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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset

arXiv

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arXiv 2022年

作者： Menezes, M.L. Samara, A. Galway, L. Sant'anna, A. Verikas, A. Alonso-Fernandez, F. Wang, H. Bond, R. Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden School of Computing and Mathematics Ulster University Belfast Belfast United Kingdom

One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user's emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell's Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the a, {3, 8, and e waves and High Order Crossing of the EEG signal. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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SVM in Classification of stage 0~II and III~IV with Breast Cancer : A Retrospective Cohort Study on a bicentric cohort 23

SVM in Classification of stage 0~II and III~IV with Breast C...

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2023 International Conference on Computer, Vision and intelligent Technology, ICCVIT 2023

作者： Guo, Yeang Tao, Tan Ronglin, Ronglin Xiao, Liangfen Ding, Lijuan Li, Qing Xie, Hui Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423000 China School of Medical Imaging Laboratory Science and Rehabilitation Xiangnan University Chenzhou423000 China Faulty of Applied Sciences Macao Polytechnic University 999078 China Department of Radiation Oncology The Third People's Hospital of Chenzhou City Chenzhou423000 China of Xiangnan University Chenzhou423000 China

ISBN: (纸本)9798400708701

Objective: The objective is to develop a predictive model utilizing Support Vector Machines (SVM) for the purpose of classifying the clinical stage of breast cancer. Materials and Methods: Accurate determination of the clinical stage of breast cancer patients holds significant importance in selecting suitable treatment options and minimizing avoidable complications. In this study, we present the application of radiomics and SVM for breast cancer computed tomography (CT) to anticipate the preoperative clinical stage in breast cancer patients. The training dataset encompasses 166 cases obtained from the Affiliated Hospital of Xiangnan University, while the test dataset comprises 91 cases from Chenzhou Third People's Hospital. The integration of clinical parameters with radiomics exhibits the most superior diagnostic efficacy in forecasting the clinical stage of breast cancer. As part of the evaluation, various metrics were calculated, including the area under the curve (AUC), the accuracy (ACC), sensitivity (Sen), specificity (Spe), positive predictive value (PPV) and negative predictive value (NPV). To differentiate between the radiomics model, clinical data model, and fusion model, the Delong test was utilized. The precision of the prediction model was evaluated by generating a calibration curve using 1,000 bootstrap weight samples. Furthermore, the decision curve analysis (DCA) was conducted to assess the model's practicality. Results: The fusion model exhibits superior predictive performance compared to both the single radiomics model and clinical model. The fusion model's test sets of AUC, ACC, Sen, Spe, PPV, and NPV are 0.824, 0.780, 0.932, 0.652, 0.707, and 0.909, respectively. Conclusion: The fusion model exhibits greater efficacy than both the single radiomics model and clinical model, and thus holds significant potential for facilitating the diagnosis of breast cancer stage and the development of individualized treatment *** © 2023 ACM.

关键词： Classification (of information)

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computing with Residue Numbers in High-Dimensional Representation

arXiv

引用

arXiv 2023年

作者： Kymn, Christopher J. Kleyko, Denis Frady, E. Paxon Bybee, Connor Kanerva, Pentti Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience University of California BerkeleyCA United States Centre for Applied Autonomous Sensor Systems Örebro University Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Neuromorphic Computing Lab Intel Santa ClaraCA United States

We introduce Residue Hyperdimensional computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show how residue numbers can be represented as high-dimensional vectors in a manner that allows algebraic operations to be performed with component-wise, parallelizable operations on the vector elements. The resulting framework, when combined with an efficient method for factorizing high-dimensional vectors, can represent and operate on numerical values over a large dynamic range using vastly fewer resources than previous methods, and it exhibits impressive robustness to noise. We demonstrate the potential for this framework to solve computationally difficult problems in visual perception and combinatorial optimization, showing improvement over baseline methods. More broadly, the framework provides a possible account for the computational operations of grid cells in the brain, and it suggests new machine learning architectures for representing and manipulating numerical data. © 2023, CC BY-SA.

关键词： Combinatorial optimization

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Optimizing wastewater treatment plant operations: a machine learning approach for energy consumption and climate dynamics analysis

Optimizing wastewater treatment plant operations: a machine ...

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8th IET Smart Cities Symposium (SCS 2024)

作者： Mahmoud Elshabrawy Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Ali E. Takieldeen Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg 24060 Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University 11961 Shaqra Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

ISBN: (数字)9781837243105

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved.

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Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob

Sentiment Analysis: Amazon Electronics Reviews Using BERT an...

引用

Language Engineering (ESOLEC), International Conference on

作者： Abdulrahman Mahgoub Hesham Atef Abdulrahman Nasser Mohamed Yasser Walaa M. Medhat M. Saeed Darweesh Passent M. El-Kafrawy Computing and Information Systems Research Center (CIS) School of Information Technology and Computer Science Nile University Egypt Faculty of Computer and AI Benha University Cairo Egypt Wireless Intelligent Networks Center (WINC) School of Engineering and Applied Sciences Nile University Egypt Computer Science Department Effat College of Engineering SA

ISBN: (纸本)9781665453233

The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in *** are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.

关键词： Sentiment analysis Bit error rate Transformers Consumer electronics Business

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How to apply remote sensing techniques to precision farming: 2 practical cases by Garnata drone

How to apply remote sensing techniques to precision farming:...

引用

2019 Regional Problems of Earth Remote Sensing, RPERS 2019

作者： Rodríguez Utrilla, Javier Nogués Palenzuela, Álvaro Cabello, Javier Alcaraz Segura, Domingo Hernández, Guirado Agroforestry Responsible in Garnata Drone SL. Granada18013 Spain Department Biology and Geology Andalusian Center for Assessment and Monitoring of Global Change Almeria04120 Spain Department of Botany University of Granada Granada18071 Spain Soft Computing and Intelligent Information Systems Research Group University of Granada Granada18071 Spain

In this report we show 2 different cases in which Remote Sensing can help Precision Farming techniques to optimize and improve profits in the agricultural sector. First, we'll show a practical case in an olive trees field and second an example of how to monitor a greenhouse crop with aerial images and deep learning techniques. © The Authors, published by EDP Sciences 2020.

关键词： Drones

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Quality Optimization of Live Streaming Services over HTTP with Reinforcement Learning

Quality Optimization of Live Streaming Services over HTTP wi...

引用

2021 IEEE Global Communications Conference (GLOBECOM)

作者： F. Tashtarian R. Falanji A. Bentaleb A. Erfanian P. S. Mashhadi C. Timmerer H. Hellwagner R. Zimmermann Christian Doppler Laboratory ATHENA Institute of Information Technology Alpen-Adria-Universität Klagenfurt Austria Sharif University of Technology Tehran Iran School of Computing National University of Singapore (NUS) Center for Applied Intelligent Systems Research (CAISR) Halmstad University Sweden

ISBN: (纸本)9781728181059

Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to $3.7\times$ .

关键词： Scalability Conferences Reinforcement learning Streaming media Internet Quality of experience Servers

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sbi reloaded: a toolkit for simulation-based inference workflows

arXiv

引用

arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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An Early Stage researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

引用

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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