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IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America)

作者： J. S. Corrêa D. L. Cavalca R. A. S. Fernandes Department of Electrical Engineering Federal University of São Carlos São Carlos Brazil Graduate Program in Computer Science Federal University of São Carlos São Carlos Brazil

The demand for electricity has increased rapidly and, for this reason, there is a need to efficiently use it. In this way, the identification of residential appliances enables such use for consumers and is crucial for demand response programs. Due to the variety of appliances in homes and their dynamic behavior, the search for patterns that explain and allow the correct labeling of temporal windows becomes a challenging task, since a window may contain more than one appliance. In this sense, the present paper proposes the transformation of time-series into images, using Gramian angular field and recurrence plots. The dataset composed of images was submitted to the labeling process, considering the use of convolutional neural networks. A comparative analysis was performed using the UK-DALE dataset. The results demonstrated the effectiveness of the proposed feature engineering stage, since the labeling task reached F1-scores until 94 %.

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Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

Multiscale Numerical Modeling for Near-Field Microwave Imped...

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CLEO: science and Innovations, S and I 2022

作者： Tami, Diego Ohlberg, Douglas A.A. Ramirez, Jhonattan C. do Rego, Cássio Gonçalves Medeiros-Ribeiro, Gilberto Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res...

ISBN: (纸本)9781557528209

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TASC4RE: a data-driven context model to support the elicitation of context-aware functionalities

TASC4RE: a data-driven context model to support the elicitat...

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2022 ER Forum and PhD Symposium, ER-Forum-PhD 2022

作者： Falcão, Rodrigo Pestana, Maria Clara Vieira, Vaninha Fraunhofer IESE Fraunhofer-Platz 1 Kaiserslautern67663 Germany Computer Science Graduate Program UFBA Av. Milton Santos s/n Salvador40170-110 Brazil Institute of Computing UFBA Av. Milton Santos s/n Salvador40170-110 Brazil

In requirements elicitation for context-aware systems, context modeling is an essential early step. However, it has been overlooked by practitioners due to its perceived high complexity, in particular when it comes to the analysis of how different contexts may influence a user task of interest. To change this situation, data-driven context modeling approaches appear to be promising: Based on contextual data, algorithms can identify how contextual elements may, in combination with each other, influence user tasks. In this paper, we contribute the Task-specific Context Model for Requirements Elicitation (TASC4RE). TASC4RE is a context model representation aimed at supporting data-driven context modeling approaches. It combines the expressiveness of natural language, which is appreciated by human readers, with formality. TASC4RE was preliminarily evaluated in a controlled experiment with professionals in the context of a software development project. The results showed a positive trend towards the acceptance of the model. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Requirements engineering

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South jakarta's air quality using PM 2.5 data at the beginning of COVID-19 Restriction 6

South jakarta's air quality using PM 2.5 data at the beginni...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Rahutomo, Reza Purwandari, Kartika Hidayat, Alam Ahmad Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

As soon as the lockdown was established in Jakarta, Indonesia, people experienced a change in Jakarta's air quality. This research was conducted to answer several challenges such as: where to find Indonesia's air quality open data platforms, how to handle the acquired data with SQL technology, and how to validate whether the air quality of Jakarta was significantly changed from a period to another or not. With particulate matter 2.5 (PM 2.5) data acquired by The United States Diplomatic Post in South Jakarta, a data visualization was generated to describe the fluctuation of PM 2.5 from February 29th to June 29th. By conducting a two-sample t-test, a significant change in South Jakarta's air quality was revealed. The test concluded that South Jakarta's air quality got worse even after a lockdown was established. © 2021 IEEE.

关键词： COVID-19

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Deep Learning as a Vector Embedding Model for Customer Churn

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Procedia computer science 2021年 179卷 624-631页

作者： Tjeng Wawan Cenggoro Raditya Ayu Wirastari Edy Rudianto Mochamad Ilham Mohadi Dinne Ratj Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

关键词： customer churn customer behavior deep learning vector embedding representation learning

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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia 2

Lockdown strategy worth lives: The SEIRD modelling in COVID-...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Nurlaila, I. Hidayat, A.A. Pardamean, B. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

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A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians

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Procedia computer science 2021年 179卷 632-639页

作者： Steven Amadeus Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.

关键词： Prognostication Polygenic Risk Model Transformer Model DeepLIFT Colorectal Cancer

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Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Telematics Work Field Review Text Classification Using the N...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Aries Maesya Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Reseach Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia

Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%.

关键词： Social networking (online) Service robots Text categorization Blogs Training data Telematics Data models

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Ant colony optimization with semi random initialization for nurse rostering problem

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International Journal for Simulation and Multidisciplinary Design Optimization 2021年 12卷

作者： Achmad, Said Wibowo, Antoni Diana, Diana Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

A nurse rostering problem is an NP-Hard problem that is difficult to solve during the complexity of the problem. Since good scheduling is the schedule that fulfilled the hard constraint and minimizes the violation of soft constraint, a lot of approaches is implemented to improve the quality of the schedule. This research proposed an improvement on ant colony optimization with semi-random initialization for nurse rostering problems. Semi-random initialization is applied to avoid violation of the hard constraint, and then the violation of soft constraint will be minimized using ant colony optimization. Semi-random initialization will improve the construction solution phase by assigning nurses directly to the shift that is related to the hard constraint, so the violation of hard constraint will be avoided from the beginning part. The scheduling process will complete by pheromone value by giving weight to the rest available shift during the ant colony optimization process. This proposed method is tested using a real-world problem taken from St. General Hospital Elisabeth. The objective function is formulated to minimize the violation of the constraints and balance nurse workload. The performance of the proposed method is examined by using different dimension problems, with the same number of ant and iteration. The proposed method is also compared to conventional ant colony optimization and genetic algorithm for performance comparison. The experiment result shows that the proposed method performs better with small to medium dimension problems. The semi-random initialization is a success to avoid violation of the hard constraint and minimize the objective value by about 24%. The proposed method gets the lowest objective value with 0,76 compared to conventional ant colony optimization with 124 and genetic algorithm with 1. © S. Achmad et al., Published by EDP sciences, 2021.

关键词： Ant colony optimization

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia 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

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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