordon Tsang, Vivian W. L. Bolton, Megan Ranger, Manon Walus, Konrad Univ British Columbia Mech Engn Dept Vancouver BC V6T 1Z4 Canada Univ British Columbia Integrated Engn Program Vancouver BC V6T 1Z4 Canada Univ British Columbia Elect & Comp Engn Dept Vancouver BC V6T 1Z4 Canada Univ British Columbia Fac Med Vancouver BC V6T 1Z4 Canada Univ British Columbia Sch Nursing Vancouver BC V6T 1Z4 Canada

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Intravenous (IV) infiltration is a common problem associated with IV infusion therapy in clinical practice. A multitude of factors can cause the leakage of IV fluids into the surrounding tissues, resulting in symptoms ranging from temporary swelling to permanent tissue damage. Severe infiltration outcomes can be avoided or minimized if the patient's care provider is alerted of the infiltration at its earliest onset. However, there is a lack of real-time, continuous infiltration monitoring solutions, especially those suited for clinical use for critically ill patients. Our design of the sensor-integrated ATTENTIV catheter allows direct detection of catheter dislodgement, a root cause of IV infiltration. We verify two detection methods: blood-tissue differentiation with a support vector machine and signal peak identification with a thresholding algorithm. We present promising preliminary testing results on biological and phantom models that utilize bioimpedance as the sensing modality.

关键词： Support vector machines Performance evaluation Animals Biological system modeling Phantoms predictive models Real-time systems

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Malicious Code Detection Based on Generative Adversarial Model 6th

Malicious Code Detection Based on Generative Adversarial Mo...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Jinzhihao Yang, Jia Zhou, Weiqi School of Computer Science and Engineering Hubei University of Technology Wuhan430000 China

ISBN: (纸本)9789819645657

With the increasing diversity and complexity of malicious code, the threats posed by malicious code are continuously growing, making malicious code detection increasingly challenging. Currently, researchers typically extract malware features and use deep-learning models for malware detection. Using image-based features for classification can improve the accuracy and efficiency of malware detection. However, traditional deep learning-based malware detection faces challenges such as data imbalance, insufficient data volume, and malware obfuscation. We propose to augment the malicious code dataset using Generative Adversarial Network (GAN) technology to improve the accuracy and efficiency of malicious code detection. First, we converted malicious software binary files into grayscale images. We used Generative Adversarial Network (GAN), Wasserstein Generative Adversarial Network (WGAN), and Wasserstein Generative Adversarial Networks-Gradient Penalty (WGAN-GP) models to augment the grayscale image dataset of malicious code. Subsequently, we obtained three augmented datasets: w1, w2, and w3. these datasets were then fed into the ResNet50 neural network model for training. Experimental results indicate that using the GAN model for data augmentation improved ResNet’s recognition accuracy by approximately 10%. When using the WGAN model for data augmentation, ResNet’s recognition accuracy increased by 2%. Similarly, using the WGAN-GP model for data augmentation also increased ResNet’s recognition accuracy by 2%. Among the three models, the GAN model demonstrated the strongest improvement effect. We demonstrate that the ResNet50 network with GAN models can significantly improve malware code detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A Power Prediction Based Model predictive Control Strategy for Dual Active Bridge Converters

A Power Prediction Based Model Predictive Control Strategy f...

引用

IEEE Asia Power and Energy engineering conference (APEEC)

作者： Yi Zhao Fujin Deng Zheng Yin Sayed Abulanwar School of Electrical Engineering Southeast University Nanjing China Faculty of Engineering Mansoura Unversity New Damietta Egypt

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

Dual active bridge bidirectional DC-DC converter has the advantages of high power density and low switching loss. Recently, a predictive control technique, named moving discretized control set model predictive control has been introduced, in order to achieve faster dynamics and stable operation. But this strategy is rarely applied to DC/DC power converters. this paper proposes a model prediction control strategy based on the transmitted power for the case that the load on the secondary side is a DC power source, which can realise the bidirectional flow of power and has better dynamic response performance and steady state performance. the proposed control strategy is simulated on Simulink software. Simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Power system measurements software packages Simulation Bridge circuits Switching loss Power transmission predictive models

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Improving Text Classification with Transformer 6

Improving Text Classification with Transformer

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6th international conference on Computer Science and engineering, UBMK 2021

作者： Soyalp, Gökhan Alar, Artun Ozkanli, Kaan Yildiz, Beytullah Department of Software Engineering Atilim University Ankara Turkey

ISBN: (纸本)9781665429085

Huge amounts of text data are produced every day. Processing text data that accumulates and grows exponentially every day requires the use of appropriate automation tools. Text classification, a Natural Language Processing task, has the potential to provide automatic text data processing. Many new models have been proposed to achieve much better results in text classification. the transformer model has been introduced recently to provide superior performance in terms of accuracy and processing speed in deep learning. In this article, we propose an improved Transformer model for text classification. the dataset containing information about the books was collected from an online resource and used to train the models. We witnessed superior performance in our proposed Transformer model compared to previous state-of-art models such as L S T M and CNN. © 2021 IEEE

关键词： Natural language processing systems

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Optimized design of Raman fiber amplifier based on improving extreme learning machine 6

Optimized design of Raman fiber amplifier based on improving...

引用

6th international conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Gong, Jiamin Wei, Xumeng Jin, Ku School of Modern Postal Xi'an University of Posts and Telecommunications China China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

An efficient method to design the broadband gain-flattened Raman fiber amplifier(RFA) with multiple pumps is proposed based on a Extreme learning machine optimized by the salp swarm optimization algorithm(SSA-ELM), and uses this method to optimize the parameter configuration of the multi-pump RFA. A multi-input multi-output SSA-ELM model is introduced to replace the complicated solving process of the nonlinear coupled Raman amplification equation. the differences between the above models and BP neural networks, traditional ELM model and GRNN model in the five evaluation indicators are analyzed. As a result, the model prediction performance proposed in this article is better. In order to improve the flatness of gain, the pump parameter is optimized by using the adaptive differential evolution algorithm to obtain the best parameter configuration. the simulation results show that the RFA designed by this method has achieved the expected effect, and the maximum error of the target gain and the predictive gain does not exceed 0.8dB. this method provides a new idea for the design of the Raman fiber amplifier in the future. © 2023 ACM.

关键词： Nonlinear equations

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A Novel Approach for Text Classification by Combining Pre-trained BERT Model with CNN Classifier 6

A Novel Approach for Text Classification by Combining Pre-tr...

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2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Wang, Chenxu Li, Yulin Wang, Ziying Yancheng Teachers University College of Information Engineering College of Software Jiangsu Yancheng China Henan University of Science and Technology College of Software Henan Luoyang China Chengdu University of Technology College of Computer Science and Cyber Security Oxford Brookes College Sichuan Chengdu China

ISBN: (纸本)9798350313444

Language model pre-training has emerged as a highly effective approach for acquiring universal language representations. Among the state-of-the-art models in this field, BERT (Bidirectional Encoder Representations from Transformers) has garnered significant attention due to its remarkable performance across various language understanding tasks. With its advanced architecture and innovative techniques, BERT has consistently delivered impressive results, solidifying its position as a leading language model pre-training model. CNN (Convolutional Neural Network) is also widely used in language model *** study presents a novel approach for text classification by combining a pre-trained BERT model with a CNN classifier. the model under consideration attains the highest level of performance currently available on the AG News and Amazon product reviews datasets. the BERT model is applied to produce contextualized embeddings for the input text data, while the CNN classifier executes the classification process. By utilizing the contextualized embeddings of BERT and the n-gram feature capturing of CNN, our model exhibits superior performance in both accuracy and F1 score when compared to the baseline models. the findings indicate the capability of our methodology in analyzing social media, determining sentiment, and classifying documents. © 2023 IEEE.

关键词： Bidirectional Encoder Representations Transformer Convolutional Neural Network Natural Language Processing text classification

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