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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Hsien-Cheng Chang, Hsiao-Huang Ke, Xiang-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City Taiwan Taipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Coronary artery disease is the most prevalent type of heart disease and has a considerable mortality rate. In diagnosing and assessing coronary artery disease, physicians must integrate a variety of clinical information and test results to evaluate the degree of coronary artery obstruction and continually learn and improve their skills in clinical practice to ensure the most accurate assessment of patients. Currently, the use of information technology to assist medical staff allows more effective and accurate analysis of symptoms. This approach not only serves as an auxiliary standard for physician diagnosis, thereby reducing the possibility of misdiagnosis, but also significantly improves patient treatment outcomes and medical quality while reducing the time burden on physicians for diagnosing patients. This study utilized image processing techniques and thinning algorithms to automate the detection of coronary artery width and, following the recommended methods of physicians in related fields, used specific vessel widths to calculate the degree of coronary artery narrowing. This provides a more accurate and efficient assessment method for evaluating the degree of vascular constriction in patients with coronary artery disease. © 2024 Copyright ACM.

关键词： Patient treatment

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From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process 25

From RAG to QA-RAG: Integrating Generative AI for Pharmaceut...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Kim, Jaewoong Hur, Minseok Min, Moohong Department of Applied Data Science Sungkyunkwan University Seoul Korea Republic of Department of Immersive Media Engineering/Convergence Program for Social Innovation Sungkyunkwan University Seoul Korea Republic of Department of Computer Education/Convergence Program for Social Innovation Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798400706295

Regulatory compliance in the pharmaceutical industry involves navigating complex and voluminous guidelines, often requiring significant amounts of human resources. Recent advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) methods provide promising enhancements to data processing and knowledge management, potentially easing these burdens. However, despite these advancements, conventional Retrieval-Augmented Generation (RAG) methods fall short in this domain due to inherent structural problems. To address these challenges, we introduce the Question and Answer Retrieval Augmented Generation (QA-RAG) framework. This framework enhances the conventional RAG framework. It integrates a dual-track retrieval mechanism tailored to the specific and dynamic nature of pharmaceutical regulations. It utilizes not only the original query but also the answers generated by a fine-tuned LLM, thus providing a more robust foundation for document retrieval. Our experiments demonstrate that QA-RAG outperforms conventional methods in various evaluation metrics including precision, recall, and F1-score. These results underscore QA-RAG's capability to enhance both the accuracy and efficiency of regulatory compliance processes in the pharmaceutical industry. This paper details the structure and efficacy of QA-RAG, emphasizing its potential to revolutionize the regulatory compliance process in the pharmaceutical industry and beyond. Copyright © 2025 held by the owner/author(s).

关键词： Data integration

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning

arXiv

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arXiv 2022年

作者： Netanyahu, Aviv Shu, Tianmin Tenenbaum, Joshua Agrawal, Pulkit Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Dept. of Brain and Cognitive Science Massachusetts Institute of Technology CambridgeMA United States

In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same task in different environments. To achieve a strong generalization, the AI agent must infer the spatial goal specification for the task. However, there can be multiple goal specifications that fit the given demonstration. To address this, we propose a reward learning approach, Graph-based Equivalence Mappings (GEM), that can discover spatial goal representations that are aligned with the intended goal specification, enabling successful generalization in unseen environments. Specifically, GEM represents a spatial goal specification by a reward function conditioned on i) a graph indicating important spatial relationships between objects and ii) state equivalence mappings for each edge in the graph indicating invariant properties of the corresponding relationship. GEM combines inverse reinforcement learning and active reward learning to efficiently improve the reward function by utilizing the graph structure and domain randomization enabled by the equivalence mappings. We conducted experiments with simulated oracles and with human subjects. The results show that GEM can drastically improve the generalizability of the learned goal representations over strong baselines.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Ant Colony Optimization based Medical Image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, V. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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Scientometric Analysis of Artificial Intelligence Research in Agriculture 4

Scientometric Analysis of Artificial Intelligence Research i...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Pramono, Andi Hamim, Miftahul Prihatin, Sukeipah Yuli Arifuddin, Rahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Politeknik Kota Malang Informatics Study Program Malang Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ($\mathrm{N}=287$), followed by the United Kingdom at second ($\mathrm{N}=139$) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index. © 2022 IEEE.

关键词： Agriculture

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Optical Spectroscopy of Excitons in ReS2 Monolayers Grown by Chemical Vapor Deposition

SSRN

引用

SSRN 2024年

作者： Ojo, Solomon Onasanya, Juwon Benamara, Mourad Hamad, Bothina Manasreh, M.O. Material Science and Engineering program University of Arkansas FayettevilleAR72701 United States Institute of nanotechnology University of Arkansas FayettevilleAR72701 United States Physics Department University of Arkansas FayettevilleAR72701 United States Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR72701 United States

Monolayers of ReS2 were grown by a chemical vapor deposition technique on SiO2/Si substrates and investigated at room temperature by using μ-Raman, μ-photoluminescence (PL) and absorbance spectroscopies. The Raman scattering spectrum exhibits several phonon modes that were confirmed by the computation analysis based on the density functional theory. The ReS2 structural integrity was confirmed by using the XRD and the energy dispersion spectroscopy that was obtained by the scanning electron microscopy. Photoluminescence spectra show excitons related to interband transition in thin monolayer flakes and bulk-like structures. Additionally, a sharp PL line located at 1.2620 eV with a second harmonic peak at 2.5420 eV was observed and explained in terms of interband excitonic transitions originating within the rhenium 5d orbit. An optical absorbance spectrum with an exciton peak around 1.4660 eV was obtained for an assembly of ReS2 flakes grown on silica substrate. © 2024, The Authors. All rights reserved.

关键词： Phonons

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Guiding Soft Robots with Motor-Imagery Brain Signals and Impedance Control

Guiding Soft Robots with Motor-Imagery Brain Signals and Imp...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Maximilian Stölzle Sonal Santosh Baberwal Daniela Rus Shirley Coyle Cosimo Della Santina The Cognitive Robotics department Delft University of Technology Mekelweg 2 CD Delft Netherlands School of Electronics Engineering Dublin City University Dublin Ireland MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA

Integrating Brain-Machine Interfaces into non-clinical applications like robot motion control remains difficult - despite remarkable advancements in clinical settings. Specifically, EEG-based motor imagery systems are still error-prone, posing safety risks when rigid robots operate near humans. This work presents an alternative pathway towards safe and effective operation by combining wearable EEG with physically embodied safety in soft robots. We introduce and test a pipeline that allows a user to move a soft robot's end effector in real time via brain waves that are measured by as few as three EEG channels. A robust motor imagery algorithm interprets the user's intentions to move the position of a virtual attractor to which the end effector is attracted, thanks to a new Cartesian impedance controller. We specifically focus here on planar soft robot-based architected metamaterials, which require the development of a novel control architecture to deal with the peculiar nonlinearities - e.g., non-affinity in control. We preliminarily but quantitatively evaluate the approach on the task of setpoint regulation. We observe that the user reaches the proximity of the setpoint in 66% of steps and that for successful steps, the average response time is 21.5s. We also demonstrate the execution of simple real-world tasks involving interaction with the environment, which would be extremely hard to perform if it were not for the robot's softness.

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Optimizing Space Communications Using Deep Learning

Optimizing Space Communications Using Deep Learning

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2021 IEEE cognitive Communications for Aerospace Applications Workshop, CCAAW 2021

作者： Robertson, Brianna I. Smith, Aaron Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA United States Nasa Glenn Research Center Cognitive Signal Processing Branch ClevelandOH United States

ISBN: (纸本)9781665412582

The paradigm shift from standard communication models to artificially intelligent solutions continues to be an exciting avenue of research. Through applying neural networks to a communication-learning model, the autonomous development of robust signal constellations emerges;however, optimal training architectures for these algorithms have not been determined. This work focuses on improving the modulation and coding solutions produced by deep neural networks by evaluating various training methods and configurations. The communications system is modeled with an autoencoder, an auto-associative neural network that learns how to transmit and recover input data, representing transmitter and receiver. Effective training of an autoencoder-based system encourages the model to learn modulation and coding schemes through optimizing symbol placement, which is achieved in this work through proper network normalization and adding channel noise. A normalization layer bounds symbol energy in a learned constellation, but these layers can impair the reproducibility of the training processes. This work assesses the effect of various normalization layers on the minimum distance between symbols in a learned constellation. Furthermore, a nonlinear noise technique, which intentionally mislabels symbols during training, is implemented to encourage symbol separation. This work concludes with a comparison between bit-wise versus symbol-wise training to determine if the learned constellation can achieve gray coding-like results. © 2021 IEEE.

关键词： Deep neural networks

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Artificial Intelligence Identification Model for Chronic Kidney Disease 1

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5th International Conference on Innovative Computing, IC 2022

作者： Cheng, Ya-Fang Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Many people suffer from Chronic Kidney Disease (CKD). Nowadays, CKD is one of the top ten causes of death. CKD should via invasive examination to understand participants health status. If a non-invasive identification model can be established, it can provide users with self-assessment which let users quickly understand their physical condition. This study used machine learning method to establish an identification model of Chronic Kidney Disease. This study found the associated factors with kidney failure from the literature. Selected MJ database as information resources. Used two different factor selection methods to training model. Compared the performance with K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, eXtreme Gradient Boosting and Vote Algorithms, used the better one to establish the model. In this study, the best model used Vote algorithm to establish the model, and can only use 13 non-invasive factors. The accuracy is 88%, the precision is 73%, the sensitivity is 69%, the specificity is 93%, and the AUC is 0.92. The contribution of this study is to use non-invasive factors to identify Chronic Kidney Disease, but it is a preliminary evaluation and ultimately requires doctors to diagnose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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