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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Lailia Rahmawati Supriadi Rustad Aris Marjuni Moch Arief Soeleman Pulung Nurtantio Andono Pujiono Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Faculty of Informatics Engineering Universitas Darul Ulum Jombang Indonesia

This paper discusses a comparison of several methods used for object detection in bad weather conditions, such as the presence of fog that interferes with the object's view. The methods compared in this paper include Faster R-CNN, Yolov3, and SSD (Single Shot Multi-Box Detector). This work contributes to evaluating the best method to overcome the object detection problem in bad weather, which is measured by mean average precision (mAP). The evaluation is executed using video objects that are converted into frames. Based on experimental results, the Faster R-CNN method is superior in terms of the mAP value where the Faster R-CNN is 87%, using SSD is 48%, and Yolov3 is 31%. Faster R-CNN provides better performance than the other two methods because the basic idea of FRCNN is the use of a Region Proposal Network (RPN) in a feature map which aims to propose many objects that can be identified in a particular image.

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Multi Socket Network Transmission with Elliptic Curve Cryptography Encryption Algorithm 1

Multi Socket Network Transmission with Elliptic Curve Crypto...

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1st International Conference on Technology, engineering, and Computing Applications, ICTECA 2023

作者： Kawet, Hetly Brinner Yusak Heikhmakhtiar, Aulia Rahardjo, Budi Mantoro, Teddy The Republic of Indonesia Defence University Sains and Defence Technology Faculty Bogor Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Sampoerna University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

Information systems play a crucial role in facilitating effective and efficient media sharing by enabling the exchange of information. However, the current technological landscape is plagued by numerous potential attacks that can compromise the security of exchanged information. Examples of such attacks include stealing user data through interception of user-server communication, spoofing, and sniffing. Robust security measures are essential to mitigate the risks of data leakage and ensure data confidentiality during network-based information exchange. This study proposes designing a communication system based on a multi-socket network, employing the elliptic curve cryptography (ECC) encryption algorithm. The system comprises a single server and multiple clients. This paper analyzes the security of ECC from the performance comparison of ECC and RSA when implemented in multi sochet server. This work uses cryptography library implementation using C programming language. The utilization of the ECC encryption algorithm in network transmission ensures a high level of security and data confidentiality during information exchange. Furthermore, the data transmission system implemented with the ECC algorithm exhibits a swift computation time, thereby expediting key processing and message transmission while maintaining a strong level of security. © 2023 IEEE.

关键词： Geometry

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Spatial Hotspot Data and Weather for Forest Fire Data Clustering 5

Spatial Hotspot Data and Weather for Forest Fire Data Cluste...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Mimboro, Prasetyo Kusrini, Kusrini Laksito, Arif Dwi Postgraduate Program Universitas Amikom Yogyakarta Yogyakarta Informatics Engineering Departement Amikom Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Amikom Yogyakarta Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Prediction of fire forest will be needing several parameters that located on the same location and on the same time frame. This is important to have data on the same location and same time periods, since forest fire mostly triggered by weather or temperature condition on certain area on the certain time. Since there are many parameters involved, the preprocessing will need to made the data have standard structure. The clustering process needed to give a label to the data with five class label, very low risk, low risk, medium risk, high risk, very high risk. Based on the data set, we measurement the best cluster number related with DB index, the results shows that 8 cluster is the best number of the data with DB Index score are 0.8856. © 2022 IEEE.

关键词： Fires

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DevSecOps: A Security Model for Infrastructure as Code over the Cloud 2

DevSecOps: A Security Model for Infrastructure as Code over ...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Ibrahim, Amr Yousef, Ahmed H. Medhat, Walaa School of Information Technology and Computer Science Nile University Center of Informatics Science Nile University Cairo Egypt Ain Shams University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9781665466776

DevSecOps includes security practice while applying DevOps. DevSecOps help secure the whole DevOps process. This paper aims to define a DevSecOps module to be used by the infrastructure team while applying infrastructure as code. The proposed module solves the problem of security by including security practice with the DevOps cycle to reach DevSecOps. The module was tested to measure time effi-ciency. A small survey was created to test other DevSecOps metrics and enhance future work. © 2022 IEEE.

关键词： Codes Computational modeling Ubiquitous computing Time measurement Security

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Exponential Moving Average for Time-Series Smoothing in Wind Speed Forecasting

Exponential Moving Average for Time-Series Smoothing in Wind...

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International Conference on informatics and Computing (ICIC)

作者： Rudi Sutomo Risma Yulistiani Melissa Indah Fianty Information Systems Faculty of Engineering & Informatics Multimedia Nusamtara University Banten Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The need for sustainable energy sources is of utmost importance, and wind power is becoming increasingly prominent. Ensuring accurate and dependable wind speed predictions is crucial for tackling energy sustainability issues. This experiment involved using Exponential Moving Average (EMA) to reduce temporal irregularities in a wind dataset time series. The categorization method Long Short-Term Memory (LSTM) was also applied. The investigation concluded with the calculation of a statistically significant R2 coefficient, which particularly reached a value of 0.935. This demonstrates how effectively the recommended method predicts wind speed to maximize renewable energy.

关键词： Renewable energy sources Smoothing methods Accuracy Wind speed Time series analysis Wind power generation Sustainable development informatics Forecasting Long short term memory

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Does Twinning Vehicular Networks Enhance Their Performance in Dense Areas?

Does Twinning Vehicular Networks Enhance Their Performance i...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Sarah Al-Shareeda Sema F. Oktug Yusuf Yaslan Gokhan Yurdakul Berk Canberk Faculty of Computer Engineering and Informatics Istanbul Technical University Turkey BTS Group Turkey School of Computing Engineering and The Built Environment Edinburgh Napier University UK

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase II, we evaluate the advantages of twinning vehicular networks through three deployment scenarios: edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis demonstrates that twinning significantly reduces network delays, with virtual twins outperforming physical networks. Virtual twins maintain low delays even with increased vehicle density, such as 15.05 seconds for 300 vehicles. Moreover, they exhibit faster computational speeds, with cloud-based twins being 1.7 times faster than edge twins in certain scenarios. These findings provide insights for efficient vehicular communication and underscore the potential of virtual twins in enhancing vehicular networks in crowded areas while emphasizing the importance of considering real-world factors when making deployment decisions.

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IoT-Based Automated Environmental Control System for Oyster Mushroom Cultivation Using ESP8266 and Telegram Bot

IoT-Based Automated Environmental Control System for Oyster ...

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International Conference on informatics and Computing (ICIC)

作者： Evi Triandini M. Kevin Afrianto Bayu Agus Irawan Azman Maricar Padma Nyoman Crisnapati Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Mechatronics Engineering Faculty of Technical Education Rajamangala University of Technology Thanyaburi

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The aim of this study was to create an Internet of Things (IoT) system that can monitor and control temperature and humidity in oyster mushroom production from a distant location. This system was developed with the goal of improving efficiency and the quality of the mushroom yield. The implementation of the system involved the utilization of a NodeMCU ESP8266 microcontroller, which was combined with DS18B20 and DHT22 sensors. The system was then managed by a Telegram bot, enabling continuous operation throughout the day. The findings revealed substantial enhancements in efficiency, as the duration for monitoring and watering decreased from 471.6 seconds to 5 seconds. Additionally, there was a noteworthy reduction in the proportion of suboptimal mushrooms, declining from 12% to 1.72%. The findings indicate that the system offers a dependable and efficient alternative for maintaining ideal environmental conditions, resulting in improved crop quality and decreased labour requirements. The deployment of this technology signifies a significant progression in precision agriculture, providing tangible advantages for small-scale mushroom cultivators and establishing the foundation for future improvements in automated agricultural systems.

关键词： Precision agriculture Temperature sensors Temperature measurement Machine learning algorithms Chatbots Sensor systems Real-time systems Sensors Temperature control Internet of Things

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An overview of data integration principles for heterogeneous databases 45

An overview of data integration principles for heterogeneous...

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Stojanovic, Aleksandar Horvat, Marko Kovacevic, Zeljko Zagreb University of Applied Sciences Department of Informatics and Computer Science Zagreb Croatia University of Zagreb Department of Applied Computing Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9789532331035

Modern large-scale information systems often use multiple database management systems, not all of which are necessarily relational. In recent years, NoSQL databases have gained acceptance in certain domains while relational databases remain de facto standard in many others. Many "legacy"information systems also use relational databases. Unlike relational database systems, NoSQL databases do not have a common data model or query language, making it difficult for users to access data in a uniform manner when using a combination of relational and NoSQL databases or simply several different NoSQL database systems. Therefore, the need for uniform data access from such a variety of data sources becomes one of the central problems for data integration. In this paper we provide an overview of the main problems, methods, and solutions for data integration between relational and NoSQL databases, as well as between different NoSQL databases. We focus mainly on the problems of structural, syntactic, and semantic heterogeneity and on proposed solutions for uniform data access, emphasizing some of the more recent proposals. © 2022 Croatian Society MIPRO.

关键词： Query languages

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm with Particle Swarm Optimization K-Means for Banking Data

Performance Comparison of the Invasive Weed Optimization K-M...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ni Luh Gede Pivin Suwirmayanti I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Emy Setyaningsih Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset.

关键词： Accuracy Consumer behavior Metaheuristics Clustering algorithms Banking Computational efficiency Particle swarm optimization Intelligent systems Cybernetics Convergence

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