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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Cloud-Based CNN Models for Automated Pulmonary Disease Diagnosis and Risk Assessment 1

Cloud-Based CNN Models for Automated Pulmonary Disease Diagn...

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1st international conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Sampoornam, M. Maria Gurulakshmanan, Gurumoorthi Sekar, Satheeshkumar Hasan, Md Ashfaqul Geetha, T. Srinivasan, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Mphasis Corporation TX United States Mphasis Corporation ChandlerAZ United States Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

This research introduces a novel method for automated pulmonary diseases detection and risk assessment using convolutional neural network (CNN) models hosted in the cloud. Pulmonary diseases are on the rise, and with them come the need for better diagnostic tools. Combining state-of-the-art machine learning methods with cloud computing infrastructure might lead to accessible and scalable healthcare solutions. The proposed CNN architecture is designed to efficiently extract features from chest radiographs, allowing for more precise illness classification and risk stratification. Using open-source data and cloud computing, the model is trained and tested on a wide variety of lung diseases and disorders, such as chronic obstructive pulmonary disease (COPD), pneumonia, and TB. Experiments show that the technique outperforms conventional diagnostics, with excellent sensitivity and accuracy across a variety of illness types. Optimizing diagnostic efficiency and enabling rapid treatments are made possible with cloud-based deployment, which also allows for real-time analysis and easy connection with current healthcare systems. This method might help with healthcare spending, patient outcomes, and the ability to provide healthcare remotely in areas with limited resources. It highlights might revolutionize automated lung disease detection and risk assessment, which in turn could improve customized medication and the management of population health. © 2024 IEEE.

关键词： Lung cancer

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Real-Time Gesture Based Sign Language Recognition System

Real-Time Gesture Based Sign Language Recognition System

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Debnath, Jeet Praveen Joe, I.R. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

Sign language is a vital mode of communication for the deaf and dumb community. This research presents a robust and real-time Gesture-Based Sign Language Detection System that leverages computer vision and deep learning techniques. The system is designed to recognize and interpret American Sign Language (ASL) gestures, enabling efficient communication between individuals who are proficient in ASL and those who are not. The core of the system utilizes Python, OpenCV (Open-Source Computer Vision Library), and MediaPipe Holistic for real-time hand and body pose estimation. By accurately tracking the movements of hands and key body parts, the system captures the nuances of sign language gestures. The captured data is then fed into a Long Short-Term Memory (LSTM) neural network, which excels in sequence modeling tasks. The LSTM model is trained on a comprehensive dataset of ASL gestures, encompassing a wide range of signs and expressions. Transfer learning techniques are also employed to fine-tune the model for improved performance in sign language recognition. The model's architecture allows it to learn the temporal dependencies and context inherent in sign language, making it capable of recognizing gestures within sentences or phrases. The system's evaluation demonstrates its effectiveness in real-world scenarios, achieving high accuracy and low latency in sign language recognition. It opens new avenues for accessible and inclusive communication, aiding both deaf and hearing individuals in bridging the communication gap. Future work may explore the integration of natural language processing (NLP) to facilitate two-way communication between sign and spoken language. In conclusion, this Gesture-Based Sign Language Detection System represents a significant step towards harnessing the power of computer vision and deep learning to make sign language more accessible and inclusive in various domains, including education, accessibility, and social interaction. © 2024 IEEE.

关键词： Long short-term memory

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Distinctive Zucchini Classification in India: Leveraging CNN and RNN Models 15

Distinctive Zucchini Classification in India: Leveraging CNN...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vats, Satvik Sharma, Rishabh Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350370249

The proposed study works at three types of zucchini-Arka Sumeet, Arka Anamika, and Ahkra Manik-in India. It uses a mix of Convolutional Neural Networks (CNN) and recurrent Neural Networks (RNN) models to classify them. A big set of 11,000 high-quality pictures showing different stages of zucchini growth was gathered and cleaned up to make all the data match. The suggested new design, a mix of CNN and RNN, was made to find complex plant pictures. It can also understand how the zucchini grows over time better by looking at its patterns. The model is very good, with a success rate of 96.9%. It does an excellent job telling zucchini types apart. Measurement tools like precision, recall, and F1-score along with specificity show if the model is good at correctly classifying things. The Confusion Matrix helps us understand where mistakes happen in classification, so can improve models better. This study shows how the mix of CNN-RNN is important for the proper grouping of zucchini types. It gives a strong base to help precision farming uses. The usefulness of the model in farming choices, getting rid of diseases, and planning specific crop strategies shows its real importance. More progress is needed in making neural networks better, adding more data, and fixing models. This will make them flexible for all kinds of farming situations and improve their performance. This study shows a good, solid way to classify types of zucchini in India. It could have important effects on broader farming tech uses. © 2024 IEEE.

关键词： Agricultural technology Crop classification Cultivar classification hybrid model machine learning Neural network Precision agriculture Zucchini variety classification

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Speaker Identification Using CNN-LSTM Model on RAVDESS dataset: A Deep learning Approach 4

Speaker Identification Using CNN-LSTM Model on RAVDESS Datas...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Department of Mathematics Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350349900

Speaker identification is used for identifying an individual based on their voice. Signal processing and deep neural networks are used for feature extraction. This paper presents a method that combines CNN and LSTM for speaker identification. Multiple models such as GMM, CNN, SVM were compared and CNN+LSTM outperformed with an accuracy of 96.52% and F1 measure of 97%. The CNN+LSTM model combines spatial and temporal information extracted by the CNN and LSTM layers which allows to capture of both local and long-term dependencies in the audio data hence making this model very efficient. The above models were evaluated on the RAVDESS (Ryerson AudioVisual database of Emotional Speech and Song) dataset. The results highlight their potential for practical applications within speaker identification systems. © 2024 IEEE.

关键词： Deep neural networks

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Intelligent computing Technology and Automation - Proceedings of the 16th international conference, ICICTA 2023

Intelligent Computing Technology and Automation - Proceeding...

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16th th international conference on Intelligent computing Technology and Automation, ICICTA 2023

ISBN: (纸本)9781643684864

The proceedings contain 141 papers. The topics discussed include: power comprehensive energy optimization scheme based on improved PSO algorithm;new energy distribution network scheduling based on optimized PSO;time domain dielectric spectrum analysis of high voltage bushing insulation state based on wavelet transform;modeling and simulation of intelligent vehicle flow guidance based on agent;a video resource recommendation system based on deep learning;human motion data acquisition system of intelligent motion bracelet based on human-computer interaction;fast estimation model of green building project cost based on support vector regression machine;intelligent recommendation method for talent resources based on big data;improved human resource allocation method based on genetic algorithm;and intelligent temperature monitoring for high voltage cable with non-invasive low power consumption.

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Globally Informed Graph Contrastive learning for Recommendation 20th

Globally Informed Graph Contrastive Learning for Recommendat...

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20th international conference on Intelligent computing (ICIC)

作者： Zheng, Yixing Li, Chengxi Dong, Junyu Yu, Yanwei Ocean Univ China Fac Informat Sci & Engn Qingdao 266400 Shandong Peoples R China

ISBN: (纸本)9789819756179;9789819756186

In recent years, the fusion of graph convolutional networks (GCNs) with contrastive learning (CL) has emerged as a promising approach in recommender systems, owing to its adeptness in extracting self-supervised signals from the original data, thus addressing the challenge of data sparsity. While this methodology has been proven to be effective, it predominantly focuses on capturing discrepancies in user preferences through the construction of contrastive views, often neglecting the underlying commonalities in user preferences. To address this limitation, we introduce a novel framework, Globally Informed Graph Contrastive learning (GIGCL), which aims to incorporate global features into the construction of contrastive views, facilitating the capture of shared features among nodes and refining the nodes' embedding outcomes. Central to our framework are four key modules, with the global features extractor module and the global informed fusion module serving as pivotal components. Following the initial embedding phase, local embeddings are fed into the global features extractor module to derive global features. Subsequently, in the global informed fusion module, the global features are seamlessly integrated with the local embeddings, generating a pair of contrastive views. Experimental results on two real-world datasets demonstrate the superiority of our proposed GIGCL over state-of-the-art baselines, and underscore the efficacy of GIGCL in reconciling divergent and convergent user preferences, thereby enhancing recommendation performance.

关键词： Graph Convolutional Networks Contrastive learning Collaborative Filtering Recommender System

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Blockchain-Based Secure data Sharing Algorithms for Cognitive Decision Management 4

Blockchain-Based Secure Data Sharing Algorithms for Cognitiv...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Alijoyo, Franciskus Antonius Prabha, B. Aarif, Mohd Fatma, Gulnaz Rao, Vuda Sreenivasa Valavan M, Ponni School of Business and Information Technology STMIK LIKMI Bandung Indonesia SRM Institute of Science and Technology Department of CSE Tamilnadu Chennai India Aligarh Muslim University Department of Commerce Uttar Pradesh Aligarh India University College Jazan University Al Ardah Jazan Saudi Arabia Koneru Lakshmaiah Education Foundation Department of CSE Andhra Pradesh Vaddeswaram India K.Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798350395914

Cognitive Decision Management in healthcare data management refers to the integration of artificial intelligence, machine learning, and cognitive computing to enhance decision-making processes. Secure data sharing is crucial in Cognitive Decision Management for healthcare as it enables seamless collaboration among stakeholders while ensuring the confidentiality, integrity, and availability of sensitive health data. This practice is essential for delivering timely and accurate insights, facilitating collaborative care, and maintaining patient privacy. Traditional approaches for secure data sharing in Cognitive Decision Management include role-based access control, encryption, and Virtual Private Networks (VPNs). Traditional approaches often face limitations such as complex key management, potential vulnerabilities in encryption algorithms, and scalability challenges in managing access controls. To overcome these limitations, this study introduces a novel approach leveraging blockchain technology. The proposed system incorporates Ethereum blockchain 2.0 for scalable and efficient healthcare data management, along with Elliptic Curve Cryptography for secure transaction verification. Blockchain ensures transparency, immutability, and decentralized control, addressing the drawbacks of traditional methods and providing a robust foundation for secure data sharing in Cognitive Decision Management within healthcare settings. Results of this study was implemented in Python tool. The throughput ranged from 450 to 520 transactions per second, showcasing system stability. © 2024 IEEE.

关键词： data integrity

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Textual Out-of-Distribution data Detection Based on Granular Dictionary

Textual Out-of-Distribution Data Detection Based on Granular...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Ouyang, Tinghui Amagasa, Toshiyuki Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350362480

As an factor influencing data quality, out-of-distribution (OOD) data detection plays a critical role in AI quality assurance. This paper presents an advanced OOD detection method based on Granular computing (GrC) and dictionary learning, specifically designed for detecting textual OOD in natural language processing (NLP) systems. First, informative data structure descriptors (information granules) are generated through GrC, which are aimed to reduce the computation overhead in big data analysis. Next, granular dictionary is constructed from these granules and used to represent original data through dictionary learning and data reconstruction. Finally, OOD detection is formalized by analyzing differences between original and reconstructed data. Finally, the proposed method formulate OOD detection via the difference between original and reconstructed data. Experiments conducted on a sentiment analysis system based on a large language model (LLM) and three OOD datasets are implemented. The constructed granular dictionary is firstly demonstrated to have good representation ability supporting effective OOD detection. Furthermore, the proposed method's effectiveness, efficiency and scalability in textual OOD detection are validated through comprehensive comparative analysis. © 2024 IEEE.

关键词： Information granules

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Evolving Knowledge Distillation with Large Language Models and Active learning 30

Evolving Knowledge Distillation with Large Language Models a...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Chengyuan Kang, Yangyang Zhao, Fubang Kuang, Kun Jiang, Zhuoren Sun, Changlong Wu, Fei College of Computer Science and Technology Zhejiang University China Institute for Intelligent Computing Alibaba Group China School of Public Affairs Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China

ISBN: (纸本)9782493814104

Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works have mainly focused on the direct use of LLMs for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model). Different from previous work, we actively analyze the student model's weaknesses, and then synthesize labeled samples based on the analysis. In addition, we provide iterative feedback to the LLMs regarding the student model's performance to continuously construct diversified and challenging samples. Experiments and analysis on different NLP tasks, namely, text classification and named entity recognition show the effectiveness of EvoKD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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3AM: An Ambiguity-Aware Multi-Modal machine Translation dataset 30

3AM: An Ambiguity-Aware Multi-Modal Machine Translation Data...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ma, Xinyu Liu, Xuebo Wong, Derek F. Rao, Jun Li, Bei Ding, Liang Chao, Lidia S. Tao, Dacheng Zhang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China Northeastern University Shenyang China The University of Sydney Sydney Australia

ISBN: (纸本)9782493814104

Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. This issue presents a significant obstacle to the development of MMT research. This paper presents a novel solution to this issue by introducing 3AM, an ambiguity-aware MMT dataset comprising 26,000 parallel sentence pairs in English and Chinese, each with corresponding images. Our dataset is specifically designed to include more ambiguity and a greater variety of both captions and images than other MMT datasets. We utilize a word sense disambiguation model to select ambiguous data from vision-and-language datasets, resulting in a more challenging dataset. We further benchmark several state-of-the-art MMT models on our proposed dataset. Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets. Our work provides a valuable resource for researchers in the field of multimodal learning and encourages further exploration in this area. The data, code and scripts are freely available at https://***/MaxyLee/3AM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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