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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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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (ai) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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A method of location privacy protection in road network environment

A method of location privacy protection in road network envi...

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2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation, SMIMA 2018

作者： Wen, Jianjun Li, Zhao Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing Engineering Research Center of Mobile Internet Data Application Chongqing City400065 China

With the widespread application of location-based services, users 'privacy concerns have become the focus of users' attention. Based on the k-anonymity method and the SpaceTwist algorithm, this paper proposes a method of incremental inquiry user privacy protection. The method preliminarily anonymizes the user's location information and points of interest on the client side, On the anonymous server side, combining the road network environment with the latitude and longitude grid generates the minimum anonymous area of random loop, instead of the user initiating incremental inquiry to the location service provider, Anonymous zones ensure k-anonymity for mobile users and road information to protect user privacy. Security and experimental analysis show that this scheme can improve the effectiveness of user query service while meeting the privacy requirements of users. © The Authors, published by EDP sciences, 2018.

关键词： Location based services

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Integration of droplet microfluidic tools for single-cell functional metagenomics: an engineering head start

TechRxiv

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TechRxiv 2021年

作者： Conchouso, David Al-Ma’abadi, Amani Behzad, Hayedeh Alarawi, Mohammed Hosokawa, Masahito Nishikawa, Yohei Takeyama, Haruko Mineta, Katsuhiko Gojobori, Takashi Departamento de Ingeniería Industrial y Mecánica Escuela de Ingeniería Universidad de las Américas Puebla Puebla72810 Mexico Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Research Organization for Nano & Life Innovation Waseda University Tokyo162–0041 Japan Department of Life Science and Medical Bioscience Waseda University Tokyo162–8480 Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Waseda University Tokyo169-8555 Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda University Tokyo169–0072 Japan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

Droplet microfluidics techniques have shown promising results to study single-cells at high throughput. However, their adoption in laboratories studying "-omics" sciences is still irrelevant because of the field’s complex and multidisciplinary nature. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single-cells in droplets at a rate of ~ 250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescent activated droplet sorting (FADS) systems to integrate the use of 4 independent fluorescence-exciting lasers (e.g., 405, 488, 561, 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger was also integrated into our method to enable adding new reagents to already made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput (> 50,000 cells/day) capabilities to mining and bioprospecting metagenomic data. © 2021, CC BY-NC-SA.

关键词： Cell proliferation

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Secure routing based on trust model and reference node strategy in Ad Hoc network

Secure routing based on trust model and reference node strat...

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2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation, SMIMA 2018

作者： Huan, Shuiyuan Wen, Jianjun Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing Engineering Research Center of Mobile Internet Data Application Chongqing City400065 China

Ad Hoc network is a kind of wireless mobile network that does not depend on any fixed infrastructure. It has the characteristics of no center, self-organization, and high dynamic and limited energy. Therefore, Ad Hoc network has security risks. The Ad Hoc secure routing based on the trust model and the reference node strategy provides a secure environment for the establishment of communication routing. The AODV routing protocol is improved. The protocol selects a trusted communication node to establish routing according to the comprehensive evaluation result of the trust model, and finds out the data message discarding the attack node by the trust model. At the same time, the reference node strategy is proposed to defend the black hole attack node. Theoretical analysis and simulation results show that the proposed secure routing scheme can effectively detect and isolate malicious nodes, resist black hole attack and data packet attack attacks, and has high security characteristics. © The Authors, published by EDP sciences, 2018.

关键词： Network security

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Feature Extraction and Classification from Planetary science datasets enabled by Machine Learning

arXiv

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arXiv 2023年

作者： Nixon, Conor A. Yahn, Zachary Duncan, Ethan Neidel, Ian Mills, Alyssa C. Seignovert, Benoît Larson, Andrew Gansler, Kathryn Liles, Charles Walker, Catherine C. Trent, Douglas M. Santerre, John Planetary Systems Laboratory NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States University of Virginia Computer Science Department 351 McCormick Road CharlottesvilleVA22904 United States School of Information University of California Berkeley 102 South Hall # 4600 BerkeleyCA94720 United States Yale University Timothy Dwight College 345 Temple St New HavenCT06511-8238 United States Baylor University Geosciences Department One Bear Place #97354 WacoTX76798-7354 United States Nantes Université Laboratoire de Planétologie et Géosciences 2 Chem. de la Houssinière Bâtiment 4 Nantes44300 France Business Intelligence Division Inntopia 782 Mountain Rd StoweVT05672 United States University of Maryland Department of Geology 8000 Regents Dr #237 College ParkMD20742 United States NASA Langley Research Center Center Operations Directorate 1 NASA Drive HamptonVA23666 United States Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution 98 Water Street Woods HoleMA02543 United States SAIC | East-2 NASA Langley Research Center OCIO Information Data & Analytics Services 1 NASA Drive HamptonVA23666 United States

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice floes (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled floes in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 30% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

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Computational intelligence and neuroscience 2023年第1期2023卷 4776770页

作者： Sujatha Krishnamoorthy Yaxi Zhang Seifedine Kadry Muhammad Attique Khan Majed Alhaisoni Nasser Mustafa Weifeng Yu Abdullah Alqahtani Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Wenzhou Zhejiang Province China. Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Wenzhou Zhejiang Province China. Department of Neurology Wenzhou Central Hospital Medical Group Wenzhou 325000 China. Department of Applied Data Science Noroff University College Kristiansand Norway. Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 UAE. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Computer Science HITEC University Taxila Pakistan. Computer Sciences Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia. Wenzhou-Kean University School of Science and Technology Wenzhou Zhejiang Province China. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia.

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

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Corrigendum to “Understanding the urban mobility community by taxi travel trajectory” [Commun. Nonlinear Sci. Numer. Simul. 101(2021) 105863/6]

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Communications in Nonlinear science and Numerical Simulation 2022年 104卷

作者： Wei-Peng Nie Zhi-Dan Zhao Shi-Min Cai Tao Zhou Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China Complexity Computation Laboratory Department of Computer Science School of Engineering Shantou University Shantou 515063 China Key Laboratory of Intelligent Manufacturing Technology (Ministry of Education) Shantou University Shantou 515063 China

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Palladium nanoparticles supported over Green tea extract-modified Zn(Al)O mixed metal oxide as a heterogeneous catalyst for cyanation of aryl halides

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Journal of Organometallic Chemistry 2025年

作者： Ali B.M. Ali Abdulla A. Al-dulaimi Shaxnoza Saydaxmetova Narinderjit Singh Sawaran Singh Elangovan Muniyandy S. Sunitha M.A. Diab Aseel Smerat Syed Waheedullah Ghori Saiful Islam Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Nursing University of Al Maarif Al Anbar 31001 Iraq Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Department of Biosciences Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences Chennai 602105 Tamil Nadu India Applied Science Research Center Applied Science Private University Amman Jordan Department of Chemistry Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Biotechnology Yeungnam University Gyeongsan Gyeongbuk 38541 Republic of Korea Water Analysis Department Central Public Health Laboratories Ministry of Health and Population Cairo 11613 Egypt Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan. Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Mechanical Engineering Department College of Engineering King Khalid University Abha Saudi Arabia Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia

Recently, modified hydrotalcite-like materials featuring layers of metal oxides have gained significant attention as improved supports for nanoparticles (NPs) due to their well electronic effects that intrinsically stabilize the deposited NPs. This research presents a refined methodology for synthesizing palladium nanoparticles (Pd NPs) using green tea extract-modified zinc-aluminum mixed oxides (Zn(Al)O/GT extract/Pd NPs). Green tea extract serves as a platform and a green reducing agent, enabling the in situ formation of Pd NPs without the use of harsh chemicals. This innovative approach represents the first occasion of designing and bio-generating Pd NPs coordinated on Zn(Al)O mixed oxides, along with their catalytic effects for the cyanation of aryl halides. The physicochemical properties of the resulting materials were evaluated utilizing a range of techniques including FE-SEM, elemental mapping, EDX, TEM, and ICP-OES analysis. The Zn(Al)O/GT extract/Pd NPs nanocatalyst showed exceptional catalytic efficiency as a recyclable platform for the cyanation of iodo- and bromo-benzene derivatives, employing K 4 [Fe(CN) 6 ] as the cyanating sorce. The obtained benzonitriles were produced in good yields, and the catalyst demonstrated the capacity to be reused up to 7 runs with only a minimal reduction in its efficacy.

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A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

A Competitive Swarm Algorithm for Image Segmentation Guided ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Diego Oliva Songfeng Lu Erik Cuevas Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong university of Science and Technology Wuhan China Department of Computer Damietta University Egypt Department of Electrical Engineering Fayoum University Fayoum Egypt Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

关键词： Image segmentation Entropy Optimization Linear programming Fuzzy sets Thresholding (Imaging) Histograms

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