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Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

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International Conference on Informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

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Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric 4th

Highsimb: A Concrete Blockchain High Simulation with Contra...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Pengfei Jie, Wanqing Voundi Koe, Arthur Sandor Hou, Ruitao Yan, Hongyang Nouioua, Mourad Thien, Phan Duc Mbous Ikong, Jacques Lancine, Camara Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Pazhou Lab Guangzhou510330 China Faculty of Mathematics and Computer Science University of Mohamed Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Faculty of Information Technology Nam Dinh University of Technology Education Nam Dinh420000 Viet Nam Department of Electrical and Telecommunications Engineering National Advanced School of Engineering - University of Yaounde I Yaounde337 Cameroon Department of Computer Science University of Social Sciences and Management Bamako2575 Mali

ISBN: (纸本)9783031200984

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Classification of Television programs Based on Public Opinion in Social Media Using Random Forest and Decision Tree

Classification of Television Programs Based on Public Opinio...

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computer science, information technology and Engineering (ICCoSITE), International Conference on

作者： Dede Kurniadi Shopi Nurhidayanti Indri Tri Julianto Teguh Wahyono Yosep Septiana Hetty Rohayani Department of Computer Science Institut Teknologi Garut Garut Indonesia Faculty of Information Technology Satya Wacana Cristian University Salatiga Indonesia Faculty of Science and Technology Muhammadiyah Jambi University Jambi Indonesia

One of the electronic media that provides a source of entertainment and information for its audience is television programs. The quality of television programs is assessed based on ratings. In addition, public opinion also has a crucial role, and this is because this opinion can be used as data to carry out sentiment analysis by classifying whether the opinion is included in the category of positive or negative sentiment. The problem requires an efficient decision-making system, such as a machine learning algorithm. This study aims to develop a classification of sentiment analysis using Random Forest and Decision Tree algorithms. The dataset used is a public opinion tweet dataset on Twitter for four television programs with five attributes: the number of retweets, id, sentiment, tv show, and tweet text. The results of testing the two algorithm models show that the Random Forest and Decision Tree models have different performance levels. The Decision Tree has lower performance with an accuracy of 74.66%, precision of 76.13%, recall of 78.75%, and an f1-score of 76.73%. Meanwhile, Random Forest has higher performance with an accuracy of 84.06%, precision of 83.65%, recall of 82.80%, and f1-score of 82.32% with a comparison of test data and training data of 20:80. Based on these results, the Random Forest model has the best performance compared to the Decision Tree in classifying television program sentiment based on user opinions in social media.

关键词： Training Sentiment analysis TV Machine learning algorithms Social networking (online) Training data Classification algorithms

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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A Cardiovascular Model for the Development of Mathematical Algorithms for Non-invasive Continuous Blood Pressure Determination 10

A Cardiovascular Model for the Development of Mathematical A...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Mahrova, Vendula Prochazka, Michal Penhaker, Marek Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Research Centre UNIZA Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781665485579

In the human body, a regulated change of hemodynamic parameters and the possibility of repetitive sensing for refinement and finding other algorithms for continuous measurement of blood pressure non-invasively is not possible. To detect relationships, processes, and dependencies in the human body without the need for invasive input, attention should be paid to the creation of an ideal controlled cardiovascular system with the possibility of controlled change of hemodynamic parameters, repetitive sensing of set simulated biological signals with a known resulting reference pressure measured by a sensor. The cardiovascular model opens the door to the possibility of tracking dependencies in the human body and thus finding more robust algorithms to measure continuous blood pressure non-invasively. The cardiovascular model allows hemodynamic parameters that cannot be changed in a controlled manner in the human body to be changed in a controlled manner, thus allowing the determination of blood pressure dependencies on hemodynamic parameters and the time required for the pressure wave to be transmitted between two locations. Attention is paid to the development of a cardiovascular model and subsequent testing of the correct function of the model using an algorithm for measuring continuous blood pressure non-invasively for physiological parameters. The results show that a model of the cardiovascular system has been developed for subsequent testing and search for further algorithms and relationships to measure continuous blood pressure non-invasively more accurately. © 2022 IEEE.

关键词： Blood pressure

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Comparison of Machine Learning Text Classification for Intent Sentiment Analysis

Comparison of Machine Learning Text Classification for Inten...

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International Conference on information technology, information Systems and Electrical Engineering (ICITISEE)

作者： Abulwafa Muhammad Sarjon Defit Gunadi Widi Nur Cahyo Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia

Tourist destination reviews on Google Maps have become a valuable point of reference for visitors seeking enjoyable spots to visit. Additionally, users can gain insight into the reasons for writing reviews. Text classification is used to determine the motivation behind these reviews. The aim of this study is to compare machine learning (ML) text classification techniques for identifying the intent behind sentiments expressed as complaints (0), suggestions (1), opinions (2), statements(3), and awards (4). The ML techniques assessed are Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To achieve this, tourist destination review data sets, comprising 738 reviews, were collected using web scraping from the Google Maps website. Preprocessing stages, including case-folding, tokenisation, filtering and stemming, are executed prior to the data being prepped for feature extraction with TF-IDF. The employed classification models perform well with KNN achieving 1.00 accuracy on a 90:10 data split, and SVM obtaining 0.90 on the same data split. Although, the Multinomial Naive Bayes algorithm with 0.70 accuracy on a 90:10 data split is classified as fair. Comparison of methods was conducted by means of a Receiver Operating Characteristic (ROC) diagram. The testing accuracy for the SVM method was found to be 95%, MNB at 91%, and KNN at 84%.

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

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Characteristics and challenges of low-code development: The practitioners perspective 21

Characteristics and challenges of low-code development: The ...

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15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2021

作者： Luo, Yajing Liang, Peng Wang, Chong Shahin, Mojtaba Zhan, Jing School of Computer Science Wuhan University Wuhan China Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science University of Illinois at Urbana-Champaign Champaign United States

ISBN: (纸本)9781450386654

Background: In recent years, Low-code development (LCD) is growing rapidly, and Gartner and Forrester have predicted that the use of LCD is very promising. Giant companies, such as Microsoft, Mendix, and Outsystems have also launched their LCD platforms. Aim: In this work, we explored two popular online developer communities, Stack Overflow (SO) and Reddit, to provide insights on the characteristics and challenges of LCD from a practitioners perspective. Method: We used two LCD related terms to search the relevant posts in SO and extracted 73 posts. Meanwhile, we explored three LCD related subreddits from Reddit and collected 228 posts. We extracted data from these posts and applied the Constant Comparison method to analyze the descriptions, benefits, and limitations and challenges of LCD. For platforms and programming languages used in LCD, implementation units in LCD, supporting technologies of LCD, types of applications developed by LCD, and domains that use LCD, we used descriptive statistics to analyze and present the results. Results: Our findings show that: (1) LCD may provide a graphical user interface for users to drag and drop with little or even no code;(2) the equipment of out-of-The-box units (e.g., APIs and components) in LCD platforms makes them easy to learn and use as well as speeds up the development;(3) LCD is particularly favored in the domains that have the need for automated processes and workflows;and (4) practitioners have conflicting views on the advantages and disadvantages of LCD. Conclusions: Our findings suggest that researchers should clearly define the terms when they refer to LCD, and developers should consider whether the characteristics of LCD are appropriate for their projects. © 2021 IEEE computer Society. All rights reserved.

关键词： Graphical user interfaces

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A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation

A Systematic Literature Review of Recent Trends and Challeng...

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International Conference on ICT For Smart Society (ICISS)

作者： Ikrar Adinata Arin Meyliana Harco Leslie Hendric Spits Warnars Dina Fitria Murad Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology.

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A Proposal of QUIC-based CYPHONIC for Encrypted End-to-End Communications

A Proposal of QUIC-based CYPHONIC for Encrypted End-to-End C...

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International Symposium on Computing and Networking (CANDAR)

作者： Shota Horisaki Kazushige Matama Katsuhiro Naito Hidekazu Suzuki Graduate School of Science and Technology Meijo University Nagoya Japan Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Japan Faculty of Information Science Aichi Institute of Technology Toyota Japan

ISBN: (纸本)9781665475310

CYber PHysical Overlay Network over Internet Communication (CYPHONIC) has been proposed as a technology to achieve both communication connectivity and mobility transparency in IPv4/IPv6 mixed environment. Using CYPHONIC, applications running on nodes can achieve end-to-end encrypted communication over an overlay network. However, since CYPHONIC control messages use UDP port 4501, it may not be possible to achieve encrypted communication without changing the firewall configuration installed in a network to which the node is connected. This paper proposes a method to realize end-to-end encrypted communication that can pass through firewalls by extending the CYPHONIC protocol specification using QUIC, which is standardized as a next-generation HTTP protocol.

关键词： Cloud computing Overlay networks Protocols Home automation Firewalls (computing) Prototypes Cryptography

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