检索结果-内蒙古大学图书馆

Design and Behavioral Analysis of Students during Examinations using Distributed Machine Learning

Design and Behavioral Analysis of Students during Examinatio...

Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Sangram Patil Akey Sungheetha Bansode G. S P. T. Kalaivaani Vijay Anand Kandaswamy Sharath Kumar Jagannathan Department of Computer Science & Engineering D Y Patil Agricultural and Technical University Talsande Kolhapur Maharashtra India Dept. of CSE Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of English KL (Deemed to be) University Vaddeswaram Guntur AP India Department of ECE Vivekanandha College of Engineering for Women (Autonomous) Tiruchengode Tamilnadu India Electronics and Communication Engineering R.M.K. Engineering College Chennai Data Science Institute Frank J. Guarini School of Business Saint Peter's University New Jersery NJ United States

This research focuses on the design and behavioral analysis of students during examinations through the utilization of Distributed Machine Learning (DML). With the increasing integration of technology in education, the ability to monitor and understand student behavior during exams has become crucial for educators and institutions. DML offers a powerful approach to process and analyze data collected from various sources, such as online exams, wearable devices, and eye-tracking sensors. This study proposes a framework that employs DML techniques to gather, process, and interpret student behavioral data in real-time. The collected data encompasses factors like gaze patterns, typing speed, hesitation intervals, and physiological responses. By employing machine learning algorithms distributed across multiple nodes, the system aims to provide insights into student engagement, cognitive load, and potential instances of academic misconduct. The analysis of behavioral patterns during examinations can aid educators in adapting teaching methodologies, providing timely interventions, and enhancing the overall learning experience. The proposed approach highlights the potential of DML in the field of education and contributes to the broader discourse on leveraging technology for pedagogical improvement.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review

引用

IEEE Transactions on Instrumentation and Measurement 2022年 1-1页

作者： Suri, Jasjit S. Agarwal, Sushant Jena, Biswajit Saxena, Sanjay El-Baz, Ayman Agarwal, Vikas Kalra, Mannudeep K. Saba, Luca Viskovic, Klaudija Fatemi, Mostafa Naidu, Subbaram Stroke Diagnosis and Monitoring Division AtheroPoint™ Roseville CA USA Advanced Knowledge Engineering Centre Global Biomedical Technologies Inc. Roseville CA USA and Dept. of CSE PSIT Kanpur UP INDIA Dept. of CSE International Institute of Information Technology Bhubaneswar INDIA Dept. of Bioengineering at the University of Louisville KY Dept. of Immunology Sanjay Gandhi Postgraduate Institute of Medical Sciences Lucknow UP INDIA Department of Radiology Massachusetts General Hospital 55 Fruit Street Boston MA USA Department of Radiology University of Cagliari ITLAY University Hospital for Infectious Diseases Zagreb CROATIA Dept. of Physiology & Biomedical Engg. Mayo Clinic College of Medicine and Science MN USA Electrical Engineering Department University of Minnesota Duluth MN USA

Coronavirus 2019 (COVID-19) has led to a global pandemic infecting 224 million people and has caused 4.6 million deaths. Nearly 80 Artificial Intelligence (AI) articles have been published on COVID-19 diagnosis. The first systematic review on the Deep Learning (DL)-based paradigm for COVID-19 diagnosis was recently published by Suri et al. [IEEE J Biomed Health Inform. 2021]. The above study used AtheroPoint’s “AP(ai)Bias 1.0” using 10 AI attributes in the DL framework. The proposed study uses “AP(ai)Bias 2.0” as part of the three quantitative paradigms for Risk-of-Bias quantification by using the best 40 dedicated Hybrid DL (HDL) studies and utilizing 39 AI attributes. In the first method, the radial-bias map (RBM) was computed for each AI study, followed by the computation of bias value. In the second method, the regional-bias area (RBA) was computed by the area difference between the best and the worst AI performing attributes. In the third method, ranking-bias score (RBS) was computed, where AI-based cumulative scores were computed for all the 40 studies. These studies were ranked, and the cutoff was determined, categorizing the HDL studies into three bins: low, moderate, and high. Using the Venn diagram, these three quantitative methods were benchmarked against the two qualitative non-randomized-based AI trial methods (ROBINS-I and PROBAST). Using the analytically derived moderate-high and low-moderate cutoff of 2.9 and 3.6, respectively, we observed 40%, 27.5%, 17.5%, 10%, and 20% of studies were low-biased for RBM, RBA, RBS, ROBINS-I, and PROBAST, respectively. We present an eight-point recommendation for AP(ai)Bias 2.0 minimization. IEEE

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Online graph learning under smoothness priors

arXiv

引用

arXiv 2021年

作者： Saboksayr, Seyed Saman Mateos, Gonzalo Cetin, Mujdat Dept. of Electrical and Computer Engineering University of Rochester RochesterNY United States Goergen Institute for Data Science University of Rochester RochesterNY United States

The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity. However, adaptation to increasingly dynamic environments as well as demands for real-time processing of streaming data pose major challenges to this end. In this context, we develop novel algorithms for online network topology inference given streaming observations assumed to be smooth on the sought graph. Unlike existing batch algorithms, our goal is to track the (possibly) time-varying network topology while maintaining the memory and computational costs in check by processing graph signals sequentially-in-time. To recover the graph in an online fashion, we leverage proximal gradient (PG) methods to solve a judicious smoothness-regularized, time-varying optimization problem. Under mild technical conditions, we establish that the online graph learning algorithm converges to within a neighborhood of (i.e., it tracks) the optimal time-varying batch solution. Computer simulations using both synthetic and real financial market data illustrate the effectiveness of the proposed algorithm in adapting to streaming signals to track slowly-varying network connectivity. © 2021, CC BY-NC-ND.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Equivariant diffusion for crystal structure prediction 24

Equivariant diffusion for crystal structure prediction

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Peijia Lin Pin Chen Rui Jiao Qing Mo Jianhuan Cen Wenbing Huang Yang Liu Dan Huang Yutong Lu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and National Supercomputer Center in Guangzhou China Dept. of Comp. Sci. Tech. Institute for AI Tsinghua University Beijing China and Institute for AIR Tsinghua University Beijing China National Supercomputer Center in Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Code is available at https://***/EmperorJia/EquiCSP.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Evaluation of Deep Training and Adapting Algorithms in Medic...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Ali Hadi Abdulwahid Girija Rani Karetla KVB. Ganesh A Suresh Kumar K. Radha K. Shivakumar Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq School of Computer Data and Mathematical Sciences Western Sydney University Australia KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of CSE Graphic Era Deemed to be University Dept. of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least - square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal- to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advancements in Multiple Sclerosis Disease Classification Through Machine Learning

Advancements in Multiple Sclerosis Disease Classification Th...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Darshanaben D. Pandya Sheshang Degadwala Dhairya Vyas Vishal Narendrakumar Solanki Sharma Vishalkumar Sureshbhai Hiteshkumar Gunvantbhai Patel Dept. of Comp. Sci. SCJCCS (BCA) Sankalchand Patel University Visnagar Gujarat India Dept. of Comm. Engg. Sigma University Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India D L Patel Institute of Mgt. & Technology (MCA College) Himatnagar Gujarat India Department of Computer Science (MCA) Sankalchand Patel University Visnagar Gujrat Quality Engineering - Data Tiger Analytics Chennai Tamil Nadu

In recent years, the field of medical diagnosis has witnessed significant advancements due to the application of machine learning algorithms, especially in complex neurological diseases like Multiple Sclerosis (MS). This study delves into the progress made in MS disease classification using various machine learning techniques, including SVM, KNN, DT, RF, NB, and Extra Trees. Through a diverse dataset of clinical and neuroimaging data, this study has systematically compared the performance of these algorithms in identifying different MS subtypes. SVM and Random Forest exhibited the highest accuracy, while KNN and Decision Trees showed competitive performance. Naive Bayes and Extra Trees also demonstrated promising results in specific scenarios. The study discusses the strengths and weaknesses of each approach and explores the interpretability of the models to understand the key features influencing the classification process. These findings contribute to the growing knowledge base of machine learning in MS disease classification and open avenues for more efficient and accurate diagnostic tools, ultimately leading to personalized patient care and improved treatment planning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

De Novo Drug Property Prediction using Graph Convolutional Neural Networks

De Novo Drug Property Prediction using Graph Convolutional N...

引用

IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Fairuz Shadmani Shishir Khan Md. Hasib Shadman Sakib Shithi Maitra Faisal Muhammad Shah BJIT Group Dhaka Sylhet Bangladesh Dept. of Computer Science and Engineering DhakaSylhetBangladesh Ahsanullah University of Science and Technology Leading University Dhaka Sylhet Bangladesh Data and BI | Engineering SureCash Dhaka Sylhet Bangladesh

Drug property prediction poses a complex task in the healthcare domain, because the drug-like molecules greatly vary in chemical structures. Inhibitory concentration (IC 50 ) prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted IC 50 . In the era of Artificial Intelligence, Drug Discovery processes can benefit from deep learning, which has been widely used in computational chemistry and bioinformatics with state-of-the-art performance. In this paper, we propose a novel (in other words, de novo) graph convolutional network approach, cross-validated by traditional methods like Lipinski's rule of five and PaDEL-described one-hot encoding. The experiment has been carried out on the ChEMBL bioactivity dataset of Acetylcholinesterase protein, achieving an R2 score of 0.52.

关键词： Drugs Proteins Molecular biophysics Medical services Predictive models Real-time systems Encoding

来源：评论

学校读者我要写书评

暂无评论

Prompt-guided Scene Generation for 3D Zero-Shot Learning

arXiv

引用

arXiv 2022年

作者： Nasiri, Majid Cheraghian, Ali Chowdhury, Townim Faisal Ahmadi, Sahar Saberi, Morteza Rahman, Shafin Business school The University of New South Wales Australia School of Engineering Australian National University Australia Dept. of Electrical and Computer Engineering North South University Bangladesh School of Computer Science and DSI University of Technology Sydney Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia

Zero-shot learning on 3D point cloud data is a related underexplored problem compared to its 2D image counterpart. 3D data brings new challenges for ZSL due to the unavailability of robust pre-trained feature extraction models. To address this problem, we propose a prompt-guided 3D scene generation and supervision method that augments 3D data to learn the network better, exploring the complex interplay of seen and unseen objects. First, we merge point clouds of two 3D models in certain ways described by a prompt. The prompt acts like the annotation describing each 3D scene. Later, we perform contrastive learning to train our proposed architecture in an end-to-end manner. We argue that 3D scenes can relate objects more efficiently than single objects because popular language models (like BERT) can achieve high performance when objects appear in a context. Our proposed prompt-guided scene generation method encapsulates data augmentation and prompt-based annotation/captioning to improve 3D ZSL performance. We have achieved state-of-the-art ZSL and generalized ZSL performance on synthetic (ModelNet40, ModelNet10) and real-scanned (ScanOjbectNN) 3D object datasets. © 2022, CC BY.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

SkyNetPredictor: Network Performance Prediction in Avionic Communication using AI

arXiv

引用

arXiv 2025年

作者： Mukhtar, Hind Schaub, Raymond Erol-Kantarci, Melike Satcom Direct Inc. Melbourne Florida University of Ottawa OttawaON Canada Dept. of Data Science Satcom Direct Inc. MelbourneFL United States Dept. of Electrical & Computer Engineering University of Ottawa OttawaON Canada

Satellite-based communication systems are integral to delivering high-speed data services in aviation, particularly for business aviation operations requiring global connectivity. These systems, however, are challenged by a multitude of interdependent factors such as satellite handovers, congestion, flight maneuvers and seasonal trends, making network performance prediction a complex task. No established methodologies currently exist for network performance prediction in avionic communication systems. This paper addresses the gap by proposing machine learning (ML)-based approaches for pre-flight network performance predictions. The proposed models predict performance along a given flight path, taking as input positional and network-related information and outputting the predicted performance for each position. In business aviation, flight crews typically have multiple flight plans to choose from for each city pair, allowing them to select the most optimal option. This approach enables proactive decision-making, such as selecting optimal flight paths prior to departure. © 2025, CC BY-NC-ND.

关键词： Satellite communication systems

来源：评论

学校读者我要写书评

暂无评论

Factor space is the adaptive and deepening theory of fuzzy sets

Factor space is the adaptive and deepening theory of fuzzy s...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Haitao Liu Runjun Wan Shanshan Xue Tiantian Wang Sizong Guo Jing He Institute of Intelligence Engineering and Mathematics College of Science Liaoning Technical University Fuxin China Dept. of Data Science and knowledge Engineering University of Maastricht Netherlands School of software and electrical engineering Swinburne University of Technology Hawthorn Australia

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

In recent years, the factor space theory has been promoted gradually as math foundation for mechanistic artificial intelligence theory. The theory was put forward in 1982 by Prof. P Z Wang, he took the fundamental space Ω in probability and the universe U of fuzzy sets both as factor spaces, but put Ω in the sky 2 U =P(U). The established fuzzy shadow theory says that the membership function on the ground is the coverage of random set in the sky, and had proved the Existence and Uniqueness Theorem on the correspondence between earth and heaven. This theory points out that the adaptive platform of intelligent description and subjective measurement is the factor space; and the core transform in between different levels is the power mapping (falling shadow). This is the mathematical secret of artificial intelligence, but also the direction of further improvement of fuzzy sets and systems.

关键词： Probability distribution Fuzzy sets Random variables Shape Artificial intelligence Aerospace electronics

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：