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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

Computer Systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Construction of fuzzy-classification expert system in cerebral palsy for learning performance facilitation 10th

Construction of fuzzy-classification expert system in cerebr...

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10th International Conference in Methodologies and Intelligent Systems for technology Enhanced Learning, MIS4TEL 2020

作者： Ivanova, Malinka Ilieva, Roumiana Lu, Zhenli College of Energy and Electronics Technical University of Sofia 8 "Kliment Ohridski" boul. Sofia Bulgaria Faculty of Management Department of Management and Business Information Systems Technical University of Sofia 8 "Kliment Ohridski" boul. Sofia Bulgaria School of Electrical Engineering and Automation Changshu Institute of Technology Chanshu215500 China

ISBN: (纸本)9783030522865

The paper presents a novel method and conceptual architecture for implementation of fuzzy-classification expert system in the domain of rehabilitation methods for cerebral palsy. The expert system includes two blocks: Fuzzy block utilizing fuzzy algorithms for multi-criteria decision making and Machine learning block based on algorithms for tree classification and KMeans clustering. The proposed solution is designed for facilitation the learning performance of university students as well as for professionals who have to make decisions in the area of cerebral palsy and corresponding rehabilitation methods. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Expert systems

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Output consensus control for linear port-Hamiltonian systems

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IFAC-PapersOnLine 2022年第30期55卷 230-235页

作者： Shuai Feng Yu Kawano Michele Cucuzzella Jacquelien M.A. Scherpen School of Automation Nanjing University of Science and Technology Nanjing 210094 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia PV Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen the Netherlands

In this paper, we study output consensus of coupled linear port-Hamiltonian systems on graphs in the presence of constant disturbances, where couplings are allowed to be both static and dynamic. Utilizing port-Hamiltonian structures, we present dynamic controllers achieving output consensus where the consensus values are determined by the disturbances. Finally, the utility of the proposed controller is illustrated by applying it to current sharing of DC microgrids.

关键词： port-Hamiltonian systems networked systems output consensus

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High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing

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Journal of the Saudi Society of Agricultural Sciences 2024年

作者： Morchid, Abdennabi Muhammad Alblushi, Ishaq G. Khalid, Haris M. El Alami, Rachid Sitaramanan, Surendar Rama Muyeen, S.M. Department of Physics Laboratory of Informatics Signals Automation and Cognitivism (LISAC) Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University BP 1796 Fes-Atlas Fez 30003 Morocco Department of Electrical and Electronics Engineering Higher Colleges of Technology Sharjah 7947 United Arab Emirates College of Engineering and Information Technology University of Dubai Academic City Dubai 14143 United Arab Emirates Department of Electrical and Electronic Engineering Science University of Johannesburg Aukland Park 2006 South Africa Samsung Austin Research and Development Centre (SARC) Advanced Computing Lab (ACL) San Jose CA United States Department of Electrical Engineering Qatar University Doha 2713 Qatar

Context: Food security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology. Objective: To meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of (1) cloud computing, (2) embedded systems, and (3) Internet-of-Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology. Methods: This paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as (1) moisture, (2) humidity, (3) temperature, and (4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach. Results and conclusion: Farmers can access comprehensive farm data from anywhere i

关键词： Cloud computing Embedded system Food security Intelligent sensors Internet-of-things (IoT) Irrigation system Smart agriculture ThingSpeak platform V model

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

DeepRicci: Self-supervised Graph Structure-Feature Co-Refine...

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IEEE International Conference on Data Mining (ICDM)

作者： Li Sun Zhenhao Huang Hua Wu Junda Ye Hao Peng Zhengtao Yu Philip S. Yu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computer Science University of Illinois at Chicago IL USA

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/.

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Structural reduction of CRNs with linear sub-CRNs

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IFAC-PapersOnLine 2021年第14期54卷 149-154页

作者： Katalin M. Hangos György Lipták Gábor Szederkényi Systems and Control Laboratory Computer and Automation Research Institute P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University Budapest Hungary

A novel systems theory-based structural reduction method is proposed in this paper that can be applied to chemical reaction networks (CRNs) including subsystems consisting of linear reactions, i.e. a linear sub-CRN. The reduced model is a delayed CRN with distributed delays having less complexes (monomials) and reactions than the original model. The reduction is based on the fact that the input/output response of a linear sub-CNR can be seen as a time-delayed input to output relationship, therefore linear sub-CNRs can be interpreted as reactions with distributed delay from their input to their output complexes. The practically important example of a kinetic model describing the spread of the COVID-19 epidemic is used as a case study to illustrate the basic concepts and the computation method.

关键词： Delay Model reduction Chemical Reaction Networks COVID

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Editorial: AI, data analytics, and mechanism design for DER integration toward net zero

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Frontiers in Energy Research 2022年 10卷

作者： Wang, Hao Ye, Yujian Chen, Yue Chen, Yize Yang, Qing Cui, Qiushi You, Pengcheng Department of Data Science and AI Faculty of Information Technology and Monash Energy Institute Monash University MelbourneVIC Australia School of Electrical Engineering Southeast University Nanjing China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China

The energy system is undergoing a fundamental energy transition by integrating low-carbon distributed 5 energy resources (DERs) in distribution networks to accelerate net zero. The increased DER uptake 6 poses significant challenges in operating energy systems to achieve net zero with high reliability and low 7 cost. In particular, the inherent variability of renewable generation, such as solar photovoltaic systems, 8 brings significant uncertainties to the energy system, causing reliability concerns. DERs also cause power 9 quality issues, such as voltage fluctuations. But the distribution system was not designed to support large Our goal in putting together this research topic is to provide a glance at the wide variety of aspects, 51 spanning from technical modeling and assessment to market-based solutions enabling energy transition 52 towards net zero by integrating DERs. We are also aware that the four papers selected are a small fraction 53 of works focusing on DER integration. We hope that these articles will succeed in attracting readers for 54 further research on this significant research topic of smart grids.

关键词： net zero energy transition smart grid distributed energy resources (DER) artificial intelligence data analytics optimization

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Ivomfuse: An Image Fusion Method Based on Infrared-to-Visible Object Mapping

SSRN

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SSRN 2022年

作者： Zhang, Xiangbo Huang, Lei Liu, Gang Ren, Qin Bavirisetti, Durga Prasad School of Automation Engineering Shanghai University of Electric Power Shanghai201306 China Library Shanghai University of Electric Power Shanghai201306 China School of Software and Microelectronics Peking University Beijing102600 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Due to the significant difference in the structure of infrared and visible images, most of the existing algorithms use globally consistent strategy, which may weaken the contrast of the target region. To solve this problem, we propose an infrared and visible image fusion method based on infrared-to-visible object mapping (IVOMFuse) First of all, we use the probability induced intuitionistic fuzzy C-means algorithm (PIIFCM) to process the infrared image, and extract the target region of the infrared image according to the image structure features. However, because of the poor contrast of the target of the visible image, it can not be segmented directly. For this reason, we map the infrared target region to the visible image, and the target region with the same coordinates of the two images can be obtained very quickly. Secondly, due to the significant differences in image structure, we set a hybrid fusion strategies for different regions. We use the fusion strategy based on the Expectation-Maximization (EM) algorithm to iterate and optimize the probability model for the target region. The fourth-order partial differential equation (FPDE) is used for the background region to decompose the source image into high-frequency texture and low-frequency approximation images respectively. Then, the principal component analysis (PCA) and the average fusion strategy are used to obtain the fused image. Finally, we evaluated our algorithm with thirteen other state-of-the-art image fusion methods on three commonly used datasets, namely TNO, CVC14, and RoadScene. Among the ten evaluation indexes, our algorithm achieved the best six times. © 2022, The Authors. All rights reserved.

关键词： Mapping

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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