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CTAG using GNN for GWAS analyses of schizophrenia to identify disease-associated genes and repurpose drugs 4

CTAG using GNN for GWAS analyses of schizophrenia to identif...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Ding, Yi Gong, Ruiyang Bai, Juzheng Ou, Ruizhi Wu, Zimeng Computer science and technology Soochow University Suzhou215031 China Oregon Episcopal School Portland97223 United States Engineering and computer science University of Southampton SouthamptonSO143QZ United Kingdom Graduate School of Arts and Science Boston University Boston02215 United States Faculty of Life sciences University of Bristol BristolBS81QU United Kingdom

ISBN: (纸本)9781510682443

Genome-wide association studies (GWAS) have demonstrated their effectiveness in discovering genetic variants associated with diseases such as Schizophrenia. However, translating these findings into therapeutic discovery and drug prediction remains a significant challenge. The NETTAG approach employs a Graph Convolutional Network (GCN) model named NOCD-G for identifying Alzheimer’s Disease (AD)-associated genes and repurposing drugs. Yet, whether there are better models for such cases, or whether this model continues to perform well in other scenarios, are questions that remain unanswered. Against this backdrop, we introduce CTAG, an optimized workflow designed to identify genes with disease-causing risks and map potential drugs for Schizophrenia. CTAG has made the following improvements over the NETTAG framework: 1. Expanding the Model Pool and Comparing Models 2. Enlarging the Dataset 3. New Findings in Drug Repurposing. © 2024 SPIE.

关键词： Genome

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program - Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively;and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively;and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset. © 2023 IEEE.

关键词： Computational linguistics

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Development of Buffer Zone into Living Lab-Garuda Smart City Framework: Systematic Review 10

Development of Buffer Zone into Living Lab-Garuda Smart City...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Emmanuel, Gunadi Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Bina Nusantara University Doctor of Computer Science Jakarta Indonesia Univ. Katolik Musi Charitas Fakultas Sains Dan Teknologi Palembang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab-Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019-2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues. © 2023 IEEE.

关键词： Smart city

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Research on ECG Classification Method Based on Convolutional Neural Network 6th

Research on ECG Classification Method Based on Convolutional...

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6th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2021

作者： Tao, Jin Shi, Jianting Wu, Rongqiang Graduate College Heilongjiang University of Science and Technology Harbin150022 China School of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin150022 China

ISBN: (纸本)9783031044083

The electrocardiogram reflects the temporal changes in the body’s cardiac potential;This is also an important technology for diagnosing cardiovascular disease, so the classification of electrocardiogram has gradually become the focus of many scholars. This paper designs an ECG classification algorithm based on convolutional neural network, which aims to automatically classify ECG using artificial intelligence algorithm. The algorithm has the characteristics of less parameters and more layers, and the classification speed is faster and has very strong real-time performance. The experimental results show that the accuracy of the algorithm reaches 98.1%, which has strong advantages in medical diagnosis and application. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Electrocardiography

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Experimentation of Integrating Roadmapping and Sci-Fi Prototyping Methods

Experimentation of Integrating Roadmapping and Sci-Fi Protot...

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2024 Portland International Conference on Management of Engineering and technology, PICMET 2024

作者： Nishinaka, Miwa Shirahada, Kunio Kishita, Yusuke Miyamoto, Dohjin Osawa, Hirotaka Kiyokawa, Sachiko Takeda, Hideaki The Graduate School of Management Kagawa University Kagawa Takamatsu Japan Ishikawa Nomi Japan The University of Tokyo Department of Precision Engineering Tokyo Japan Communication in Science and Technology Education and Research Program Hokkaido University Sapporo Japan Keio University Faculty of Science and Technology Kanagawa Yokohama Japan Graduate School of Education The University of Tokyo Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781890843458

In 2023, we proposed a hypothetical knowledge management framework that incorporates science fiction prototyping (SFP) into the traditional roadmapping (RM) method to make RM more innovative and create a future beyond what we can currently imagine. Our main objective is to create a new organizational knowledge co-creation method for generating innovative ideas by incorporating humanistic factors utilizing SFP into RM. In the current article, we present the exploratory study we conducted to examine the hypothetical framework. Experimental workshops were held in November 2023 to compare the integrated method, in which SFP is applied to RM, with the original RM method. We followed a crossover design in the workshops and analyzed the results qualitatively and quantitatively following the perspectives proposed in the hypothetical framework. The qualitative analysis revealed successful integration regarding diverse and broader perspectives. The quantitative analysis utilizing Wilcoxon signed rank tests showed that the scores of the integrated method were significantly higher than those of RM for variables evaluating the effectiveness of the integration, while not for variables related to participants' feelings and satisfaction with the workshops, and sustainability. These findings indicate that the integration was successful in terms of its effectiveness as a method, while participants' subjective feelings toward the workshops were not different in both methods. As for sustainability, which requires a long-term perspective, no apparent differences were observed in these short-term workshops. © 2024 PICMET.

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Multi-Step Hindsight Experience Replay with Bias Reduction for Efficient Multi-Goal Reinforcement Learning

Multi-Step Hindsight Experience Replay with Bias Reduction f...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Yang, Yu Yang, Rui Lyu, Jiafei Yan, Jiangpeng Luo, Feng Luo, Dijun Li, Xiu Li, Lanqing Tsinghua University Shenzhen International Graduate School Shenzhen China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Ai Lab Tencent Inc. Shenzhen China

ISBN: (纸本)9798350301113

Multi-goal reinforcement learning has emerged as a powerful approach for planning and robot manipulation tasks, but it faces challenges such as sparse rewards and sample inefficiency. Hindsight Experience Replay (HER) has been proposed as a solution to these challenges by relabeling goals, but it still requires a large number of samples and significant computation. To address these issues, we propose Multi-step Hindsight Experience Replay (MHER), which incorporates multi-step relabeling to improve sample efficiency. Despite the advantages of n -step relabeling, we theoretically and experimentally prove the off-policy n -step bias introduced by n -step relabeling may lead to poor performance in many environments. To address this issue, two bias-reduced MHER algorithms, MHER ( λ ) and Model-based MHER (MMHER) are presented. MHER ( λ ) exploits the λ return while MMHER benefits from model-based value expansions. Experimental results on numerous multi-goal robotic tasks show that our solutions can successfully alleviate the off-policy n -step bias and achieve significantly higher sample efficiency than previous multi-goal RL baselines with little additional computation beyond HER. © 2023 IEEE.

关键词： Reinforcement learning

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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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Exploration of Human Emotion Recognition in Speech Data Based on Convolutional Neural Network with Mel and Delta Scale Features 2

Exploration of Human Emotion Recognition in Speech Data Base...

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2nd International Conference on Information technology Research and Innovation, ICITRI 2023

作者： Krisnandi, Dwi Pardede, Hilman Ferdinandus Graduate School of Computer Science Universitas Nusa Mandiri Faculty of Information Technology Jakarta Indonesia Research Center for Artificial Intelligence and Cyber Security Bandung Indonesia

ISBN: (纸本)9798350324945

This exploration of human emotion recognition in voice data can provide information related to human emotions based on the intonation of a person's voice that describes that person's emotions. The selection of voice data to determine emotions compared to facial expressions is aimed at knowing the emotions of a person because, during this pandemic, most people wore masks so they could not see the person's facial expressions clearly. In this study, we explored emotion recognition using the Mel and Delta scales to obtain accuracy from the RAVDESS dataset used in this study using the 2-dimensional CNN algorithm used in this study. The predicted labels in this study are 4 labels per group: angry, sad, neutral, and happy. CNN has the ability to recognize and distinguish complex audio frequency alloys and shapes, such as emotional voices, which increases the accuracy of emotion recognition. The disadvantages of the CNN model are highly dependent on the features selected, and selecting the wrong features can reduce accuracy. In LR 0.01 we can see that the delta-delta-mfcc with an f1 score result of 57.95% and an accuracy score of 63.88% is the best value and in LR 0.001 we can see that the delta-delta-mfcc with an f1 score result of 56.65% and an accuracy score of 62.50% is the best value. © 2023 IEEE.

关键词： Emotion Recognition

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Recognition of Junk Short Messages Based on Local Sensitive Hash KNN Algorithm

Recognition of Junk Short Messages Based on Local Sensitive ...

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2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Fan, Jihui Yuan, Fengshan St. Paul University Philippines Department of Graduate School Tuguegarao Philippines School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (纸本)9781665462877

At present, the recognition and filtering of spam short messages mainly include the following technologies: black and white list recognition technology, transmission frequency limit recognition technology, keyword matching recognition technology, and machine learning based spam short message recognition technology. These commonly used spam short message recognition technologies have their own limitations. The K-Nearest Neighbor algorithm (KNN, K-Nearest Neighbor) based on locally sensitive hashing proposed in this paper can find the samples as close as possible when the eigenvectors are similar but not identical. First, define the features, and then use the local sensitive hash method(LSH). The task of calculating the similarity of the matrix is to find a measure, quantify the similarity of the matrix, compile and train the model, generate the matrix based on the text content of the short message, use TF-IDF algorithm, use Local Sensitive Hash to solve the most similar samples, record the category of the samples, import the training results to the KNN classifier, and obtain the best nearest neighbor through continuous experiments. The accuracy rate is evaluated on the test set or verification set. After KNN classifier, the classification accuracy rate reaches 98.7%. © 2022 IEEE.

关键词： Nearest neighbor search

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Hybrid approach sentiment analysis using Transformer-LSTM in the Indonesian language

Hybrid approach sentiment analysis using Transformer-LSTM in...

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International Seminar on Research of Information technology and Intelligent Systems (ISRITI)

作者： Angel Karentia Ferdinand Winaya Derwin Suhartono Computer Science Department BINUS Graduate Program – Master of Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Sentiment analysis is widely used as a tool to find valuable insight from texts without explicitly expressed. Lots of techniques have already been used to get it but there still have shortcomings in data source or the model strategy itself. Indonesian language approximately has 199 million speakers across the world where 44 million speakers natively. Even with that great number, the resources for the Indonesian language's natural language processing are still limited, and hard to find the perfect way to define sentiment analysis in the Indonesian language. The state-of-the-art sentiment analysis method uses LSTM on a small corpus while the best in town is Transformer where it's easy to transfer learning from the Transformer pre-trained model into specific tasks. From this potential, combining the Transformer pre-trained model with LSTM can be an innovative strategy. This research compared the hybrid model of Transformer-LSTM build using three Indonesian languages’ Transformer-based pre-trained model on sentiment analysis task which can surpass the Transformer model with the highest increased 2.40% accuracy and 3.02% on F1 score.

关键词： Seminars Sentiment analysis Analytical models Accuracy Law Soft sensors Transfer learning Medical services Transformers Long short term memory

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