检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Heshan, Adithya B.M. Wickramathilake, Shamilka K.W.D. Gunathilake, Dulmi W.M.P. Herath, Hiruni H.M.S. Samarasekara, Piyumika de Silva, Dilshan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The web application described above has the potential to make a significant contribution to the conservation of endangered wildlife and the responsible management of domestic animals. The application addresses a critical issue that has far-reaching implications for the health of ecosystems and the well-being of animals. By providing users with tools to track and manage animal populations, monitor their health and behavior, and implement conservation efforts, the application empowers individuals and organizations to take action and make a positive impact on the environment. In addition to its practical benefits, the web application also includes educational resources and supports community engagement to raise awareness about the importance of preserving biodiversity and protecting animal welfare. This holistic approach recognizes the complex and interconnected nature of conservation and management efforts, and highlights the need for collaboration and cooperation among stakeholders. The user-friendly interface of the application ensures that it is accessible to a wide range of users, including individuals with limited technical expertise. The ability to collaborate and share data also promotes transparency and accountability, which are essential for effective conservation and management efforts. Overall, this web application has the potential to be a valuable tool for those involved in animal conservation and management, and it offers hope for the protection of endangered wildlife and the responsible management of domestic animals. © 2023, CC BY.

关键词： Animals

来源：评论

学校读者我要写书评

暂无评论

Wearable Wellness: Depression Screening via Fitbit Data Collected During COVID-19 Pandemic

Wearable Wellness: Depression Screening via Fitbit Data Coll...

引用

IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Nikola Grozdani America Muñoz Alexander Pietrick Ricardo Flores Avantika Shrestha Xingtong Guo Shichao Liu Elke A. Rundensteiner Computer Science Worcester Polytechnic Institute Worcester MA USA Computer Science/Information Systems National Louis University Chicago IL USA Computer Science/Data Science Worcester Polytechnic Institute Worcester MA USA Data Science Worcester Polytechnic Institute Worcester MA USA Architectural Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

The COVID-19 pandemic has greatly increased depression among adolescents. The current depression diagnosis process requires significant patient effort and can be costly. Prior research through passively collected data has shown promising depression screening results but is limited by complex data collection and privacy concerns. In this research, we create multiple machine learning models to screen physiological data collected from Fitbit, a wearable biomarker, and depression screening surveys across 166 college students. The highest-scoring model on these physiological modalities achieved an F1-score of 0.92. Our research findings highlight the potential impact of digital technology development in current clinical practices.

关键词： COVID-19 Training Surveys Wearable Health Monitoring systems Pandemics Biological system modeling Machine learning

来源：评论

学校读者我要写书评

暂无评论

Difficulty-Aware Data Augmentor for Scene Text Recognition

Difficulty-Aware Data Augmentor for Scene Text Recognition

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guanghao Meng Tao Dai Bin Chen Naiqi Li Yong Jiang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Shenzhen China College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Technology Harbin Institute of Technology Shenzhen

Deep neural network (DNN) based scene text recognition (STR) methods usually require a large amount of annotated data for training, which is time-consuming and cost-expensive in practice. To address this issue, many data augmentation methods have been developed to train recognizers by improving the diversity of training samples. However, most existing methods neglect the difficulty inherent in samples, and easily suffer from the problem of over-diversity, i.e., the distribution of the augmented data significantly deviates from that of clean data. In this paper, we propose a novel difficulty-aware data augmentation framework for scene text recognition, which jointly considers the difficulty of samples and the strength of augmentations. Specifically, our framework first predicts the sample difficulty, followed by an adaptive data augmentation strategy. Furthermore, we build a more diverse set of augmentation methods for STR and integrate it into our augmentation framework. Extensive experiments on scene text recognition benchmarks show that our augmentation framework significantly improves the performance of recognizers.

关键词： Training Measurement Deep learning Text recognition Neural networks Speech recognition Signal processing

来源：评论

学校读者我要写书评

暂无评论

Sign Language to Text Conversion using RNN-LSTM

Sign Language to Text Conversion using RNN-LSTM

引用

Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Amutha Seviappan Kothai Ganesan Anna Anbumozhi A. Sameer Reddy B. Vamsi Krishna D.Sampath Reddy Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of CSE (AIML) KPR Institute of Engineering and Technology Coimbatore India Department of Data Science and Business systems SRM Institute of Science and Technology Chennai India

The speech-impaired community only uses sign language; the rest of society interacts verbally. Our research intends to fill this communication gap by proposing a state-of-the-art method for comprehending both static and moving signs in Indian Sign Language and translating them into text. Data is collected, pre-processed, and hand recognition is finished using media pipe holistic before being categorized into suitable speech output. Because LSTM networks can develop long-term dependencies, they were investigated and employed for the classification of gesture data. The constructed model exhibited a 100% accuracy rate while categorizing 26 motions, highlighting the usefulness of LSTM-based neural networks for sign language translation. For those who are deaf or dumb, translating sign language into text allows them to interact with each other or with people in the general public by using hand gestures. According to a survey we conducted on sign language comprehension, most people are unable to identify the hand gestures and mode of communication used by sign language users. After hearing about all the difficulties, they have communicating with regular people, we developed this initiative on sign language transcription. The community of deaf and mute people will benefit from this initiative because it explains the meaning of each hand gesture and uses hand gestures to communicate various signs. In this project, hand gestures were recognized using a pipeline method called media pipe, and training and testing were carried out using deep learning techniques. Sign language was then converted to text format.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images

引用

Journal of Healthcare engineering 2023年第1期2023卷 1566123页

作者： Naga Srinivasu, Parvathaneni Krishna, T. Balamurali Ahmed, Shakeel Almusallam, Naif Khaled Alarfaj, Fawaz Allheeib, Nasser Department of Computer Science and Engineering Prasad v Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada521139 India Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems College of Computer and Information Science King Saud University Riyadh Saudi Arabia

Over the past few years, a tremendous change has occurred in computer-Aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-Term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-Art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result. © 2023 Parvathaneni Naga Srinivasu et al.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Learning dynamics of kernel-based deep neural networks in manifolds

引用

science China(Information sciences) 2021年第11期64卷 105-119页

作者： Wei WU Xiaoyuan JING Wencai DU Guoliang CHEN School of Computer Science Wuhan University Institute of Deep-sea Science and Engineering Chinese Academy of Sciences School of Computer Guangdong University of Petrochemical Technology Institute of Data Science City University of Macau College of Computer Science and Software Engineering Shenzhen University

Convolutional neural networks(CNNs) obtain promising results via layered kernel convolution and pooling operations, yet the learning dynamics of the kernel remain obscure. We propose a continuous form to describe kernel-based convolutions through integration in neural manifolds. The status of spatial expression is proposed to analyze the stability of kernel-based CNNs. We divide CNN dynamics into the three stages of unstable vibration, collaborative adjusting, and stabilized fluctuation. According to the system control matrix of the kernel, the kernel-based CNN training proceeds via the unstable and stable status and is verified by numerical experiments.

关键词： learning dynamics kernel-based convolution manifolds control model network stability

来源：评论

学校读者我要写书评

暂无评论

Pursuit-Evasion on a Sphere and When It Can Be Considered Flat 63

Pursuit-Evasion on a Sphere and When It Can Be Considered Fl...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Milutinovic, Dejan Moll, Alexander Von Manyam, Satyanarayana G. Casbeer, David W. Weintraub, Isaac E. Pachter, Meir Electrical and Computer Engineering Dept. University of California Santa Cruz Santa CruzCA United States Control Science Center Aerospace Systems Directorate Air Force Research Laboratory DaytonOH45435 United States Dcs Corporation DaytonOH United States Air Force Institute of Technology Wright-Patterson Afb Dept. of Electrical Engineering OH United States

ISBN: (纸本)9798350316339

In planar pursuit-evasion differential games considering a faster pursuer and slower evader, the interception points resulting from equilibrium strategies lie on the Apollonius circle. This property is instrumental for leveraging geometric approaches for solving multiple pursuit-evasion scenarios in the plane. Here, we study a pursuit-evasion differential game on a sphere and generalize the planar Apollonius set to the spherical domain. We find that the interception point from the equilibrium strategies can leave the Apollonius set boundary and present a condition to keep the intercept point on the boundary. This condition allows for generalizing planar pursuitevasion strategies to the sphere. © 2024 IEEE.

关键词： Spheres

来源：评论

学校读者我要写书评

暂无评论

Improved Dingo Optimization Algorithm for Human-robot Collaborative U-shaped Disassembly Line Balancing Problem

Improved Dingo Optimization Algorithm for Human-robot Collab...

引用

Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jialong Li XiWang Guo Jiacun Wang ShuJin Qin Liang Qi Qi Kang Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA Economics and Management Shangqiu Normal University Shangqiu China Computer Science and Technology Shandong University of Science and Technology Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

There is increasing attention on remanufacturing, with the primary focus in the industry on recovering reusable products to generate profit. In the process of optimizing remanufacturing, addressing the balance issue in the operation of the disassembly line is crucial. Therefore, this study proposes the use of an improved Dingo Optimization Algorithm (IDOA) and designs a mathematical model for human-robot collaboration to explore how to achieve higher profits under these conditions. Finally, the improved IDOA is experimentally compared with the commercial optimization solver CPLEX and another intelligent optimization method—the Battle Royale Optimization (BRO). The results demonstrate that this method offers high quality and efficiency.

关键词： Industries Human-robot interaction Collaboration Mathematical models Social intelligence Optimization

来源：评论

学校读者我要写书评

暂无评论

Phase- and Angle-Sensitive Terahertz Hot-Electron Bolometric Plasmonic Detectors Based on Fets with Graphene Channel and Composite H-BN/Black-P/H-BN Gate Layer

引用

International Journal of High Speed Electronics and systems 2024年第4期33卷

作者： Ryzhii, Victor Tang, Chao Otsuji, Taiichi Shur, Michael S. Ryzhii, Maxim Mitin, Vladimir Research Institute For Electrical Communication Tohoku University Miyagi Sendai980-8577 Japan Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department Of Computer Science And Engineering University Of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan Department Of Electrical Engineering University At Buffalo Suny BuffaloNY United States

In this paper, we propose and analyze the terahertz (THz) bolometric vector detectors based on the graphene-channel field-effect transistors (GC-FET) with the black-P gate barrier layer or with the composite b-BN/black-P/b-BN gate layer. The phase difference between the signal received by the FET source and drain substantially affects the plasmonic resonances. This results in a resonant variation of the detector response on the incoming THz signal phase shift and the THz radiation angle of incidence. © 2024 World Scientific Publishing Company.

关键词： Graphene

来源：评论

学校读者我要写书评

暂无评论

Fair Resource Allocation Policies in Reverse Auction-Based Cloud Market

引用

SN computer science 2021年第6期2卷 483页

作者： Kumar, Dinesh Baranwal, Gaurav Vidyarthi, Deo Prakash Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad UP Prayagraj 211004 India Department of Computer Science Institute of Science Banaras Hindu University UP Varanasi 221005 India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi 110067 India

The increasing number of Internet of Things (IoT) applications and their dependence on cloud computing for computational services has resulted in the cloud market’s growth. This growth has attracted many business organizations to offer cloud services, leading to significant competition amongst the cloud service providers. Reverse auction has been widely used to model such competition when there are many cloud service providers. A study on fair resource allocation mechanisms is performed in this work, and a family of such mechanisms is proposed. This work considers a reverse auction-based cloud market where users submit their combinatorial bids. This work emphasizes the importance of fairness in cloud resource allocation and its implementation in a cloud market. The proposed priority-based fair resource allocation mechanisms remove the bidder drop problem in a cloud market where a few major cloud providers dominate and control the whole market. Performance of the proposed fair resource allocation mechanisms is studied based on various metrics such as the number of winning auction rounds, providers’ revenue, users’ procurement cost, etc., in a simulated environment. It is observed that the naïve and not well-established providers in the market also win when fair mechanisms based on priority methods are implemented. They also get a chance to win the auction and can offer the resources successfully to the customer. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Combinatorial auction Fairness Internet of Things (IoT) Resource allocation

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：