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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Kurniadi, Felix Indra Satyaning Pradnya Paramita, Ni Luh Putu Angela Sihotang, Erna Fransisca Anggreainy, Maria Susan Zhan, Rongfang School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science University of North Texas Department of Rehabilitation and Health Service United States

ISBN: (纸本)9798350390025

Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data. © 2024 IEEE.

关键词： Social networking (online)

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GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information 17th

GeoRefineNet: A Multistage Framework for Enhanced Cephalome...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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Classification Prediction of Heart Disease Using Machine Learning Techniques 4

Classification Prediction of Heart Disease Using Machine Lea...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Setiawan, Kevis Jonathan Beratha, Putu Satria Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector Machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively. © 2023 IEEE.

关键词： Support vector machines

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Investigation of Colors Categorized into Color Names on a Liquid Crystal Display to Enhance CAPTCHAs

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International Journal of the Society of Material engineering for Resources 2022年第2期25卷 212-217页

作者： Ishizawa, Chikako Kobayashi, Nozomu Kageyama, Yoichi Department of Mathematical Science and Electrical-Electronic-Computer Engineering Graduate School of Engineering Science Akita University Akita010-8502 Japan

This study focuses on the human ability to tolerate subtle diff erences in color and categorize similar colors into a single mass to enhance completely automated public turing test to tell computers and humans apart (CAPTCHA). First, an experiment was conducted to check whether the color selection condition could control the color categorization results. Consequently, it was clarifi ed that when the selection condition was added, the color to be categorized changed, implying the possibility of controlling the categorized result. Subsequently, an investigation was conducted to clarify the range of liquid crystal display colors categorized as major color names. As a result, it was clarified that the hue angle size of the colors categorized by more than half of the participants was sometimes greater than the hue angle size between the primary and complementary colors. It was also clarifi ed that among the categorized colors, there were categorized colors in the two color names. Finally, results from the fi rst and second experiments were collated. It was confi rmed that by using the categorized color range and selection conditions, it is possible to obtain an answer with a color name that is diff erent from the original color name. These results suggest that the ability of humans to categorize similar colors into a single mass enhances CAPTCHA. © 2022 Society of Materials engineering for Resources of Japan. All rights reserved.

关键词： Color

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Ship Navigational Status Classification Based on the Geometrical and Spatiotemporal Features of the AIS-Generated Trajectory 9

Ship Navigational Status Classification Based on the Geometr...

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9th International Conference on Big Data Analytics, ICBDA 2024

作者： Wijaya, Wayan Mahardhika Nakamura, Yasuhiro Graduate School of Science and Engineering National Defense Academy Yokosuka Japan National Defense Academy Department of Computer Science Yokosuka Japan

ISBN: (纸本)9798350352962

Studies on the Automatic Identification System (AIS) data mining have proven the feasibility of discovering knowledge from the AIS tracks to understand vessels' behavior. One of its purposes is to help maritime surveillance efforts for safety and security matters. Research on vessel behavior often relies on the spatiotemporal features of the AIS tracks, while human operators recognize vessel movement by comprehending their trajectory patterns. Additionally, many works do not comprehensively discuss a thorough AIS data preprocessing stage before implementing their novelty method to analyze raw AIS data. Thus, this paper proposes a trajectory segmentation technique, a data preprocessing procedure, for AIS data to provide an analytics-ready dataset that facilitates a practical implementation of AIS-based vessel behavior analytics. The approach utilizes the AIS-generated vessel trajectories' spatiotemporal and geometrical features to split a ship trajectory into segments according to its navigational status, such as stopping and underway. The spatiotemporal features are speed, course, heading, and position timestamp, while the geometrical features include the diameter and bounding box of the trajectory. Every trajectory is divided into stopping and underway segments. Stopping segment extraction aims to facilitate stopping event detection and classification, which helps understand vessel activities in port areas. Mean-while, the underway segment is dedicated to streamlining vessel movement analytics for loitering detection and other anomalies in maritime traffic. The experiment result demonstrates the efficacy of the method. It outperformed the existing stopping segments classification approach with 0.98 accuracy and improved the currently published loitering detection to 0.97 accuracy and 0.91 F-score. © 2024 IEEE.

关键词： Ships

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AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses

AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of O...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lu, Xiaotian Li, Jiyi Takeuchi, Koh Kashima, Hisashi Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (纸本)9798891761681

Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP). Answers to open-ended questions are highly diverse and difficult to quantify, and cannot be simply evaluated as correct or incorrect, unlike close-ended questions with definitive answers. While large language models (LLMs) have demonstrated strong capabilities across various tasks, they exhibit relatively weaker performance in evaluating answers to open-ended questions. In this study, we propose a method that leverages LLMs and the analytic hierarchy process (AHP) to assess answers to open-ended questions. We utilized LLMs to generate multiple evaluation criteria for a question. Subsequently, answers were subjected to pairwise comparisons under each criterion with LLMs, and scores for each answer were calculated in the AHP. We conducted experiments on four datasets using both ChatGPT-3.5-turbo and GPT-4. Our results indicate that our approach more closely aligns with human judgment compared to the four baselines. Additionally, we explored the impact of the number of criteria, variations in models, and differences in datasets on the results. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Bending the Rule: Naphthobisthiadiazole-Based Terpolymers with Both High Solubility and Crystallinity for Organic Photovoltaics

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ACS Applied Polymer Materials 2024年第5期6卷 2586-2593页

作者： Chou, Li-Hui Chen, Fengkun Nakano, Kyohei Yamanaka, Kodai Mikie, Tsubasa Saito, Masahiko Liu, Cheng-Liang Tajima, Keisuke Osaka, Itaru Department of Applied Chemistry Graduate School of Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8527 Japan Department of Chemical and Materials Engineering National Central University Taoyuan32001 Taiwan Saitama Wako351-0198 Japan Applied Chemistry Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8527 Japan Department of Materials Science and Engineering National Taiwan University Taipei10617 Taiwan

Achieving a good balance between the crystallinity and solubility is of great importance in the design of π-conjugated polymers. Here, we synthesized a series of terpolymers in which small fractions of a planar V-shaped fused ring, triphenyleno[1,2-c:7,8-c′]bis([1,2,5]thiadiazole) (TPTz), are incorporated into the linear-shaped backbone of a highly crystalline polymer based on naphtho[1,2-c:5,6-c′]bis([1,2,5]thiadiazole) (NTz), PNTz4T. Due to the "bending" structure induced by the TPTz unit, the terpolymers had greatly improved solubility by about 5-9 times compared to the linear-shaped PNTz4T. In particular, the terpolymers preserved the crystallinity of PNTz4T to a considerable extent with a higher fraction of face-on oriented domains in films. As a result, the highly solution-processable terpolymers exhibited high photovoltaic performances and slightly outperformed PNTz4T. Our results indicate that the incorporation of a V-shaped unit into a linearly shaped polymer is a good strategy to develop π-conjugated polymers with high solubility and crystallinity. © 2024 American Chemical Society.

关键词： Solubility

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Enhancing Healthcare Monitoring Systems: A Case Analysis in Optimization 18

Enhancing Healthcare Monitoring Systems: A Case Analysis in ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Lalit, Mohit Bathla, Gaurav Singh, Surender Chandigarh University Computer Science and Engineering Department Mohali India Career Program Code Quotient Mohali India

ISBN: (纸本)9789380544519

In the past few years, fog computing (FC) has emerged as a promising complement to cloud computing. It offers reduced latency, minimal bandwidth consumption, and real-time data transfer. In healthcare, particularly in IoT-enabled systems, FC integration has become pivotal for real-time monitoring and immediate data transfer to health experts. The researchers all around the world proposed and developed various communication frameworks and come up with certain outputs in terms of quality-of-service parameters (QoS). However, these QoS parameters are not always appropriate for effective execution of the application. Following that, this study explores the integration of FC with multi-objective Optimization Algorithms (such as FFLY and GWO) to optimize crucial quality of service (QoS) metrics using a proposed linear function to optimize multi objectives, essential for sustained communication. The research focuses on optimizing multi objectives in healthcare monitoring systems. Experimental results reveal the superiority of the Grey Wolf Optimizer (GWO) over the Firefly Algorithm (FFLY). This study emphasizes the crucial significance of multi-objective optimisation algorithms in optimizing critical parameters for successful healthcare communication frameworks, which can lead to improvements in healthcare monitoring efficiency. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Fog Computing Healthcare 4.0 Industry 4.0 IoT Optimization

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Porphyrin-Grafted Poly(ethylene terephthalate) as a Reusable and Highly Selective Colorimetric Probe for Mercuric Ion Contaminants in Aqueous Samples

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ACS Applied Materials and Interfaces 2024年第30期16卷 39195-39205页

作者： Atayde, Eduardo C. Takenaka, Yasumasa Abe, Hideki Wu, Ming-Rou Wu, Kevin C.-W. Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Department of Chemistry National Tsing Hua University Department of Chemical Engineering National Taiwan University Bioplastic Research Team RIKEN Center for Sustainable Resource Science Department of Chemical Engineering and Materials Science Yuan Ze University Department of Chemical Engineering Chung Yuan Christian University

Heavy metals are the most hazardous water pollutants, with severe health and environmental consequences. Among these, mercuric (Hg2+) ions are known to cause detrimental health issues in both humans and aquatic life. ... 详细信息

Heavy metals are the most hazardous water pollutants, with severe health and environmental consequences. Among these, mercuric (Hg²⁺) ions are known to cause detrimental health issues in both humans and aquatic life. Due to this, several analytical techniques have been devised to detect and quantify the amount of this ion. However, most of these require advanced instrumentation, prolonged analysis time, and sample preparation. In this study, a low-cost and highly reusable colorimetric probe was developed by grafting porphyrin to poly(ethylene terephthalate) sheets using an oxazoline polymer as covalent adhesive. Upon exposure to trace amounts of Hg²⁺ in solution, the fabricated material visually transitioned from faint brownish pink to green by the complexation mechanism. Additionally, the transparency of this probe allowed the quantitative spectrophotometric determination of the Hg²⁺ concentration in aqueous samples. It was also shown that the material is highly stable, which can be reused for more than 50 times without significant decline in its performance, hence, making it suitable for the onsite monitoring of mercuric ion contamination in different bodies of water. © 2024 The Authors. Published by American Chemical Society.

关键词： colorimetric probe mercuric ion oxazoline PET porphyrin

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Multimodal Information Extraction:A systematic Review of subtask, modal types and applications based on Deep Learning in Banking sector 5

Multimodal Information Extraction:A systematic Review of sub...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Srinivsan, Samundiswary Krishna, R. Sathya Bama Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India SIES Graduate School of Technology Navi Mumbai India

ISBN: (纸本)9798350361155

Deep Learning has been widely applied in all application domains namely Healthcare, Entertainment, Banking & finance, Agriculture, Aerospace & defence etc. Due to stupendous achievement of deep learning as a data processing technique has fascinated in all research areas. Handling of Deep learning in Banking and Financial sector is pervasive. Advancement of Information extraction especially because of different formats of information such as text, image, sound, video can be extracted simultaneously by the usage of deep learning techniques. This study reviews and analyses multimodal information extraction based on deep learning techniques especially on banking and financial sector. It aims to enhance the efficiency of document-intensive business tasks in the financial sector. Deep review analysis will be carried out to understand subtasks of Information extraction and techniques involved in Banking domain. Finally, how multimodal information extraction using deep learning enhances the efficiency than unimodal and its use cases in current AI based automation world will be highlighted. © 2024 IEEE.

关键词： Finance

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