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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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Critical factors to inhibit water-splitting side reaction in carbon-based electrode materials for zinc metal anodes

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Carbon Energy 2022年第6期4卷 1080-1092页

作者： Dong Hyuk Kang Eunji Lee Beom Sik Youn Son Ha Jong Chan Hyun Juhee Yoon Dawon Jang Kyoung Sun Kim Hyungsub Kim Sang Moon Lee Sungho Lee Hyoung-Joon Jin Hyung-Kyu Lim Young Soo Yun KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoulSeongbuk-guSouth Korea Division of Chemical Engineering and Bioengineering Kangwon National UniversityChuncheonGangwon-doSouth Korea Department of Eco-Polymer Science and Engineering Inha UniversityIncheonMichuhol-guSouth Korea Carbon Composite Materials Research Center Institute of Advanced Composite MaterialsKorea Institute of Science and Technology(KIST)Jeollabuk-doSouth Korea Department of Chemical and Biological Engineering Korea UniversitySeoulSeongbuk-guSouth Korea Neutron Science Division Korea Atomic Energy Research Institute(KAERI)DaejeonYuseong-guSouth Korea Research Center for Materials Analysis Korea Basic Science Institute(KBSI)DaejeonYuseong-guSouth Korea Department of Integrative Energy Engineering Korea UniversitySeoulSeongbuk-guSouth Korea

Zinc metal anodes(ZMA)have high theoretical capacities(820 mAh g−1 and 5855 mAh cm−3)and redox potential(−0.76 V *** hydrogen electrode),similar to the electrochemical voltage window of the hydrogen evolution reaction(HER)in a mild acidic electrolyte system,facilitating aqueous zinc batteries competitive in next-generation energy storage ***,the HER and byproduct formation effectuated by water-splitting deteriorate the electrochemical performance of ZMA,limiting their *** this study,a key factor in promoting the HER in carbon-based electrode materials(CEMs),which can provide a larger active surface area and guide uniform zinc metal deposition,was investigated using a series of threedimensional structured templating carbon electrodes(3D-TCEs)with different local graphitic orderings,pore structures,and surface *** ultramicropores of CEMs are the determining critical factors in initiating HER and clogging active surfaces by Zn(OH)2 byproduct formation,through a systematic comparative study based on the 3D-TCE series *** the 3D-TCEs had a proper graphitic structure with few ultramicropores,they showed highly stable cycling performances over 2000 cycles with average Coulombic efficiencies of≥99%.These results suggest that a well-designed CEM can lead to high-performance ZMA in aqueous zinc batteries.

关键词： aqueous batteries carbon electrode hydrogen evolution reaction multivalent ion zinc metal anode

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

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Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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Efficient Photothermal Catalytic Oxidation Enabled by Three-Dimensional Nanochannel Substrates

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Environmental science and Technology 2024年第11期58卷 5153-5161页

作者： Li, Yifei Zhang, Qianpeng Chong, Yanan Huang, Wei-Hsiang Chen, Chi-Liang Jin, Xiaojing Chen, Guangxu Fan, Zhiyong Qiu, Yongcai Ye, Daiqi School of Environment and Energy State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control South China University of Technology Guangdong Guangzhou510000 China State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong National Synchrotron Radiation Research Center 101 Hsin-Ann Road Hsinchu Science Park Hsinchu30076 Taiwan Taipei10607 Taiwan College of Light Chemical Industry and Materials Engineering Shunde Polytechnic Foshan528333 China

Photothermal catalysis exhibits promising prospects to overcome the shortcomings of high-energy consumption of traditional thermal catalysis and the low efficiency of photocatalysis. However, there is still a challenge to develop catalysts with outstanding light absorption capability and photothermal conversion efficiency for the degradation of atmospheric pollutants. Herein, we introduced the Co3O4 layer and Pt nanoclusters into the three-dimensional (3D) porous membrane through the atomic layer deposition (ALD) technique, leading to a Pt/Co3O4/AAO monolithic catalyst. The 3D ordered nanochannel structure can significantly enhance the solar absorption capacity through the light-trapping effect. Therefore, the embedded Pt/Co3O4 catalyst can be rapidly heated and the O2 adsorbed on the Pt clusters can be activated to generate sufficient O2- species, exhibiting outstanding activity for the diverse VOCs (toluene, acetone, and formaldehyde) degradation. Optical characterization and simulation calculation confirmed that Pt/Co3O4/AAO exhibited state-of-the-art light absorption and a notable localized surface plasmon resonance (LSPR) effect. In situ diffuse reflectance infrared Fourier transform spectrometry (in situ DRIFTS) studies demonstrated that light irradiation can accelerate the conversion of intermediates during toluene and acetone oxidation, thereby inhibiting byproduct accumulation. Our finding extends the application of AAO’s optical properties in photothermal catalytic degradation of air pollutants. © 2024 American Chemical Society.

关键词： Catalysis

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Implementation of a Geographical Information System (GIS) for E-commerce 4th

Implementation of a Geographical Information System (GIS) fo...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Mohamed, Shili Sethom, Kaouthar Obaid, Ahmed J. Alkhafaji, Mohammed Ayad Mohsen, Karrar Shareef Innov’COM at Sup’Com Laboratory University of Carthage Carthage Tunisia Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (纸本)9789819937158

Nowadays Geographic information systems (GIS) have developed quickly in recent years and are now utilized in a wide range of industries, including e-health, agriculture, and more. However, compared to other industries, the application of GIS in electronic commerce lags far behind. This paper consists of a web GIS application that aims to help businesses reduce driving time and costs by generating an optimal route between the delivery drivers and the customer’s locations. Using the programmable APIs provided by Google maps, we built an online shopping system with map features to research the potential of GIS in online commerce. The system incorporates Google maps into the system website using the Google maps API and adheres to the three-tier architectural paradigm. The geographical data is based on Google maps data, and the system data is stored in a MySQL database. The system MySQL database and the spatial data from Google maps are connected by latitude and longitude. These can drastically reduce the work and cost of development while simultaneously greatly increasing the system's extensibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Information systems

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Bond sensitive graph neural networks for predicting high temperature superconductors

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Materials Genome engineering Advances 2024年第2期2卷 126-133页

作者： Liang Gu Yang Liu Pin Chen Haiyou Huang Ning Chen Yang Li Turab Lookman Yutong Lu Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijingChina National Supercomputer Center in Guangzhou School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhouChina School of Materials Science and Engineering University of Science and Technology BeijingBeijingChina Department of Engineering Science and Materials University of Puerto RicoMayaguezPuerto RicoUSA AiMaterials Research LLC Santa FeNew MexicoUSA

Finding high temperature superconductors(HTS)has been a continuing challenge due to the difficulty in predicting the transition temperature(Tc)of ***,the efficiency of predicting Tc has been greatly improved via ma-chine learning(ML).Unfortunately,prevailing ML models have not shown adequate generalization ability to find new HTS,*** this work,a graph neural network model is trained to predict the maximal Tc(Tc max)of various *** model reveals a close connection between Tc max and chemical *** sug-gests that shorter bond lengths are favored by high Tc,which is in coherence with previous domain *** importantly,it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc,which is new even to the human *** can provide a convenient guidance to the materials scientists in search of HTS.

关键词： graph neural network machine learning superconductivity superconductors transition temperature

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advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication 35

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Dutta, Joy Eldeeb, Hossien B. Ho, Tu Dac Khalifa University C2PS Research Center Department of Computer Science Abu Dhabi United Arab Emirates University of Cambridge Electrical Engineering Division CambridgeCB3 0FA United Kingdom Faculty of Information Technology and Electrical Engineering Trondheim Norway

ISBN: (纸本)9798350362244

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

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Real-Time Tunable Gas Sensing Platform Based on SnO_(2) Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

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Nano-Micro Letters 2024年第12期16卷 103-119页

作者： Gi Baek Nam Jung-El Ryu Tae Hoon Eom Seung Ju Kim Jun Min Suh Seungmin Lee Sungkyun Choi Cheon Woo Moon Seon Ju Park Soo Min Lee Byungsoo Kim Sung Hyuk Park Jin Wook Yang Sangjin Min Sohyeon Park Sung Hwan Cho Hyuk Jin Kim Sang Eon Jun Tae Hyung Lee Yeong Jae Kim Jae Young Kim Young Joon Hong Jong-In Shim Hyung-Gi Byun Yongjo Park Inkyu Park Sang-Wan Ryu Ho Won Jang Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Research Laboratory of Electronics Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeMA 02139USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCA 90089USA Department of Display Materials Engineering Soonchunhyang UniversityAsan 31538Republic of Korea Department of Photonics and Nanoelectronics BK21 FOUR ERICA-ACE CenterHanyang University ERICAAnsan 15588Republic of Korea Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok 25913Republic of Korea Advance Institute of Convergence Technology Seoul National UniversitySuwon 16229Republic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Physics Chonnam National UniversityGwangju 500-757Republic of Korea

Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power ***,despite these benefits,challenges still exist such as a limited range of detectable gases and slow *** this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power *** optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain ***,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)*** noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and ***-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.

关键词： Micro-LED Gas sensor array Low power consumption Metal decoration Real-time detection

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