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Effective Building Data Warehouse Infrastructure by Code in Cloud Platform 24

Effective Building Data Warehouse Infrastructure by Code in ...

引用

24th International Electronics Symposium, IES 2022

作者： Sabil, Andi Abdul Salam Martono, Hero Yudo Permatasari, Desy Intan Electronic Engineering Polytechnic Institute of Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Over time the data generated by a market is getting bigger and more diverse. So, it takes a larger container to store the data. The solution is to use a data warehouse. On the other hand, network infrastructure is needed to handle data collection. Improper infrastructure can potentially stop the storage system and cause data loss. So, we need a method to build a network infrastructure that can handle this. Considering the availability of infrastructure (high availability) and the ease of construction. This study aims to build a network infrastructure with code to improve consistency and speed until it is ready to be used for Business Intelligence needs. BIAPP is an application that is used to process network infrastructure development using code. In addition, this application can immediately be used for Business Intelligence needs when the infrastructure has been created. Speed results are obtained when building network infrastructure using code and conventional. An average of 207.63% of code usage was obtained faster than the conventional method. In addition to getting results from infrastructure speed, the results of this application can already carry out Business Intelligence processes. One of the results obtained is the number of products repurchased by customers. The data can be used to determine the number of products to be purchased on the next purchase. © 2022 IEEE.

关键词： Data warehouses

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Developing Energy Autonomous and Cable-less Multi-gateway LoRa Networks

Developing Energy Autonomous and Cable-less Multi-gateway Lo...

引用

2023 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2023

作者： Valkanis, Anastasios Beletsioti, Georgia A. Kantelis, Konstantinos Nicopolitidis, Petros Papadimitriou, Georgios Louta, Malamati Aristotle University of Thessaloniki Department of Informatics Thessaloniki Greece University of Western Macedonia Faculty of Engineering Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350302561

Long Range (LoRa) networks are a reliable and efficient solution to serve Internet of Things (IoT) applications in a long range rural and forest areas. The capability of the gateways, both for wireless connection with the backbone network and be powered by renewable energy resources, makes possible the development of energy autonomous and without the need of cabling LoRa networks. This perspective greatly facilitates the network installation in hard-to-reach areas, as well as reducing their operating costs. An important obstacle, for the operation of energy autonomous LoRa networks, is the need for continuous operation of the gateways defined by the LoRaWAN protocol, leading to increased power consumption. For this reason, it is necessary the creation of a protocol that will reduce the energy consumption of the gateways, while maintaining the quality of the services provided in the IoT applications. In this paper we present a new hybrid protocol for energy autonomous and cable-less multi-gateway LoRa networks. The simulation results showed that the proposed protocol ensures a more than 50% reduced energy consumption for the gateways, as well as increased efficiency and reliability compared to the existing protocol, at the cost of slightly increased delay in data transmission. © 2023 IEEE.

关键词： Energy efficiency

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Decision Making in Precision Agriculture - The Case of VEL OS Intelligent Decision Support System 14

Decision Making in Precision Agriculture - The Case of VEL O...

引用

14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Theodorou, Dimitrios Mantas, Nikolaos Karampelia, Ioanna Dimokas, Nikos Kyriakidis, Thomas Louta, Malamati University of Western Macedonia Faculty of Engineering Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece

ISBN: (纸本)9798350318067

Precision agriculture includes Integrated Pest Management (IPM), which focuses on preventing and controlling pests and diseases through a combination of biological, traditional, and chemical practices. An Intelligent Decision Support System (VELOS) was developed for plant protection in bean cultivations in the Prespa region of Greece. VELOS utilizes remote sensing, GPS mapping, and sensor technologies to assist in collecting and generating massive amounts of high-quality data. This information is then used to monitor crop development, detect pests and diseases, and assess the efficacy of pest management strategies. The collected information is exploited by the Decision Support Systems (DSS) at the core of IPM. The DSS assists in saving resources and minimizing the overall cost while improving overall pest management efficiency. It also provides an intelligent decision support mechanism for pest management methods, such as which pesticides to apply and when to use them. A DSS can also help pest control practitioners monitor the effectiveness of pest management measures and modify them appropriately. This feedback loop can provide effective disease management, avoiding the danger of serious crop losses or property damage. The proposed Intelligent Decision Support System (VELOS) is responsible for generating suitable plant protection recommendations as well as scheduling their implementation. © 2023 IEEE.

关键词： Precision agriculture

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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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Javanese Script Single Letters Classification using GoogLeNet Architecture and Adam Optimizer Based on Convolutional Neural Networks (CNN)

Javanese Script Single Letters Classification using GoogLeNe...

引用

2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Susanto, Ajib Sari, Christy Atika Mulyono, Ibnu Utomo Wahyu Rachmawanto, Eko Hari Setiadi, De Rosal Ignatius Moses Ali, Rabei Raad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq

ISBN: (纸本)9798350339215

This study examines the decline in the usage of the Javanese language, which has experienced a decrease in the number of speakers from approximately 82 million in 2007 to 68.2 million in 2015. The convergence of the Javanese script and Optical Character Recognition (OCR) technology is proposed as a solution, allowing for the preservation and accessibility of the Javanese script in the digital age. The integration of Convolutional Neural Networks (CNNs) in Javanese script classification achieved a high accuracy rate of 92.95% in identifying positive and negative cases. The dataset used for training consisted of 8440 sample images, which were divided into 20 subfolders for training and testing. The results presented in Table IV demonstrate the successful implementation of the classification model, achieving a 98.87% sensitivity, 100% precision, and 98.88% specificity. This research contributes to the preservation and understanding of the culturally significant Javanese script while addressing the decline in its usage. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

引用

13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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Using Google data for assessing the relation between Covid-19 spread and social distancing

Using Google data for assessing the relation between Covid-1...

引用

2022 Global Information Infrastructure and Networking Symposium, GIIS 2022

作者： Polykalas, Spyros E. Vlachos, Kyriakos G. Prezerakos, George N. Ionian University Digital Media and Communication Kefalonia Greece University of Patras Computer Engineering and Informatics Rio Greece University of West Attica Informatics and Computer Engineering Athens Greece

ISBN: (数字)9781665490955

ISBN: (纸本)9781665490955

The paper examines the relation between the spread of the Covid-19 epidemic and the respective measures adopted by various countries collectively labeled as 'social distancing'. The progress of the disease is publicly available from the data published by the Johns Hopkins University Center for Systems Science and engineering (JHU CSSE) that contain the new cases of Covid-19 and deaths from Covid-19 per country on a daily basis. Moreover, Google reports the situation regarding social distancing in a number of social activity categories in several countries based on data gathered by users of mobile phones using Google mobile applications. The paper analyzes these two sets of data for 22 countries (20 in Europe plus the USA and Canada) and shows a significant correlation between the decrease in the levels of social distancing and the daily rate of increase of new Covid-19 cases. Consequently, the discussion is concerned with the effect of social distancing measures per category of social activity, the level of conformance displayed by citizens in each country as well as the number of days required between the imposition of the measures and their effect on the spread of the pandemic. This discussion provides additional insight that can assist policy makers in imposing the most effective set of measures as well as the proper sequence of measures withdrawing in in similar situations. © 2022 IEEE.

关键词： COVID-19 Correlation Pandemics Human factors Particle measurements Social factors Time measurement

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LIME: Long-Term Forecasting Model for Desalination Membrane Fouling to Estimate the Remaining Useful Life of Membrane 1

引用

36th International Conference on Industrial, engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023

作者： Eltanbouly, Sohaila Erradi, Abdelkarim Tantawy, Ashraf Said, Ahmed Ben Shaban, Khaled Qiblawey, Hazim Computer Science and Engineering Department College of Engineering Qatar University Doha Qatar School of Computer Science and Informatics De Montfort University Leicester United Kingdom Chemical Engineering Department College of Engineering Qatar University Doha Qatar

ISBN: (数字)9783031368226

ISBN: (纸本)9783031368219

Membrane fouling is one of the major problems in desalination processes as it can cause a severe drop in the quality and quantity of the permeate water. This paper presents a data-driven approach for long-term forecasting of fouling behavior in membrane-based desalination processes. The proposed Long-term forecastIng ModEl (LIME) consists of two intertwined machine learning models trained separately by historical operating conditions of ultrafiltration for pretreatment of reverse osmosis seawater where transmembrane pressure is used as a fouling indicator. The first model predicts the increase in fouling due to filtration. This output is fed to the second model to predict the fouling reduction due to membrane cleaning. In turn, this output is used as the initial fouling condition for predicting the next filtration cycle. The forecasted fouling is used to estimate the membrane’s remaining useful life (RUL), which ends when cleaning no longer reduces the fouling below a safety threshold. Evaluation results show that the model can predict the membrane fouling for 1400 cycles with an R-squared score of 0.8. Moreover, the RUL is estimated for various thresholds with an average percentage error of 7%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Desalination

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LINE Chatbot For Covid-19 Wellness Support using Sentence Similarity Measurement

LINE Chatbot For Covid-19 Wellness Support using Sentence Si...

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2022 International Conference on Electrical engineering and informatics, ICELTICs 2022

作者： Liliana, Dewi Yanti Nalawati, Rizky Elisa Nugrahani, Fitria Khairunisa, Hana Lestari, Raihan Agro Aulia, Vaia Sanu Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

The development of Mobile Applications in Indonesia is proliferating, especially in the development of Information Technology (IT) through social media and Instant Messaging (IM). The chatbot feature in some IMs is used daily to obtain information, one of which is LINE Messenger. Based on a survey conducted by authors to 221 respondents, the average number of respondents was 75.1% who were LINE users. In addition, the Covid-19 outbreak has hit the world. Covid-19 is a pandemic that is happening in many countries around the world. Therefore, many people are worried about the impact, and the authority must be ready to overcome the problem caused by this. Various research and collaborative efforts for Covid-19 wellness support by utilizing IT were conducted. This paper reports the use of LINE Chatbot as a platform to support the Covid-19 wellness program with the Sentence Similarity Measurement method. In addition to the chatbot, we created a website to make it easier for users to access the chatbot and obtain information generated from user queries. The accuracy rate of the SSM method for the Covid-19 chatbot is 85.71% in responding to user natural language questions. © 2022 IEEE.

关键词： Surveys

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One-Class Classification of 3D Point Clouds Using Dynamic Graph CNN 7

One-Class Classification of 3D Point Clouds Using Dynamic Gr...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Gencer, Zeki Sahin, Yusuf H. Unal, Gozde Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Anomaly detection is defined as a binary classification of normal and abnormal samples in a given data. In this task, anomalous samples are considered very rare and thus they are not used in training the deep learning model. Anomaly detection is thoroughly studied in 2D images, but 3D applications are not common yet. In this work, we combine studies in the field of 3D point cloud and anomaly detection and present a one-class classification method for 3D data. This method uses a pretrained Dynamic Graph Convolutional Neural Network (DG-CNN) to extract features from 3D point cloud data, and then fits a separate multivariate Gaussian distribution for each class. In the test phase, features are extracted from the test samples, and the distances to the distribution obtained in training indicate how anomalous the sample is for that class. Our method achieves 94.2% AUROC accuracy for the given setup. This is the first attempt to handle 3D point cloud classification as an anomaly detection case, and thus our findings and methods serve as a baseline for further studies. © 2022 IEEE.

关键词： Anomaly detection

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