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34th Medical informatics Europe Conference, MIE 2024

作者： Jaffe, Shai Ben Shahar, Bruria Shahar, Yuval Goldstein, Ayelet Shalom, Erez Selivanova, Maya Rimon, Ephraim Cohen, Odeya Department of Nursing Faculty of Health Sciences Ben-Gurion University of the Negev Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Computer Science Department Hadassah Academic College Israel Herzfeld Geriatric Rehabilitation Medical Center Israel

ISBN: (纸本)9781643685335

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge. © 2024 The Authors.

关键词： Nursing

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Lip-reading: Phoneme-to-text Machine Translation using Large Language Models

Lip-reading: Phoneme-to-text Machine Translation using Large...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Dounia El-Ghomari Randa El-Bialy Daqing Chen School of Engineering Polytech Nantes Nantes France Faculty of Informatics and Computer Science British University in Egypt School of Engineering London South Bank University London UK

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

An effective conversion of recognised phonemes to words has a significant impact on the overall performance of an automatic lip-reading system. In this paper, transformer-based models have been examined for phoneme-to-text translation. The benchmark BBC Lip-reading Sentence 2 (LRS2) data is used in this study, and the T5 model (Text-To-Text Transfer Transformer) and the GPT-2 model are considered the conversion models. A detailed discussion is provided on the analysis procedure, data pre-processing, model implementation, and the metrics employed for model evaluation. The experimental results have demonstrated that the T5 model can achieve a substantial improvement over the GPT-2 model with a superior accuracy and fluency in the phoneme-to-text translation, offering a potentially promising solution for enhancing the precision of automatic translation and speech processing systems. This work contributes to the development of more robust applications in the field of speech recognition and communication technologies.

关键词： Measurement Analytical models Translation Large language models Speech recognition Transformers Data models Social intelligence Machine translation Speech processing

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A Combination of Image Enhancement and U-Net Architecture for Segmentation in Identifying Brain Tumors on CT-SCAN Images 4

A Combination of Image Enhancement and U-Net Architecture fo...

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

作者： Desiani, Anita Adrezo, Muhammad Chika Marselina, Nyayu Arhami, Muhammad Salsabila, Aulia Gibran Al-Filambany, Muhammad Uiversitas Sriwijaya Mathematics Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Computer Science Faculty Jakarta Indonesia Politeknik Negeri Lhokseumawe Informatics Engineering Aceh Indonesia

ISBN: (纸本)9781665473279

A brain tumor is a condition where the abnormal growth of brain cells around the brain spreads to any part of the body uncontrollably. Brain tumors can be detected by recognizing tumor patterns in brain images by segmentation that separates brain tumor features in brain images. Sometimes the image data still has low quality, so image enhancement is needed to improve image quality. This paper proposed to improve the quality of a 2D brain tumor image and the performance of segmentation. Image enhancement consists of two stages, namely contrast enhancement and noise reduction. The contrast enhancement used is Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma correction, while the noise reduction used is a Gaussian filter. Parameters in measuring the results of the quality of the image are Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM). U-Net architecture is applied in the brain tumor segmentation process. Parameters in evaluating the performance of the segmentation model are accuracy, sensitivity, specificity, and mean Intersection over Union (IoU). The results of brain tumor image improvement obtained are MSE of 0.018 and SSIM of 0.9748. The results of brain tumor segmentation obtained were 98% accuracy, 77.85% sensitivity, 99.12% specificity, and 82.31 % mean IoU. Sensitivity below 80% is caused by too small a portion of the tumor. Even though the sensitivity was below 80%, the model was able to separate the tumor from the background. © 2022 IEEE.

关键词： Image enhancement

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Utilization of COBIT 2019 for an Advanced Strategic Evaluation of IT Risk Management in the Application of e-Government at Palembang City Governance

Utilization of COBIT 2019 for an Advanced Strategic Evaluati...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： M. Arief Algiffary Widya Cholil Tjahjanto Yesi Novaria Kunang Zaid Amin Prihandoko Magister of Informatics Engineering Bina Darma University Palembang Indonesia Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Intelligent Systems Research Group Bina Darma University Palembang Indonesia Informatics Engineering Bina Darma University Palembang Indonesia Faculty of Computer Science Gunadarma University Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Digitalization is transforming information, data, and activities across various sectors such as business, communication, education, and government through technologies like Artificial Intelligence, Cloud Computing, and the Internet of Things (IoT). This transformation presents new opportunities but also raises concerns about privacy and data security. Palembang City Governance is currently enhancing urban development through e-Government initiatives, and the Department of Communication and informatics, as the driving force behind this transformation, has yet to evaluate its IT risk management processes, which are crucial for addressing system disruptions, data breaches, and cyberattacks. This study evaluates IT risk management in the e-Government application by the Department of Communication and informatics using COBIT 2019, a comprehensive and integrated framework for managing information technology that helps organizations achieve their business objectives by leveraging technology. The processes assessed, such as APO12, have already reached a maturity level of 4 (Quantitative), while EDM03, APO13, DSS04, and DSS05 have achieved a maturity level of 3 (Defined), and BAI10 remains at a maturity level of 2 (Managed). To improve it's maturity level, as well as to ensure that all risks can be managed and to mitigate potential risks, ongoing improvement efforts and gradual enhancements to systems and performance are needed in the e-Government Management Division of the Department of Communication and informatics of Palembang City.

关键词： Prevention and mitigation Multimedia systems Data security Urban areas Force Education Internet of Things informatics Information technology Electronic government

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data

Ensemble based unsupervised anomaly detection with concept d...

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Smart World Congress (SWC), IEEE

作者： Danlei Li Nirmal Nair Kevin I-Kai Wang Kouichi Sakurai Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Department of Informatics Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework’s longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability.

关键词： Adaptation models Accuracy Concept drift Time series analysis Stability analysis Sensors Sea surface temperature Reliability Anomaly detection Thermal stability

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An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

An observer comparison study to evaluate a machine learning ...

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Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

作者： Abdihamzehkolaei, Alireza Mirniaharikandehei, Seyedehnafiseh Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. School of Electrical and Computer Engineering University of Oklahoma NormanOK United States School of Systems Engineering and Informatics Universidad Nacional de San Agustín de Arequipa Arequipa Peru Medicine Faculty Universidad Nacional de San Agustín de Arequipa Arequipa Peru

ISBN: (数字)9781510660403

ISBN: (纸本)9781510660397

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the abstract

关键词： COVID-19

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Optimal Broadcast Schedules in Logarithmic Time with Applications to Broadcast, All-Broadcast, Reduction and All-Reduction

arXiv

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arXiv 2024年

作者： Träff, Jesper Larsson TU Wien Faculty of Informatics Institute of Computer Engineering Research Group Parallel Computing 191-4 Treitlstrasse 3 Vienna1040 Austria

We give optimally fast O(log p) time (per processor) algorithms for computing roundoptimal broadcast schedules for message-passing parallel computing systems. This affirmatively answers difficult questions posed in a SPAA 2022 BA and a CLUSTER 2022 paper. We observe that the computed schedules and circulant communication graph can likewise be used for reduction, all-broadcast and all-reduction as well, leading to new, round-optimal algorithms for these problems. These observations affirmatively answer open questions posed in a CLUSTER 2023 paper. The problem is to broadcast n indivisible blocks of data from a given root processor to all other processors in a (subgraph of a) fully connected network of p processors with fully bidirectional, one-ported communication capabilities. In this model, n - 1 + log2 p communication rounds are required. Our new algorithms compute for each processor in the network receive and send schedules each of size log2 p that determine uniquely in O(1) time for each communication round the new block that the processor will receive, and the already received block it has to send. Schedule computations are done independently per processor without communication. The broadcast communication subgraph is an easily computable, directed, log2 p-regular circulant graph also used elsewhere. We show how the schedule computations can be done in optimal time and space of O(log p), improving significantly over previous results of O(p log2 p) and O(log3 p), respectively. The schedule computation and broadcast algorithms are simple to implement, but correctness and complexity are not obvious. The schedules are used for new implementations of the MPI (Message-Passing Interface) collectives MPI Bcast, MPI Allgatherv,MPI Reduce and MPI Reduce scatter. Preliminary experimental results are given. Carefully engineered and extensively evaluated implementations will be presented elsewhere. © 2024, CC BY.

关键词： Message passing

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Heart Disease Detection from PSAX Echocardiography View using Ultrasound Portable Based on Machine Learning Method

Heart Disease Detection from PSAX Echocardiography View usin...

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International Electronics Symposium (IES)

作者： Jovan Josafat Jaenputra Riyanto Sigit Tri Harsono Abraham Ahmad Ali Firdaus Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Faculty of Medicine Universitas Nahdlatul Ulama Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Heart disease is one of the highest causes of death in Indonesia and even the world. Based on data from the World Health Organization (WHO) in 2021, 17.8 million people died or one third each year due to heart and blood vessel disease. Delayed treatment of heart disease is the main trigger of death by heart disease. In Indonesia itself, health services for heart disease are still relatively low where patients need to do heart examinations in large hospitals that have special rooms for heart examinations. With echocardiographic examination using sound waves (ultrasound), many parameters such as hemodynamics, heart mass, and myocardial deformation can be measured. Nowadays, portable ultrasound heart examination devices are available to allow doctors to perform heart examinations flexibly and effectively. This tool will capture data in the form of video of the patient's heart condition and then perform image preprocessing stages with median filter high boost filter, morphology, thresholding, and canny filter. Segmentation stage with region filter, collinear filter, and triangle equation. Tracking stage with Optical flow Lucas-Kanade method. Feature extraction stage using trigonometry equation, and the classification stage using Support Vector Machine (SVM) with above 80% of testing accuracy

关键词： Heart Optical filters Support vector machines Performance evaluation Image segmentation Ultrasonic imaging Feature extraction

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Matko Glučina Nikola Žigulic Domagoj Frank Ivan Lorencin Dario Matika Department of Engineering Istrian University of Applied Sciences Pula Croatia Faculty of Engineering University of Rijeka Rijeka Croatia Department of Computer Science and Informatics University North Koprivnica Croatia Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Department of Electrical engineering University North Varazdin Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996.

关键词： Technological innovation computer vision Weapons Urban areas Artificial neural networks Safety Security Intelligent systems informatics

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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