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Metaheuristic-Driven Two-Stage Ensemble Deep Learning for Lung/Colon Cancer Classification

computers, Materials & Continua 2024年第9期80卷 3855-3880页

作者： Pouyan Razmjouei Elaheh Moharamkhani Mohamad Hasanvand Maryam Daneshfar Mohammad Shokouhifar department.of electrical&computer engineering Valie-e-Asr University of RafsanjanKerman***Iran International Academic Relations Office(IRO) Computer Science DepartmentUniversity of HalabjaHalabja46018Iraq department.of computer engineering University of Mohaghegh ArdabiliArdabil***Iran department.of Biomedical engineering Islamic Azad University of MashhadMashhad***Iran department.of electrical&computer engineering Shahid Beheshti UniversityTehran***Iran

This study investigates the application of deep learning,ensemble learning,metaheuristic optimization,and image processing techniques for detecting lung and colon cancers,aiming to enhance treatment efficacy and improve survival *** introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer *** diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks(CNNs)in feature extraction and model constructions,and utilizing the power of various Machine Learning(ML)algorithms for final ***,we consider different scenarios consisting of two-class colon cancer,three-class lung cancer,and fiveclass combined lung/colon cancer to conduct feature extraction using four *** extracted features are then integrated to create a comprehensive feature *** the next step,the optimization of the feature selection is conducted using a metaheuristic algorithm based on the Electric Eel Foraging Optimization(EEFO).This optimized feature subset is subsequently employed in various ML algorithms to determine the most effective ones through a rigorous evaluation *** top-performing algorithms are refined using the High-Performance Filter(HPF)and integrated into an ensemble learning framework employing weighted *** findings indicate that the proposed ensemble learning model significantly surpasses existing methods in classification accuracy across all datasets,achieving accuracies of 99.85%for the two-class,98.70%for the three-class,and 98.96%for the five-class datasets.

关键词： Lung cancer colon cancer feature selection electric eel foraging optimization deep learning ensemble learning

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information science and engineering Hunan Normal University School of computer science Shaanxi Normal University Faculty of computer science and Control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences computer science department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

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Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

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工程与科学中的计算机建模(英文) 2024年第1期138卷 125-165页

作者： Haiyu Chen Haijian Shao Xing Deng Lijuan Wang Xia Wang School of computer Jiangsu University of Science and TechnologyZhenjiang212003China department.of electrical and computer engineering University of Nevada Las VegasLas VegasNV89154USA department.of electrical and computer engineering National University of Singapore117583Singapore

The concept of the digital twin,also known colloquially as the DT,is a fundamental principle within Industry 4.0 *** recent years,the concept of digital siblings has generated considerable academic and practical ***,academia and industry have used a variety of interpretations,and the scientific literature lacks a unified and consistent definition of this *** purpose of this study is to systematically examine the definitional landscape of the digital twin concept as outlined in scholarly literature,beginning with its origins in the aerospace domain and extending to its contemporary interpretations in the manufacturing ***,this investigationwill focus on the research conducted on Industry 4.0 and smartmanufacturing,elucidating the diverse applications of digital twins in fields including aerospace,intelligentmanufacturing,intelligent transportation,and intelligent cities,among others.

关键词： Digital twins Industry 4.0 smart manufacturing digital thread modeling simulation

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Broadband angular spectrum analog processors based on all-dielectric metasurfaces

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science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 188-190页

作者： Lin Deng Yongmin Liu department.of electrical and computer engineering Northeastern UniversityBoston 02115USA department.of Mechanical and Industrial engineering Northeastern UniversityBoston 02115USA

Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital ***,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.

关键词： dielectric spectrum hardware

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Next generation multiple access for IMT towards 2030 and beyond

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science China(Information sciences) 2024年第6期67卷 448-450页

作者： Zhiguo DING Robert SCHOBER Pingzhi FAN H.Vincent POOR department.of computer and Information engineering Khalifa University department.of electrical and Electronic engineering University of Manchester Institute for Digital Communications Friedrich Alexander-University Erlangen-Nurnberg (FAU) CSNMT International Cooperation Research Center (MoST) Southwest Jiaotong University department.of electrical and computer engineering Princeton University

Multiple access techniques are fundamental to the design of wireless communication systems,since many crucial components of such systems depend on the choice of the multiple access technique[1].For example,the use of ...

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High-rate metal-free MXene microsupercapacitors on paper substrates

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Carbon Energy 2024年第5期6卷 94-104页

作者： Han Xue Po‐Han Huang Lee‐Lun Lai Yingchun Su Axel Strömberg Gaolong Cao Yuzhu Fan Sergiy Khartsev Mats Göthelid Yan‐Ting Sun Jonas Weissenrieder Kristinn BGylfason Frank Niklaus Jiantong Li School of electrical engineering and computer science KTH Royal Institute of TechnologyStockholmSweden School of engineering sciences KTH Royal Institute of TechnologyStockholmSweden

MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(>1000 mV s^(−1))on-paper MSCs,mainly due to the reduced electrical conductance of MXene films deposited on ***,ultrahigh-rate metal-free on-paper MSCs based on heterogeneous MXene/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)-stack electrodes are fabricated through the combination of direct ink writing and femtosecond laser *** a footprint area of only 20 mm^(2),the on-paper MSCs exhibit excellent high-rate capacitive behavior with an areal capacitance of 5.7 mF cm^(−2)and long cycle life(>95%capacitance retention after 10,000 cycles)at a high scan rate of 1000 mV s^(−1),outperforming most of the present on-paper ***,the heterogeneous MXene/PEDOT:PSS electrodes can interconnect individual MSCs into metal-free on-paper MSC arrays,which can also be simultaneously charged/discharged at 1000 mV s^(−1),showing scalable capacitive *** heterogeneous MXene/PEDOT:PSS stacks are a promising electrode structure for on-paper MSCs to serve as ultrafast miniaturized energy storage components for emerging paper electronics.

关键词： direct ink writing femtosecond laser scribing MXene on-paper microsupercapacitors PEDOT:PSS ultrahigh rate capability

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Breakdown of effective medium theory:A study of photonic spin Hall effect in deep-subwavelength all-dielectric multilayers

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science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 173-174页

作者： Yongmin Liu department.of Mechanical and Industrial engineering Northeastern UniversityBoston 02115USA department.of electrical and computer engineering Northeastern UniversityBoston 02115USA

Effective medium theory(EMT)has been widely applied in material science,electromagnetics and photonics to determine the effective material properties for inhomogeneous composites comprising subwavelength *** versatility of this foundational approach has been established through its application in classical Maxwell-Garnet models,which encompass relatively simple structures。

关键词： composites theory dielectric

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Resilence Control of DC Microgrid Integrated with Multi-Nanogrids Considering Connected-Islanded States

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 786-796页

作者： Hossien Faraji Reza Hemmati Pierluigi Siano department.of electrical engineering Amirkabir University of TechnologyTehranIran department.of electrical engineering Kermanshah University of TechnologyKermanshahIran department.of Management&Innovation Systems University of Salernoand also with the Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg 2006South Africa

In this paper, a DC microgrid (DCMG) integrated with a set of nano-grids (NG) is studied. DCMG exchanges predetermined active and reactive power with the upstream network. DCMG and NGs are coordinately controlled and managed in such a way the exchanged P-Q power with external grid are kept on scheduled level following all events and operating conditions. The proposed control system, in addition to the ability of mutual support between DCMG and NGs, makes NGs support each other in critical situations. On the other hand, in all operating conditions, DCMG not only feeds three-phase loads with time-varying active and reactive power on the grid side but also injects constant active power into the grid. During events, NGs support each other, NGs support DCMG, and DCMG supports NGs. Such control strategies are realized by the proposed control method to increase resilience of the system. For these purposes, all resources and loads in DCMG and NGs are equipped with individual controllers. Then, a central control unit analyzes, monitors, and regularizes performance of individual controllers in DCMG and NGs. Nonlinear simulations show the proposed model can effectively control DCMG and NGs under normal and critical conditions.

关键词： Control system DC microgrid nanogrid resilience

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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on computer Vision Techniques

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computer Modeling in engineering & sciences 2024年第4期139卷 759-782页

作者： Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi department.of computer science and engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India department.of computer science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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Analysis and Optimization of Boost Converter Parameters in Internal Model Control for Voltage Source Converter-based AC Microgrids

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2038-2054页

作者： Chiebuka Eyisi Qifeng Li department.of electrical and computer engineering University of Central FloridaOrlandoFL 32816 USA.

This paper investigates integration of distributed energy resources(DERs)in microgrids(MGs)through two-stage power conversion structures consisting of DC-DC boost converter and DC-AC voltage source converter(VSC)*** contrast to existing investigations that treated DC-link voltage as an ideal constant voltage,this paper considers the non-ideal dynamic coupling between both subsystems for completeness and higher accuracy,which introduces additional DC-side dynamics to the *** analysis shows parameters of the boost converter’s power model that impact stability through the *** selecting these parameters can mitigate this effect on stability and improve dynamic performance across the ***,an optimization framework is developed to facilitate in selecting adequate boost converter parameters in designing a stable voltage source converter-based microgrid(VSC-MG).The developed optimization framework,based on particle swarm optimization,considers dynamic coupling between both subsystems and is also effective in avoiding inadequate boost converter parameters capable of propagating instability through the DC-link to the *** are performed with MATLAB/Simulink to validate theoretical analyses.

关键词： AC microgrid boost converter dead-time droop control internal model control particle swarm optimization small-signal analysis time delay virtual impedance voltage source converter

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