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arXiv 2024年

作者： Rodrigues, Livia Bocchetta, Martina Puonti, Oula Greve, Douglas Londe, Ana Carolina França, Marcondes Appenzeller, Simone Rittner, Leticia Iglesias, Juan Eugenio Massachusetts General Hospital Harvard Medical School United States Universidade Estadual de Campinas School of Electrical and Computer Engineering Brazil Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London United Kingdom Centre for Cognitive and Clinical Neuroscience Division of Psychology Department of Life Sciences College of Health Medicine and Life Sciences Brunel University London United Kingdom Universidade Estadual de Campinas School of Medical Sciences Brazil Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods. Recently, new techniques using synthetic images have emerged, reducing the need for manual annotation. Here we provide HELM, Hypothalamic ex vivo Label Maps, a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, which can be used to develop segmentation methods using synthetic data. The label maps are obtained with a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains. We also provide the pre-processed ex vivo scans, as this dataset can support future projects to include other structures after these are manually segmented. © 2024, CC BY.

关键词： Magnetic resonance imaging

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A Chaotic Image Encryption Scheme Using Novel Geometric Block Permutation and Dynamic Substitution

arXiv

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arXiv 2025年

作者： Ali, Muhammad Ahmad, Jawad Khan, Muhammad Abdullah Hussain Ullah, Safee Rehman, Mujeeb Ur Shah, Syed Aziz Khan, Muhammad Shahbaz Department of Electrical Engineering HITEC University Taxila47080 Pakistan Cyber Security Center Prince Mohammad Bin Fahd University Al Khobar31952 Saudi Arabia School of Computer Science and Informatics De Montfort University LeicesterLEl 9BH United Kingdom Research Centre for Intelligent Healthcare Coventry University CoventryCVl 5FB United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEHlO 5DT United Kingdom

In this digital era, ensuring the security of digital data during transmission and storage is crucial. Digital data, particularly image data, needs to be protected against unauthorized access. To address this, this paper presents a novel image encryption scheme based on a confusion diffusion architecture. The diffusion module introduces a novel geometric block permutation technique, which effectively scrambles the pixels based on geometric shape extraction of pixels. The image is converted into four blocks, and pixels are extracted from these blocks using L-shape, U-shape, square-shape, and inverted U-shape patterns for each block, respectively. This robust extraction and permutation effectively disrupts the correlation within the image. Furthermore, the confusion module utilises bit-XOR and dynamic substitution techniques. For the bit-XOR operation, 2D Henon map has been utilised to generate a chaotic seed matrix, which is bit-XORed with the scrambled image. The resultant image then undergoes the dynamic substitution process to complete confusion phase. A statistical security analysis demonstrates the superior security of the proposed scheme with high uncertainty and unpredictability, achieving an entropy of 7.9974 and a correlation coefficient of 0.0014. These results validate the proposed scheme's effectiveness in securing digital images. © 2025, CC BY-NC-ND.

关键词： Digital image storage

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Hybrid Convolutional Neural Network with Change Detection on Hyperspectral Imagery

Hybrid Convolutional Neural Network with Change Detection on...

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Industrial Electronics Research and Applications (ICIERA), International Conference on

作者： Indira Bidari Satyadhyan Chickerur Abhishek THM School of Computer Science and Engineering KLE Technological University Hubballi India Centre for High Performance Computing KLE Technological University Hubballi India

ISBN: (纸本)9781665435437

Change Detection (CD) is one of the major research areas in the field of remote sensing. Hyperspectral Images (HSI's) boosted the change detection technology with their high spectral resolution features. Traditional CD techniques process the low dimensional images not well suited for high dimensional HSI's. Even tiny details can be captured using hyperspectral images, but processing these images is difficult because of their complex high-dimensional data. HSI contains noise and redundancy that affect the spectral features of hyperspectral imagery. We present a hybrid convolution neural network method to address processing complex high dimensional data of hyperspectral images. The proposed network extracts the spectral-spatial information of hyperspectral images by decomposing the change Tensor in three directions. The spectral dimension of the change Tensor is reduced by using I-D Convolution” and then the Tensor is decomposed from two spatial dimensions. A 2-D convolution is applied to extract the spectral and spatial features along different spatial dimensions to improve accuracy. The results on three hyperspectral image datasets illustrate the performance improvement than most state-of-the-art techniques.

关键词： Industrial electronics Tensors Image resolution Convolution Neural networks Redundancy Feature extraction

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Enhanced Electrochemical Response and Promising Anti-Cancer Potential of a Novel Azo Dye: From Sensor Technology to Bcl-Xl Inhibition

SSRN

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SSRN 2024年

作者： Harisha, S. Nagaraj, Kalyan Reddy, Mohan R. Sridhar, Amulyashree Kumar, S.R. Kiran Basavegowda, Nagaraj Centre for Nanosciences Department of Chemistry K.S. Institute of Technology Karnataka Bengaluru560 109 India B.M.S. College of Engineering Karnataka Bengaluru560019 India Department of Chemistry B.M.S. College of Engineering Karnataka Bengaluru560019 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Karnataka Bengaluru560035 India Department of Biotechnology Yeungnam University Gyeongbuk Gyeongsan38451 Korea Republic of

We report the synthesis of heterocyclic azo dye 1,3-dimethyl-5-[(E)-(5-sulfanyl-1,3,4-thiadiazol-2-yl)diazenyl]pyrimidine-2,4,6(1H,3H,5H)-trione (i.e. DSTDPT). The structure of DSTDPT was characterized using UV-Visible, FTIR, 1HNMR, and LCMS analyses. Furthermore, electrochemical properties were examined in a 0.2 M phosphate buffer (pH=7.2) with a carbon paste electrode (CPE), which was in turn altered using surfactants CTAB, SDS, and Triton X-100 to boost sensitivity and selectivity metrics towards paracetamol detection. The nonlinear optical metrics and electronic structure of DSTDPT was investigated by performing density functional theory (DFT) computations using 6-31G (d, p)/B3LYP basis set. The compound revealed promising optical characteristics, with significant first-order hyperpolarizability, which was 19 times that of urea. In addition, molecular docking studies revealed favourable binding energies of DSTDPT towards B-cell lymphoma-extra-large (Bcl-xL) protein. To further validate the solidity and instabilities within Bcl-xL protein and Bcl-xL-DSTDPT complex, molecular dynamics (MD) simulations was performed. The outcomes established DSTDPT as a promising lead molecule with potent anti-cancer property. © 2024, The Authors. All rights reserved.

关键词： Molecular docking

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Resource-Efficient Hybrid Quantum-Classical Simulation Algorithm

arXiv

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arXiv 2024年

作者： Chee, Chong Hian Leykam, Daniel Mak, Adrian M. Bharti, Kishor Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117543 Singapore 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore Centre for Quantum Engineering Research and Education TCG CREST Sector V Salt Lake Kolkata700091 India School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece AngelQ Quantum Computing 531A Upper Cross Street #04-95 Hong Lim Complex 051531 Singapore

Digital quantum computers promise exponential speedups in performing quantum time-evolution, providing an opportunity to simulate quantum dynamics of complex systems in physics and chemistry. However, the task of extracting desired quantum properties at intermediate time steps remains a computational bottleneck due to wavefunction collapse and no-fast-forwarding theorem. Despite significant progress towards building a Fault-Tolerant Quantum computer (FTQC), there is still a need for resource-efficient quantum simulators. Here, we propose a hybrid simulator that enables classical computers to leverage FTQC devices and quantum time propagators to overcome this bottleneck, so as to efficiently simulate the quantum dynamics of large systems initialized in an unknown superposition of a few system eigenstates. It features no optimization subroutines and avoids barren plateau issues, while consuming fewer quantum resources compared to standard methods when many time steps are required. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Retraction Note: Novel fuzzy logic expert system-based edge detection for X-ray images

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Soft computing 2024年第1期28卷 269-269页

作者： Thirugnanasambandam, Kalaipriyan Prabu, U. Mahto, Dindayal Rajendiran, P. R. Venkatesan, R. Raghav, R. S. Centre for Smart Grid Technologies School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India School of Computing SASTRA Deemed To Be University Thanjavur India

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TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big Data Streaming

TransformerFusionNet: A Real-Time Multimodal Framework for I...

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International Conference on computer and Applications (ICCA)

作者： Hager Saleh Michael McCann Shaker El-Sappagh John G. Breslin Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Insight Research Ireland Centre for Data Analytics University of Galway Galway Ireland Atlantic Technological University Letterkenny Ireland Department of Computing Atlantic Technological University Letterkenny Ireland Faculty of Computer Science and Engineering Galala University Suez Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt School of Engineering University of Galway Galway Ireland

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings.

关键词： Heart Accuracy Recurrent neural networks Biological system modeling Pipelines Mortality Medical services Big Data Transformers Real-time systems

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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