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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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

作者： Plati, Daphni K. Konstantakopoulos, Fotios S. Kalatzis, Theofanis Manousos, Dimitrios Kassiotis, Thomas Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Forth Institute of Computer Science Crete HeraklionGR 70013 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy

ISBN: (纸本)9798350383386

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition;(ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital. © 2023 IEEE.

关键词： Hospitals

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Development of the Function Check Stability of Digital Media Receivers using Interference Mitigation Techniques

Development of the Function Check Stability of Digital Media...

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International Electrical engineering Congress (iEECON)

作者： Pontakan Lhuangchon Chaiyong Soemphol Department of Electrical and Computer Engineering Faculty of Engineering at Mahasarakham University Mahasarakham Thailand Faculty of Engineering at Mahasarakham University Research Unit for Computational Electromagnetics and Optical Systems (CEMOS) Mahasarakham Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This study explores techniques to mitigate interference in assessing functions related to AM radio wave signals in digital media receivers. Signal transmission cable testing focused on BNC RG59 75-ohm cables with length of 300 mm. Six methods were employed: Method 1 used a Ferrite core, Method 2 used copper tape, Method 3 connected a grounding wire, Method 4 combined grounding wire connection with a Ferrite core, Method 5 combined grounding wire connection with copper tape, and Method 6 combined grounding wire connection with both a Ferrite Core and copper tape. The result has been shown that Method 6 yielded optimal results, with AM 1000 kHz Usable Sens, AM Interstation Noise L, and AM Interstation Noise R functions differing from reference values by −3.53 dBm, −5.05 dBm, and −5.26 dBm, respectively, emphasizing its efficacy in enhancing the assessed functions.

关键词： Ferrites Grounding Wires Noise Receivers Interference Media

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Postural Data Analysis using AI-powered Classification Models

Postural Data Analysis using AI-powered Classification Model...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Luca Bacco Matteo Coletta Martina Zaltieri Daniela Lo Presti Carlo Massaroni Emiliano Schena Mario Merone Department of Engineering Research Unit of Computer Systems and Bioinformatics Università Campus Bio-Medico di RomaVia Alvaro del Portillo 21 Rome Italy Department of Engineering Research Unit of Measurements and Biomedical Instrumentation Università Campus Bio-Medico di RomaVia Alvaro del Portillo 21 Rome Italy

Nowadays, more and more people are working remotely or in professions that require them to sit for long periods of time. Unfortunately, spending too much time in a seated position can lead to a range of physical and mental health problems, such as musculoskeletal discomfort, headaches, and respiratory issues. These problems are often exacerbated by poor posture, which is common when sitting for extended periods of time. To address this issue, we have developed a system for classifying sitting postures using sensors and machine learning algorithms, achieving 100% of accuracy with a set of seven fiber Bragg grating sensors. We have further optimized the multisensor system by studying the optimal number of sensors and their positioning on the spine, achieving over 95% accuracy in classifying upright, kyphotic, and lordotic positions with as little as only two devices.

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Interconnectedness of Circular Economy, Artificial Intelligence and Vertical Farming as a Technosignature Model for Sustainable Space Food Production 15

Interconnectedness of Circular Economy, Artificial Intellige...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Ii, Ronnie Concepcion Cabaccan, Carlo Ducut, Jullian Dominic Relano, R-Jay Alejandrino, Jonnel Agulto, Ronnel Bandala, Argel Mohamad, Roslina Dadios, Elmer Culaba, Alvin Department of Manufacturing Engineering and Management De la Salle University Manila Philippines De la Salle University Center for Engineering and Sustainability Development Research Intelligent Systems Research Unit Manila Philippines De la Salle University Bio-energy Environment and Renewables Laboratory Center for Engineering and Sustainability Development Research Manila Philippines Plant and Soil Health Research Unit Center for Natural Sciences and Environmental Research De la Salle University Manila Philippines School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of Electrical Engineering College of Engineering Universiti Teknologi Mara Shah Alam Malaysia De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Mechanical Engineering Manila Philippines

ISBN: (纸本)9798350381177

The degradation of Earth's natural resources and environmental health due to improper anthropological activities, mainly through unoptimized food production, is manifested through climate change that negatively alters the productivity in the agricultural sector. Because of this challenge in open terrestrial agriculture, the concept of space farming is believed to be significant in delivering fresh and quality food for the people on top of providing for astronauts and making them healthy in space. However, another problem emerges in the view of circular economy (CE) which is a production and consumption idealism that avoids degradation of natural resources and environment in producing one food unit and despite the collaborative advances in space technology, particularly in astrobotany, the materialization of CE and the absolutism of artificial intelligence (AI) are still partly imaginary. This study employed a bibliometric, scientometric and systematic analysis to highlight the advantages, limitations, and connections of CE, AI, and vertical farming (VF) which led to establishing a technosignature model for sustainable space food production. Here, CE, AI and VF were singularized as a model for sustainable space food production and identified their potential interplay. Based on scientific correlations, it is undoubtedly true that CE, AI, and VF have existing mutualism in Earth-based systems, but the degree of its importance as a whole is not yet completely appreciated for space farming. The developed CE-AI-VF technosignature model is scalable and adaptable if the government and scientific community put it into action accompanied by science policies. © 2023 IEEE.

关键词： Climate change

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bioinformatics and systems biology approaches to identify molecular targets and pathways shared between Schizophrenia and bipolar disorder

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Informatics in Medicine Unlocked 2024年 49卷

作者： Khatun, Mst Tania Rana, Humayan Kabir Hossain, Md Arju Lakshmanna, Kuruva Rahman, Md Mafizur Parvin, Anzana Rahman, Md Habibur Department of Biotechnology and Genetic Engineering Islamic University Department of Computer Science and Engineering Green University of Bangladesh Department of Microbiology Primeasia University School of Computer Science Engineering and Information Systems VIT Department of Computer Science and Engineering Islamic University Center for Advanced Bioinformatics and Artificial Intelligence Research Islamic University

Schizophrenia is a heterogeneous spectrum disorder of unknown etiology that is going to be a public concern around the world. The molecular association between the pathophysiology of schizophrenia (SP) and bipolar disorder (BD) is still not clear enough. To address this gap, we developed an integrated bioinformatics approach to identify common molecular signatures and possible interactive pathways that are common in the molecular pathogenic process between SP and BD. We employed pluripotent stem cell (iPSC)-derived cortical interneuron transcriptomic data and post-mortem brain transcriptomic data from patients with SP and BD, respectively. Twenty common differentially expressed genes (DEGs) were identified between SP and BD. Positive regulation of cyclic AMP (cAMP)-mediated signaling and regulation of the metabolic process were found to be common biological pathways between these two disorders. Protein-protein interactions and transcriptional regulatory interactions identified with hub-bottleneck proteins (LY6G5B, DLGAP2, DXO, EGR3, BAG6, DL-GAP2, SKIV2L, CSNK2B and RXRB), regulatory transcription factors (FOXC1, GATA2, RELA and ELK4), and regulatory microRNAs (hsa-mir-34a-5p, hsa-mir23b-3p, hsa-mir-146a-5p, hsa-mir-27a-3p, and hsa-mir-1-3p) are as key molecular signatures between SP and BD. Protein-chemical and protein-drug interactions revealed valproic acid, antirheumatic agents, trichostatin A, aarsenic, and 4-(5-benzo (1,3) dioxol-5-yl-4-pyridin-2yl-1H-imidazole-2-yl) benzamide and isopropyl alcohol compounds could be used as a potential therapeutic target molecule against SP and BD. The research findings would be used in wet lab-based research to further clarify potential therapeutic targets for the treatment of SP and BP disorders. © 2024 The Authors

关键词： Bipolar Differentially expressed genes microRNAs Pathways Schizophrenia Transcription factors

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Class Balancing Diversity Multimodal Ensemble for Alzheimer’s Disease Diagnosis and Early Detection

arXiv

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arXiv 2024年

作者： Francesconi, Arianna di Biase, Lazzaro Cappetta, Donato Rebecchi, Fabio Soda, Paolo Sicilia, Rosa Guarrasi, Valerio Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Operative Research Unit of Neurology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Eustema S.p.A. Research and Development Centre Naples Italy Department of Diagnostic and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Alzheimer’s disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addresses these challenges by introducing a novel approach: multImodal enseMble via class BALancing diversity for iMbalancEd Data (IMBALMED). IMBALMED integrates multimodal data from the Alzheimer’s Disease Neuroimaging Initiative database, including clinical assessments, neuroimaging phenotypes, biospecimen and subject characteristics data. It employs an ensemble of model classifiers, each trained with different class balancing techniques, to overcome class imbalance and enhance model accuracy. We evaluate IMBALMED on two diagnostic tasks (binary and ternary classification) and four binary early detection tasks (at 12, 24, 36, and 48 months), comparing its performance with state-of-the-art algorithms and an unbalanced dataset method. IMBALMED demonstrates superior diagnostic accuracy and predictive performance in both binary and ternary classification tasks, significantly improving early detection of MCI at 48-month time point. The method shows improved classification performance and robustness, offering a promising solution for early detection and management of AD. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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The Sustainability Component of the Brand and the Purchase Intention of Gen Z: State of the Art

The Sustainability Component of the Brand and the Purchase I...

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International Conference on Marketing and Technologies, ICMarkTech 2023

作者： Coutinho, Evelina Maria Oliveira Au-Yong-Oliveira, Manuel Department of Economics Management Industrial Engineering and Tourism Campus Universitário de Santiago University of Aveiro Aveiro3810-193 Portugal Research Unit On Governance Competitiveness and Public Policies Campus Universitário de Santiago University of Aveiro Aveiro3810-193 Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (纸本)9789819736973

In recent decades, the concept of sustainability has taken on a key role in the agendas of political decision-makers, businesses, and society. Brands have incorporated attributes associated with the concept of sustainability, making it essential to understand how these attributes are perceived and how they influence purchasing decisions. Young consumers tend to be well informed about social and environmental aspects and are more sensitive than previous generations to environmental issues. Although Gen Z’s purchasing intentions are influenced by the sustainable practices of organizations, their preferences are not fully understood. Recent studies have shown that the sustainability component associated with the brand influences Gen Z’s purchase intentions, and that social and economic aspects are at the root of this, emphasizing the importance of corporate social responsibility and transparent brand communication in terms of sustainability. Understanding how the sustainability component of brands influences the purchasing decisions of young consumers is fundamental for defining future branding strategies. The aim of this paper is to describe the state of the art of the relationship between the sustainability component of the brand and the purchase intention of Gen Z, based on the literature. The topic has not yet been widely studied, so it leaves clues for future development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sustainable development goals

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A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values

arXiv

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arXiv 2023年

作者： Caruso, Camillo Maria Guarrasi, Valerio Ramella, Sara Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Operative Research Unit of Radiation Oncology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Background and Objective: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. Methods: We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. Results: We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. Conclusions: The results show that our model not only outperforms the state-of-the-art’s performance but also simplifies

关键词： Lung cancer

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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