检索结果-内蒙古大学图书馆

10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Andriyanov, Nikita Kim, Alexandr Fao, Xenin Data Analysis And Machine Learning Department Financial University Under The Government Of The Russian Federation Moscow Russia Data Science Department Huazhong University Of Science And Technology Huazhong China

ISBN: (纸本)9798350372939

The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy. It is found that the use of generative models to expand the training sample is on average 1-2% more effective than the use of standard augmentations. The combined method of expanding the training image base can improve the accuracy by 4-5%. In addition, the right approach in generative prompts can also improve the accuracy metrics of the models. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A Natural Language Processing Based Framework for Early Detection of Anorexia via Sequential Text Processing 25

A Natural Language Processing Based Framework for Early Dete...

引用

25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Sarangi, Prateek Kumar, Sumit Agarwal, Shraddha Basu, Tanmay Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

Task 2 of eRisk shared tasks in CLEF 2024 aims to develop text mining solutions for early prediction of anorexia using sequentially posted texts over social media. Anorexia is an eating disorder, a kind of mental illness, where people have distorted perceptions of their body weights and accordingly manipulate their food habits, which often results in deficient body weight. The aim here is to identify anorexia by processing the interactions over social media of an individual through text mining models. The organisers provided training data with ground truths for training the models and test data for evaluating their performance. The BioNLP research group at the Indian Institute of science Education and Research Bhopal (IISERB) participated in Task 2 and submitted five runs for five text mining frameworks. Five different classifiers and individual feature engineering techniques were used to develop frameworks. The bag-of-words model and transformer-based embedding were used as features for individual classifiers. The performance of multiple classifiers was evaluated using the training corpus. Then Random Forest, Adaptive Boosting, Logistic Regression, Support Vector Machine (SVM), and Longformer classifiers were chosen to run on the test set. Experimental results show that SVM and AdaBoost classifiers using the TF-IDF-based weighting schemes achieved the highest precision score among all submitted runs in Task 2 of eRisk 2024. However, the performance of our models in terms of the other metrics, like recall, f-score, ERDE, etc., is not reasonably good compared to the other runs. Hence, we plan to develop transformer-based embeddings from scratch using data collected from multiple social media platforms. © 2024 Copyright for this paper by its authors.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Enhanced Face Recognition Performance Through Convolutional Neural Networks 2

Enhanced Face Recognition Performance Through Convolutional ...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Prasanna, D.S.John Deva Ragupathi Kumar, Pratik SRM Institiute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350368109

In recent days, Convolutional Neural Networks (CNNs) has demonstrated significant efficacy in the realm of facial recognition owing to their adeptness in extracting discerning features. This study introduces a facial recognition frame work employing a face identification and pre processing using Multi-Task Convolutional Neural Networks (MTCNN).Subsequently, this work employs the devised CNN model and a softmax function used for classification purposes. Experimental validation is conducted on the Olivetti Research Laboratory facial database. Results from our implementation showcase superior performance compared to prevalent models, achieving an accuracy rate above 95%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Wheat Yield Prediction using Temporal Fusion Transformers 2

Wheat Yield Prediction using Temporal Fusion Transformers

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Junankar, Tejas Sondhi, Jasleen Kaur Nair, Akhil M Department of Data Science Pune Lavasa India

ISBN: (纸本)9798350320923

In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use *** this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation

NewsRecLib: A PyTorch-Lightning Library for Neural News Reco...

引用

2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Iana, Andreea Glavaš, Goran Paulheim, Heiko Data and Web Science Group University of Mannheim Germany Center For Artificial Intelligence and Data Science University of Würzburg Germany

NewsRecLib1 is an open-source library based on Pytorch-Lightning and Hydra developed for training and evaluating neural news recommendation models. The foremost goals of NewsRecLib are to promote reproducible research and rigorous experimental evaluation by (i) providing a unified and highly configurable framework for exhaustive experimental studies and (ii) enabling a thorough analysis of the performance contribution of different model architecture components and training regimes. NewsRecLib is highly modular, allows specifying experiments in a single configuration file, and includes extensive logging facilities. Moreover, NewsRecLib provides out-of-the-box implementations of several prominent neural models, training methods, standard evaluation benchmarks, and evaluation metrics for news recommendation. © 2023 Association for Computational Linguistics.

关键词： Lightning

来源：评论

学校读者我要写书评

暂无评论

Customer Churn Prediction Using Machine Learning Algorithms 2

Customer Churn Prediction Using Machine Learning Algorithms

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Balaji, Garray Gowtham, Neralla Aditya Tarun, Suthapalli Prajapati, Gaurav Manikandan, N. SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798331534950

This study explores the application of machine learning algorithms for predicting customer churn within the telecommunications sector, using a dataset containing key customer attributes like service usage indicators, and account details. Customer churn prediction seeks to determine the likelihood of customers discontinuing service, a crucial insight for retention strategies. Multiple predictive models were developed and compared, including logistic regression, decision trees, random forests, and gradient boosting classifiers, to assess their individual effectiveness and performance. In this analysis, extensive feature engineering was employed to enhance data representation, followed by targeted feature selection to refine model precision and reduce overfitting. Each model was evaluated using essential metrics: accuracy, precision, recall (sensitivity), and the area under the ROC curve, with comparative analysis highlighting the relative strengths of each approach. Results indicate that ensemble methods such as gradient boosting and random forests perform best in capturing complex churn patterns. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Speech emotion recognition system using MLP Classifier Algorithm

Speech emotion recognition system using MLP Classifier Algor...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Madan, Puneet Dixit, Devansh Shobana, J. Srm Institute Of Science And Technology Department Of Data Science And Business Systems Chennai India

ISBN: (纸本)9798350352689

Research on speech emotion recognition (SER) is ongoing and has numerous applications in fields like healthcare, education, and human-computer interaction. The RAVDESS dataset for SER, which includes a varied collection of emotional speech recordings from 24 actors, is examined in this research as a potential resource for SER. Using the RAVDESS dataset, we specifically investigate the effects of several feature extraction methods, machine learning algorithms, and data augmentation strategies on SER accuracy. Our findings demonstrate that our proposed method was able to perform better with higher accuracy than most models, showcasing the RAVDESS dataset's potential for SER research. We also explore the generalizability of our results and contrast them with those from other datasets. This research can offer practical insights for enhancing SER systems' accuracy and it can have a significant impact on the development of emotion-aware systems. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Automated Theorem Provers Help Improve Large Language Model Reasoning 25th

Automated Theorem Provers Help Improve Large Language Model ...

引用

25th Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR 2024

作者： McGinness, Lachlan Baumgartner, Peter School of Computer Science Australian National University and Data61 CSIRO Australia Data61 CSIRO and School of Computer Science Australian National University Australia

In this paper we demonstrate how logic programming systems and Automated first-order logic Theorem Provers (ATPs) can improve the accuracy of Large Language Models (LLMs) for logical reasoning tasks where the baseline performance is given by direct LLM solutions. We first evaluate LLM reasoning on steamroller problems using the PRON-TOQA benchmark. We show how accuracy can be improved with a neuro-symbolic architecture where the LLM acts solely as a front-end for translating a given problem into a formal logic language and an automated reasoning engine is called for solving it. However, this approach critically hinges on the correctness of the LLM translation. To assess this translation correctness, we secondly define a framework of syntactic and semantic error categories. We implemented the framework and used it to identify errors that LLMs make in the benchmark domain. Based on these findings, we thirdly extended our method with capabilities for automatically correcting syntactic and semantic errors. For semantic error correction we integrate first-order logic ATPs, which is our main and novel contribution. We demonstrate that this approach reduces semantic errors significantly and further increases the accurracy of LLM logical reasoning. © 2024, EasyChair. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

An artificial intelligence framework for explainable drift detection in energy forecasting

引用

Energy and AI 2024年第3期17卷 368-379页

作者： Chamod Samarajeewa Daswin De Silva Milos Manic Nishan Mills Harsha Moraliyage Damminda Alahakoon Andrew Jennings Centre for Data Analytics and Cognition La Trobe UniversityAustralia Department of Computer Science Virginia Commonwealth UniversityRichmondUSA

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

关键词： Building energy consumption forecasting Explainable drift detection Energy embedding Dimensional modeling

来源：评论

学校读者我要写书评

暂无评论

An Efficient Pairing-Free Certificateless Signcryption Scheme Under the Standard Model for VANET

引用

IEEE Internet of Things Journal 2025年第12期12卷 22142-22154页

作者： Liu, Xianxiang Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China City University of Macau Faculty of Data Science Macau999078 China Hubei University of Technology School of Computer Wuhan430068 China

With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advancements in vehicle ad hoc networks (VANET). Therefore, efficient data transmission is a critical challenge in IoT, especially VANET. The certificateless signcryption (CLS) scheme has high authentication efficiency, which has become increasingly important in IoT. However, most existing schemes rely on bilinear pairing, resulting in high calculation costs or failure to achieve their claimed security. Furthermore, the security of some CLS schemes is proved in the random oracle, which limits the usefulness of the CLS scheme. To further address these issues, we propose an efficient and secure CLS scheme in the standard model for VANET, which enhances its practical applicability in VANET. Our construction employs formal security proofs and cost simulations to ensure the scheme's security and low calculation costs, making it suitable for VANET. Finally, the security and efficiency analyses prove that our scheme offers enhanced security features and superior performance compared to existing schemes. © 2014 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：