检索结果-内蒙古大学图书馆

International Conference on Computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Antó nio Correia Jano Moreira De Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Tr&#x00E1 s-os-Montes e Alto Douro Vila Real Portugal Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil NCE/UFRJ T&#x00E9 rcio Pacitti Institute of Computer Applications and Research Rio de Janeiro Brazil

In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digital nomads (DN). To do this, we collected textual data from posts in a Reddit community. We argue that, in order to understand how to design technical solutions for the so-called ‘new normal’ working conditions, one way to approach this is to understand how digital nomads are being impacted in their work practices and routines, and also how they are seeing the future of their technology-mediated work-life space. Finally, we show how evidence collected from DNs about their experiences and difficulties perceived during the pandemic period can inform CSCW researchers worldwide about future design-oriented strands.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

引用

IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Rahmad Kurniawan Iwan Iskandar Fitra Lestari Habibi Al Rasyid Harpizon Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Informatics Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

arXiv

引用

arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Modular boundaries in recurrent neural networks

arXiv

引用

arXiv 2023年

作者： Tanner, Jacob C. Mansour, Sina L. Coletta, Ludovico Gozzi, Alessandro Betzel, Richard F. Cognitive Science Program 2 School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States National University of Singapore Singapore Fondazione Bruno Kessler Trento Italy Functional Neuroimaging Lab Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems Rovereto Italy Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States

Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all- or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs [1, 2]. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? We find evidence that they do. First, we find that these boundaries neatly divide the representational content and role of neurons. Next, we find that these boundaries can be directly inferred from features of the weight matrix in feed-forward neural networks, and are related to clustering of the Jacobian matrix in recurrent neural networks. We also find that the weights of input neurons to recurrent neurons partially inform their modular structure, an observation that we corroborated using structural and functional imaging data from mice and humans. Finally, we find that the dynamics of these RNNs reflected the boundaries between modules. Collectively, our results suggest that neural populations in RNNs sometimes form modu

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Ensemble for Multi-Step Solar Irradiance Forecasting in Brazilian's Sites

Heterogeneous Ensemble for Multi-Step Solar Irradiance Forec...

引用

Workshop on Communication Networks and Power Systems (WCNPS)

作者： Domingos Sávio de O. Santos Douglas A. P. Dantas Paulo S. G. de Mattos Neto Hugo Valadares Siqueira Aranildo R. Lima Francisco Madeiro João. F. L. de Oliveira Alex C. Pereira Manoel H. N. Marinho Escola Politécnica de Pernambuco Universidade de Pernambuco Recife PE Brazil Centro de Informática Universidade Federal de Pernambuco Recife PE Brazil Graduate Program in Electrical Engineering Federal University of Technology-Paraná Ponta Grossa PR Brazil Aquatic Informatics Vancouver Canada São Francisco Hydroelectric Company (Chesf) Recife PE Brazil

Multi-step solar irradiance forecast is an important part of a successful power grid management. However that forecast can be difficult due to nonlinear patterns and random fluctuation. This work applies statistical and machine learning methods to forecast solar irradiance time series from three different locations of Brazil. The applied models are: Autoregressive Integrated Moving Average (ARIMA), Deep Belief Network (DBN), Extreme Learning Machine (ELM), Multi Layer Perceptron neural network (MLP), Random Forest (RF), and Support Vector Regression (SVR). Taking into account these methods, an ensemble of them is proposed, which has the objective of reducing: the uncertainty of selecting a single model, overfitting, and underfitting. Compared with single models and based on two performance metrics, the ensemble found good overall results when applied in the time series with horizons of 6, 12 and 24 hours ahead.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Content design: E-learning module for study on cultural tourism information systems based on local wisdom 2

Content design: E-learning module for study on cultural tour...

引用

2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Pageh, I.M. Permana, A.A.J. Historical Education Faculty of Law and Social Sciences Ganesha University of Education Singaraja Indonesia Information Technology Department Informatics Management Study Program Faculty of Engineering and Vocational Ganesha University of Education Singaraja Indonesia

Facing the 4.0 industrial revolution, it is necessary to carry out reforms and innovations related to national education. Reforms and innovations in the implementation of components of the education system involve changes in curriculum, graduate competencies and assessments, teacher qualifications, funding, facilities and infrastructure, decentralization and educational autonomy. Bali as a world tourist destination must be maintained and passed on to the younger generation. The younger generation can use information technology to help promote Balinese culture. Bali has a lot of local wisdom that can be implemented in the tourism sector. Therefore to promote those local wisdoms, a module that can be accessed through e-learning is developed to help students develop a cultural tourism package based on local wisdom to be promoted online. The modules developed have been tested by content and media experts, which can be used independently by students. Modules can be accessed online at e-learning and they are ready to use instructions for students so they can work on assignments and projects independently. Modules containing this theory and practice are for making brochures, tour packages, videos, and websites, all of which are connected to promote tourism based on local Balinese wisdom. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Exergy evaluation while drying fish waste in the forced convection rotary dryer

引用

AIP Conference Proceedings 2024年第1期3145卷

作者： I. G. N. B. Catrawedarma Akhmad Afandi Anis Usfah Prastujati Endi Sailul Haq Department of Mechanical Engineering Politeknik Negeri Banyuwangi Banyuwangi Indonesia Livestock Product Processing Technology Study Program Politeknik Negeri Banyuwangi Banyuwangi Indonesia Department of Informatics Engineering Politeknik Negeri Banyuwangi Banyuwangi Indonesia

This study focuses on the exergy analysis of the fish waste drying system using a rotary dryer. The effect of the initial mass of fish waste on the exergy parameters was carried out. The exergy balance was applied in the drying chamber. The exergy parameters analyzed include exergy inflow, exergy outflow, exergy loss, and exergy efficiency. In terms of exergy sustainability, the sustainability index, environmental impact factor, waste energy ratio, and improvement potential were also analyzed. As a result, it was found that the exergy inflow for all initial masses was not significantly different. Meanwhile, if the fish waste’s initial mass is decreasing, the exergy outflow and exergy efficiency increase, and exergy loss decrease. The sustainability index decreases, and the environmental impact factor increases with increasing initial mass. Waste energy ratio and improvement potential increase with increasing initial mass.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

引用

Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Bonifacius Vicky Indriyono Natalinda Pamungkas Fatima Hashim Abbas Ery Mintorini Mohammed Almohamadi Sarmad Nozad Mahmood Sameer Alani Ahmed H. Alkhayyat Eko Hari Rachmawanto Information System University of Dian Nuswantoro Kediri Indonesia Medical Laboratories Techniques Department Al-Mustaqbal University College Hillah Babil Iraq Visual Communication Design University of Dian Nuswantoro Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Medical Laboratories Techniques University of Mashreq Research center Baghdad Iraq College of technical engineering University of Mashreq The Islamic University Najaf Iraq Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments; they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A DISEASE-SPECIFIC FOUNDATION MODEL USING OVER 100K FUNDUS IMAGES: RELEASE AND VALIDATION FOR ABNORMALITY AND MULTI-DISEASE CLASSIFICATION ON DOWNSTREAM TASKS

arXiv

引用

arXiv 2024年

作者： Jang, Boa Ahn, Youngbin Choe, Eun Kyung Yoon, Chang Ki Choi, Hyuk Jin Kim, Young-Gon Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Korea Republic of Department of Surgery Seoul National University Hospital Healthcare System Gangnam Center Korea Republic of Department of Ophthalmology Seoul National University College of Medicine Korea Republic of Department of Ophthalmology Seoul National University Hospital Healthcare System Gangnam Center Korea Republic of Department of Medicine Seoul National University College of Medicine Korea Republic of

Artificial intelligence applied to retinal images offers significant potential for recognizing signs and symptoms of retinal conditions and expediting the diagnosis of eye diseases and systemic disorders. However, developing generalized artificial intelligence models for medical data often requires a large number of labeled images representing various disease signs, and most models are typically task-specific, focusing on major retinal diseases. In this study, we developed a Fundus-Specific Pretrained Model (Image+Fundus), a supervised artificial intelligence model trained to detect abnormalities in fundus images. A total of 57,803 images were used to develop this pretrained model, which achieved superior performance across various downstream tasks, indicating that our proposed model outperforms other general methods. Our Image+Fundus model offers a generalized approach to improve model performance while reducing the number of labeled datasets required. Additionally, it provides more disease-specific insights into fundus images, with visualizations generated by our model. These disease-specific foundation models are invaluable in enhancing the performance and efficiency of deep learning models in the field of fundus imaging. Copyright © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

Supply chain information system for product management control using rule based reasoning model

引用

AIP Conference Proceedings 2023年第1期2665卷

作者： Mufadhol Mufadhol Mustafid Mustafid Suryono Suryono Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Informatics Engineering STEKOM University Semarang Indonesia

Accuracy, fast and efficiency in performing digital transactions are needed especially during the pandemic covid 19, this need to be done for persevering and improving customer confidence. The supply chain is a concept applied by companies to analyze production needs from raw material providers, transportation to be used, production processes and even determine expeditions to send goods to customers according to a predetermined time. This research has never been done by other researchers where the supply chain process is carried out by a company by involving algorithms in computer science in the form of applying rule based reasoning methods to obtain optimal results. The supply chain information system combined with the rule based reasoning method allows companies to control production management that can improve product quality even up to the marketing model that will be used accompanied by consultants who are experts in their fields and can communicate actively to customers so that customers feel satisfied and feel well served. Computational rules will be run through the system developed in the form of rule based reasoning so that the company can obtain fast and precise analysis to take a decision with the purpose of obtaining the maximum benefit from any activities and transactions.

关键词：

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：