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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Data preparation for artificial intelligence in medical imaging: Experiences from the ProCAncer-I initiative

Data preparation for artificial intelligence in medical imag...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Kalokyri, Varvara Tachos, Nikolaos Sfakianakis, Stelios Nikiforaki, Katerina Karatzanis, Ioannis Kondylakis, Haridimos Mazzetti, Simone Regge, Daniele Papanikolaou, Nikolaos Marias, Konstantinos Fotiadis, Dimitrios Tsiknakis, Manolis FORTH-ICS Foundation of Research and Technology Hellas Institute of Computer Science Nik. Plastira 100 Crete Heraklion70013 Greece University Campus of Ioannina FORTH-BRI Ioannina45110 Greece University of Turin Department of Surgical Sciences Turin Italy Candiolo10060 Italy Champalimaud Foundation Champalimaud Research Computational Clinical Imaging Group Lisbon1400-038 Portugal FORTH-BRI Greece The Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University Campus of Ioannina Ioannina45110 Greece

ISBN: (纸本)9798350383386

The ProCAncer-I project, an important initiative in the field of prostate cancer research, is exploiting the power of Artificial Intelligence (AI) to advance prostate cancer diagnosis, prognosis, and treatment. The success of AI in prostate cancer imaging within this context relies on meticulous data preparation, especially when dealing with clinical data that need to be integrated with imaging data. This paper explores the importance of data preparation to implement AI tools for prostate cancer imaging, and outlines the approach followed and challenges needed to be addressed in the context of ProCAncer-I, for ensuring data quality, privacy, and reliability. © 2023 IEEE.

关键词： Medical imaging

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and research Opportunities

arXiv

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arXiv 2023年

作者： Song, Yanjie Wu, Yutong Guo, Yangyang Yan, Ran Suganthan, Ponnuthurai Nagaratnam Zhang, Yue Pedrycz, Witold Das, Swagatam Mallipeddi, Rammohan Ajani, Oladayo Solomon Feng, Qiang National Defense University Haidian District Beijing China Department of Analytics Operations and Systems University of Kent Kent Canterbury United Kingdom School of Systems Science Beijing Jiaotong University Beijing China School of Civil and Environmental Engineering Nanyang Technological University Singapore KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar School of Reliability and Systems Engineering Beihang University China Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India School of Electronics Engineering Kyungpook National University Taegu Korea Republic of

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering fur

关键词： Reinforcement learning

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference

Gaussian Process-based Active Learning for Efficient Cardiov...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Stavroula C. Tassi Konstantinos D. Polyzos Dimitrios I. Fotiadis Antonis I. Sakellarios Department of Material Science and Engineering Unit of Medical Technology and Intelligent Information Systems (MEDLAB) University of Ioannina Ioannina Greece Department of Mechanical and Aeronautics Engineering University of Patras Patras Greece Department of Electrical and Computer Engineering University of Minnesota Minnesota USA Biomedical Reasearch Institute Foundation for Research and Technology-Hellas(FORTH) Ioannina Greece Department of Mechanical and Aeronautics Engineering Laboratory of Biomechanics and Biomedical Engineering University of Patras Patras Greece Unit of Medical Technology and Intelligent Information Systems (MEDLAB) University of Ioannina Ioannina Greece

Cardiovascular disease (CVD) poses a significant global health challenge, and accurate inference methods are vital for early detection and intervention. However, the quality of prediction relies heavily on the availability of labeled data, which are often limited in medical applications. To cope with the challenge of limited labeled data, we are the first to propose an active learning (AL) approach that leverages a weighted ensemble of Gaussian processes to effectively infer CVD by strategically selecting the few most informative data points to label. Through experiments conducted on the SMARTool dataset, we demonstrate the effectiveness of the advocated approach, achieving superior performance in CVD inference compared to baseline methods. Our findings highlight the potential impact of the proposed AL framework in CVD diagnosis and treatment clinical cases, particularly in scenarios where labeled data are scarce, due to data confidentiality concerns or high sampling costs.

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Enhanced Rheumatoid Arthritis Treatment Optimization Through Deep Learning

Enhanced Rheumatoid Arthritis Treatment Optimization Through...

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Disruptive Technologies (ICDT), International Conference on

作者： Siva Venkatesh Arcot T. Buvaneswari K.R. Kavitha R. Manickavasagam Suguna M M Muthulekshmi Contact Center Business Unit Cisco Systems INC Little Elm Texas USA Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Biomedical Engineering Alpha College of Engineering Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Inflammation and joint destruction are hallmarks of Rheumatoid Arthritis (RA), a chronic autoimmune condition. To improve patient outcomes, it is essential to optimize treatment plans. According to this research, integrating deep learning (DL) methods into a Convolutional Neural Network (CNN) provides a new method to optimize RA treatments. The CNN model is used to decipher intricate medical data, such as imaging and patient records, find trends, and predict how patients react to treatment. To assess the efficacy of the proposed model in predicting ideal treatment methods, it was trained on a large dataset that included patient images and clinical data. The CNN model offers a potential tool for doctors to adapt RA management plans since results show it significantly improves prediction accuracy and treatment personalization. This method allows for data-driven decisions in RA management, improving patient care and enhancing treatment performance.

关键词： Deep learning Adaptation models Accuracy Soft sensors Predictive models Arthritis Data models Convolutional neural networks Optimization Biomedical imaging

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GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

arXiv

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arXiv 2024年

作者： Kavouras, Loukas Psaroudaki, Eleni Tsopelas, Konstantinos Rontogiannis, Dimitrios Theologitis, Nikolaos Sacharidis, Dimitris Giannopoulos, Giorgos Tomaras, Dimitrios Markou, Kleopatra Gunopulos, Dimitrios Fotakis, Dimitris Emiris, Ioannis Institute for the Management of Information Systems Athena Research Center Athens Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece Université Libre de Bruxelles Bruxelles Belgium Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Department of Informatics Athens University of Economics and Business Athens Greece Archimedes Research Unit Athena Research Center Athens Greece

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. Effectiveness is measured by the fraction of the population that is provided recourse, ensuring that the actions benefit as many individuals as possible. Keeping the cost of actions low ensures the proposed recourse actions remain practical and actionable. Limiting the number of actions that provide global counterfactuals is essential to maximize interpretability. The primary challenge, therefore, is balancing these trade-offs—maximizing effectiveness, minimizing cost, while maintaining a small number of actions. We introduce GLANCE, a versatile and adaptive framework, comprising two algorithms, that allows the careful balancing of the trade-offs among the three key objectives, with the size objective functioning as a tunable parameter to keep the actions few and easy to interpret. C-GLANCE employs a clustering approach that considers both the feature space and the space of counterfactual actions, thereby accounting for the distribution of points in a way that aligns with the structure of the model. T-GLANCE provides additional features to enhance flexibility. It employs a tree-based approach, that allows users to specify split features, to build a decision tree with a single counterfactual action at each node that can be used as a subgroup policy. Our extensive experimental evaluation demonstrates that our method consistently shows greater robustness and performance compared to existing methods across various datasets and models. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Fully automated evaluation of paraspinal muscle morphology and composition in patients with low back pain

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Intelligence-Based Medicine 2024年 9卷 100130-100130页

作者： Giaccone, Paolo Russo, Fabrizio Volpecina, Manuel Mallio, Carlo Augusto Papalia, Giuseppe Francesco Vadalà, Gianluca Denaro, Vincenzo Vollero, Luca Merone, Mario Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome 00128 Italy Research Unit of Orthopaedic and Trauma Surgery Fondazione Policlinico Universitario Campus Bio-Medico Via Alvaro del Portillo 200 Rome 00128 Italy Research Unit of Orthopaedic and Trauma Surgery Department of Medicine and Surgery Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome 00128 Italy Research Unit of Diagnostic Imaging and Interventional Radiology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome 00128 Italy

Chronic Low Back Pain (LBP) is one of the most prevalent musculoskeletal conditions and is the leading cause of disability worldwide. The morphology and composition of lumbar paraspinal muscles, in terms of infiltrated adipose tissue, constitute important guidelines for diagnosis and treatment choice but still require manual procedures to be assessed. We developed a fully automated artificial intelligence based algorithm both to segment paraspinal muscles from MRI scans through a U-Net architecture and to estimate the amount of fatty infiltrations by a home-made intensity- and region-based processing;we further validated our results by statistical assessment of the accuracy and agreement between our automated measures and the clinically reported values, achieving dice scores greater than 95 % on the preliminary segmentation task, as well as an excellent degree of agreement on the following area estimates (ICC_2,1 = 0.89). Furthermore, we employed an external public dataset to validate our model generalization abilities, reaching dice scores greater than 94 % with an average processing time of 21.92s(±3.38s) per subject. Hence, a deterministic and reliable measuring tool is proposed, without any manual confounding effect, to efficiently support daily clinical practice in LBP management. © 2023 The Authors

关键词： Artificial intelligence computer vision Deep learning Fat infiltrations Low back pain Neural networks

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Automatic neonatal cranial ultrasound segmentation using deep learning: A review

Automatic neonatal cranial ultrasound segmentation using dee...

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2022 International Workshop in Neurodevelopmental Impairments in Preterm Children - Computational Advancements, DETERMINED 2022

作者： Khaled, Roa'a Ortega-Leon, Arantxa Pizarro, Joaquín Fernández, Isabel Benavente Lubián López, Simón P. Gontard, Lionel C. Applied Optics and Magnetism Research Group University of Cádiz Puerto Real11510 Spain Department of Computer Engineering University of Cádiz Puerto Real11519 Spain Intelligent Modelling of Systems Research Group University of Cádiz Algeciras11202 Spain Research Unit Puerta del Mar University Hospital Cádiz11009 Spain IMEYMAT University of Cádiz Puerto Real11510 Spain

Ultrasound is widely used as a clinical routine tool for neonates’ brain assessment, especially for preterm neonates. This population is at high risk of developing serious complications leading to neurocognitive and motor impairments. However, the analysis of Cranial Ultrasound requires experienced personnel to perform a time-consuming visual assessment, which is nontrivial due to the low quality and artifacts in the images. For this analysis to be more objective, fast, and accurate, many automatic methods have been proposed. Such methods usually rely on segmenting brain structures or regions of interest for the extraction of subsequent clinically useful measurements. Deep Learning methods are being more adopted recently as they proved to have a huge potential in many medical image analysis tasks. In this review article, we present and discuss the Deep Learning-based methods developed for the automatic segmentation of preterm neonatal ultrasound images, more specifically the methods developed for segmenting the Cerebral Ventricle System. The performance and evaluation results of these methods are compared, and their major contributions are outlined. Furthermore, we discuss the main challenges of neonatal ultrasound automatic segmentation and possible ways to address these challenges. Finally, we discuss the future directions in this very specific context. © 2022 Copyright for this paper by its authors.

关键词： Ultrasonics

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