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

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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BERT-based Topic Modeling Approach for Malaria Research Publication

BERT-based Topic Modeling Approach for Malaria Research Publ...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017–2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years

关键词： Text mining Heuristic algorithms Multimedia systems Time series analysis Sociology Semantics Pipelines

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Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification

Convolutional Neural Network Using Res-Net For Organic and A...

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International Conference of computer science and Information Technology

作者： Abba Suganda Girsang Rezky Yunanda Muhammad Edo Syahputra Ezra Peranginangin Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08.

关键词： Image recognition Computational modeling Transfer learning Metaheuristics Inorganic materials Iron Classification algorithms

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Mobile application to track people in covid19 monitoring and patients under covid19 supervision 2

Mobile application to track people in covid19 monitoring and...

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

作者： Chrysler, A. Warnars, H.L.H.S. Utomo, W.H. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Magister Science of Information Technology Faculty of Computing President University Bekasi17550 Indonesia

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients. © Published under licence by IOP Publishing Ltd.

关键词： Mobile computing

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Text-based Depression Detection on Social Media Posts: A Systematic Literature Review

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Procedia computer science 2021年 179卷 582-589页

作者： David William Derwin Suhartono Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Due to the huge increase of awareness of mental health well-being, the detection of mental illness itself is starting to become a huge concern. Many psychiatrists found difficulties in identifying the existence of mental illness in a patient because of the complicated nature of each mental disorder, thus making it hard to give the appropriate treatment to the patient before it’s too late. However, due to the integration of social media into people’s daily life, this create an environment that may provide additional information regarding the mental disorder a patient bear. This study has been undertaken as a Systematic Literature Review (SLR), which is defined as a process of identifying, assessing, and interpreting the available resources to provide answers for a set of research questions. Analysis is made to answer questions regarding text-based mental illness detection based on the social media activity of people with mental disorders, and reveals that it indeed is possible to do early detection of depression on social due to the existence of a particular characteristics in the way these subjects use their social media. This SLR found that from the small amount of research using text-based approach, most studies use deep learning models such as RNN on the early detection of depression cases due to the limitation of data availability. However, this study will look to find method that may prove to be more effective.

关键词： Linguistic analysis Natural language processing Depression detection Social media

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment

AI-Based Feature Extraction and Cosine Similarity for Automa...

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Information Technology (ACIT)

作者： Bambang Dwi Wijanarko Bachtiar Rosilah Binti Hassan Dina Fitria Murad Ridho Bramulya Ihsan Yaya Heryadi Engkos Achmad Kuncoro Eka Julianti Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Master of English Education Graduate School Universitas Terbuka Jakarta Indonesia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Manajemen Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department Universitas Terbuka Jakarta Indonesia

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well.

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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Networking, Electrical Engineering, computer science, and Technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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Attention is Everything You Need: Case on Face Mask Classification

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Procedia computer science 2023年 227卷 372-380页

作者： Nanda Pratama Dody Harianto Stefan Filbert Harco Leslie Hendric Spits Warnars Maybin K. Muyeba School of Computer Science Bina Nusantara University West Jakarta Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University West Jakarta Jakarta 11480 Indonesia MIST Consulting Manchester M410SA United Kingdom

Automated face mask classification has surfaced recently following the COVID-19 mask wearing regulations. The current State-of-The-Art of this problem uses CNN-based methods such as ResNet. However, attention-based models such as Transformers emerged as one of the alternatives to the status quo. We explored the Transformer-based model on the face mask classification task using three models: Vision Transformer (ViT), Swin Transformer, and MobileViT. Each model is evaluated with a top-1 accuracy score of 0.9996, 0.9983, and 0.9969, respectively. We concluded that the Transformer-based model has the potential to be explored further. We recommended that the research community and industry explore its integration implementation with CCTV.

关键词： Face Mask Classification Convolutional Neural Network Attention Transformer Deep Learning

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Information system design on tobacco addiction measurement protocol for smokers 6

Information system design on tobacco addiction measurement p...

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

作者： Mahesworo, Bharuno Budiarto, Arif Parung, Deby Erina Amirulloh, Dian Simon, Simon Pardamean, Bens Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Genetics Indonesia Product Department Jakarta Indonesia Genetics Indonesia Laboratory Department Jakarta Indonesia Board of Director Genetics Indonesia Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Smoking habits in Indonesia has become a national health burden despite their huge contribution to national economics. With easy access to inexpensive cigarettes in Indonesia, the percentage of teenage smokers in Indonesia is very high. More than 70% of these smokers have tried to quit, but only approximately 5% have succeeded. These facts show the importance of developing precision stop smoking treatments. We proposed an information system design of smoking measurement questionnaire that is able to describe the characteristic differences between smokers and former smokers. This information system design was implemented in genetic service company's web-based application and is expected to help us extract the necessary variables to help us finding biomarkers associated with smoking cessation in our future study. The information system design has been developed by combining and modifying questionnaires from previous studies. The focus is on strategic variables and the compactness of the questionnaire, and it can be used by other researchers to describe the smoking dependency of their samples. The information system design has been tested on pilot respondents/customers and shows results that meet our expectations. © 2021 IEEE.

关键词： Tobacco

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