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6th International Workshop on Big Data and Information Security, IWBIS 2021

作者： Briliyant, Obrina Candra Tirsa, Nusranto Pratama Hasditama, Maulana Andika Politeknik Siber Dan Sandi Negara Cyber Security Engineering Program Department of Cyber Security Bogor Indonesia National Cyber and Crypto Agency Depok Indonesia Faculty of Electrical Engineering and Informatics Budapest Computer Engineering Budapest Hungary

ISBN: (数字)9781665424516

ISBN: (纸本)9781665424516

Cyber Threat Intelligence (CTI) is an actionable, evidence-based knowledge management system of cyber threats data. CTI consists of 3 (three) major parts, namely information gathering, analysis, and dissemination. To carry out dissemination in CTI, Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Intelligence Information (TAXII) have been developed as open and interoperable standards for the CTI dissemination. An effective CTI is a CTI that can be integrated into the organization's security operations processes. Many organizations today runs CTI with a mechanism for reporting and distributing information in the dissemination process or known as cyber threat information sharing which has significant effects on security operations performance. In this study, user requirements analysis and design of cyber threat information sharing processes were carried out. From the results of the requirement analysis, a suitable business process re-engineering were proposed to accelerate the automation of CTI, especially in the information sharing part. Lastly, the designed processes were tested by simulating the processes using STIX and TAXII-based platform. The results of this study indicate that the designed processes can be run on STIX and TAXII-based platforms, ergo a step toward to a fully automated CTI. © 2021 IEEE.

关键词： Information dissemination

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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Stroke Prognostication in Patients Treated with Thrombolysis Using Random Forest

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Open Neuroimaging Journal 2024年 17卷

作者： Yunus, Reyhan Eddy Harris, Salim Sidipratomo, Prijo Kekalih, Aria Jatmiko, Wisnu Pandelaki, Jacub Rachman, Andhika Syahrul, Syahrul Valindria, Vanya Vabrina Rachmadi, Muhammad Febrian Muzakki, Muhammad Faris Tjuatja, Andrew Wijaya, Anthony Eka Teresa, Devina Faculty of Medicine Universitas Indonesia Rumah Sakit Umum Pusat Nasional Cipto Mangunkusumo Doctoral Program in Medical Sciences Faculty of Medicine Universitas Indonesia Faculty of Computer Science Universitas Indonesia Faculty of Medicine Universitas Syiah Kuala School of Electrical Engineering and Informatics Institut Teknologi Bandung

Background: Early identification and accurate prognostication of acute ischemic stroke are crucial due to the narrow time frame for treatment and potential complications associated with thrombolysis intervention. Objectives: This pilot study in the Southeast Asian region using Indonesian data, aims to develop a novel machine learning model for predicting the clinical outcome of acute ischemic stroke patients following thrombolysis. The model seeks to aid clinicians in identifying eligible candidates for thrombolysis therapy. Methods: This retrospective study at Cipto Mangunkusumo Hospital’s medical records from 2014 to 2023 used non-contrast brain CT, clinical, and lab data to develop a Random Forest (RF) algorithm predicting Δ NIHSS (National Institutes of Health Stroke Scale) score, indicating functional outcome. The developed RF model was applied to a validation dataset, with performance evaluated. The study also compared RF with a previous Convolutional Neural Networks (CNN) algorithm. Results: This study included 145 acute ischemic stroke patients treated with thrombolysis. It demonstrated the promising feasibility of using machine learning algorithms to predict clinical outcomes in this population. Integration of CT, clinical, and laboratory data as inputs to the RF models shows the best prediction performance (Accuracy = 0.75, AUC = 0.72, F1=0.50, Precision=0.60, Sensitivity=0.43, Specificity=0.88) Conclusions: The application of machine learning shows the potential to enhance the selection process for thrombolysis intervention in treating acute ischemic stroke. Further research with larger multicenter datasets and additional imaging modalities is required to improve predictive ability. © 2024 The Author(s).

关键词： Artificial intelligence International license Machine learning Prognostication Stroke Thrombolysis

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

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Journal of King Saud University - computer and Information Sciences 2024年第2期36卷

作者： Yousefi, Shamim Najjar-Ghabel, Samad Danehchin, Ramin Band, Shahab S. Hsu, Chung-Chian Mosavi, Amir Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning data. This paper proposes a hybrid mechanism for automated melanoma detection on dermoscopic images based on Discrete Cosine Transform features and metadata. It is composed of three steps. First, extra information/artifacts are deleted;the remaining pixels are standardized for accurate processing. Second, the reliability of the mechanism is improved by the Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of features is reduced by Locality Sensitive Discriminant Analysis. The third step divides the images into learning and test ones to create image-based models using learning data. Finally, the best model is selected based on test data and improved by a metadata-based model. Simulation results show that the decision tree provides the most practical image-based model by improving accuracy and sensitivity. Besides, the comparison results demonstrate that our model improves the F-Value to detect melanoma superior to other approaches. © 2024 The Author(s)

关键词： Artificial intelligence Big data Data science Dermoscopic image Discrete cosine transform features Machine learning Melanoma detection Metadata

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer Science 2021年第1期2卷 1-11页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer Science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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