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检索条件"机构=Data and Data Science"

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Privacy federation learning framework based on principal component analysis

Privacy federation learning framework based on principal com...

引用

2024 International Conference on Computer Network Security and Software Engineering, CNSSE 2024

作者： Yang, Jiaheng Feng, Xia Liu, Yueming Faculty of Data Science City University of Macau China

ISBN: (纸本)9781510680234

Federal learning is an effective distributed learning technology that allows machine learning model training while protecting data privacy. However, with the increase of the number of user -side devices, the calculation burden of users in federal learning will increase. Researchers explore the use of dimension reduction technology to reduce the calculation burden required for model training, but this trigger a problem with low accuracy. This article extracts the dimensions of gradient data by improving the main component analysis method to extract the dimensions of gradient data, and reduce communication and calculation burden while protecting the privacy of the client. The experimental results of this article show that under large -scale data sets, the method of this article increases the speed of 50%training and reaches 96%accuracy. © 2024 SPIE.

关键词： data privacy

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Facial Expression Recognition with Transfer Learning: A Deep Dive 2

Facial Expression Recognition with Transfer Learning: A Deep...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Debroy, Moushreeta Thomas, K.T. Mathew, Lian Dept. of Data Science Lavasa Pune India

ISBN: (纸本)9798350384277

In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. © 2024 IEEE.

关键词： Contrastive Learning

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Assessing GPT's Legal Knowledge in Japanese Real Estate Transactions Exam

Assessing GPT's Legal Knowledge in Japanese Real Estate Tran...

引用

2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Inoshita, Keito Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9798331533137

In recent years, large language models (LLMs) like GPT-3.5 and GPT-4, developed by OpenAI, have demonstrated impressive capabilities in tasks such as natural language understanding, code generation, and even passing professional exams. Trained on vast datasets, these models have shown high accuracy across various fields. One challenging area for LLMs is professional certification exams, which require specialized knowledge and understanding of complex regulations. The Real Estate Transaction Specialist Examination (RETSE) in Japan is a key certification for professionals managing real estate transactions. Passing the RETSE qualifies individuals to handle legal responsibilities like ensuring the legality of property deals. The exam covers civil law, tax law, and real estate regulations, making it a comprehensive test of legal knowledge. With a pass rate of around 15%, it is considered one of Japan’s more difficult professional exams, making it a strong benchmark for evaluating LLM performance. This study evaluates the ability of GPT-3.5 and GPT-4 to answer RETSE questions, comparing their performance to official passing criteria and analyzing the effect of prompt engineering. While GPT-4 outperformed GPT-3.5, neither model met the passing standard. Prompt strategies such as "Consider customary laws" improved response accuracy, especially for complex legal questions. These findings suggest that while GPT models cannot fully replace human expertise in legal exams like RETSE, they offer potential as supplementary learning tools for verifying basic knowledge. To mitigate the risk of job displacement by AI, it is crucial for professionals to enhance their expertise in areas where LLMs struggle, such as recent legal changes and ethical judgment. This study highlights both the limitations and potential of GPT in legal contexts, providing insights into how AI can support learners and practitioners in real estate law. ©2024 IEEE.

关键词： Geological surveys

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Empowering Deaf with Indian Sign Language Interpreter using Deep Learning

Empowering Deaf with Indian Sign Language Interpreter using ...

引用

2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Nimbalkar, Shivanjali Vijay Vaidya, Soham Nilesh Gade, Mayuri Mahesh Hagare, Pranali Sandip Shendage, Pradip N. VPKBIET Artificial Intelligence and Data Science Baramati India

ISBN: (纸本)9798350362879

In the ever-changing global landscape, the diversity of sign languages, with over 140 distinct variants worldwide, poses a significant challenge in developing universally applicable recognition models. This complexity is compounded by the dynamic nature of sign languages, requiring constant adaptation to incorporate emerging signs tied to technological advancements. This research aims to improve communication accessibility for the deaf community in India through a Real-Time Indian Sign Language (ISL) recognition system. Specifically addressing the communication gap experienced by deaf children, our approach leverages advanced technologies such as deep learning, computer vision, and neural networks to convert ISL gestures into a textual format. The system relies on a carefully curated dataset, including emergency words, Devanagari script, and English alphabets in ISL. Using methodologies like the mediapipe library and cvzone hand tracking module, we construct the SignVaria and implement dynamic hand gesture recognition modules with advanced deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The goal is to overcome regional specificity challenges in sign language recognition, ensuring the integration of cutting-edge technologies for enhanced robustness and accessibility for the target demographic. Our research yielded the most impressive results using the Convolutional Neural Network (CNN), achieving an accuracy of 96.1%. © 2024 IEEE.

关键词： Long short-term memory

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An Encoder-Agnostic Weakly Supervised Method For Describing Textures

An Encoder-Agnostic Weakly Supervised Method For Describing ...

引用

2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Mao, Shangbo Rajan, Deepu College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331510831

Recent advances in Large Language Models (LLMs) have enabled the semantic description of textures in natural language, aiming to capture them in richer detail. However, most methods are confined to either depending on supervised training with pairs of images and manually annotated visual attributes that most texture datasets lack or using Vision-Language Models (VLMs) such as CLIP. In this paper, we develop an encoder-agnostic Weakly supervised Texture Description Generator (WTDG) that employs a novel Scaled Ranked Kullback-Leibler divergence (SR-KL) loss between image and text modalities. Within the SR-KL loss formulation, we leverage category information, which is always available as ground-truths for all benchmark texture recognition datasets. We further extend our proposed WTDG to assist in texture recognition by using its generated texture descriptions. Thus, we develop a multimodal framework, called Tex2, which is adept at simultaneous generation of texture description and recognition. Our approach exhibits promising performance in describing and recognizing textures on benchmark datasets. © 2025 IEEE.

关键词： Visual languages

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Design and Implementation of a Customizable EEG dataset Construction System 17

Design and Implementation of a Customizable EEG Dataset Cons...

引用

17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Han, Liyong Liu, Jianwen Cheng, Ye Huang, Zhihua College of Computer and Data Science Fuzhou University China

ISBN: (纸本)9798331507398

Electroencephalography (EEG) plays a pivotal role in diagnosing neurological conditions, such as epilepsy and cognitive impairments. The efficacy of EEG analysis heavily relies on the quality and diversity of the underlying dataset. A significant challenge in EEG data analysis is the pronounced class imbalance, where certain neurological patterns are underrepresented, potentially skewing predictive models and diagnostic accuracy. Moreover, the absence of negative examples in EEG datasets can lead to misclassifications and limit the generalizability of findings. To address these issues, we developed a sophisticated, modular EEG dataset construction system designed to enhance the integration of both positive and negative examples, thereby mitigating class imbalance. This system is highly flexible and scalable, allowing for the tailored creation of datasets that meet specific research and clinical demands. This paper details the design, implementation, and validation of the system, demonstrating its capability to construct balanced datasets that are essential for robust EEG analysis. The results affirm the system's efficacy in fostering more accurate and comprehensive diagnostic and research outcomes in the field of neurology. © 2024 IEEE.

关键词： Electroencephalography

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Latent Vector Autoregressive Modeling with Maximum Predicted Variance for Dynamic Process Monitoring

Latent Vector Autoregressive Modeling with Maximum Predicted...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Shumei Qin, S. Joe School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9781665410205

In this paper, we define reduced dimensional predictors in latent dynamic systems in contrast to the traditional full-dimensional predictor models. Then the estimation of the new latent vector autoregressive model is developed with an objective to maximize the predicted variance for a given number of latent variables. A new dynamic predictive monitoring index that accounts for variations in the prediction residual and the predictor is developed. The residuals are modeled with a subsequent principal component analysis and a comprehensive monitoring method is developed to detect abnormal situations in industrial and operational systems. The new algorithm is tested on a simple closed-loop control system and the revamped Tennessee Eastman simulated process to show its effectiveness compared to other state-of-the-art methods. © 2024 IEEE.

关键词： Prediction models

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Implementation of Movie Recommendation System Using Hybrid Filtering Methods and Sentiment Analysis of Movie Reviews 2

Implementation of Movie Recommendation System Using Hybrid F...

引用

2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Naskar, Ishika Joseph, Niju P. Department of Statistics and Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350383287

In present era of digitization of entertainment, immense volume of movies are produced, which results in the necessity of sophisticated recommendation systems. In the streaming platform these systems empower users to discover new and relevant movies, benefiting both viewers and the entertainment industry. This research paper offers a comprehensive method for incorporating movie review sentiment analysis into a hybrid recommendation system. The study focuses on 4890 movies using a broad dataset containing the detailed descriptions of the movies along with the reviews. To employ the demographic filtering, the popularity score of the movies were calculated, then to apply the collaborative filtering, the textual movie descriptions were vectorized using the countvectorizer method. To predict the sentiment of the movie reviews, the high accuracy model "ControX/Sen1"was used. This hybrid recommendation system ranked the movies based on the user's preferences by employing cosine similarity, the sorted list was further filtered with the positive sentiment reviews. By including sentiment analysis, this research advances sophisticated movie recommendation systems by providing a comprehensive method for addressing user preferences and emotional resonance in film selections. © 2024 IEEE.

关键词： Recommender systems

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Segmentation of Brain MRI Tumors by MedSAM with Prompts Generated by Object Detection 5

Segmentation of Brain MRI Tumors by MedSAM with Prompts Gene...

引用

5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Zhou, Kai Yu, Dan City University of Macau Faculty of Data Science China

ISBN: (纸本)9798350385557

This research paper explores the use of deep learning technology in the segmentation of multimodal brain tumor medical images, particularly emphasizing the application of the customized and fine-tuned MedSAM (Segment Anything in Medical Images) model. MedSAM, an adaptation of the original SAM (Segment Anything Model), is specifically tailored for the nuances of medical imaging. In the model's training and fine-tuning phase, it retains the robust image feature extraction capabilities of the pre-trained model's image and prompt encoders. At the same time, the mask decoder is specifically modified to boost the precision of tumor segmentation. The results of the study indicate that the refined MedSAM model surpasses both the standard SAM model and the initial version of MedSAM in Dice coefficient scores, confirming the method's efficacy in enhancing brain tumor image segmentation accuracy. This research demonstrates that a general model, when appropriately modified, can be effectively utilized for specialized medical image segmentation tasks, not only elevating the accuracy of brain tumor segmentation but also offering innovative approaches and insights for future clinical diagnostic applications. Furthermore, the study sheds light on the model's versatility and its potential applicability to diverse dataset types, providing a foundation for subsequent research avenues. © 2024 IEEE.

关键词： Image segmentation

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Melodic Precision: Unveiling the Impact of Recursive Feature Elimination on Music Genre Classification 11

Melodic Precision: Unveiling the Impact of Recursive Feature...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Neware, Govindram Poulose, Alwin School of Data Science Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9798350379136

Music genre classification has experienced significant advancements due to the integration of machine learning techniques. This study delves into the impact of the Recursive Feature Elimination (RFE) technique on the performance of music genre classification models. Utilizing the GTZAN dataset, which includes various audio features, we implement machine learning models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). By employing the RFE technique, we aim to enhance model accuracy and interpretability. Analyzing classification metrics such as accuracy, precision, recall, F1 score, and confusion matrices, we provide a nuanced understanding of how feature selection and elimination using RFE refine the accuracy and interpretability of music genre classification models. Our findings reveal the effectiveness of RFE in improving the discriminative power of music genre classifiers, highlighting the importance of individual feature contributions. Additionally, our study compares the impact of RFE with other feature selection and elimination techniques such as variance threshold, Chisquare test, and information gain, offering valuable insights into optimizing music genre classification models for practical applications. These results underscore the potential of RFE to advance the field of music genre classification by improving model performance and interpretability, making it a valuable tool for researchers and practitioners in the domain. © 2024 IEEE.

关键词： Feature Selection

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