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DeepADNet: A CNN-LSTM model for the multi-class classification of Alzheimer's disease using multichannel EEG

Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057573-e057573页

作者： Thi Kieu Khanh Ho YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: Early and accurate diagnosis of Alzheimer's disease (AD) - an incurable and progressive brain disorder, along with a sharp upward trend in the incidence rate, is significantly important for patients to take prevention and appropriate treatments. However, the pathophysiological mechanism at the levels of AD severity is still poorly understood in spite of the worldwide financial and research efforts. Using an inexpensive and noninvasive modality, such as Electroencephalography (EEG) coupled with Deep Learning (DL)-derived diagnostic tool to retain the accuracy and versatility, therefore, has gained much attention in AD multi-class classification. METHODS: The present study proposes DL model, named DeepADNet, for the AD multi-class classification using multichannel EEG. First, we extracted the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands relative to a specific experimental event. Second, the hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built to exploit the wide range of ERSP patterns on a time-frequency domain and to generate discriminant features to classify healthy controls (HC) with two AD subject groups. To tackle the imbalance problem and improve the classification accuracy, we also applied different over-sampling techniques RESULTS: The EEG data were collected from the Oddball - a cognitive ability test with 63 subjects (23 HC, 17 Pre-symptomatic AD (aAD) and 23 Prodromal AD (pAD) subjects) and the N-back - a memory ability test with 36 subjects (13 HC, 11 aAD and 12 pAD subjects). We demonstrated that the ERSP patterns displayed significant differences among three subject groups during the two experimental stages. Classification results also reveal that a CNN-LSTM model could overcome the existing methods utilizing the hand-engineered features which demand on prior knowledge of AD analysis and achieved the highest

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Bottom-Up Meta-Policy Search

arXiv

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arXiv 2019年

作者： Melo, Luckeciano C. Maximo, Marcos R.O.A. da Cunha, Adilson M. Autonomous Computational Systems Lab Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil Software Engineering Research Group Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil

Despite of the recent progress in agents that learn through interaction, there are several challenges in terms of sample efficiency and generalization across unseen behaviors during training. To mitigate these problems, we propose and apply a first-order Meta-Learning algorithm called Bottom-Up Meta-Policy Search (BUMPS), which works with two-phase optimization procedure: firstly, in a meta-training phase, it distills few expert policies to create a meta-policy capable of generalizing knowledge to unseen tasks during training;secondly, it applies a fast adaptation strategy named Policy Filtering, which evaluates few policies sampled from the meta-policy distribution and selects which best solves the task. We conducted all experiments in the RoboCup 3D Soccer Simulation domain, in the context of kick motion learning. We show that, given our experimental setup, BUMPS works in scenarios where simple multi-task Reinforcement Learning does not. Finally, we performed experiments in a way to evaluate each component of the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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EntreCompOnto: An Ontology for Semantic Representation of Entrepreneurship Competences

EntreCompOnto: An Ontology for Semantic Representation of En...

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2018 IEEE International Conference on Teaching, Assessment, and Learning for engineering, TALE 2018

作者： Piedra, Nelson Caro, Edmundo Tovar Knowledge-Based Systems Research Group Universidad Técnica Particular de Loja Loja Ecuador Department of Computer Languages and Systems and Software Engineering Technical University of Madrid Madrid Spain

ISBN: (纸本)9781538665220

The development of citizens' and organizations' entrepreneurial capacity is a key policy objective of any country. The information required for monitoring development of curricula and learning activities fostering entrepreneurship as a competence is obtained from different data sources in different information systems. The semantic interoperability between these systems with resources relevant to the development of entrepreneurship stimulates more productive sharing of information among all stakeholders. Building semantic interoperable systems, ensuring in this way a collaborative performance, is not without difficulties. This paper presents EntreCompOnto, an ontology based on the EntreComp framework that could support European citizens and organizations in attaining entrepreneurship competence. EntreCompOnto sets the ground for identifying the knowledge, skills and attitudes that comprise 'entrepreneurial mindset', the ultimate aim being to facilitate exchange among users of EntreComp's adaptation families and eventually to positively impact the mobility, employability and participation of citizens in society and the economy. © 2018 IEEE.

关键词： Curricula

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Improving the multi-class classification of Alzheimer's disease with machine learning-based techniques: An EEG-fNIRS hybridization study

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057565-e057565页

作者： Thi Kieu Khanh Ho Minhee Kim YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jae Gwan Kim Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: As an incurable neurodegenerative disorder, Alzheimer's Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) reveals an ideal technique-derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi-class classification tasks. METHODS: In this study, the hybrid EEG-fNIRS was employed in constructing the classification machine learning (ML)-based models to categorize four subject groups, including healthy controls (HC) and three AD patient classes. First, a concurrent EEG-fNIRS setup was utilized to record data from 41 subjects during the Oddball - a cognitive ability test and 1-back - a memory ability test. Second, while fNIRS features were computed and extracted, the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands were extracted. Third, a conventional neural network and a hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built for the multi-class classification using the fNIRS and EEG features, respectively. To boost the classification accuracy, the output of different models was combined by majority voting methods. RESULTS: The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (71.01% ± 0.004) by combing their complementary properties, compared to using EEG (66.35% ± 0.016) or fNIRS (67.15% ± 0.017) alone. In addition, fNIRS and ERSP feature patterns displayed a significant difference between subject

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Symmetric Encryption-Based Cloud Data Security Transmission System in Communication networks

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Journal of Circuits, Systems and computers 2025年

作者： Li, Tong Xu, Chang-Bin Huang, Wen-Si Yang, Chao Wu, Xiao-Bo The Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd. Liaoning Province Shenyang City110006 China School of Computer Science And Engineering Northeastern University Liaoning Province Shenyang City110819 China State Grid Information & Telecommunication Group Co. Ltd. Beijing102211 China Northeast Electric Power University Jilin Province Jilin City132012 China State Grid Liaoning Electric Power Co. Ltd. Liaoning Province Shenyang City110004 China North China Electric Power University Beijing102206 China Union Tech Software Technology Co. Ltd. Beijing102600 China

This paper addresses information transmission security issues within cloud computing security management platforms. Focusing on the symmetric encryption algorithm, it proposes a cloud computing data security transmission system, followed by model validation and simulation analysis. The key findings are as follows: (1) The use of a symmetric encryption algorithm ensures both user data security and the secure distribution of encryption keys. (2) The proposed encryption algorithm effectively conceals the distribution characteristics of explicit data, enhancing the information transmission security of the ship communication network. (3) The joint robust optimization algorithm outperforms other benchmark methods in terms of average safety rate. (4) The maximum speed between sampling nodes is calculated, revealing a speed difference of 0.3 km/h between the embedded data and the original data. The average speed difference is 0.001 km/h, with a speed change rate of only 0.01%. These results indicate that the algorithm proposed in this paper provides high secrecy and a large hidden capacity, making it suitable for secure information transmission in vehicle-to-network communications and from roadside intelligent facilities to cloud servers. © 2025 World Scientific Publishing Company.

关键词： Publishing

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IoT-Based Architecture for Data Analytics of Arboviruses in Smart Cities

IoT-Based Architecture for Data Analytics of Arboviruses in ...

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IEEE Symposium on computers and Communications

作者： Priscylla da S. Tavares Emanuel B. Rodrigues Group of Computer Networks Software Engineering and Systems (GREat) Graduate Program in Computer Science (MDCC) Federal University of Ceará (UFC) Fortaleza Brazil

With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, forming the basis of the Internet of Things (IoT). Such devices enable the development of new applications capable of making cities more intelligent, providing improvements to society and urban infrastructure. As a result of the interaction between different applications, there is a large set of data, from which useful knowledge can be extracted. An important problem to be faced is the recurrent occurrence of arbovirus outbreaks in big cities with tropical climate. This research proposes an architecture capable of aggregating data from different types of IoT devices, and manipulating them using data analytics techniques to assist the surveillance of arbovirus outbreaks. As an example of case study, we present an exploratory analysis of climatology data and cases of arbovirus diseases in the city of Fortaleza, Ceara, Brazil, between the years of 2011 and 2017.

关键词： Internet of Things Data analytics Health care Smart city

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Bi-directional mission offloading for agile space-air-ground integrated networks

arXiv

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arXiv 2019年

作者： Zhou, Sheng Wang, Guangchao Zhang, Shan Niu, Zhisheng Shen, Xuemin Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Beihang Univesity Beijing100191 China State Key Laboratory of Software Development Environment Beijing Key Laboratory of Computer Networks Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada

Space-air-ground integrated networks (SAGIN) provide great strengths in extending the capability of ground wireless networks. On the other hand, with rich spectrum and computing resources, the ground networks can also assist space-air networks to accomplish resource-intensive or power-hungry missions, enhancing the capability and sustainability of the space-air networks. Therefore, bi-directional mission offloading can make full use of the advantages of SAGIN and benefits both space-air and ground networks. In this article, we identify the key role of network reconfiguration in coordinating heterogeneous resources in SAGIN, and study how network function virtualization (NFV) and service function chaining (SFC) enable agile mission offloading. A case study validates the performance gain brought by bi-directional mission offloading. Future research issues are outlooked as the bi-directional mission offloading framework opens a new trail in releasing the full potentials of SAGIN. Copyright © 2019, The Authors. All rights reserved.

关键词： Network function virtualization

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Online Intelligent Resource Management for Power-Delay Tradeoff in Backhaul-Limited Mobile Edge Computing Systems

Online Intelligent Resource Management for Power-Delay Trade...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yuan Sun Gang Yang Xing-She Zhou Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of China Electronics Technology Group Corporation Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Computer Science and Engineering Northwestern Polytechnical University Xi???an China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance the quality of experience for mobile device users, as it can offload computation intensive tasks from mobile devices to edge clouds. Nevertheless, task offloading also incurs additional overhead in both power consumption and execution delay. In essence, energy consumption and task delay cannot be optimized simultaneously as these two objectives contradict each other. Although many studies have been conducted to make tradeoff between them, the backhaul limitation of MEC-enabled wireless networks has not been tackled. In this paper, we study the power-delay tradeoff for MEC in backhaul-limited wireless networks. An average weighted sum power consumption minimization problem with task buffer stability constraints is formulated to investigate the problem, and a Lyapunouv Optimization based online intelligent computation and communication resource management algorithm is also proposed to solve it. In particular, at each time slot, optimal CPU-cycle frequencies, transmit power and backhaul capacity allocation are efficiently determined. Performance analysis indicates that under the proposed algorithm an [O(1/V), O(V)] tradeoff exists between the power consumption and execution delay, where V is a control parameter. Finally, simulations are conducted to validate the analytic results and demonstrate the changes of the system performance with various algorithm parameters.

关键词： Task analysis Mobile handsets Power demand Resource management Delays Optimization Wireless communication

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