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Deep Learning as a Vector Embedding Model for Customer Churn

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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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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Enhancing Branch Office Network Availability Using Cloud EoIP Gateway

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Procedia computer science 2021年 179卷 574-581页

作者： Fransiskus W.P. Dharma Suharjito Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia 11480

Since internet service is mostly available in many countries, tunneling solution over an internet link often used in the company for network communication between its multi-regional branches. The current state of the art solution consisting of an EoIP tunnel combined with IPsec installed on the Head Quarter of the company. Researchers see this solution as prone to total network disruption when the HQ loses its internet service. With the rising trend of cloud computing development, researchers then propose a multi-region network infrastructure design consist of MikroTik devices and Cloud platforms using EoIP IPsec protocol. Cloud platform used as an EoIP IPsec gateway due to its high availability. From the implementation and testing, the EoIP Cloud gateway proved to have high network availability. Branch network communication proved to be running well with no impact on the HQ loss of internet services. IPsec also proved to be secure enough to secure user data transmitted over the internet. From the QoS findings, there is a 24ms drop in delay performance and 0,66 ms drop in jitter performance. The EoIP IPsec on cloud platform also proved to have a 0,97% improvement in throughput performance and has a 0% packet loss rate.

关键词： EoIP tunnel Secure EoIP MikroTik Availability MikroTik Cloud Network Infrastructure

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Designing Wireless Sensor Network Routing on Agriculture Area Using the LEACH Protocol 3

Designing Wireless Sensor Network Routing on Agriculture Are...

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3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

作者： Gitakarma, Made Santo Priyambodo, Tri Kuntoro Suyanto, Yohanes Sumiharto, Raden Wijayanto, Danur Universitas Gadjah Mada Post Graduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Master Program of Computer Science Yogyakarta Indonesia

ISBN: (纸本)9781728184067

Microelectronic technology that supports the establishment of wireless sensor networks (WSN) has brought hope to the ease of Internet of Things (IoT) technology that can generate smart environments. A WSN consists of a collection of sensor nodes that are small, intelligent, and inadequate in storage, energy, and processing power. One of the well-known routing techniques in WSN is LEACH. There have been many LEACH studies and their development with random node distribution. Here we designed several scenarios in WSN routing with the position of the sensor nodes specified in the mapping area. MATLAB simulation results show network lifetime can be extended by reducing data packet size. In five experiments, the number of 40 nodes was the optimal number to extend network lifetime. Each scenario has advantages and disadvantages that can be referenced in developing the LEACH protocol. © 2020 IEEE.

关键词： Internet of things

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Optimizing data cube visualization for web. Applications performance and user-friendly data aggregation

arXiv

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arXiv 2021年

作者： Szelogowski, Daniel UW - Whitewater Computer Science Master’s Program WhitewaterWI United States

Current open-source applications which allow for cross-platform data visualization of OLAP cubes feature issues of high overhead and inconsistency due to data oversimplification. To improve upon this issue, there is a need to cut down the number of pipelines that the data must travel between for these aggregation operations and create a single, unified application which performs efficiently without sacrificing data, and allows for ease of usability and extension. © 2021, CC BY.

关键词： Visualization

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Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm 1

Predicting Relegation Clubs in Italian Serie A with Method b...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Syukmana, F. Wahyudi, E. Gata, W. Wahono, H. Febianto, N.I. Kuntoro, A.Y. Nitra, R.O. Effendi, L. Saputra, D.D. Sulaeman, O.R. Computer Science Postgraduate Program STMIK Nusa Mandiri Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Indonesia

The purpose of this study is to help small clubs from Italian Serie A in finding the minimum targets to avoid relegation into Serie B competition (below Serie A league). Relegation will reduce the club's income from TV revenues and the decline of enthusiastic supporters. Based on the data from the final standings (seasons 2006 until 2018), this can be explained by the Decision Tree method using the C4.5 algorithm. The methods used in this study are data collection, data pre-processing, model proposal, model testing, and model validation. In this study, it is expected that the value of accuracy exceeds 85% to achieve a proper classification. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Lightweight End to end Pose-Robust face recognition system with Deep Residual Equivariant Mapping

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Procedia computer science 2021年 179卷 648-655页

作者： Kevin William Gunawan Noptovius Halimawan Suharjito Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta 11480 Indonesia

In the face recognition field of study, pose-robustness and lightness of a model are few of the critical improvement factors of face recognition. However, these fields are still providing challenge for researchers. Even though pose variance is proven to drop the accuracy of deep learning-based models, pose-robustness is not studied often in lightweight face recognition models. Existing pose-robust models have heavier implementation costs compared to lightweight models. We propose a deep learning architecture that implements Deep Residual Equivariant Mapping (DREAM) to improve pose-robustness of a lightweight MobileFaceNets model as a solution to the underlying issue. In the proposed model, the DREAM block is stitched to the MobileFaceNets stem CNN architecture. The evaluation process compares the speed, file size, and accuracy on pose diverse datasets, such as the CFP and IJB-A dataset. The evaluation results of the proposed model show an accuracy improvement of 0.07% with verification speed difference of 0.17 ms. Both of the results show a better performance compared to the baseline naive model.

关键词： face recognition pose-robust lightweight deep learning

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Development of Portable Temperature and Air Quality Detector for Preventing Covid-19 1

Development of Portable Temperature and Air Quality Detector...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Budiharto, Widodo Irwansyah, Edy Dewanti, Retno Santoso Gunawan, Alexander Agung Widhyatmoko, Danu Sembodo, Jarot Soeroso Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Business School Management Department Jakarta Indonesia Bina Nusantara University School of Design Dept. of Visual Communication Design Jakarta Indonesia Bina Nusantara University Master of Information System Program BINUS Graduate Program Jakarta Indonesia

ISBN: (纸本)9781728133331

Due to Covid-19, body temperature measurement is mandatory and become an important consideration in determining whether an individual is healthy or not. This paper presents the development of portable temperature and air quality detector to prevent suspect of Covid-19 with the main symptom is the body of temperature above 38° Celsius. We propose an algorithm and architecture used for temperature detector with maximum distance 80cm and CO2 and Volatile Organic Compounds (VOC) measurement are as indicator of good air quality. Based on experiment, we can detect temperature accurately until 0.3° Celsius using digital temperature sensor MLX90614 comparing with commercial one, furthermore the system able to give information about the quality of air, allowing/not allowing someone to enter a room with accuracy 92.5%. © 2021 IEEE.

关键词： Air quality

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Cloud Quantum Computing Concept and Development: A Systematic Literature Review

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Procedia computer science 2021年 179卷 944-954页

作者： Haryono Soeparno Anzaludin Samsinga Perbangsa Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480

A cloud quantum computer is a quantum computer that can be accessed in a cloud environment through a network. Today, there are numbers of cloud quantum computing services that can be accessed by users. They are used to solve complex problems that require powerful computing. Different cloud quantum computing services deliver different architecture and performances. In our study, we conducted a research on some services to test and evaluate the performances of different cloud quantum computing services and make a comparison out of it. The test will be conducted using two different methods such as visual programming and qiskit. From the result, we can see that the amount of qubit per backend and shots per run pretty much affect the execution time of a cloud quantum computing. This test will give the users some insight and enables them to decide which cloud quantum computing services deliver better performance or faster execution time based on the specification each cloud quantum computer offers.

关键词： Cloud Quantum Computing Benchmarking Comparison

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Classification of Acute Intracerebral Hemorrhage Using Radiomics on Brain Computed Tomography Images

Classification of Acute Intracerebral Hemorrhage Using Radio...

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International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Phattanun Thabarsa Salita Angkurawaranon Chakri Madla Withawat Vuthiwong Kittisak Unsrisong Papangkorn Inkeaw Master's Degree Program in Data Science Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Radiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand Department of Computer Science Data Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand

Acute intracerebral hemorrhage (ICH) entity accounts for 10 to 15% of all strokes and is associated with a higher mortality rate ischemic stroke or subarachnoid hemorrhage. Causes of ICH are divided into primary, and secondary, including vascular malformation and tumorous. Primary ICH accounts for approximately 80% of all ICH cases. Vascular anomalies rank as the second most common cause of spontaneous ICH overall. Furthermore, hemorrhage resulting from brain tumors can occur in up to 10% of all primary or metastatic tumors. Early recognizing of these three causes of bleeding is critical for clinicians in precise diagnosis, effective treatment management, and helps avoid delayed diagnosis. We proposed a radiomics approach for classifying multiple causes of acute ICH as vascular malformation, tumorous, and primary-related hematoma. Non-contrast brain computed tomography with clinical features was used as input. The regions of both hematoma and perihematomal edema were delineated by using manual segmentation approach. Four feature selection methods were adopted. Also, three classification models were investigated in this study. The results showed that using the features selected by F-value applied with SVM classifier outperformed the other models, achieving weighted average accuracy (± SD) of 0.84 (± 0.07). Additionally, the model demonstrated average sensitivity and positive predictive value of 0.84 (± 0.06) and 0.86 (± 0.05), respectively. We also evaluate the overall performance of discriminating each class from the rest using AUC. The result suggested that our proposed model achieved the weighted average AUC of 0.90. Our proposed method highlights the potential in identifying multiple causes of acute and nontraumatic ICH, which has not been previously explored.

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