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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images

Scientific reports 2025年第1期15卷 19369页

作者： Liang Zhou Achin Jain Arun Kumar Dubey Sunil K Singh Neha Gupta Arvind Panwar Sudhakar Kumar Turki A Althaqafi Varsha Arya Wadee Alhalabi Brij B Gupta Jiading District Central Hospital Shanghai University of Medicine and Health Sciences Shanghai 201318 China. Department of Information Technology Bharati Vidyapeeth's College of Engineering New Delhi India. Department of CSE Chandigarh College of Engineering and Technology Chandigarh India. Galgotias Multi-Disciplinary Research & Development Cell (G-MRDC) Galgotias University Greater Noida Uttar Pradesh 201308 India. Department of CSE Chandigarh College of Engineering and Technology Chandigarh India. sudhakar@ccet.ac.in. Information Systems Department HECI School Jeddah Saudi Arabia. Hong Kong Metropolitan University Hong Kong SAR China. Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India. Immersive virtual reality research group Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia. Computer Science Department HECI School Dar Alhekma University Jeddah Saudi Arabia. Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan. bbgupta@asia.edu.tw. Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan. bbgupta@asia.edu.tw. Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India. bbgupta@asia.edu.tw. School of Cybersecurity Korea University Seoul South Korea. bbgupta@asia.edu.tw. Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul 02447 Korea. bbgupta@asia.edu.tw.

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the deadliest forms and severely impacts human life. Early diagnosis and appropriate treatment significantly increase patient survival rates. Computed Tomography (CT) is a preferred imaging modality for detecting lung cancer, as it offers detailed visualization of tumor structure and growth. With the advancement of deep learning, the automated identification of lung cancer from CT images has become increasingly effective. This study proposes a novel lung cancer detection framework using a Flower Pollination Algorithm (FPA)-based weighted ensemble of three high-performing pretrained Convolutional Neural networks (CNNs): VGG16, ResNet101V2, and InceptionV3. Unlike traditional ensemble approaches that assign static or equal weights, the FPA adaptively optimizes the contribution of each CNN based on validation performance. This dynamic weighting significantly enhances diagnostic accuracy. The proposed FPA-based ensemble achieved an impressive accuracy of 98.2%, precision of 98.4%, recall of 98.6%, and an F1 score of 0.985 on the test dataset. In comparison, the best individual CNN (VGG16) achieved 94.6% accuracy, highlighting the superiority of the ensemble approach. These results confirm the model's effectiveness in accurate and reliable cancer diagnosis. The proposed study demonstrates the potential of deep learning and neural networks to transform cancer diagnosis, helping early detection and improving treatment outcomes.

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Strengthening some complexity results on toughness of graphs

arXiv

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arXiv 2019年

作者： Katona, Gyula Y. Varga, Kitti Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary MTA-ELTE Numerical Analysis and Large Networks Research Group Hungary HAS Alfréd Rényi Institute of Mathematics Hungary

Let t be a positive real number. A graph is called t-tough if the removal of any vertex set S that disconnects the graph leaves at most |S|/t components. The toughness of a graph is the largest t for which the graph is t-tough. The main results of this paper are the following. For any positive rational number t ≤ 1 and for any k ≥ 2 and r ≥ 6 integers recognizing t-tough bipartite graphs is coNP-complete (the case t = 1 was already known), and this problem remains coNP-complete for k-connected bipartite graphs, and so does the problem of recognizing 1-tough r-regular bipartite graphs. To prove these statements we also deal with other related complexity problems on toughness. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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A Novel Congestion-Aware Interest Flooding Attacks Detection Mechanism in Named Data Networking

A Novel Congestion-Aware Interest Flooding Attacks Detection...

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International Conference on computer Communications and networks (ICCCN)

作者： Ahmed Benmoussa Abdou el Karim Tahari Nasreddine Lagaa Abderrahmane Lakas Farhan Ahmad Rasheed Hussain Chaker Abdelaziz Kerrache Fatih Kurugollu Department of Computer Science University of Laghouat Algeria College of Information Technology United Arab Emirates University Al Ain UAE Cyber Security Research Group University of Derby Derby UK Institute of Security and Cyber-Physical Systems Innopolis University Innopolis Russia Department of Maths and Computer Science University of Ghardaia Ghardaia Algeria

Named Data Networking (NDN) is a promising candidate for future internet architecture. It is one of the implementations of the Information-Centric Networking (ICN) architectures where the focus is on the data rather than the owner of the data. While the data security is assured by definition, these networks are susceptible of various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). IFAs overwhelm an NDN router with a huge amount of interests (Data requests). Various solutions have been proposed in the literature to mitigate IFAs; however; these solutions do not make a difference between intentional and unintentional misbehavior due to the network congestion. In this paper, we propose a novel congestion-aware IFA detection and mitigation solution. We performed extensive simulations and the results clearly depict the efficiency of our proposal in detecting truly occurring IFA attacks.

关键词： Internet Security computer architecture Floods IP networks Delays computer science

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Cross-Device Radio Map Generation via Crowdsourcing

Cross-Device Radio Map Generation via Crowdsourcing

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Georgios Pipelidis Nikolaos Tsiamitros Efdal Ustaoglu Romeo Kienzler Petteri Nurmi Huber Flores Christian Prehofer Ariadne Maps Munich Germany Technical University of Munich Software and Systems Engineering Research Group Munich Germany IBM Watson Internet of Things Zurich Switzerland Department of Computer Science University of Helsinki Helsinki

Crowdsourcing is a powerful technique for bootstrapping sensing systems that are based on wireless signals. For example, wireless sensing systems can ask users to contribute training data and localization systems (such as WiFi fingerprinting) can take advantage of wireless measurements provided by users of the system. Indeed, previous research has demonstrated that crowdsourcing can reduce labor efforts needed to deploy and initialize wireless sensing systems by several orders of magnitude, without compromising on system performance. Despite the many benefits of crowdsourcing, current methods suffer from one significant drawback, namely that they are highly sensitive to variations in devices capturing the measurements. Indeed, as we demonstrate in this paper, cross-device variations can decrease performance of crowdsourced bootstrapping approaches up to 70of radio maps used for localization and to make them robust against cross-device variations in wireless signals. We evaluate our framework by considering WiFi fingerprinting based localization as a representative example of applications that benefit from our approach. Our results demonstrate up to 1.8m localization error, and 18.7.

关键词： Wireless communication Wireless sensor networks Elbow Indoor navigation Crowdsourcing Sensors Wireless fidelity

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Performance of machine learning approaches on prediction of esophageal varices for Egyptian chronic hepatitis C patients

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Informatics in Medicine Unlocked 2019年 17卷

作者： Abd El-Salam, Shimaa M. Ezz, Mohamed M. Hashem, Somaya Elakel, Wafaa Salama, Rabab ElMakhzangy, Hesham ElHefnawi, Mahmoud Department of Systems and Information Engineering Research Division National Research Centre Egypt Bioinformatics Group Centre of Excellence for Medical Research National Research Centre Giza Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Department of Systems and Computers Faculty of Engineering Al-Azhar University Cairo Egypt Department of Endemic Medicine and Hepatology Faculty of Medicine Cairo University Cairo Egypt

Esophageal Varices is one of the most common side-effects of liver cirrhosis diseases which is detected by Upper endoscopy. Screening all patients implies many endoscopies will be needed, which increases the workload of endoscopy units. The aim of this study is to find solutions to diagnose the disease, by analyzing the patterns found in the data through classification analysis, using machine learning techniques for early prediction in cirrhotic patients based on their clinical examination. This research study attempts to propose a quicker and more efficient technique for disease diagnosis, leading to timely patient treatment. Our method analyzed 4962 patients with chronic hepatitis C from fifteen different centers in Egypt between 2006 and 2017. The dataset included twenty-four individual clinical laboratory variables. Esophageal Varices was present in 2218 patients and absent in 2,744 patients. Different types of feature selection (Filter-Wrapper) Approaches were applied to select the most significant features. The proposed model used six common algorithms including Neural networks, Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest and Bayesian Network to achieve our objective. The results showed that correlation and (p-value) based on filter method and Bayesian Network algorithm are well-suited for this analysis. Only nine variables: Gender, Platelet, Albumin, Total Bilirubin, Baseline_PCR, Liver, Spleen, Stiffness, and prothrombin concentration were the most significant predictors for Esophageal Varices. The Bayesian Network algorithm showed the highest performance;it achieved 74.8% and 68.9% for Area Under Receiver Operating Characteristic curves and accuracy, respectively. To conclude, machine learning techniques were able to predict Esophageal Varices in cirrhotic patients. The experimental results show that the Bayesian Network achieved better results than the other approaches. © 2019

关键词： Esophageal varices Hepatitis C virus Machine learning Medical diagnosis Prediction algorithms

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2021年 1237 AISC卷 v-vi页

作者： Dong, Yucheng Herrera-Viedma, Enrique Matsui, Kenji Omatsu, Shigeru Briones, Alfonso González González, Sara Rodríguez Business School Sichuan University Chengdu China Dept. of System Design Osaka Institute of Technology Osaka Japan GRASIA Research Group Facultad de Informática Universidad Complutense de Madrid Madrid Spain University of Granada Granada Spain Hiroshima University Osaka Japan IoT European Digital Innovation Hub Bioinformatics Intelligent Systems and Educational Technology Research Group Department of Computer Science Faculty of Science University of Salamanca Salamanca Spain

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Tensor network models of AdS/qCFT

arXiv

引用

arXiv 2020年

作者： Jahn, Alexander Zimborás, Zoltán Eisert, Jens Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Institute for Particle and Nuclear Physics Wigner Research Centre for Physics Budapest1121 Hungary BME-MTA Lendület Quantum Information Theory Research Group Budapest1111 Hungary Institute for Mathematics Budapest University of Technology and Economics Budapest1111 Hungary Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

The study of critical quantum many-body systems through conformal field theory (CFT) is one of the pillars of modern quantum physics. Certain CFTs are also understood to be dual to higher-dimensional theories of gravity via the anti-de Sitter/conformal field theory (AdS/CFT) correspondence. To reproduce various features of AdS/CFT, a large number of discrete models based on tensor networks have been proposed. Some recent models, most notably including toy models of holographic quantum error correction, are constructed on regular time-slice discretizations of AdS. In this work, we show that the symmetries of these models are well suited for approximating CFT states, as their geometry enforces a discrete subgroup of conformal symmetries. Based on these symmetries, we introduce the notion of a quasiperiodic conformal field theory (qCFT), a critical theory less restrictive than full CFT with characteristic multi-scale quasiperiodicity. We discuss holographic code states and their renormalization group flow as specific implementations of a qCFT with fractional central charges and argue that their behavior generalizes to a large class of existing and future models. Beyond approximating CFT properties, we show that these can be best understood as belonging to a new paradigm of discrete holography. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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Adaptive routing for quantum memory failures in the quantum internet

arXiv

引用

arXiv 2019年

作者： Gyongyosi, Laszlo Imre, Sandor School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Networked Systems and Services Budapest University of Technology and Economics Budapest1117 Hungary MTA-BME Information Systems Research Group Hungarian Academy of Sciences Budapest1051 Hungary

We define an adaptive routing method for the management of quantum memory failures in the quantum Internet. In the quantum Internet, the entangled quantum states are stored in the local quantum memories of the quantum nodes. A quantum memory failure in a particular quantum node can destroy several entangled connections in the entangled network. A quantum memory failure event makes the immediate and efficient determination of shortest replacement paths an emerging issue in a quantum Internet scenario. The replacement paths omit those nodes that are affected by the quantum memory failure to provide a seamless network transmission. In the proposed solution, the shortest paths are determined by a base-graph, which contains all information about the overlay quantum network. The method provides efficient adaptive routing in quantum memory failure scenarios of the quantum Internet. The results can be straightforwardly applied in practical quantum networks, including long-distance quantum communications. Copyright © 2019, The Authors. All rights reserved.

关键词： Network routing

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Proceedings of the EuBIC-MS 2020 Developers’ Meeting

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EuPA Open Proteomics 2020年 24卷 1-6页

作者： Christopher Ashwood Wout Bittremieux Eric W. Deutsch Nadezhda T. Doncheva Viktoria Dorfer Ralf Gabriels Vladimir Gorshkov Surya Gupta Andrew R. Jones Lukas Käll Dominik Kopczynski Lydie Lane Ludwig Lautenbacher Marc Legeay Marie Locard-Paulet Bart Mesuere Yasset Perez-Riverol Eugen Netz Sander Willems European Bioinformatics Community for Mass Spectrometry (info@***) Denmark Department of Cellular and Integrative Physiology University of Nebraska Medical Center Omaha USA Department of Computer Science University of Antwerp Antwerp Belgium Institute for Systems Biology Seattle WA 98109 USA Novo Nordisk Foundation Center for Protein Research University of Copenhagen Denmark Bioinformatics Research Group University of Applied Sciences Upper Austria Hagenberg Austria VIB-UGent Center for Medical Biotechnology VIB Ghent Belgium Department of Biochemistry and Molecular Biology University of Southern Denmark Denmark Institute of Systems Molecular and Integrative Biology University of Liverpool UK Science for Life Laboratory KTH - Royal Institute of Technology Solna Stockholm Sweden Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V. Dortmund Germany SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine Faculty of Medicine Geneva University Geneva Switzerland Technical University of Munich Chair for Proteomics and Bioanalytics Freising Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SD UK Applied Bioinformatics Dept. of Computer Science University of Tübingen Tübingen Germany Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA 92093 USA Center for non-coding RNA in Technology and Health Department of Veterinary and Animal Sciences University of Copenhagen Denmark Department of Biomolecular Medicine Ghent University Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Biomolecular Interactions Max Planck Institute for Developmental Biology Tübingen Germany

The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13 th to January 17 th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://***/10.5281/zenodo.3890181 .

关键词： Computational mass spectrometry Mass spectrometry Proteomics Bioinformatics Spectrum clustering Phosphoproteomics XIC extraction Proteomics graph networks Predicted spectra Metaproteomics Functional annotations Benchmark development

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