检索结果-内蒙古大学图书馆

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

Fraud Detection in Financial Transactions: A Machine Learning Approach

Fraud Detection in Financial Transactions: A Machine Learnin...

引用

International Conference on science technology engineering and Management (ICONSTEM)

作者： Shashank Patel Mudita Pandey Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector.

关键词： Analytical models Accuracy Computational modeling Finance data models Fraud Security

来源：评论

学校读者我要写书评

暂无评论

Speech Emotion Recognition Using data Augmentation

Speech Emotion Recognition Using Data Augmentation

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Tanisha Kapoor Arnaja Ganguly D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Speech emotion recognition (SER) is a vital aspect of human-computer interaction systems, enabling applications such as virtual assistants, affective computing, and mental health monitoring. In this work, deep learning methods to audio data to suggest a fresh approach to SER are applied. While still in its infancy, CNN is capable of accurately identifying emotions in human speech, but it requires careful study in order to produce reliable *** studywas done using a heterogeneous dataset that included recordings of different emotional states from many sources, such as TESS, RAVDESS, SAVEE, CREMAD, and others. A deep neural network model was built with many fully connected layers and ReLU activation functions after preprocessing and feature extraction. The suggested method’ s effectiveness was assessed using common performance indicators like F1-score, recall, accuracy, and precision. Our tests produced encouraging outcomes, with an accuracy of 84% on the test dataset. This demonstrates how well the suggested technique works to discern emotions from speech audio information.

关键词： Emotion recognition Accuracy Virtual assistants Speech recognition Mental health Feature extraction Recording

来源：评论

学校读者我要写书评

暂无评论

Deepfake Detection system

Deepfake Detection System

引用

Networking and Communications (ICNWC), International Conference on

作者： Shaswat Shrivas Aditya Rai M. Lakshmi Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet_pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet_pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies.

关键词： Deepfakes Visualization Social networking (online) Law enforcement Forensics Entertainment industry Biodiversity

来源：评论

学校读者我要写书评

暂无评论

Skin Cancer Classification using CNN and Transfer Learning (TL)

Skin Cancer Classification using CNN and Transfer Learning (...

引用

International Conference on Inventive Computation Technologies (ICICT)

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Skin cancer is a predominant and possibly lethal condition that distresses people across the world. Primary detection and precise finding are crucial for leveraging efficacious treatment and enhanced patient consequences. This research study explores the application off Artificial Intelligence (AI) resolutions for the primary finding and analysis of skin cancer. The study leverages a dataset of dermatological images and employs various ML methods, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, to analyse the existence of skin cancer with a higher degree of accuracy. By utilizing a combination of image processing and feature extraction, the AI model demonstrates higher performance in classifying skin lesions into malignant or benign categories. The proposed model has achieved an accuracy rate of 98.52%, making the AI-based system a promising tool for dermatologists and healthcare professionals.

关键词： Support vector machines Technological innovation Dermatology Transfer learning Collaboration Medical services Skin

来源：评论

学校读者我要写书评

暂无评论

Utilizing Dynamic Spectrum Allocation to Optimize Capacity in High-Speed Fiber Optic Communications 3

Utilizing Dynamic Spectrum Allocation to Optimize Capacity i...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Narmadha, T. Sahoo, Laxman Yadav, Dhananjay Sharmila, P. Mahalle, Parikshit N. Parmar, Yuvraj Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Dynamic spectrum allocation can extensively improve the ability of high-speed fiber optic communications, permitting Fiber-to-the-home (FTTH) and different deployments to be optimized for max performance. This approach makes use of a decentralized approach to allocate spectrum resources on demand, taking into account scalability and efficient aid usage. With the aid of the use of a restrained set of hardware and software, dynamic spectrum allocation can quick and correctly update the communications community to meet changing demands. This generation takes gain of the increasingly more sophisticated optical additives available these days to alter the spectrum in real time rather than relying on static allocation algorithms. This capability reduces the want to frequently reassign spectrum allocations, in addition to the amount of time and sources it requires, permitting the communications network to be managed and controlled extra efficiently and efficiently. Moreover, dynamic spectrum allocation improves the overall performance of current fiber communications structure with the aid of better using current optical components, which may be applied retroactively. © 2023 IEEE.

关键词： Communications Decentralized Dynamic Retroactively Sophisticated

来源：评论

学校读者我要写书评

暂无评论

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEARNING

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEA...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, R. Harsha, B.K. Sharmiladevi, D. Sajitha, L.P. Rao, Vuda Sreenivasa Nirmala, G. Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of ECE CMR Institute of Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem India Department of Computer Science and Business System R. M. K Engineering College Tamilnadu Kavaraipettai India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (纸本)9798350305708

One of the most important jobs of a self-driving vehicle's visual system is to identify and categorize obstacles along the route. Cars need to be detected, categorized, localized, and linked across frames if they are to be tracked. With the low cost and high utility of visual information, vision-based technologies have gained popularity for this purpose. In this paper, we develop a deep learning-based motion tracking from high-speed video frames. The model is developed using deep convolutional neural network that makes it to be applicable for classification of objects in the video frames. The deep convolutional neural network classifies the obstacles present in the pathway and offers a better classification pattern than the conventional models. © 2023 IEEE.

关键词： Application Convolutional Neural Network Deep Learning High-Speed Video Motion Vehicle Video Frames

来源：评论

学校读者我要写书评

暂无评论

Incorporating Transfer Learning in Engine Health Assessment for Accurate Remaining Useful Life Prediction 3

Incorporating Transfer Learning in Engine Health Assessment ...

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karpagavalli, C. Nelson, A. Raja, R. Saranya, E. Soudagar, Manzoore Elahi M. Ramaian, Christu Paul Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India Pandian Saraswathi Yadav Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivagangai630561 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology and Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350396157

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective;it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft. © 2024 IEEE.

关键词： Aircraft engines

来源：评论

学校读者我要写书评

暂无评论

Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

引用

地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

来源：评论

学校读者我要写书评

暂无评论

Exploring the Use of Machine Learning Techniques for Enhancing Real-Time data Access 3

Exploring the Use of Machine Learning Techniques for Enhanci...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rampal, Sourav Shreenidhi, H.S. Shrivastava, Manish Singh, Akhilendra Pratap Reena, R. Kumar, Santosh D. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts. © 2023 IEEE.

关键词： Machine learning

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：