检索结果-内蒙古大学图书馆

10th Brazilian Conference on Intelligent Systems, BRACIS 2021

作者： Vargas, Daniel P. Paulus, Gustavo B. Silva, Luis A. L. Graduate Program in Computer Science Federal University of Santa Maria – UFSM RS Santa Maria Brazil

ISBN: (纸本)9783030917012

Deception is an essential behavior in many card games. Despite this fact, it is not trivial to capture the intent of a human strategist when making deceptive decisions. That is even harder when dealing with deception in card games, where components of uncertainty, hidden information, luck and randomness introduce the need of case-based decision making. Approaching this problem along with the investigation of the game of Truco, a quite popular game in Southern regions of South America, this work presents an approach that combines active learning and Case-Based Reasoning (CBR) in which agents request a human specialist to review a reused game action retrieved from a case base containing played Truco hands. That happens when the agents are confronted with game situations that are identified as opportunities for deception. The goal is to actively capture problem-solving experiences in which deception can be used, and later employ such case knowledge in the enhancement of the deceptive capabilities of the Truco agents. Experimental results show that the use of the learned cases enabled different kinds of Truco agents to play more aggressively, being more deceptive and performing a larger number of successful bluffs. © 2021, Springer Nature Switzerland AG.

关键词： Case based reasoning

来源：评论

学校读者我要写书评

暂无评论

Pantanal wetland wildfires are associated with drought in interconnected biomes

引用

Natural Hazards 2025年 1-27页

作者： de Sousa, Bruna Rodrigues Fonseca, Humberto Paiva Arévalo, Santiago Maria Márquez Lorenzon, Alexandre Simões Torres, Fillipe Tamiozzo Pereira do Amaral, Cibele Hummel Applied Meteorology Graduate Program Federal University of Vicosa Viçosa Brasil Forest Science Graduate Program Federal University of Vicosa Viçosa Brasil Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder USA Environmental Data Science Innovation and Inclusion Lab University of Colorado Boulder USA Earth Lab University of Colorado Boulder USA

Pantanal is the largest tropical wetland in the world and has been extremely affected by these wildfires recently, such as in 2020 and 2024. The largest rivers that supply the Pantanal originate in the Brazilian highlands (“plateau”), so the availability of surface water in the biome (“plain”) is strongly associated with the hydrological dynamics and land use and cover of surrounding biomes such as the Amazon, Cerrado, and Atlantic Forest. Although the hydrological connections between these biomes and the local factors contributing to wildfires have been well explored, there appears to be a lack of comprehensive studies at a macro-scale that demonstrate the associations between historical above-average fires in the Pantanal and the climatic and biospheric conditions of its hydrologically interconnected biomes. Therefore, this study aimed to analyze a 32-year historical series (1989–2020) of burned areas in the Pantanal and identify the drivers of above-average wildfires. We explored local environmental variables as well as those of surrounding biomes. We found that 13 out of 32 analyzed years stand out for a total burned area above normal, and only 1999 showed a larger burned area than 2020. The results of this study demonstrate that water dynamics from soil to atmosphere, such as decreased soil moisture, vegetation moisture and evapotranspiration, and increased drought, including during previous years, in the Pantanal and the surrounding forested biomes (Amazon and Atlantic Forest) are associated with the occurrence of above-average wildfires in the Pantanal wetland. In addition, the 32-year trend analysis revealed that widespread drought has increased in the Pantanal and all analyzed interconnected biomes, indicating that the world’s largest tropical wetland will likely see more above-average wildfires. Our results have implications for macro-scale adaptive management, integrated watershed management, fire risk early detection, and the preparedness of teams respon

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mapping the Landscape of Social Learning: Integrating Key Success Factors, Challenges, and Digital Platforms 9

Mapping the Landscape of Social Learning: Integrating Key Su...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Marcel Meyliana Warnars, Harco Leslie Hendric Spits Mursitama, Tirta Nugraha BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Investigating the Impact on Data Recovery in computer Forensics

Investigating the Impact on Data Recovery in Computer Forens...

引用

Cryptography, Informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Benfano Soewito Computer Science Department Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

Defragmentation can potentially be employed as a tactic by perpetrators to conceal, misrepresent, or eliminate digital evidence. This study explores the effects of minor defragmentation, a potential method to conceal digital evidence, on recovering file system data in digital forensics. Our investigation sought to determine the influence of minor defragmentation on the effectiveness of data recovery and to identify methods that can augment the success rate post-defragmentation. We limited the scope of this study to defragmentation in Hard Disk Drives (HDDs), solid-state drives (SSDs), and USB drives. A mixed-method approach employs a literature review, case studies, and controlled experiments. Comparative analysis was used as the main data analysis technique to investigate its impact. Preliminary findings suggest that minor defragmentation hampers data recovery; however, certain strategies can augment success rates. These results can significantly influence the development of data recovery policies, particularly those of digital forensic analysts and law enforcement. The primary objective of this study is to bolster the efficiency and dependability of file system data recovery post-defragmentation while upholding ethical and legal standards.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Antibiotic resistant bacteria and genes (ARB and ARG) in water and sewage treatment units: A review

引用

Environmental Nanotechnology, Monitoring and Management 2024年 21卷

作者： Flach, Kauane Andressa Bones, Ubiratan Alegransi Wolff, Delmira Beatriz de Oliveira Silveira, Andressa da Rosa, Genesio Mario Carissimi, Elvis Silvestri, Siara Graduate Program in Environmental Engineering Federal University of Santa Maria Brazil Graduate Program in Environmental Science and Technology Federal University of Santa Maria Brazil

Microorganisms have historically coexisted with humans, animals, and the environment in a natural way. Like any other living being, microorganisms are subjected to environmental pressures that can eventually extinguish or strengthen their existence. In recent years, the emergence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG) has raised an alert about their influence on human and animal health. In addition to ARBs found in nosocomial environments, recent studies point to the environment as an auxiliary hotspot for the spread of antimicrobial resistance. In this sense, this review covers research that investigated ARB and ARG in E. coli and Salmonella, occurrence and potential environmental reservoirs for the spread of antimicrobial resistance, as well as a discussion on how bacterial resistance affects public health, the detection and treatment methods currently employed to reduce or remove this pollutant from water treatment plants (WTP) and sewage treatment plants (WWTP). The results indicated that the great increase in bacterial resistance may be linked, among other reasons, to repetitive contact with residual concentrations of antibiotics present in the environment, causing the bacteria to suffer from selective pressure and if resistant to one or more antibiotics. Thus, public health is compromised, since commonly used antibiotics are often ineffective in treating disease-resistant pathogenic bacteria. From this perspective, studies have shown the importance of consistent detection methods, which allow the tracking and analysis of sources of antimicrobial resistance on a global scale, in addition to the need to improve advanced treatment to reduce the ARG and ARB of WTPs and WWTPs. The first two sections of this review article present an overview of the problems related to the occurrence and dissemination of antimicrobial resistance (AMR) in bacteria, with a focus on E. coli and Salmonella, identified by the WHO as bacteria of globa

关键词： Advanced treatments Analytical determination Antimicrobial resistance Environmental compartments Public health

来源：评论

学校读者我要写书评

暂无评论

Perfomance Improvement of mpdCNN as Ensemble Learning Model on Low Resolution Image-Face Recognition System

Perfomance Improvement of mpdCNN as Ensemble Learning Model ...

引用

International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Daniel Anando Wangean Derwin Suhartono Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.

关键词： Measurement Deep learning Accuracy Image resolution Image recognition Face recognition Instruments Robustness Ensemble learning Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Evaluating a LSTM Neural Network and a Word2vec Model in the Classification of Self-admitted Technical Debts and Their Types in Code Comments 22nd

Evaluating a LSTM Neural Network and a Word2vec Model in the...

引用

22nd International Conference on Enterprise Information Systems, ICEIS 2020

作者： Santos, Rafael Meneses Santos, Israel Meneses Júnior, Methanias Colaço Mendonça, Manoel Graduate Program in Computer Science Federal University of Bahia Salvador Brazil Department of Information Systems Federal University of Sergipe Itabaiana Brazil

ISBN: (纸本)9783030754174

Context: Software development teams constantly opt for faster, lower quality solutions to solve current problems without planning for the future. This situation will have a negative long-term impact and is called technical debt. Similar to a financial debt, technical debts require interest payments and must be managed and detected so that the team can evaluate the best way to deal with them. One way to detect technical debts is through classification of source code comments. Developers often insert comments warning of the need to improve their own code in the future. This is known as Self-Admitted Technical Debt (SATD). Objective: Combine Word2vec for word embedding with a Long short-term memory (LSTM) neural network model to identify SATDs from comments in source code and compare with other studies and LSTM without word embedding. Method: We plan and execute an experimental process with model’s effectiveness data validation. Results: In general, the classification improves when all SATD types were grouped in a single label. In relation to other studies, the LSTM model with Word2vec achieved better recall and f-measure. The LSTM model without word embedding achieves greater recall, but perform worse in precision and f-measure. Conclusion: We found evidence that LSTM models combined with word embedding are promising for the development a more effective SATD classifier. © 2021, Springer Nature Switzerland AG.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Indoor Positioning System Classification with Wi-Fi Signal Data: A Machine Learning Approach

Indoor Positioning System Classification with Wi-Fi Signal D...

引用

International Conference on Knowledge and Smart Technology (KST)

作者： Ivan Alexander Gede Putra Kusuma Yaya Heryadi Widodo Budiharto Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement School of Computer Science - Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Indoor positioning systems (IPS) are critical for enabling accurate navigation, asset tracking, and emergency response in indoor environments where GPS fails. This study evaluates five machine learning algorithms-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree, and Random Forest-using two benchmark datasets, UJIIndoorLoc and UTSIndoorLoc. The results reveal that SVM achieves the highest accuracy, with 81.3 % on UJIIndoorLoc and 92 % on UTSIndoorLoc, followed by MLP with $\mathbf{7 8. 8 \%}$ and 90.9 %, respectively. Random Forest provides stable performance, with 77.6 % and 86.08 %, while KNN reaches 75 % and 89.7 %, performing well in structured environments. Decision Tree shows the lowest accuracy, 71.2 % and 76.54 %, highlighting its limitations with complex data. The UTSIndoorLoc dataset consistently yields higher accuracies, demonstrating its structured signal distribution. These findings underscore SVM and MLP as optimal algorithms for Wi-Fi fingerprinting IPS, offering robust and scalable solutions for indoor localization.

关键词： Support vector machines Accuracy Machine learning algorithms Nearest neighbor methods Fingerprint recognition Decision trees IP networks Random forests Wireless fidelity Indoor positioning systems

来源：评论

学校读者我要写书评

暂无评论

Lung and Colon Cancer Detection from CT Images Using Deep Learning

引用

Machine Graphics and Vision 2023年第1期32卷 85-97页

作者： Akinyemi, Joseph D. Akinola, Akinkunle A. Adekunle, Olajumoke O. Adetiloye, Taiwo O. Dansu, Emmanuel J. Department of Computer Science University of Ibadan Ibadan Nigeria Department of Data Science Bowling Green State University Bowling GreenOH United States BigCodeGen LLC Fort WorthTX United States Department of Mathematical Sciences Federal University of Technology Akure Nigeria Ecological Integration Laboratory Graduate School of Life Sciences Tohoku University Sendai Japan

Cancer is a deadly disease that has gained a reputation as a global health concern. Further, lung cancer has been widely reported as the most deadly cancer type globally, while colon cancer comes second. Meanwhile, early detection is one of the primary ways to prevent lung and colon cancer fatalities. To aid the early detection of lung and colon cancer, we propose a computer-aided diagnostic approach that employs a Deep Learning (DL) architecture to enhance the detection of these cancer types from Computed Tomography (CT) images of suspected body parts. Our experimental dataset (LC25000) contains 25 000 CT images of benign and malignant lung and colon cancer tissues. We used weights from a pre-trained DL architecture for computer vision, EfficientNet, to build and train a lung and colon cancer detection model. EfficientNet is a Convolutional Neural Network architecture that scales all input dimensions such as depth, width, and resolution at the same time. Our research findings showed detection accuracies of 99.63%, 99.50%, and 99.72% for training, validation, and test sets, respectively. © 2023 Faculty of Applied Informatics and Mathematics - WZIM, Warsaw university of Life sciences - SGGW. All rights reserved.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Comparing the Effect of Attention Mechanism and Aspect Embedding on the Performance of Long Short-term Memory Model for Aspect-Based Sentiment Analysis

Comparing the Effect of Attention Mechanism and Aspect Embed...

引用

2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The attention mechanism is one of the key enablers which have positioned transformer models as the state-of-the-art models in Natural Language Processing. By having the attention mechanism, the first version (vanilla) transformer model can retrieve the hidden state but at the same time can maintain a low requirement on the context vector dimension by selectively choosing to which encoder hidden states to give priority attention. The objective of this study is to evaluate the effect of modified Long Short-term Memory models architecture on the model performance as a predictive model. The first model is modified by adding an attention mechanism to its architecture;whilst, the second model is modified by adding additional input to its architecture. The performance of both models is compared by assigning each model the same task: to address the aspect-based sentiment analysis task. The experiment results show that adding an attention mechanism is not significantly increase the performance of the Long Short-term Memory model in comparison to adding input to the same model. In particular, the average training accuracy of the former model is 0.885;whilst, the average training accuracy of the latter model is 0.942. © 2023 IEEE.

关键词： Sentiment analysis

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：