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Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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Real Market Basket Analysis using Apriori and Frequent Pattern Tree Algorithm

Real Market Basket Analysis using Apriori and Frequent Patte...

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2021 International Conference on Computer, Control, informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Christian, Michael Albert Nathanael, Nathanael Mauliani, Annisa Indrawati, Ariani Manik, Lindung Parningotan Akbar, Zaenal Informatics Engineering Program Study Kwik Kian Gie School of Business Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9781450385244

Recently, data mining has been implemented in various fields, including business and telecommunications. Data mining is a technique for extracting and detecting patterns in massive data sets that combines machine learning, statistics, and database systems. One of the most important use-cases in data mining is finding the high-frequency patterns between the set of itemset called association rules. Association rule mining is a well-researched technique for finding some relations between variables in large databases. This paper aims to measure the performance of the Apriori and Frequent Pattern Tree algorithms by comparing them using several points of comparison. Then we compared the outputs, whether they produce the same or different rules, to find out whether the way the two algorithms work is similar or not. After that, we looked for the itemsets that best match the reality in the market by giving them to a user who had transaction data from his spare parts shop. © 2021 ACM.

关键词： Association rules

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LE-DTA: Local Extrema Convolution for Drug Target Affinity Prediction 48

LE-DTA: Local Extrema Convolution for Drug Target Affinity P...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Langore, Tanoj Hsu, Te-Cheng Hsieh, Yi-Hsien Lin, Che Taipei10617 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9781728163277

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset. © 2023 IEEE.

关键词： Graph neural networks

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Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients 10

Supervised Learning Models for Early Detection of Albuminuri...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Muharram, Arief Purnama Tahapary, Dicky Levenus Lestari, Yeni Dwi Sarayar, Randy Dirjayanto, Valerie Josephine School of Electrical Engineering and Informatics Master Program of Informatics Institut Teknologi Bandung Bandung Indonesia Indonesia Medical Education and Research Institute Universitas Indonesia Metabolic Disorder Cardiovascular and Aging Research Center Faculty of Medicine Jakarta Indonesia Universitas Indonesia Faculty of Medicine Cipto Mangunkusumo General Hospital Ophthalmology Department Jakarta Indonesia Universitas Indonesia Faculty of Medicine Jakarta Indonesia

ISBN: (纸本)9798350329919

Diabetes, especially T2DM, continues to be a significant health problem. One of the major concerns associated with diabetes is the development of its complications. Diabetic nephropathy, one of the chronic complication of diabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing diabetic nephropathy involves considering various criteria, one of which is the presence of a pathologically significant quantity of albumin in urine, known as albuminuria. Thus, early prediction of albuminuria in diabetic patients holds the potential for timely preventive measures. This study aimed to develop a supervised learning model to predict the risk of developing albuminuria in T2DM patients. The selected supervised learning algorithms included Naive Bayes, Support Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost, and Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries of diabetes complications risk factors, was used to train the algorithms. It consisted of 10 attributes as features and 1 attribute as the target (albuminuria). Upon conducting the experiments, the MLP demonstrated superior performance compared to the other algorithms. It achieved accuracy and f1-score values as high as 0.74 and 0.75, respectively, making it suitable for screening purposes in predicting albuminuria in T2DM. Nonetheless, further studies are warranted to enhance the model's performance. © 2023 IEEE.

关键词： Forecasting

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Development of Urban Data Sensing for Smart City Platform

Development of Urban Data Sensing for Smart City Platform

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International Conference on ICT For Smart Society (ICISS)

作者： Okyza Maherdy Prabowo Suhono Harso Supangkat Eueung Mulyana STMIK AMIK Bandung Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Smart City and Community Innovation Center School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

The idea of a “smart city” is one in which cities may effectively and efficiently manage their resources to raise the standard of living of its residents. Cities are already capable of performing sensing, understanding, and acting at a certain degree because to the existence of information technology as an enabler. Several studies have created various physical sensors for sensing in order to produce data. However, urban data is not limited to factual information on the environment and infrastructure. In order to collect data from the digital world, including social media, online news, and other digital social sensors, this research aims to create an urban data collecting system. The creation of digital urban sensor data can enhance the accuracy of the information that can help decision-makers.

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Revealing a Country’s Government Discourse Through BERT-based Topic Modeling in the US Presidential Speeches

Revealing a Country’s Government Discourse Through BERT-bas...

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International Conference on Computer, Control, informatics and Its Applications (IC3INA)

作者： Rafi Mahmud Zain Sajarwo Anggai Tukiyat Ahmad Musyafa A. A. Waskita Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

This paper analyzes presidential speeches from the United States, delivered by the current president, Joe Biden, from his inauguration until April 3, 2024, using BERT-based topic modeling techniques. Since the role and actions of the United States affect global affairs, inferring latent topics from these speeches are important for making informed decisions and also useful to researchers. We provide experiments combining different embedding methods and clustering algorithms for dimensionality reduction. Our experiment results demonstrated that using DistilBERT embeddings followed by HDBSCAN clustering and UMAP dimensionality reduction consistently produced meaningful and interpretable topics, and this paper introduces the UMASS and NPMI metrics for evaluating topic coherence, with the best values of these metrics among the other schemes investigated. Some of the dominant themes conveyed in the American president’s state of the union speech that were successfully revealed in this study include international cooperation, alongside significant attention to environmental, community, health, and economic issues.

关键词： Measurement Dimensionality reduction Economics Digital control Visualization Government Coherence Medical services Security informatics

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Development of A Deep Learning Hybrid Model for Classification of Plastic Waste Objects in the Coastal Environment

Development of A Deep Learning Hybrid Model for Classificati...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Imran Djafar Indrabayu Syafruddin Syarif Doctoral Program of Electrical Engineering Department Hasanuddin University Dipa Makassar University Makassar Indonesia Department of Informatics Hasanuddin University Makassar Indonesia Department of Electrical Engineering Hasanuddin University Makassar Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Plastic, glass, metal, and wood waste has become a global scourge and nightmare to the marine environment and the development of the coastal areas. This paper aims at developing a hybrid model of deep learning technique using Convolutional Neural Networks (CNN) and Vision transformers (ViT) in the performance of detecting underwater waste materials. The model is designed with the use of transfer learning and high-resolution imaging sourced from underwater drones to help overcome the challenges of underwater work such as poor visibility and insufficient ambient light. The local features are extracted using CNNs and the global spatial relationships of the images are attended to by the ViTs. The results achieved a waste detection accuracy rate of about 90%, which is effective even in difficult environmental conditions. The concept is for a waste management system where drones or underwater robotics systems can assist automated waste management, waste treatment processes, and aid marine renovation activities.

关键词： Waste management Deep learning Transfer learning Sea measurements Metals Glass Feature extraction Transformers Convolutional neural networks Drones

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The Effect of Regularization on Deep Learning Methods for Detection of Malaria Infection 1

The Effect of Regularization on Deep Learning Methods for De...

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1st International Conference on Converging Technology in Electrical and Information engineering, ICCTEIE 2021

作者： Swastika, Windra Widodo, Romy Budhi Balqis, Ginza Alfarizha Sitepu, Rehmadenta Universitas Ma Chung Informatics Engineering Malang Indonesia Universitas Ma Chung Program of Pharmacy Malang Indonesia

ISBN: (纸本)9781665400817

Malaria is an infectious disease caused by peripheral blood parasites of the genus Plasmodium. The estimated global malaria cases reached 229 million cases in 2019, of which 250,644 cases occurred in Indonesia. The large number of cases makes the early and accurate diagnosis of malaria very important because it can reduce the severity and prevent death. The most widely used method of diagnosis by far is examining a thin blood smear under a microscope and looking for infected cells. This research examines the effect of regularization applied to several Convolutional Neural Network (CNN) architectures to obtain the best accuracy of malaria parasite detection on thin blood smear images. The regularization techniques used are dropout layer, L2 regularization, and data augmentation. The results show that the use of BaselineNet without regularization achieved 94.92% accuracy. The use of regularization on ResNet-50, MicroVGGNet, BaselineNet-1 obtained 97.12%, 95.64% and 96,28% accuracy respectively. © 2021 IEEE.

关键词： Diagnosis

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The Recommendation System for Increasing the Independence of Micro, Small and Medium Enterprises (MSMEs) Using the Normalized Rating Frequency (NRF) Method 4

The Recommendation System for Increasing the Independence of...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Lestari, Sri Yulmaini Aswin Kurniawan, Rio Sulyono Pratama, Yan Aditiya Faculty of Computer Science Informatics Engineering Study Program Bandar Lampung Indonesia Faculty of Business Economics Management Study Program Bandar Lampung Indonesia

ISBN: (纸本)9781665433945

Micro, Small, and Medium Enterprises (MSMEs) have an important role in improving the economy of small communities and the stability of the Indonesian economy. However, they still face various problems such as capital, raw materials, distribution, licensing, and others. This study developed a special treatment recommendation system to increase the independence of MSMEs by adopting a ranking-based method to produce recommendations related to problem-solving steps from MSMEs. The ranking method used Normalized Rating Frequency (NRF). This method performed an aggregation process of the ratings given by MSMEs in looking at the priority level of problem-solving based on previous experience. The result of the experiment showed that an average NDCG value was 0.7916. This explained that the quality of the ranking was good so that it was feasible to be recommended in solving problems in the form of special treatment to acquire to the independence of MSMEs. © 2021 IEEE

关键词： Recommender systems

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An Investigation of Ultrashort Pulses Propagation in Atmospheric Turbulence of FSO Communications 9

An Investigation of Ultrashort Pulses Propagation in Atmosph...

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9th International Conference on Electrical engineering, Computer Science and informatics, EECSI 2022

作者： Darusalam, Ucuk Priambodo, Purnomo Sidi Universitas Nasional Informatics Study Program Universitas Siber Asia of IEEE Photonics Society Informatics Faculty of Information and Communications Technology Jakarta Indonesia Universitas Indonesia IEEE Photonics Society Faculty Engineering Department of Electrical Engineering Depok Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

The atmospheric turbulence as the natural media of optical propagation for the free-space optical (FSO) communications is the great advantage to reach flexible connection and low-cost deployment but also is the major problem as well. Since, the phenomena of optical propagation in the atmosphere are random process, an optical signal is induced that produces severe effects to the performance. Thus, a method to reach best performance of FSO must be developed intensively to reduce the severe effects of atmospheric turbulence. One of the solutions to reduce those aforementioned effects is an ultrashort pulse implemented in FSO to modulate frequency carrier as the signal of optical propagation. In this paper, an investigation of an ultrashort pulse was performed through simulation. An ultrashort pulse has a great chance to be implemented in FSO since it has shorter width of pulse duration than a short pulse. An ultrashort pulse is generated using optical second harmonic pulse generator that produce a pulse at 50 fs of width. Based on the results of simulation, the ultrashort pulses showing better performance of FSO than short pulses have. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： atmospheric turbulence free-space optical communications optical propagation optical scintillation temporal broadening turbulence effects ultrashort pulses

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