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7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Utama, Ditdit Nugeraha Asrol, Muhammad Suharjito Harito, Christian Napthaleni Syafrudin, Muhammad Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Sejong University Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of

ISBN: (纸本)9798331519643

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89. © 2024 IEEE.

关键词： Sugar industry

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An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques

An Evaluation Of Hospital Accreditation From The Survey With...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Dittaphong Prapunwattana Aurawan Imsombut Picha Suwannahitatorn Thammakorn Saethang Biomedical Data Science Master's Program Faculty of Science Kasetsart University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Thai Board of Prevention Medicine (Epidemiology) Phramongkutklao College of Medicine Bangkok Thailand

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring.

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method

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Procedia computer science 2023年 216卷 136-143页

作者： Muhammad Titan Hafizal Dian Pratama Putra Herry Wirianata Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Faculty of Forestry STIPER Institute of Agriculture Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants’ condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-to-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants.

关键词： agriculture cocoa expert system forward chaining recommendation system

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Classification of Acute Intracerebral Hemorrhage Using Radiomics on Brain Computed Tomography Images

Classification of Acute Intracerebral Hemorrhage Using Radio...

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International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Phattanun Thabarsa Salita Angkurawaranon Chakri Madla Withawat Vuthiwong Kittisak Unsrisong Papangkorn Inkeaw Master's Degree Program in Data Science Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Radiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand Department of Computer Science Data Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand

Acute intracerebral hemorrhage (ICH) entity accounts for 10 to 15% of all strokes and is associated with a higher mortality rate ischemic stroke or subarachnoid hemorrhage. Causes of ICH are divided into primary, and secondary, including vascular malformation and tumorous. Primary ICH accounts for approximately 80% of all ICH cases. Vascular anomalies rank as the second most common cause of spontaneous ICH overall. Furthermore, hemorrhage resulting from brain tumors can occur in up to 10% of all primary or metastatic tumors. Early recognizing of these three causes of bleeding is critical for clinicians in precise diagnosis, effective treatment management, and helps avoid delayed diagnosis. We proposed a radiomics approach for classifying multiple causes of acute ICH as vascular malformation, tumorous, and primary-related hematoma. Non-contrast brain computed tomography with clinical features was used as input. The regions of both hematoma and perihematomal edema were delineated by using manual segmentation approach. Four feature selection methods were adopted. Also, three classification models were investigated in this study. The results showed that using the features selected by F-value applied with SVM classifier outperformed the other models, achieving weighted average accuracy (± SD) of 0.84 (± 0.07). Additionally, the model demonstrated average sensitivity and positive predictive value of 0.84 (± 0.06) and 0.86 (± 0.05), respectively. We also evaluate the overall performance of discriminating each class from the rest using AUC. The result suggested that our proposed model achieved the weighted average AUC of 0.90. Our proposed method highlights the potential in identifying multiple causes of acute and nontraumatic ICH, which has not been previously explored.

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HAR-CTGAN: A Mobile Sensor data Generation Tool for Human Activity Recognition

HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Ac...

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IEEE International Conference on Big data

作者： Joshua DeOliveira Walter Gerych Aruzhan Koshkarova Elke Rundensteiner Emmanuel Agu Data Science Program Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781665480468

Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features — features that are represented by real numbers — recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves.

关键词： Training Medical services Big data Generative adversarial networks data models Stability analysis Human activity recognition

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LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation

arXiv

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arXiv 2024年

作者： Song, Steven Subramanyam, Anirudh Madejski, Irene Grossman, Robert L. Department of Computer Science University of Chicago United States Medical Scientist Training Program Pritzker School of Medicine United States Center for Translational Data Science University of Chicago United States Department of Medicine University of Chicago United States

In the current paradigm of image captioning, deep learning models are trained to generate text from image embeddings of latent features. We challenge the assumption that these latent features ought to be high-dimensional vectors which require model fine tuning to handle. Here we propose Label Boosted Retrieval Augmented Generation (LaB-RAG), a text-based approach to image captioning that leverages image descriptors in the form of categorical labels to boost standard retrieval augmented generation (RAG) with pretrained large language models (LLMs). We study our method in the context of radiology report generation (RRG), where the task is to generate a clinician's report detailing their observations from a set of radiological images, such as X-rays. We argue that simple linear classifiers over extracted image embeddings can effectively transform X-rays into text-space as radiology-specific labels. In combination with standard RAG, we show that these derived text labels can be used with general-domain LLMs to generate radiology reports. Without ever training our generative language model or image feature encoder models, and without ever directly "showing" the LLM an X-ray, we demonstrate that LaB-RAG achieves better results across natural language and radiology language metrics compared with other retrieval-based RRG methods, while attaining competitive results compared to other fine-tuned vision-language RRG models. We further present results of our experiments with various components of LaB-RAG to better understand our method. Finally, we critique the use of a popular RRG metric, arguing it is possible to artificially inflate its results without true data-leakage. Our code is available at: https://***/uccdis/label-boosted-RAG-for-RRG. © 2024, CC BY.

关键词： Visual languages

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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IEEE International Conference on Big data

作者： Ruofan Hu Dongyu Zhang Dandan Tao Huayi Zhang Hao Feng Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose USA College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University 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

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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Base on GAN Combined with CNN Architecture to Generate Brain Stroke CT Images 24

Base on GAN Combined with CNN Architecture to Generate Brain...

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

作者： Shih, Guan-Yu Ting, Hsien-Wei Chen, Yi-Xiang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program inBiomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

In recent years, many fields have expanded their research methods through the integration of artificial intelligence. In the current medical field, it is widely used in image recognition to diagnose patient symptoms, train clinical prediction models, and improve clinical workflow processes to avoid missing treatment opportunities. These methods have all achieved good results so far. Now, generative AI is also impacting the development of the medical field, not only generating prescriptions that match patient conditions but also being applied in generating medical images for data augmentation and simulating various pathological conditions, thereby aiding in training deep learning models. Stroke is a leading cause of death or disability, ranking as the second leading cause of death worldwide, next to cancer. If cancers were categorized individually, stroke would surpass all single diseases, making it a primary focus of research in brain disease treatment. There are already studies where generative AI has been used to create medical images, which are then manually evaluated by doctors to determine if they were generated by AI. In this project, we plan to generate CT images of strokes using generative AI and use a trained model (to diagnose stroke) to improve accuracy. Through this project, we hope to solve the problem of scarce stroke CT images while also providing support for training stroke-related models. © 2024 Copyright ACM.

关键词： Deep learning

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