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Dynamically Contrastive Clustering For Sentence Embedding 6

Dynamically Contrastive Clustering For Sentence Embedding

引用

6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Shi, Haidong Zhou, Xiaotang Li, Lingbo School of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798331541750

U nsupervised contrastive learning to obtain sentence embedding has become a widely adopted and effective method. However, existing techniques mainly focus on the construction of positive-negative sample pairs, where instance contrastive learning is performed on samples by determining the positive and negative examples, while these methods ignore the effect of temperature coefficients on the model and tend to use fixed temperature coefficients. To address these issues, we propose a new approach named DCCSE(Dynamically Contrastive Cluster for Sentence Embedding), our model smooths the sample embedding while enhancing the sample linkage. Specifically, we use sample embeddings to dynamically update the cluster embeddings, combine contrastive learning and cluster centroid while employing cluster embed dings to smooth the sample embeddings and design a dynamic temperature coefficient function to improve the linkage between sample pairs. We evaluate our approach on a standard STS dataset, and the experimental results show that our model has relatively significant advantages over other baseline models. © 2024 IEEE.

关键词： Contrastive Learning

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Performance Analysis of Machine Learning Algorithms for Covid-19 Detection using Cough Analysis

Performance Analysis of Machine Learning Algorithms for Covi...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Subitha, D. Kavitha, J.C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309140

With an alarming rate of Covid-19 cases worldwide, the challenge for early detection of this disease is growing. There are many methods available for the detection of Covid-19 but they can be tiresome and difficult. The more common method of testing is reverse transcriptase-polymerase chain reaction (RTPCR) testing which requires direct contact with the patient during the testing that further causes more chance for disease spread. But, the method of testing that uses coughing sounds was found to be more safe method of testing as this does not require the presence of patient in the lab rather the pre-recorded audio wave is enough to perform the testing. Machine Learning algorithms are a great promise for detection of Covid-19 disease. There are many different approaches for detection involving wide range of inputs. Likewise, this paper shows about the possibility of detecting Covid-19 using cough sound of the patient, by extracting required input features from the audio file using short time Fourier transform method. Simulation results shows the performance of different classification algorithms in terms of Accuracy, Precision, Recall and F1-Score for the detection of Covid-19. This paper has developed a web application which can detect the presence of corona virus. The audio recording of cough is loaded as the input in to the app which is then sent to deep learning model at the back end that returns the Covid infection result at the front end of the application. This also shows the accuracy of various algorithms in the web application. © 2024 IEEE.

关键词： COVID-19

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A Novel Hybrid Approach for Brain Tumour MRI Segmentation Leveraging Transfer Learning 5

A Novel Hybrid Approach for Brain Tumour MRI Segmentation Le...

引用

5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Sudheer Kumar, E. Anumandla, Sai Keerthana Krishnakumar, Anagha Bhavana, Gondi Vidya Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376685

Integrating multiple deep learning architectures often significantly improves the performance of a model. This approach is particularly beneficial for challenging tasks like brain tumour detection. This paper provides a detailed examination of a novel DenseNet-ResNet fusion model that leverages DenseNet's strength in feature extraction and ResNet's robust learning process. This helps in overcoming the limitations of each separate model and, hence, leads to increased accuracy and improved training efficiency. Additionally, various augmentation techniques were applied to the data to improve the performance;these included brightness adjustments, rotation, shifting, shear transformation, zooming, and horizontal flipping. We also employed histogram equalization, to improve contrast, making the image clearer for visualizing the tumours. Furthermore, image registration algorithms were embedded for the alignment of flipped images, and Grad-CAM visualization was utilized for a more comprehensive evaluation of the results. Our results have shown that the DenseNet-ResNet fusion model not only gives higher classification accuracy but is also better at generalization. © 2024 IEEE.

关键词： Image registration

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Leveraging Data Analytics in Azure for Effective Churn Management 2

Leveraging Data Analytics in Azure for Effective Churn Manag...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Gokulnath, Shreejith Suthraye Revathi, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research aims to develop optimized and cost-effective Azure architecture solutions by strategically selecting and utilizing Microsoft Azure services for specific use cases. The study leverages a simulated data-driven churn management case study within a social network context, emulating real-world data scenarios. The methodology employed involves a comprehensive understanding of the existing churn management script, enabling adaptability to diverse scenarios. This necessitates an in-depth exploration of Microsoft Azure's service offerings to ascertain their necessity and relevance. As the case study entails real-time streaming of event data, it is channeled into a staging area while concurrently undergoing batch processing for both account and subscription data. This synthesis culminates in a unified storage space tailored for analytics purposes. The implications of this research are twofold. First, the study yields a high-impact automated asset from a business standpoint. Second, it contributes expertise in the intricate process of migrating an entire asset from on-premises infrastructure to the cloud. Furthermore, using streamlined cost optimization achieved by judiciously selecting and configuring only essential resources, culminating in efficient outcomes. This paves the way for client-side stakeholders to make informed, data-driven decisions, leveraging the insights harnessed from the system. © 2024 IEEE.

关键词： Cost effectiveness

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Multi-Scale Prompt Memory-Augmented Model for Black-Box Scenarios

Multi-Scale Prompt Memory-Augmented Model for Black-Box Scen...

引用

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Kuang, Xiaojun Philip Chen, C.L. Li, Shuzhen Zhang, Tong School of Computer Science & Engineering South China University of Technology China

ISBN: (纸本)9798891761148

Black-box few-shot text classification handles text classification in limited data without accessing the parameters and gradients of language models (LMs). Existing black-box optimization methods have demonstrated strong few-shot learning capabilities. However, they still require numerous LMs’ calls to search optimal prompts, thus resulting in overfitting performance and increasing computational cost. To address this issue, we present MuSKPrompt (Multi-scale Knowledge Prompt for Memory Model), an efficient multi-scale knowledge prompt-based memory model in black-box few-shot text classification task. MuSKPrompt extracts instance-level and class-level knowledge at different scales and stores them in memory banks during training. Then, it references multi-scale memory banks to perform quick inference on new samples via a novel scoring module. MuSKPrompt achieves competitive performance in limited data through multi-scale instance-level and class-level knowledge. Moreover, it realizes gradient-free optimization with zero training parameters in the black-box scenario. Experiments on different benchmarks and parameter analysis demonstrate the effectiveness and efficiency of MuSKPrompt in black-box few-shot text classification tasks. ©2024 Association for Computational Linguistics.

关键词： Classification (of information)

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RVTS: retrieval-based text-to-speech 3

RVTS: retrieval-based text-to-speech

引用

3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Yang, Jin Su, Xuesong Xu, Wenke School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9781510680531

The single-stage text-to-speech model has made significant advancements in recent years. A comparison between two prominent models, VITS and VITS2, reveals that training with the same individual's voice dataset led to the voiceprint model generated by VITS closely aligning with the original voiceprint;however, its segmentation and fluency were subpar. On the other hand, VITS2 showed the opposite characteristics. Through the implementation of Retrieval-based Speech (RVTS), involving the extraction of intonation and fragmented sentences from the VITS2 model followed by code utilization, we were able to achieve speech synthesis. Our experimental findings demonstrate that our approach surpasses both VITS and VITS2, leveraging the strengths of both models. © 2024 SPIE.

关键词： Machine learning

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Interpretable Clustering of Spatio-Temporal Data by using SHAP Method 10

Interpretable Clustering of Spatio-Temporal Data by using SH...

引用

10th International Conference on Web Research, ICWR 2024

作者： Younesi, Sara Rahmani, Hossein Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

Nowadays, the simultaneous consideration of spatial features and temporal features in the form of spatiotemporal data has been considered in various applications. Among the most important of these applications, we can mention the classification of drivers, the personality of users, the prediction and monitoring of the route and many other cases. Due to the lack of labels for this type of data, unsupervised methods are the only analytical methods on the table. Today, machine learning or deep learning algorithms are very popular and widely used due to the many successes they have had in solving various problems, but due to the black box nature of these methods, the internal mechanism of these models is unclear to users. On the other hand, in addition to the high accuracy of the model, the interpretability and root-finding of analyzes based on spatial-temporal data are very important. In this paper, we intend to cluster spatio-temporal data by focusing on the interpretability of results and selected interpretable features. © 2024 IEEE.

关键词： Learning algorithms

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Face Identification of Suspects Using Sequential -Deep Convolutional Neural Network 2

Face Identification of Suspects Using Sequential -Deep Convo...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Munusamy, Vaishnavi Senthilkumar, Sudha School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In terms of increasing terrorism, criminal behaviour, and anti-social events, there has been a need for safety systems to identify criminals. Face Recognition is one of the vibrant technologies that are very useful for person identification, especially in criminal investigation. Convolutional layers are used in our proposed system for feature extraction, and dense layers are used for classification. ReLU activation functions use four convolutional layers with 3 × 3 kernels and 128/64 filters to add non-linearity. Carefully placing dropout layers prevents overfitting, and Max Pooling layers down sample spatial dimensions. The model produces encouraging outcomes on a customized dataset comprising 4288 photos that capture different angles and concealed/hidden images of four subjects. © 2024 IEEE.

关键词： Face recognition

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Vision Beyond the Haze: Advancements in Cataract Detection and Diagnosis 2

Vision Beyond the Haze: Advancements in Cataract Detection a...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, E. Sudheer Reddy, N U. Praneeth Reddy, Depatla Hemika Kanish, Tulasi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368413

Eye diseases are among the most critical health issues impacting human life. One of the most frequently encountered eye problems is cataracts, due to which people suffer as they age. The cloudiness that forms on eye lenses is known as cataracts which can significantly damage vision. Diagnosing the disease in its acute stage rather than its onset should not blind the patient. This study presents an improved Cataract detection method using pre-trained convolutional neural networks (CNNs) on an Ocular Disease Intelligent Recognition (ODIR) dataset, Comparing the performance of ResNet50, ResNet101, and VGG19 architectures to find the most effective model for this project. The proposed approach uses deep learning to analyse fundus images, aiming to increase diagnostic accuracy, speed, and reliability. several preprocessing and data Augmentation techniques were utilized to prepare the data set, which provided robust training and improved model generalization. Samples were refined using pre-trained ImageNet weights and adjusted for binary classification of Cataract presence. Among the tested models, ResNet50 exhibited the best performance, achieving an overall accuracy of 99.08% It also showed good evaluation metrics, with a precision rate of 98.15% and 100% recall for cataract images, and 100% precision with 98.23% recall for normal images. The results highlight the potential of ResNet50 to provide accurate and effective cataract diagnosis, especially in areas where access to trained ophthalmologists is inaccessible. This study highlights the importance of using deep learning technologies to improve the early detection and treatment of cataracts, ultimately contributing to better patient outcomes and global eye health progress. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Knee Osteoarthritis Severity from X-Ray Images Using Ensemble Learning 5

Prediction of Knee Osteoarthritis Severity from X-Ray Images...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Navyeesh, Kothoju Hinduja, Kanakala Jani Anbarasi, L. Charitha, Nagavarapu Divya Varshitha, Pasala Sri Varshitha, Uppuluri Venkata Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

Millions of people worldwide are affected by knee osteoarthritis (OA), a prevalent condition involving the deterioration of joint cartilage and underlying bone. Consequently, it causes discomfort, restricted movement, and diminished mobility. It is crucial to accurately and promptly assess the severity grading of the disease for effective clinical management. In this study, the important issue of predicting knee OA severity using X-ray images is addressed through the utilization of deep learning models, specifically convolutional neural networks (CNNs). An ensemble model that combines the strengths of different architectures was proposed to show the enhanced accuracy in Knee OA. The proposed work used the best features extracted using EfficientNet, DenseNet and ensemble models to attain an accuracy rate of 94.76%. The exceptional performance of the ensemble model underscores its reliability in classifying knee OA severity and provides a comprehensive and precise solution for health assessment. The proposed systematic approach demonstrates how deep learning can enhance diagnostic capabilities for knee osteoarthritis and improve clinical decision-making. © 2024 IEEE.

关键词： Grading

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