检索结果-内蒙古大学图书馆

2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saranya, R. Pushpakumar, R. Srinivasan, P. Aparna, N. Karthika, K. Chauhan, Amit Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of ECE Sona College of Technology Tamilnadu Salem India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Tamil Nadu Veerappanur India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Karnataka Bengaluru India

ISBN: (纸本)9798350340235

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. © 2023 IEEE.

关键词： Brain Tumor Segmentation Central Nervous System (CNS) FuzzyClustering Method (FCM)

来源：评论

学校读者我要写书评

暂无评论

Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

引用

Revue d'Intelligence Artificielle 2019年第6期33卷 461-466页

作者： Reddy Chirra, Venkata Rami Uyyala, Srinivasulu Reddy Kishore Kolli, Venkata Krishna Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Department of Computer Science and Engineering VFSTR Guntur522213 India

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Overview of Shared-task on Abusive Comment Detection in Tamil and Telugu 3

Overview of Shared-task on Abusive Comment Detection in Tami...

引用

3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Priyadharshini, Ruba Chakravarthi, Bharathi Raja Subramanian, Malliga Navaneethakrishnan, Subalalitha Chinnaudayar Shanmugavadivel, Kogilavani Premjith, B. Murugappan, Abirami Kumaresan, Prasanna Kumar Prashanth, Karnati Sai Reddy, Mangamuru Sai Rishith Chandu, Janakiram Gandhigram Rural Institute-Deemed to be University India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland Kongu Engineering College Tamil Nadu India SRM Institute of Science and Technology Kattankulathur Chennai India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham India Department of Information Science and Technology Anna University India

ISBN: (纸本)9789544520854

This paper discusses the submissions to the shared task on abusive comment detection in Tamil and Telugu codemixed social media text conducted as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. The task encourages researchers to develop models to detect the contents containing abusive information in Tamil and Telugu codemixed social media text. The task has three subtasks - abusive comment detection in Tamil, Tamil-English and Telugu-English. The dataset for all the tasks was developed by collecting comments from YouTube. The submitted models were evaluated using macro F1-score, and prepared the rank list accordingly. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Differentially private stochastic optimization: new results in convex and non-convex settings 21

Differentially private stochastic optimization: new results ...

引用

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Raef Bassily Cristóbal Guzmán Michael Menart Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Department of Applied Mathematics University of Twente Inst. for Mathematical and Comput. Eng. Pontificia Universidad Católica de Chile Department of Computer Science & Engineering The Ohio State University

ISBN: (纸本)9781713845393

We study differentially private stochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (GLLs). Our algorithm for the l2 setting achieves optimal excess population risk in near-linear time, while the best known differentially private algorithms for general convex losses run in super-linear time. Our algorithm for the ℓ1 setting has nearly-optimal excess population risk $\tilde{O}\left(\sqrt{\frac{\log{d}}{n\varepsilon}}\right)$ and circumvents the dimension dependent lower bound of [AFKT21] for general non-smooth convex losses. In the differentially private non-convex setting, we provide several new algorithms for approximating stationary points of the population risk. For the ℓ1-case with smooth losses and polyhedral constraint, we provide the first nearly dimension independent rate, $\tilde O\left(\frac{\log^{2/3}{d}}{{(n\varepsilon)^{1/3}}}\right)$ in linear time. For the constrained ℓ1-case with smooth losses, we obtain a linear-time algorithm with rate $\tilde O\left(\frac{1}{n^{1/3}}+\frac{d^{1/5}}{(n\varepsilon)^{2/5}}\right)$. Finally, for the ℓ2-case we provide the first method for non-smooth weakly convex stochastic optimization with rate $\tilde O\left(\frac{1}{n^{1/4}}+\frac{d^{1/6}}{(n\varepsilon)^{1/3}}\right)$ which matches the best existing non-private algorithm when d = O(√n). We also extend all our results above for the non-convex ℓ2 setting to the ℓp setting, where 1 < p ≤ 2, with only polylogarithmic (in the dimension) overhead in the rates.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of a Hybrid Brain-computer Interface and Virtual Reality System for Post-Stroke Rehabilitation

引用

IFAC-PapersOnLine 2020年第2期53卷 16010-16015页

作者： Mengjie Huang Yuting Zheng Jingjing Zhang Bin’ao Guo Chenyu Song Rui Yang Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool University Suzhou 215123 China Research Institute of Big Data Analytics Xi’an Jiaotong-Liverpool University Suzhou 215123 China

As one of common diseases among elderly, stroke often leads to motor impairment and even serious disability. Post-stroke rehabilitation is of great importance to restore the motor function and improve the life quality of stroke survivors. This study therefore sets out to propose a hybrid system based on brain-computer interface and virtual reality, which can provide various training programs including action observation, motor imagery and physical therapy for post-stroke patients with different motor control levels and training demands. The present work offers new insights into the way in which the conventional rehabilitation programs can be turned into innovative and interactive training experiences with advanced technologies to make optimal rehabilitation outcomes for stroke survivors.

关键词： Rehabilitation engineering Stroke Virtual reality Brain-computer interface Motor imagery

来源：评论

学校读者我要写书评

暂无评论

Understanding the dynamics between vaping and cannabis legalization using Twitter opinions

arXiv

引用

arXiv 2021年

作者： Adhikari, Shishir Uppal, Akshay Mermelstein, Robin Berger-Wolf, Tanya Zheleva, Elena Computer Science University of Illinois at Chicago United States Psychology Institute for Health Research and Policy University of Illinois at Chicago United States Computer Science and Engineering Electrical and Computer EngineeringEvolution Ecology and Organismal Biology Translational Data Analytics Institute Ohio State University United States

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes. © 2021, CC BY-SA.

关键词： Tobacco

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

引用

IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

引用

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

作者： Legapriyadharshini, N. Thirumalaikumari, T. Senbagam, K. Sarasu, R. Basha, H. Anwer Malathi, P. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350309140

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers. © 2024 IEEE.

关键词： Legacy systems

来源：评论

学校读者我要写书评

暂无评论

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and Novel Controller Framework to Meet Real-World Innovations

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Syambabu, V. Kalaiselvi, K. Mahil, S. Derisha Hemalatha, S. Alekhya, Badi Kanagavalli, N. Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Aiml Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors. © 2024 IEEE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

A Novel Development of Artificial Intelligence Enabled Learning Methodology for Human Depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ramkumar, G. Priyadharshini, R. Selvi, K. Kalaiselvi, K. Rathika, S. Kanagavalli, N. Saveetha University Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Human Depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human Depression Prediction Scheme (HDPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HDPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment. © 2024 IEEE.

关键词： Forecasting

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：