检索结果-内蒙古大学图书馆

8th International Conference on Informatics and Computing, ICIC 2023

作者： Putri, Delfia Nur Anrianti Wijaya, Raden Bagus Muhammad AdryanPutra Adhy Nailfaaz Wahyono Computer Science Undergraduate Program Faculty of Mathematics and Natural Science Sleman Indonesia Faculty of Mathematics and Natural Science Department of Computer Science and Electronics Sleman Indonesia

ISBN: (纸本)9798350342604

Urban Green Space (UGS) plays a vital role in maintaining the balance of urban ecosystems. In Yogyakarta City, urban development and an increase in population accompanied by a decrease and insufficiency in the proportion of UGS correlate with rising urban temperatures. Monitoring the availability of UGS can serve as a foundation for decision-making in managing the city's UGS. With advancements in technology and computing in the field of computer Vision (CV), it's possible to recognize UGS from the geometric patterns of satellite images. This study proposes a vegetation segmentation method using satellite images and a Deep Learning (DL) method with the U-Net architecture to estimate the availability of UGS. Evaluation results show that the image dataset without preprocessing with the Red Green Blue (RGB) color channel gives an average Intersection over Union (IoU) of 80.74%. These results are better than data that underwent grayscaling and contrast stretching preprocessing. Based on a case study conducted in three sub-districts in the Yogyakarta region, the constructed U-Net model gave an average IoU evaluation of 60.88%. These results demonstrate the model's capability to provide a reference in calculating the UGS area at the sub-district level, which can then be utilized by the Yogyakarta City government to analyze the availability of UGS more efficiently. With this analysis, the government can make strategic decisions to manage UGS and create a sustainable urban environment. © 2023 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Robust Human Interaction Recognition Using Extended Kalman Filter

引用

computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

来源：评论

学校读者我要写书评

暂无评论

Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

引用

Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

来源：评论

学校读者我要写书评

暂无评论

Using Deep Learning Models for COVID-19 Related Sentiment Analysis on Twitter Data

Using Deep Learning Models for COVID-19 Related Sentiment An...

引用

2023 Human-Centered cognitive Systems, HCCS 2023

作者： Bangyal, Waqas Haider Amina, Saeeda Shakir, Rabia Ubakanma, George Iqbal, Muddesar Department of Computer Science Murree Pakistan Department of Computer Science Sialkot Pakistan Fuuast Department of Computer Science Islamabad Pakistan London South Bank University School of Engineering School of Engineering Cognitive Systems Research Centre United Kingdom

ISBN: (纸本)9798350359183

The significance of sentiment analysis has increased in modern times due to the extensive use of social media platforms as a medium for individuals to express their opinions. Twitter is widely acknowledged as a popular social media site mostly used for microblogging. People often voice their opinions on current events, making it challenging for researchers to accurately classify the mood expressed in these opinions. This research study presents a novel and efficient method for identifying and detecting false or misleading information pertaining to the COVID-19 pandemic. The dataset including artificially created news stories is obtained from a collection of texts and processed via the cycle of natural language processing (NLP). For this research study, three advanced deep learning models were used to predict the emotion of news items, accurately differentiating between genuine and fraudulent ones. This study utilizes Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers. Subsequently, it compares the outcomes achieved from these classifiers. The findings suggest that the BiGRU deep learning classifier has exceptional accuracy and efficiency, achieving a remarkable accuracy rate of 91%. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

引用

2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Survey of Refactoring Techniques to Maximize Code Coverage Metric 15

A Survey of Refactoring Techniques to Maximize Code Coverage...

引用

15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Simatupang, Johannes Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Refasctoring is a technique used in software development to improve the quality of code without changing its functionality. One metric that is often used to measure code quality is Code Coverage. This study aims to examine refactoring techniques that can maximize Code Coverage Metric. Through the study, identification, evaluation, and summary of empirical evidence from various literature sources are carried out. The results of this study provide guidance on effective refactoring techniques to improve Code Coverage as well as other positive impacts for software development. There are ten refactoring techniques that can be used to improve Code Coverage Metrics in software testing. © 2023 IEEE.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

Service Oriented Smart Model for Flood Controlling 9

Service Oriented Smart Model for Flood Controlling

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Rachmat Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Opening or closing dam-gate activities manually conducted in Manggarai dam to control the dam water level. The controlling action operated to avoid the flood possibility occurring in Jakarta city (the Indonesian capital). The study was conducted to develop a smart model for flood controlling based on service or called a service-oriented smart model (SOSM). The water-flow algorithm (WFA), fuzzy logic, object and service-oriented are four main methods operated in the study. The WFA is a central method to model the real water flow in the river coming from Katulampa dam (in Bogor city) until Manggarai dam (in Jakarta). The fuzzy logic functioned to simulate the dam's water level and the gate open/close decision should be decided by avoiding the bias value. The object-oriented model analysis and design approach, where the unified modelling language (UML) tools are operated to analyze and design the constructed model. Then, the service-oriented conception is used to integrate all sides in implementing the model. Finally, the constructed model can simulate the flood status in Jakarta via status value in decimal numbers with 6 numbers behind the point. © 2023 IEEE.

关键词： Floods

来源：评论

学校读者我要写书评

暂无评论

Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology 11

Implementation of Fine-Tuned BERT for Enzyme Classification ...

引用

11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Henry, Matthew Martianus Kenneth, Christian Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331520311

Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach. © 2024 IEEE.

关键词： Gene Ontology

来源：评论

学校读者我要写书评

暂无评论

Service Oriented Fuzzy Decision Model for Health Insurance Claim 5

Service Oriented Fuzzy Decision Model for Health Insurance C...

引用

5th International Conference on Advancement in Data science, E-Learning and Information Systems, ICADEIS 2023

作者： Utama, Ditdit Nugeraha Susanto, Sumiatie Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350303414

Insurance claim is a fascinating issue to study. A potential loss for the insurance company is major coming from this issue. Thus, many studies performed already to answer such an issue. This study takes aim to develop a computational decision model based on service technology. Four fundamental methods operated in constructing the model and its implementation in service technology. The analytical hierarchical process (AHP) and fuzzy logic methods are two methods benefited to construct the model;where the AHP used to prioritize thirteen parameters considered and the fuzzy logic with its inference capability operated to generate the decision. Object oriented is an analysis and design method to analyze and design the model implanting it in service oriented architecture (SOA). Then, SOA conception functioned to deploy the model in the service architecture. Ultimately, the suggested framework comprising three layers of service-oriented architecture (SOA), namely business process, service interface, and application, has been established, alongside the integration of eight essential services that connect these three applications. The model demonstrates simulation outcomes indicating that 31.47% of claims are categorized as low risk and have been approved, 17.64% of claims are considered moderate risk with currently pending decision status (requiring additional investigation), while 50.89% of claims are classified as high risk with also pending decision status. © 2023 IEEE computer Society. All rights reserved.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Neural Network and Resampling to Handle Imbalance Class on Estimate Software Defect 15

Neural Network and Resampling to Handle Imbalance Class on E...

引用

15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Setiawan, Tri Agus Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Rapid progress in the field of artificial intelligence has created opportunities for extensive applications in software development. One area that receives attention is the evaluation of code quality using machine learning techniques. In this investigation, we examined the possible application of machine learning to predict the likelihood of defects in computer code. We employ NASA archival data as case studies. Machine learning models employ neural network algorithms. Our exploration involves partitioning the dataset into training data and test data for performance evaluation. The findings indicate that the Neural Organize technique with resampling yields a high level of accuracy in predicting software defects. Our simulated neural network is capable of identifying intricate patterns in the data and providing precise measurements of the size and intensity of defects. These results have significant implications in the software business, enabling developers to promptly identify possible vulnerabilities and take preventive measures before product release. © 2023 IEEE.

关键词： Machine learning

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：