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Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things

Computers, Materials & Continua 2022年第10期73卷 965-979页

作者： Sultan Ahmad Shakir Khan Mohamed Fahad Al.Ajmi Ashit Kumar Dutta L.Minh Dang Gyanendra Prasad Joshi Hyeonjoon Moon Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia College of Pharmacy King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Saudi Arabia Department of Information Technology FPT UniversityHo Chi Minh CityVietnam Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

In recent times,Internet of Medical Things(IoMT)gained much attention in medical services and healthcare management *** healthcare sector generates massive volumes of data like personal details,historical medical data,hospitalization records,and discharging records,IoMT devices too evolved with potentials to handle such high quantities of *** and security of the data,gathered by IoMT gadgets,are major issues while transmitting or saving it in *** advancements made in Artificial Intelligence(ai)and encryption techniques find a way to handle massive quantities of medical data and achieve *** this view,the current study presents a new Optimal Privacy Preserving and Deep Learning(DL)-based Disease Diagnosis(OPPDL-DD)in IoMT ***,the proposed model enables IoMT devices to collect patient data which is then preprocessed to optimize *** order to decrease the computational difficulty during diagnosis,Radix Tree structure is *** addition,ElGamal public key cryptosystem with Rat Swarm Optimizer(EIG-RSO)is applied to encrypt the *** the transmission of encrypted data to cloud,respective decryption process occurs and the actual data gets ***,a hybridized methodology combining Gated Recurrent Unit(GRU)with Convolution Neural Network(CNN)is exploited as a classification model to diagnose the *** sets of simulations were conducted to highlight the performance of the proposed model on benchmark *** experimental outcomes ensure that the proposed model is superior to existing methods under different measures.

关键词： Internet of medical things privacy security encryption radix tree deep learning

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Current Status and Recent Advances in Preimplantation Genetic Testing for Structural Rearrangements

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Reproductive and Developmental Medicine 2020年第1期4卷 1-6页

作者： Shuo zhang Cai-Xia Lei Xiao-Xi Sun Cong-Jian Xu Department of Genetics Shanghai Ji Ai Genetics and IVF InstituteObstetrics and Gynecology HospitalFudan UniversityShanghai 200011China Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases Obstetrics and Gynecology HospitalFudan UniversityShanghai 200011China

Preimplantation genetic testing(PGT)is an early form of prenatal genetic diagnosis,which can identify the abnormal embryos cultured in vitro,allow only transfer of genetically normal embryos,and improve the pregnancy *** recent years,the rapid development of microarrays and next-generation sequencing(NGS)technologies has remarkably accelerated the clinical application of *** particular,a variety of detection methods have emerged and achieved significant progress in PGT for structural rearrangements(PGT-SR).The detection-related abilities of these methods range from the detection of limited chromosome aneuploidy to comprehensive chromosome screening of the whole genome to differentiation of embryos with normal or balanced translocation/inversion *** this study,we reviewed PGT-SR-related detection techniques to provide a better reference for clinical application and *** have also discussed the potential development of novel techniques in the future.

关键词： Chromosome Aneuploidy Inversion Karyotype Preimplantation Genetic Testing Structural Chromosomal Rearrangement Structural Rearrangement Translocation Karyotype

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Predicting visual acuity with machine learning in treated ocular trauma patients

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International Journal of Ophthalmology(English edition) 2023年第7期16卷 1005-1014页

作者： Zhi-Lu Zhou Yi-Fei Yan Jie-Min Chen Rui-Jue Liu Xiao-Ying Yu Meng Wang Hong-Xia Hao Dong-Mei Liu Qi zhang Jie Wang Wen-Tao Xia Department of Forensic Medicine Guizhou Medical UniversityGuiyang 550009Guizhou ProvinceChina Shanghai Key Laboratory of Forensic Medicine Shanghai Forensic Service PlatformInstitute of Forensic ScienceMinistry of JusticeShanghai 200063China The SMART(Smart Medicine and ai-based Radiology Technology)Lab Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghai 200444China School of Communication and Information Engineering Shanghai UniversityShanghai 200444China Basic Medical College Jiamusi UniversityJiamusi 154007Heilongjiang ProvinceChina

aiM:To predict best-corrected visual acuity(BCVA)by machine learning in patients with ocular trauma who were treated for at least ***:The internal dataset consisted of 850 patients with 1589 eyes and an average age of *** initial visual acuity was 0.99 log *** test dataset consisted of 60 patients with 100 eyes collected while the model was *** different machine-learning algorithms(Extreme Gradient Boosting,support vector regression,Bayesian ridge,and random forest regressor)were used to predict BCVA,and four algorithms(Extreme Gradient Boosting,support vector machine,logistic regression,and random forest classifier)were used to classify BCVA in patients with ocular trauma after treatment for 6mo or *** features were obtained from outpatient records,and ocular parameters were extracted from optical coherence tomography images and fundus *** features were put into different machine-learning models,and the obtained predicted values were compared with the actual BCVA *** best-performing model and the best variable selected were further evaluated in the test ***:There was a significant correlation between the predicted and actual values[all Pearson correlation coefficient(PCC)>0.6].Considering only the data from the traumatic group(group A)into account,the lowest mean absolute error(MAE)and root mean square error(RMSE)were 0.30 and 0.40 log MAR,*** the traumatic and healthy groups(group B),the lowest MAE and RMSE were 0.20 and 0.33 log MAR,*** sensitivity was always higher than the specificity in group A,in contrast to the results in group *** classification accuracy and precision were above 0.80 in both *** MAE,RMSE,and PCC of the test dataset were 0.20,0.29,and 0.96,*** sensitivity,precision,specificity,and accuracy of the test dataset were 0.83,0.92,0.95,and 0.90,***:Predicting BCVA using machine-learning models in patients wit

关键词： ocular trauma predicting visiual acuity best-corrected visual acuity visual dysfunction machine learning

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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Computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(ai),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Porous Pt nanoframes decorated with Bi(OH)3 as highly efficient and stable electrocatalyst for ethanol oxidation reaction

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Nano Research 2020年第1期13卷 265-272页

作者： Xiaolei Yuan Bei Jiang Muhan Cao Congyang zhang Xiaozhi Liu Qinghua zhang Fenglei Lyu Lin Gu Qiao zhang School of Chemistry and Chemical Engineering Nantong University9 Seyuan RoadNantong226019China Institute of Functional Nano and Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials and DevicesSWC for Synchrotron Radiation ResearchSoochow University199 Ren’ai RoadSuzhou215123China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing100049China Key Laboratory of Green Chemistry and Technology Ministry of EducationCollege of ChemistrySichuan UniversityChengdu610064China

High-quality Pt-based catalysts are highly desirable for ethanol oxidation reaction(EOR),which is of critical importance for the commercial applications of direct ethanol fuel cells(DEFCs).However,most of the Pt-based catalysts have suffered from high cost and low operation *** a two-step method has been developed to synthesize porous Pt nanoframes decorated with Bi(OH)3,which show excellent catalytic activity and operation durability in both alkaline and acidic *** example,the nanoframes show a mass activity of 6.87 A·mgPt−1 in alkaline media,which is 13.5-fold higher than that of commercial Pt/*** importantly,the catalyst can be reactivated simply,which shows negligible activity loss after running for 180,000 *** in situ attenuated total reflection-infrared(ATR-IR)absorption spectroscopy and CO-stripping experiments indicate that surface Bi(OH)3 species can greatly facilitate the formation of adsorbed OH species and subsequently remove carbonaceous poison,resulting in a significantly enhanced stability towards *** work may favor the tailoring of desired electrocatalysts with high activity and durability for future commercial application of DEFCs.

关键词： Pt nanoframe ethanol oxidation reaction bismuth hydroxide CO tolerance in situ infrared spectroscopy durability

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MOD-Net:A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs

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Communications in Computational Physics 2022年第7期32卷 299-335页

作者： Lulu zhang Tao Luo Yaoyu zhang Weinan E Zhi-Qin John Xu Zheng Ma Institute of Natural Sciences and School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai200240China School of Mathematical Sciences and Institute of Natural Sciences Shanghai Jiao Tong UniversityCMA-ShanghaiShanghai200240China MOE-LSC and Qing Yuan Research Institute Shanghai Jiao Tong UniversityShanghai200240China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai200031China School of Mathematical Sciences Peking UniversityBeijing100871China ai for Science Institute Beijing100080China

In this paper,we propose a machine learning approach via model-operatordata network(MOD-Net)for solving PDEs.A MOD-Net is driven by a model to solve PDEs based on operator representationwith regularization *** linear PDEs,we use a DNN to parameterize the Green’s function and obtain the neural operator to approximate the solution according to the Green’s *** train the DNN,the empirical risk consists of the mean squared loss with the least square formulation or the variational formulation of the governing equation and boundary *** complicated problems,the empirical risk also includes a fewlabels,which are computed on coarse grid points with cheap computation cost and significantly improves the model ***,the labeled dataset works as a regularization in addition to the model *** MOD-Net solves a family of PDEs rather than a specific one and is much more efficient than original neural operator because few expensive labels are *** numerically show MOD-Net is very efficient in solving Poisson equation and one-dimensional radiative transfer *** nonlinear PDEs,the nonlinear MOD-Net can be similarly used as an ansatz for solving nonlinear PDEs,exemplified by solving several nonlinear PDE problems,such as the Burgers equation.

关键词： Deep neural network radiative transfer equation Green’s method neural operator

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Preimplantation genetic testing for aneuploidy improves clinical outcomes in patients with repeated implantation failure

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Reproductive and Developmental Medicine 2023年第1期7卷 12-19页

作者： Rui-Huan Gu Jing Fu Nai-Dong Ge Zhi-Chao Li bin Huang Yan Xu Yao-Yu Zou Lu Li Yi-Juan Sun Xiao-Xi Sun Shanghai Ji’ai Genetics&IVF Institute Obstetrics&Gynecology HospitalFudan UniversityShanghai 200011China Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases Obstetrics&Gynecology HospitalFudan UniversityShanghai 200011China

Objective:The objective of this study is to study whether preimplantation genetic testing for aneuploidy(PGT-A)improves the clinical outcomes of infertile patients with repeated implantation failure(RIF)undergoing frozen-thawed embryo ***:This is a retrospective analysis of clinical pregnancy,live birth,miscarriage rates,and obstetric and perinatal outcomes of women with RIF with or without *** analyses of categorical data were performed using propensity score matching(PSM),χ^(2)test,and Student’s t ***:We enrolled 466 patients with RIF,of which,209 were in the RIF-PGT-A *** rate of euploid blastocysts was significantly associated with age and day 5 or 6 *** were significant differences between the RIF-PGT-A group and the RIF-non-PGT-A group across several *** PSM,positive serum human chorionic gonadotropin(56.9%and 33.9%,P<0.01),clinical pregnancy(49.5%and 31.2%,P<0.01),live birth(43.1%and 25.7%,P<0.01),and fetal heart rates(50.0%and 29.8%,P<0.01)per transfer were significantly higher in the RIF-PGT-A ***:Elective single-embryo transfer PGT-A can minimize the risk of obstetric and perinatal outcomes,especially fetal body weight,in women with ***,PGT-A can significantly improve pregnancy and live birth rates.

关键词： Preimplantation genetic testing Aneuploidy Repeated implantation failure Clinical outcomes

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Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

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Computers, Materials & Continua 2022年第4期71卷 255-268页

作者： R.Krishnaswamy B.Sivakumar B.Viswanathan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India School of Computing SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Factually of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (ai) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

关键词： Biomedical signals ecg disease diagnosis artificial intelligence parameter tuning gru model

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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Computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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A magnetically enabled simulation of microgravity represses the auxin response during early seed germination on a microfluidic platform

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Microsystems & Nanoengineering 2022年第1期8卷 97-109页

作者： Jing Du Lin Zeng Zitong Yu Sihui Chen Xi Chen Yi zhang Hui Yang Laboratory of Biomedical Microsystems and Nano Devices Center for Bionic Sensing and IntelligenceInstitute of Biomedical and Health EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Center for Medical ai Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

For plants on Earth,the phytohormone auxin is essential for gravitropism-regulated seedling establishment and plant ***,little is known about auxin responses under microgravity conditions due to the lack of a tool that can provide an alteration of *** this paper,a microfluidic negative magnetophoretic platform is developed to levitate Arabidopsis seeds in an equilibrium plane where the applied magnetic force compensates for gravitational *** the benefit of the microfluidic platform to simulate a microgravity environment on-chip,it is found that the auxin response is significantly repressed in levitated *** microgravity statistically interrupts auxin responses in embryos,even after chemical-mediated auxin alterations,illustrating that auxin is a critical factor that mediates the plant response to gravity ***,pretreatment with an auxin transportation inhibitor(N-1-naphthylphthalamic acid)enables a decrease in the auxin response,which is no longer affected by simulated microgravity,demonstrating that polar auxin transportation plays a vital role in gravity-regulated auxin *** presented microfluidic platform provides simulated microgravity conditions in an easy-to-implement manner,helping to study and elucidate how plants correspond to diverse gravity conditions;in the future,this may be developed into a versatile tool for biological study on a variety of samples.

关键词： magnetic auxin microgravity

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