检索结果-内蒙古大学图书馆

Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data

International Journal of Intelligent Computing and Cybernetics 2024年第2期17卷 395-414页

作者： Abba Suganda Girsang Bima Krisna Noveta Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakartaIndonesia

Purpose-The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter *** Twitter text is extracted by using named entity recognition(NER)with six classes hierarchy location in ***,the tweet then is classified into eight classes of natural disasters using the support vector machine(SVM).Overall,the system is able to classify tweet and mapping the position of the content ***/methodology/approach-This research builds a model to map the geolocation of tweet data using *** research uses six classes of NER which is based on region *** data is then classified into eight classes of natural disasters using the ***-Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data *** results also show good performance in geocoding such as match rate,match score and match ***,with SVM,this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region,which originate from the tweets *** limitations/implications-This study implements in Indonesia ***/value-(a)NER with six classes is used to create a location classification model with StanfordNER andArcGIS *** use of six location classes is based on the Indonesia regionalwhich has the large ***,it hasmany levels in its regional location,such as province,district/city,sub-district,village,road and place names.(b)SVMis used to classify natural *** of types of natural disasters is divided into eight:floods,earthquakes,landslides,tsunamis,hurricanes,forest fires,droughts and volcanic eruptions.

关键词： Twitter geolocation Natural disaster detection Support vector machine Natural language processing Named entity recognition

来源：评论

学校读者我要写书评

暂无评论

Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

引用

computers, Materials & Continua 2023年第12期77卷 3637-3657页

作者： Derwin Suhartono Alif Tri Handoyo Franz Adeta Junior Computer Science Department School of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia Cyber Security Program Computer Science DepartmentSchool of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online *** study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and *** research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training *** inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original ***,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text *** exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original *** methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation ***,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using *** increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in *** proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

关键词： Data augmentation Generative Adversarial Network(GAN) Reverse GAN(RGAN) sarcasm detection

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Threat Detection

Machine Learning for Threat Detection

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Sutanto, Dustin Fransisca, Febri Liem, Steven Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila School of Computer Science Cyber Security Program Computer Science Department Bina Nusantara Jakarta Indonesia School of Computer Science Computer Science Program Computer Science Department Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

These days almost all people in the world use the Internet as the internet is constantly evolving. Cyber attack scale are increased thanks to cybercriminals that have become sophisticated in employing threats. Since the 1980s, Intrusion Detection Systems (IDS) have been developed to defend computer networks against malicious activity and unauthorized access. Companies choose to implement IDS the part of their Cyber Security strategy to protect their data and network from potential threats. in this case, IDS has played a crucial role in detecting abnormal traffic and protecting digital assets. furthermore, a high-accuracy IDS is needed in concern of having false positive feedback from the IDS machine itself that can cause various problems. to accompany the need for high accuracy and trainable IDS machines, we experimented with a hybrid system using machine learning that is combined with traditional IDS machines resulting in higher accuracy and lower error rate. By adjusting the parameter the accuracy improved and could achieve an accuracy of 99.66% of classifying the traffic. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Security Challenges and Issues in Cloud Computing

Security Challenges and Issues in Cloud Computing

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Zakiyyah, Rika Permana, Daniel Valenecio, Devan Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia Computer Science Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding their infrastructure to meet the demand from the customer. With such advantages there will also be disadvantages such as security problems. This paper will discuss about security problems such as encryption, access control, multi-factor authentication, regular audits, secure configurations, incident response planning. This paper will also talk about security measures to deal with threats such as malicious insiders, data abuse, unsecured interfaces and APIs, shared technology complications, data loss or leakage, hijacking, and enigmatic risk profiles. This study examines and evaluates a range of cloud computing security concerns and issues through a systematic analysis of the literature. The goal of the research is to raise public awareness of the need for cloud computing security and to offer possible remedies. Identify and classify network security threats and vulnerabilities in cloud computing by doing an extensive examination of the body of research and empirical investigations is one of the answer to the questions from the paper. In the end, this take a look at unearths vulnerabilities and their practical implications through very well reading network safety in cloud computing. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media 30

Uncovering Agendas: A Novel French & English Dataset for Age...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Katsios, Gregorios A. Sa, Ning Bhaumik, Ankita Strzalkowski, Tomek Department of Computer Science United States Department of Cognitive Science Rensselaer Polytechnic Institute United States

ISBN: (纸本)9782493814104

The behavior and decision making of groups or communities can be dramatically influenced by individuals pushing particular agendas, e.g., to promote or disparage a person or an activity, to call for action, etc.. In the examination of online influence campaigns, particularly those related to important political and social events, scholars often concentrate on identifying the sources responsible for setting and controlling the agenda (e.g., public media). In this article we present a methodology for detecting specific instances of agenda control through social media where annotated data is limited or non-existent. By using a modest corpus of Twitter messages centered on the 2022 French Presidential Elections, we carry out a comprehensive evaluation of various approaches and techniques that can be applied to this problem. Our findings demonstrate that by treating the task as a textual entailment problem, it is possible to overcome the requirement for a large annotated training dataset. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Impacts of Dynamic Game Balancing in a Survival Roguelike Chess Game

Analyzing the Impacts of Dynamic Game Balancing in a Surviva...

引用

2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Mario Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

This research investigates the novel application of Dynamic Game Balancing (DGB) techniques in the context of a hybrid chess-survival roguelike game, a unique combination of genres not widely explored in previous studies. The study addresses two primary research questions: the identification of suitable DGB techniques for these genres and the enhancement of user experience through DGB. A game prototype was developed using Unity, where players control a chess knight in a turn-based system, facing increasingly challenging enemy pieces. The DGB system dynamically adjusts the game's difficulty based on player performance, utilizing various inputs such as damage received or dealt, health points, powerup usage, and decision speed. Preliminary results from 14 beta-testers show overall positive feedback, with players reporting higher engagement and enjoyment due to the seamless difficulty adjustments, as reflected by the high mean scores from surveys like the User Experience Questionnaire (UEQ). This research fills a gap in the literature by demonstrating the feasibility and benefits of integrating DGB in a chess-survival roguelike setting. These findings highlight the potential for DGB to significantly improve player experience in hybrid game genres, suggesting that its application can create more adaptive and immersive gaming environments in various other genres as well. © 2025 IEEE.

关键词： Game design

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Recurrent Neural Network with Mini-Batch Gradient Descent Method for Multiple Sclerosis Disease Prediction

A Hybrid Recurrent Neural Network with Mini-Batch Gradient D...

引用

International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Kavipriya, E. Sasikala, S. Department of Computer Science With Cognitive Systems and AIML Hindusthan College of Arts & Science Tamil Nadu Coimbatore India

ISBN: (纸本)9789819795062

Machine Learning techniques on different sectors plays a vital role for prediction analysis. In healthcare sector, diagnosis support and prognostic evaluation has been done using various algorithms of machine learning. The Multiple Sclerosis (MS) is one of the chronic diseases which affects the central nervous system of human. There are multiple symptoms and disabilities indicate the issues of Multiple Sclerosis. This neurological disorder has various patterns and disease progression rate among worldwide. This causes a variety of symptoms including fatigue, numbness, visual difficulties, urinary incontinence, and difficulty moving and cognitive deficiencies. This paper analyzes the risk factors of Multiple Sclerosis and early prediction using Machine learning methods. This work proposed hybrid Recurrent Neural Network with mini-batch gradient descent method for Multiple Sclerosis prediction. The performance of this proposed method is evaluated and the achieved accuracy rate is 92%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class Classification of Network Packet Attacks

Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class C...

引用

2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Setiawan, Valentino Soewito, Benfano Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

This exponential proliferation of IoT devices is creating an ever-growing demand for efficient cybersecurity solutions in resource-constrained environments. In this study, we propose Edge-IoTDistilBERT, a fine-tuned DistilBERT model, intended for the multi-class classification of network packet attacks within IoT ecosystems. Our proposed model was trained on the Edge-IIoT dataset, representative of all traffic classes and attack variants with high accuracy of up to 99.99%, outperforming state-of-the-art solutions. The new preprocessing pipeline of PCAP to text would transform the network traffic to a textual form allowing the model to learn the pattern in the dataset and making it able to generalize its content. Our fine-tuned model show a robust result on imbalanced dataset with two diffrent splits namely 80-20 and 70-30. The result shown by Edge-IoTDistilBERT place this model as a feasible effective cybersecurity solution for IoT networks, offering a good trade-off between high performance and resource constraints linked to edge devices. © 2025 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Contextual Understanding of Teen & Youth Privacy Perceptions with Social Robots 25

Contextual Understanding of Teen & Youth Privacy Perceptions...

引用

20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Levinson, Leigh Indiana University Bloomington Cognitive Science Program Department of Informatics BloomingtonIN United States

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378931

One of the biggest concerns voiced by families about child-centered robots in homes is privacy. My work uses co-design to better understand how youth, their family, and their clinicians formalize a youth's right to privacy with social robots. In particular, I highlight teen voices who are often overlooked in HRI. My main takeaways emphasize how the unique positioning of a robot as a non-authoritative, yet prosocial 'other' presence means stakeholders have lower perceptions of privacy when data collection and sharing is clearly defined by the robot's task. Furthermore, a social robot is situated among a teen's pre-existing relationships and responsibilities, framing privacy with robots from a relational perspective and elevating the need to clearly define the appropriate social roles for robots. In future work, I will couple these takeaways with youth's lived experiences with robots to see how privacy is contextualized further by the use of social robots in youth spaces. © 2025 IEEE.

关键词： Social robots

来源：评论

学校读者我要写书评

暂无评论

Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

引用

2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Limicia, Jennifer Ardelia Chandra, Wilbert Gunawan, Alexander Agung Santoso Tedjasulaksana, Jeffrey Junior School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions. © 2025 IEEE.

关键词： Convolutional neural networks

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：