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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2023

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

ISBN: (纸本)9798350313635

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. © 2023 IEEE.

关键词： Sentiment analysis

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An Integrated Fire Scene Information System Based on YOLOv4-Tiny and DeepSort Technologies

An Integrated Fire Scene Information System Based on YOLOv4-...

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Chien Ho Chen Yu Cheng Wang Shao An Yu Department of Leisure Industry and Health Promotion National Taipei University of Nursing and Health Sciences Taipei City Taiwan Department of Computer Information and Network Engineering & Master Program Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (数字)9798350366976

ISBN: (纸本)9798350366983

This research introduces a pioneering framework for flame, smoke detection, and personnel monitoring, developed to address the increasing frequency of fire-related incidents in Taiwan. These incidents have caused significant loss of life and property damage. To tackle these issues, we employ YOLOv4-Tiny and DeepSort technologies to detect flames, smoke, and monitor personnel within surveillance footage, providing essential information to users. Unlike previous studies, this system integrates flame and smoke detection with personnel monitoring, enhancing rescue efficiency and ensuring personnel safety. During testing, the system effectively analyzed danger levels, monitored personnel, and provided necessary information. However, smoke can reduce the accuracy of personnel monitoring, so future research will aim to improve this issue and optimize the detection and identification of individuals moving between different footage. In summary, this investigation proposes a system that analyzes danger levels and monitors personnel in fire scenes. A survey of firefighters and security personnel concluded that this system helps improve rescue efficiency and reduce personnel loss due to fire incidents.

关键词： Training Adaptation models Accuracy Target tracking Computational modeling Heuristic algorithms Fires

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Artificial Intelligence Assisted IoT Data Intrusion Detection 4

Artificial Intelligence Assisted IoT Data Intrusion Detectio...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Shukla, Amogh Ahamad, Shahanawaj Rao, G.Nageswara Al-Asadi, Avein Jabar Gupta, Ankur Kumbhkar, Makhan Research Assistant Vellore Institute of Technology Vellore India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Lakireddy Bali Reddy College of Engineering Dept. of EEE Mylavaram India Computer Institute of Sulaimani Dept. of Computer Program Iraq Vaish College of Engineering Dept. of Computer Science and Engineering Rohtak India Christian Eminent College Dept. of Computer Science Indore India

ISBN: (纸本)9781665414470

It has become increasingly important since the introduction of Internet of Things (IoT) technology to ensure the security of IoT networks. Network anomalies and threats can be identified and predicted using a variety of intrusion detection systems (IDS). Despite the fact that the Internet of Things is vulnerable to attacks, early detection of malicious behavior can prevent data from being compromised. In this work, the primary goal is to develop machine artificial intelligence energy efficient models that can be used to detect attacks on the Internet of Things network. Normal and attack data from the IoT environment must be collected in order to construct a model. The Bayesian Network, the Artificial Neural Network, and the Support Vector Machine are considered to have the most promise. Using roundtrip time and power consumption data, a traditional three-layer Artificial Neural Network is put through its paces in the real world. © 2021 IEEE.

关键词： Internet of things

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KISS-Matcher: Fast and Robust Point Cloud Registration Revisited

arXiv

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arXiv 2024年

作者： Lim, Hyungtae Kim, Daebeom Shin, Gunhee Shi, Jingnan Vizzo, Ignacio Myung, Hyun Park, Jaesik Carlone, Luca Massachusetts Institute of Technology CambridgeMA02139 United States Daejeon34141 Korea Republic of Dexory United Kingdom Computer Science Engineering and Interdisciplinary Program of AI Seoul National University Seoul08826 Korea Republic of

While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-source and versatile C++ library for point cloud registration, called KISS-Matcher. KISS-Matcher combines a novel feature detector, Faster-PFH, that improves over the classical fast point feature histogram (FPFH). Moreover, it adopts a k-core-based graph-theoretic pruning to reduce the time complexity of rejecting outlier correspondences. Finally, it combines these modules in a complete, user-friendly, and ready-to-use pipeline. As verified by extensive experiments, KISS-Matcher has superior scalability and broad applicability, achieving a substantial speedup compared to state-of-the-art outlier-robust registration pipelines while preserving accuracy. Our code will be available at https://***/MIT-SPARK/KISS-Matcher. © 2024, CC BY-NC-ND.

关键词： C++ (programming language)

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Minimization of High Computational Cost in Data Preprocessing and Modeling Using MPI4Py

SSRN

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SSRN 2023年

作者： Ebenezer, Oluwasakin Thomas, Torku Tingting, Sun Yinusa, Ahmeed Hamden, Sammi Poudel, Samir Vargas, Jorge Poudel, Khem Narayan Computation and Data Science Program Middle Tennessee State University TN United States Engineering Technology Middle Tennessee State University TN United States Computer Science Program Middle Tennessee State University TN United States

Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall. In this research, we developed a novel approach that effectively deployed tools such as MPI and MPI4Py from parallel computing to handle data preprocessing and deep learning modeling processes. As a case study, the technique is applied to COVID-19 data from state of Tennessee, USA. Finally, the effectiveness of our approach is demonstrated by comparing it with existing methods without parallel computing concepts like MPI4Py. Our results demonstrate promising outcome for the deployment of parallel computing in modeling to minimize high computational cost. © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Study Case on Implementation Elevator 2 Floor System on PLC OMRON CP1E N30

Study Case on Implementation Elevator 2 Floor System on PLC ...

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International Conference on Education and technology (ICET)

作者： Muhammad Afnan Habibi Arif Wibawa Irham Fadlika Mochamad Farhan Ali Irfani Mohamad Farel Firmansyah Angger Baskoro Study Program of Power Plant Engineering Technology Malang Indonesia Dept. of Electrical Engineering Universitas Negeri Malang Malang Indonesia Dept. of Electrical Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

The swift progression of industrial technology underscores the need for adept professionals capable of skillfully implementing and managing intricate systems. Vocational education assumes a pivotal role in cultivating competent intermediaries proficient in translating theoretical knowledge into practical applications. At the core of industrial automation lies the programmable Logic Controller (PLC), a specialized digital computer that interfaces with sensors, switches, and actuators to execute real-time control programs, thus facilitating meticulous industrial processes. This study concentrates on the practical deployment of a two-floor elevator system utilizing the OMRON CP1E N30 PLC. Elevators, integral to contemporary structures, mandate precision for secure vertical conveyance. Leveraging input signals from elevator buttons, sensors, and switches, the PLC orchestrates efficient motor control and dependable door operations. This hands-on application underscores the PLC’s significance within real-time control contexts. Nonetheless, Vocational Higher Education graduates frequently grapple with the challenge of transitioning to industry settings due to limited practical exposure. This partnership harmonizes with the center’s objective of enriching vocational education by integrating sophisticated technologies like PLCs, equipping the workforce with the acumen to bridge the gap between theoretical foundations and the pragmatic demands of industry.

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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computer Science, Information technology and engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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Networking, Electrical engineering, computer Science, and technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Data Warehouse-Based Ad Archive for Media Analysis

Data Warehouse-Based Ad Archive for Media Analysis

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International Conference on Edge Computing and Applications (ICECAA)

作者： G. Indra B. Praveen Kumar G. Hemanth Kumar MS Srikanth Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering KL University Vijayawada Andhra Pradesh India School of Electronics and Communication Engineering REVA University Bengaluru Karnataka India Program Specialist TNS India Foundation Bangalore Karnataka India

Media influence's public opinion and decision-making today. Advertisements especially affect audience perceptions, actions, and purchases. A comprehensive data library on commercials and their performance is needed to understand how media affects society. Ad content, placement, and performance are stored in a data warehouse-based ad archive. The archive can evaluate marketing campaigns and discover media trends. Ad servers, social media platforms, and media monitoring tools may build the data warehouse-based ad archive. A dimensional data model helps retrieve and analyze data. Structured Query Language (SQL) queries, Online Analytical Processing (OLAP) cubes, and data visualization tools may access the archive. Media academics, marketers, and politicians may study the media environment using the data warehouse-based ad archive. Media academics may utilize the collection to study the marketing campaigns, media trends and media's influence on society. The archive may help advertisers analyze their ad campaigns, optimize media placement, and understand their target demographic. Policymakers may use the archive to monitor media outlets' advertising compliance and assess policy changes' media landscape effects.

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