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Machine Learning Approaches in Detecting Autism Spectrum Disorder

Procedia computer science 2023年 227卷 1070-1076页

作者： Daniel Nicholas Dominic Tjeng Wawan Cenggoro 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

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Envisioning Risk Management for Smart Contracts: A Conceptual Model

Envisioning Risk Management for Smart Contracts: A Conceptua...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

作者： Semi Yulianto Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

This study proposes a smart contract risk management model built on the NIST Risk Management Framework (RMF) to help identify, assess, and manage the risks of smart contracts. While smart contracts are beloved as a means to automate and disintermediate business processes, their security vulnerabilities can be critical. The main issue discussed in this paper is the lack of a holistic approach to risk management smart contracts. The resulting framework consists of six steps: Risk identification, assessment, prioritization, mitigation, testing, and continuous monitoring (and was developed through reviewing existing literature on smart contract security and the NIST RMF). It is recommended that a case study be performed to prove the proposed model's effectiveness in managing the risks of smart contracts and minimizing financial losses and reputational harm. The paper presents a risk management framework for smart contracts to increase trust and adoption to enhance security while reducing financial losses and reputation damage. This has wider implications for the security of smart contracts and can be used as a starting point for future work. This study is expected to significantly contribute to smart contract security by introducing an organized way to address these contracts' risks using the NIST RMF.

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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats.

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Using SYCLomatic to Migrate CUDA Code to oneAPI Adapting NVIDIA GPU

Using SYCLomatic to Migrate CUDA Code to oneAPI Adapting NVI...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Wentao Liang Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Program in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

Nowadays, the use of accelerators in high performance computing has become more common than ever before. The most used accelerators must be the Graphics Processing Unit (GPU). It has emerged as an important component in most of the parallel computing scenarios, surpassing the capabilities of the traditional Central Processing Unit (CPU) in perspective of both performance and energy efficiency.

关键词： Codes Conferences High performance computing Graphics processing units Cluster computing Parallel processing Energy efficiency Central Processing Unit

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

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Procedia computer science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Human Activity Classification: A LSTM and MediaPipe Approach for Intelligent Systems Application 6

Human Activity Classification: A LSTM and MediaPipe Approach...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Chen, Yi-An Yang, Pei-Ching Chang Jung Christian University Master Degree Program in Information and Design Tainan Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan Chang Jung Christian University Artificial Intelligence Research Center Tainan Taiwan

ISBN: (纸本)9798331518158

With advancements in artificial intelligence, computer vision technologies have seen significant progress, enabling applications like facial recognition, autonomous driving, and medical imaging. This study leverages the Multi-Camera Action Dataset (MCAD) and MediaPipe Pose to create a skeletal dataset for training a Long Short-Term Memory (LSTM) model to recognize 18 types of daily actions. Evaluation using confusion matrix and F1 score reveals that Action 16 (Object Carry) is often misclassified as Action 7 (Walk) due to similar leg movements, and Action 9 (Cell to Ear) as Action 1 (Wave) due to similar hand movements. Future work aims to enhance model accuracy and explore broader applications in physical activity and exercise analysis. © 2024 IEEE.

关键词： Intelligent systems

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ICT-Based Conceptual Model for Local Culture Communities using Soft System Methodology 7

ICT-Based Conceptual Model for Local Culture Communities usi...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Nindito, Hendro Prabowo, Harjanto Sfenrianto Hendric, Spits Warnars Harco Leslie BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The problems that exist in the field of art and culture preservation experienced by the arts and culture community side are the limitations on physical facilities for disseminating works, exchanging information between arts and culture communities, as well as the need to increase professionalism so that they can be independent in order to help improve the economy, where the role of regulation in this regard also affects this condition. Based on the facts above, this community requires an interaction model that is integrated with the use of information technology. This study uses a Soft System Methodology approach to create a model that is suitable for the arts and cultural community. The result is an ICI-based conceptual model for a local culture community. (Abstract) © 2022 IEEE.

关键词： Information use

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Recommendation System Journalist for Getting Top News Based on Twitter Data

Recommendation System Journalist for Getting Top News Based ...

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2019 International Conference of science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Girsang, Abba Suganda Isa, Sani Muhamad Harvy, Ikrar Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

One application that can be utilized in finding the latest news is by utilizing the development of information and communication technology such as seeing the delivery of public information through social media such as twitter. This study implements the system which is able to get the trending or top news based on twitter data. This information will connect to the journalist via mobile application. It helps journalists to get the potential news to be reported in their media. The application will group each tweets into various themes based on the cosine similarity. The tweets which are more the certain threshold, can be indicated as the top news. The top news is then classified into one category news by using SVM algorithm. The training data becomes from the source data of one media news. The specific topic of this result will ease to order the journalist cover this top news. The system can be implemented well. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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