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Medical Image Synthesis Using Autoencoder with Vision Transformer

Medical Image Synthesis Using Autoencoder with Vision Transf...

引用

international conference on Image and Vision Computing New Zealand, IVCNZ

作者： Zakia Zinat Choudhury Brendan McCane Sean Coffey Department of Computer Science University of Otago Dunedin New Zealand Department of Medicine University of Otago Dunedin New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Medical imaging plays a crucial role in clinical diagnostics, with modalities like Transthoracic Echocardiography (TTE) and Cardiac Magnetic Resonance (CMR) offering distinct advantages and limitations in cardiovascular assessment. While TTE provides real-time, non-invasive imaging, it is operator-dependent and may yield incomplete views. In contrast, CMR offers comprehensive evaluations but is time-consuming and costly. This paper proposes a novel architecture for synthesizing CMR images from TTE inputs using an integrated autoencoder and vision transformer. The autoencoder captures TTE patterns and transforms them into CMR-like representations, enhanced by the vision transformer's attention mechanisms. Evaluation through quantitative and qualitative metrics demonstrates the system's ability to generate realistic CMR images, potentially enhancing diagnostic accuracy and workflow efficiency in cardiac imaging.

关键词： measurement computer vision Computational modeling Training data Transforms Transformers Data models Real-time systems Medical diagnostic imaging Testing

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One-Pass Generation of Multivariate Time Series through Conditional Multivariate modeling

One-Pass Generation of Multivariate Time Series through Cond...

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international joint conference on Neural Networks (IJCNN)

作者： Abdellah Madane Florent Forest Hanane Azzag Mustapha Lebbah Jerôme Lacaille DAVID Lab UVSQ Paris-Saclay University Versailles France IMOS Lab EPFL Lausanne Switzerland LIPN UMR CNRS 7030 Sorbonne Paris Nord University Villetaneuse France DataLab Safran Aircraft Engines Moissy-Cramayel France

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In recent years, exploring deep generative models for generating time series has garnered significant interest within the research community. These models have found wide-ranging applications in areas such as data augmentation, scenario simulation, and the imputation of missing data. The authenticity of the generated time series has seen remarkable advancements with the integration of recurrent neural networks (RNNs) and generative adversarial networks (GANs). RNNs used to represent the state-of-the-art (SOA) in processing sequence dependencies until the advent of Transformers, which redefined the SOA, especially in Natural Language Processing and computer Vision. The introduction of a transformer-based GAN represented an innovative step forward, aiming to address the limitations inherent in RNNs. However, this model’s efficacy is constrained when faced with unimodal data distribution assumptions, leading to arbitrary outputs in complex distribution scenarios. This paper introduces a novel Multivariate Time Series Conditional GAN (MTS-CGAN), that leverages transformer-based architectures in generator and discriminator networks. MTS-CGAN conditions the generation process on a specific encoded context (categorical and MTS inputs), enabling one-pass generation of multivariate time series, and accommodating mixed distribution frameworks, outperforming existing models. We evaluate MTS-CGAN using quantitative metrics across multiple multivariate time series datasets. Furthermore, we propose also an innovative adaptation of the Frechet Inception Distance (FID), tailored for time series, to assess the quality of the generated data. This research demonstrates the potential of MTS-CGAN in generating high-fidelity multivariate time series.

关键词： measurement Training Adaptation models Recurrent neural networks Atmospheric modeling Time series analysis computer architecture

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Fusion of Multi-Scale CNN for Motor Imagery EEG Classification

Fusion of Multi-Scale CNN for Motor Imagery EEG Classificati...

引用

computer, Electronics & Electrical Engineering & their Applications (IC2E3), international conference on

作者： Kshetrimayum Shalu Devi Samit Ari Department of ECE NIT Rourkela Rourkela India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

Brain-computer interface (BCI) is a groundbreaking and innovative paradigm in human-computer interaction, with the potential to enhance the living standard with technological advancement. The electroencephalogram (EEG) signals associated with motor imagery (MI) have been widely employed in several BCI systems. However, the challenge of individual variability in signal patterns hinders the classifier's efficacy. This article presents a fusion multi-scale convolutional neural network (MCNN) to perform an end-to-end MI EEG classification BCI system. The proposed multi-scale CNN fusion employed spatial and depthwise filters to learn temporal and spatial MI EEG features. Multiple scales of CNN are used, since characteristics at multiple scales to develop a subject-dependent model are crucial to learn, as each individual exhibits a unique signal pattern. The evaluation for classification performance is executed using the BCI Competition IV 2a (BCIC IV 2a) dataset. Furthermore, t-SNE visualisation is shown to illustrate features extracted for a subject. Compared with some existing state-of-the-art methodologies, the proposed method demonstrates enhancements in performance metrics.

关键词： Training measurement Human computer interaction Motors Feature extraction Brain modeling Electroencephalography Convolutional neural networks Time-domain analysis Standards

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Analysing LSTM and Transformer Models for Chatbot Development: A Comparative Study

Analysing LSTM and Transformer Models for Chatbot Developmen...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), international conference on

作者： Shivank Solanki Sudeep Batra Mrinal Pandey Monika Goyal Mamta Arora Computer Science & Technology Manav Rachna University Haryana India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In today’s digital landscape, the development of chatbots plays a pivotal role in enhancing user engagement across various applications. This paper conducts a comparative study between Long Short-Term Memory (LSTM) and Transformer models for chatbot development, focusing on their efficacy in generating contextually coherent responses. LSTM, known for its sequential data processing capabilities, is compared with Transformers, which excel in capturing long-range dependencies and context understanding. Leveraging the Hugging Face Transformers library and TensorFlow/Keras for implementation, we evaluate metrics such as accuracy, perplexity, and computational efficiency on structured conversational datasets. Through empirical experiments and evaluations, this study provides insights into optimal model selection and training strategies to improve chatbot performance in real-world applications. The findings contribute to understanding the strengths and limitations of each architecture, informing best practices for developing robust conversational AI systems.

关键词： Training measurement Analytical models Computational modeling computer architecture Transformers Chatbots Libraries Information and communication technology Long short term memory

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Enhancing Software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection Software algorithms Software quality Predictive models Nearest neighbor methods Software measurement Classification tree analysis Software engineering

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Advancing Bangla NLP: Transformer-Based Question Generation using Fine-Tuned LLM

Advancing Bangla NLP: Transformer-Based Question Generation ...

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international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Washik Wali Faieaz Sayma Jannat Pronoy Kumar Mondal Sadman Sadik Khan Shuvo Karmaker Md Sadekur Rahman Dept. of CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This research explores the development of a question generation model for Bangla text using the Bangla T5 base model, a transformer-based architecture tailored for the language. The study addresses the underrepresentation of Bangla in natural language processing (NLP) by leveraging a carefully curated dataset of Bangla paragraphs and corresponding questions. The Bangla T5 model, pre-trained on a large Bangla corpus, is fine-tuned to generate contextually relevant and coherent questions from input paragraphs. The model’s performance is evaluated using standard metrics such as Exact Match (EM) and ROUGE, achieving a 28.65% Exact Match score. The ROUGE scores demonstrate strong alignment with the reference questions, with ROUGE-1 F1 at 73.64%, ROUGE-2 F1 at 57.86%, and ROUGE-L F1 at 70.77%. These results highlight the model’s ability to capture the linguistic nuances of Bangla and generate questions that are semantically and syntactically accurate. This work contributes to the advancement of Bangla NLP and underscores the potential for extending similar methodologies to other low-resource languages, promoting inclusivity and linguistic diversity in AI systems.

关键词： Training measurement Pipelines Linguistics Transformers Question generation Decoding Artificial intelligence Standards Context modeling

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Audio-Visual Person Verification Based on Recursive Fusion of joint Cross-Attention

Audio-Visual Person Verification Based on Recursive Fusion o...

引用

international conference on Automatic Face and Gesture Recognition

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Canada

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Person or identity verification has been recently gaining a lot of attention using audio-visual fusion as faces and voices share close associations with each other. Conventional approaches based on audio-visual fusion rely on score-level or early feature-level fusion techniques. Though existing approaches showed improvement over unimodal systems, the potential of audio-visual fusion for person verification is not fully exploited. In this paper, we have investigated the prospect of effectively capturing both intra- and inter-modal relationships across audio and visual modalities, which can play a crucial role in significantly improving the fusion performance over unimodal systems. In particular, we introduce a recursive fusion of a joint cross-attentional model, where a joint audio-visual feature representation is employed in the cross-attention framework in a recursive fashion to progressively refine the feature representations that can efficiently capture the intra- and inter-modal relationships. To further enhance the audio-visual feature representations, we have also explored BLSTMs to improve the temporal modeling of audio-visual feature representations. Extensive experiments are conducted on the Voxceleb1 dataset to evaluate the proposed model. Results indicate that the proposed model shows promising improvement in fusion performance by adeptly capturing the intra- and inter-modal relationships across audio and visual modalities. Code is available at https://***/praveena2j/RJCAforSpeakerVerification

关键词： Training Visualization Codes Gesture recognition Faces

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Traffic Sign Recognition using Shift In-variant 2-D ConvNet

Traffic Sign Recognition using Shift In-variant 2-D ConvNet

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international conference on Inventive Computation Technologies (ICICT)

作者： Ramsundar B Sudharsan S A. Pravin Dept of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This research introduces a novel method for traffic sign identification utilizing a variant 2-D convolutional neural network (CNN) architecture with a shift-invariant operation. Addressing challenges such as variations in size, illumination, occlusions, and perspective, the proposed approach efficiently learns and identifies traffic signs regardless of their location in the input image. The CNN architecture employs common convolutional layers followed by separate shift-invariant convolutional layers for each type of traffic sign, enabling the network to extract both shared and individual traits effectively. Tests conducted on benchmark datasets including the LISA Traffic Sign Dataset (LISA-TSD) and German Traffic Sign Recognition Benchmark (GTSRB) demonstrate superior recognition accuracy compared to state-of-the-art methodologies. The hierarchical design of the proposed model enhances recognisability, making it suitable for intelligent transportation systems. Overall, the study presents a promising variant 2-D CNN model for traffic sign identification with significant potential for real-world applications.

关键词： Training measurement Adaptation models Computational modeling Supervised learning Transportation computer architecture

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Ripple Sparse Self-Attention for Monaural Speech Enhancement 48

Ripple Sparse Self-Attention for Monaural Speech Enhancement

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Qiquan Zhu, Hongxu Song, Qi Qian, Xinyuan Ni, Zhaoheng Li, Haizhou University of New South Wales School of Electrical Engineering and Telecommunications Australia National University of Singapore Department of Electrical and Computer Engineering Singapore Alibaba Group China Meta United States The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility. © 2023 IEEE.

关键词： Speech enhancement

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Specific Emitter Identification of ADS-B Signal Based on Mamba

Specific Emitter Identification of ADS-B Signal Based on Mam...

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Electronics and Communication, Network and computer Technology (ECNCT), international conference on

作者： Liu Yang Xiaofang Wu Institution of Systems Engineering Academy of Military Science Beijing China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

Specific emitter identification (SEI) has significant demands for application and broad application prospects in radar, communication and many other fields. With the development of deep learning, a large number of researchers have introduced it into SEI and achieved good performance. Recently, researchers have proposed the Mamba model, which improves the computing speed and alleviates the drawback of long time-consuming in deep learning, and has attracted widespread attention. We propose the complex-valued convolutional Mamba (CVCM) model for SEI. Firstly, the signal is transformed into patches that can be input into Mamba through a complex-valued convolutional neural network (CVCNN). Then, the patches are fed into a multi-layer Mamba structure for feature extraction, and finally, the features are fed into a fully connected network for classification. We validated the proposed method using automatic dependent surveillance- broadcast (ADS-B) signal that is easily accessible to peer researchers. The average recognition accuracy of CVCM for 30 categories of targets is 96.58%.

关键词： Deep learning measurement Accuracy Target recognition Convolution Computational modeling Surveillance Feature extraction Radar applications Convolutional neural networks

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