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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications engineering department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt department of Artificial Intelligence engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt department of Electrical engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia department of Electronics and Communications engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Effect of fineness of ash on pozzolanic properties and acid resistance of sugarcane bagasse ash replaced cement mortars

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Frontiers of Structural and Civil engineering 2022年第10期16卷 1287-1300页

作者： Shan E ALI Rizwan AZAM Muhammad Rizwan RIAZ Mohamed ZAWAM department of Civil engineering University of Engineering&Technology LahoreLahore 54890Pakistan Faculty of engineering Cairo UniversityCairo 12613Egypt

This paper addresses the potential use of Sugar Cane Bagasse Ash(SCBA)as a pozzolanic material for partial cement replacement in concrete mixtures.Cement mortars containing SCBA having five different particle size distributions at a replacement rate of 20%by weight were used to study the chemical and physical pozzolanic properties of SCBA.The durability of SCBA replaced mortars was also evaluated.SCBA with 0%retained on sieve No.325 was used to replace 20%by weight of cement and create mortar specimens that were subjected to sulfuric acid attack of varying concentrations(1%–3%by weight of water).The tested samples were observed to check visual distortion,mass loss,and compressive strength loss at 1,7,14,28,and 56 d of acidic exposure,and the results were compared to those for the control sample,that was lime water cured,at the same ages.The SCBA sets were found to meet the requirements for pozzolan class N specified by ASTM C 618.Mortars containing SCBA with 0%or 15%retention produced better compressive strength than the control mortars after 28 d.Additionally,X-ray fluorescence and X-ray diffraction analysis showed that the SCBA had favorable chemical properties for a pozzolanic material.Furthermore,SCBA replaced samples at all ages showed improved resistance against acidic attack relative to that of the control mortars.Maximum deterioration was seen for 3%concentrated solution.This study’s findings demonstrated that SCBA with an appropriate fineness could be used as a pozzolanic material,consistently with ASTM C 618.

关键词： durability cement replacement sugarcane bagasse ash fineness of ash pozzolanic properties mortar acid resistance

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Titanium dioxide-graphene composite electrochemical sensor for detection of hexavalent chromium

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International Journal of Minerals,Metallurgy and Materials 2022年第3期29卷 529-535页

作者： Natpichan Pienutsa Krittamet Yannawibut Jetthana Phattharaphongmanee Oukrit Thonganantakul Sira Srinives Nanocomposite engineering Laboratory(NanoCEN) Department of Chemical EngineeringFaculty of EngineeringMahidol University25/25 PuttamonthonNakhon Pathom73170Thailand

Hexavalent chromium(Cr(VI))compound is useful to various industries but is toxic and carcinogenic.In this research work,we fab-ricate an amperometric sensor for the determination of Cr(VI),using a titanium dioxide(TiO2)-reduced graphene oxide(rGO)composite as the sensing element.The composite was synthesized following sol−gel chemistry,yielding TiO2 nanoparticles of~50 nm in size,immobilized on chemically exfoliated rGO sheets.The composite was employed in a 3-electrode electrochemical cell and operated in an amperometric mode,exhibiting good responses to the 50 to 500 ppb Cr(VI).Our best result from pH 3 Mcilvane’s buffer medium reveals the sensitivity of 9.12×10−4 ppb−1 and a detection limit of 6 ppb with no signal interference from 200 ppm Ca(II),150 ppm Mg(II),and 50 ppb Pb(II).The excellent results of the TiO2-rGO sensor can be attributed to synergic effects between TiO2 and rGO,resulting from the presence of n-p heterojunctions and the formation of the TiO2 nanoparticles on rGO.

关键词： photocatalyst electrochemical sensor hexavalent chromium graphene

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Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

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Computers, Materials & Continua 2022年第8期72卷 2683-2696页

作者： M.Saravanan J.Jayanthi U.Sakthi R.Rajkumar Gyanendra Prasad Joshi L.Minh Dang Hyeonjoon Moon department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India department of Computer Science and engineering Sona College of TechnologySalem636005India department of Computer Science and engineering Saveetha School of EngineeringSIMATSChennai602105India department of Electronics and Instrumentation engineering Kongu Engineering CollegePerundurai638060India department of Computer Science and engineering Sejong UniversitySeoul05006Korea

Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing components.But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images.Therefore,image compression techniques can be utilized to process remote sensing images.In this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image construction.The process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving it.With this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing images.The goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of codebook.Besides,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is employed.The design of SBO algorithm for remote sensing image compression depicts the novelty of the work.To showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

关键词： Remote sensing images image compression vector quantization sand bowerbird optimizer metaheuristics space savings

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Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

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Computers, Materials & Continua 2022年第12期73卷 5041-5061页

作者： Mehrdad Ahmadi Kamarposhti Hassan Shokouhandeh Ilhami Colak Kei Eguchi department of Electrical engineering Jouybar BranchIslamic Azad UniversityJouybarIran department of Electrical engineering Semnan UniversitySemnanIran department of Electrical and Electronics engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbulTurkey department of Information Electronics Fukuoka Institute of TechnologyFukuokaJapan

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.

关键词： Maximum power tracking photovoltaic system adaptive fuzzy control whale optimization algorithm particle swarm optimization

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De Novo Dissecting the Three‑Dimensional Facial Morphology of 2379 Han Chinese Individuals

Phenomics

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Phenomics 2024年第1期4卷 1-12页

作者： Hui Qiao Jingze Tan Shaoqing Wen Menghan Zhang Shuhua Xu Li Jin State Key Laboratory of Genetic engineering Center for Evolutionary BiologyCollaborative Innovation Center for Genetics and DevelopmentSchool of Life SciencesFudan UniversityShanghai 200438China Ministry of Education Key Laboratory of Contemporary Anthropology Human Phenome InstituteZhangjiang Fudan International Innovation CenterFudan UniversityShanghai 201203China Institute of Archaeological Science Fudan UniversityShanghai 200433China Institute of Modern Languages and Linguistics Fudan UniversityShanghai 200433China department of Liver Surgery and Transplantation Liver Cancer Institute Zhongshan HospitalFudan UniversityShanghai 200032China

Phenotypic diversity,especially that of facial morphology,has not been fully investigated in the Han Chinese,which is the largest ethnic group in the world.In this study,we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional(3D)manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals.Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions:Zhengzhou,Taizhou,and Nanning.We identifed 1560 shared features from extracted phenotypes,which characterized well the basic facial morphology of the Han Chinese.In particular,heterogeneous phenotypes showing population structures corresponded to geographical subpopulations.The greatest facial variation among these geographical populations was the angle of glabella,left subalare,and right cheilion(p=3.4×10^(−161)).Interestingly,we found that Han Chinese populations could be classifed into northern Han,central Han,and southern Han at the phenotypic level,and the facial morphological variation pattern of central Han Chinese was between the typical diferentiation of northern and southern Han Chinese.This result was highly consistent with the results revealed by the genetic data.These fndings provide new insights into the analysis of multidimensional phenotypes as well as a valuable resource for further facial phenotype-genotype association studies in Han Chinese and East Asian populations.

关键词： Phenotypes Three-dimensional facial imaging Facial morphology Han Chinese

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Effect of intensity and duration of anthropic noises on European mink locomotor activity and fecal cortisol metabolite levels

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Current Zoology 2022年第6期68卷 688-699页

作者： Lorena ORTIZ-JIMENEZ Carlos IGLESIAS-MERCHAN Alba Itzel MARTıNEZ-SALAZAR Isabel BARJA department of Biology Zoology UnitUniversidad Autonoma de MadridMadrid28049Spain department of forest and Environmental engineering and Management Universidad Politecnica de MadridMadridSpain Geographic Information Systems Laboratory Institute of BiologyUniversidad Nacional Autonoma de MexicoMexico CityMexico Biodiversity and Global Change Research Centre(CIBC-UAM) Universidad Autonoma de MadridMadridSpain

Human activities involving noise emission can affect wild animals.European mink was exposed to road noise and human voice playbacks to analyze how sound intensity level and duration of both noises altered the time that individuals were active and if their fecal cortisol metabolite(FCM)levels varied.A Hierarchical Analysis Cluster was performed to establish 2 mink groups with respect to both noise source type:short duration/low intensity(SL)and long duration/high intensity(LH).We performed general linear mixed models to evaluate the variation in locomotor activity duration(s)and FCM(nanogram per gram)levels,respectively.The results showed both road noise and human voices decreased locomotor activity duration in SL more sharply compared with LH,and human voices were the triggers that induced the most pronounced response to both exposure conditions.FCM(ng/g)levels increased in SL compared with LH during road noise while the opposite happened during human voices.Differences based on sex and age of individuals were observed.In conclusion,noise characteristics given by the sound type determined the variations in locomotor activity duration while noise exposure level determined the variations in FCM(ng/g)levels.Attention should be paid to noisy activities(e.g.,recreational activities for visitors in protected natural areas)and loud groups of people to conserve wildlife,especially noise sensitive species.

关键词： anthropic disturbances European mink fecal cortisol metabolites noise exposure locomotor activity behavioral response

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Synthetic Aperture Positioning: A Review

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Journal of Beijing Institute of Technology 2024年第2期33卷 89-102页

作者： Guangcai Sun Wenlong Dong Yuqi Wang Mengdao Xing National Key Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China School of Communication and Information engineering Xi’an University of Science and TechnologyXi’an 710054China National Key Laboratory of Radar Signal Processing and the Academy of Advanced Interdisciplinary Research DepartmentXidian UniversityXi’an 710071China

Obtaining precise position of interested emitters passively has wide applications in both civilian and military fields.Different from traditional parameter measurement and direct position determination(DPD)method,recently a new passive localization method based on synthetic aper-ture technique,named synthetic aperture positioning(SAP),has been proposed.The method com-pensates for the nonlinear phase produced by relative motion between the moving platform and the emitter,achieving coherent summation of intercepted signals.The SAP can obtain high-resolution and high-precision localization results at a low signal-to-noise ratio.This paper summarizes the research progress of SAP,including localization principles,spaceborne applications,and application scope analysis.Besides,the possible future outlook of SAP is considered.

关键词： passive localization synthetic aperture positioning coherent processing

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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Computer Systems Science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam department of Computer Science and engineering Prathyusha Engineering CollegeThiruvallur602025India department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia department of Electronics and Communication engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India department of Electrical and Electronics engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway department of Information and Communication engineering Soonchunhyang UniversityAsanKorea department of Computer Science and engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of Computer Vision(CV)and Natural Language Processing(NLP)for generating the image descriptions.Itfinds use in several application areas namely recommendation in editing applications,utilization in virtual assistance,etc.The development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual semantics.In this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)technique.The OHHO-DLIC technique involves the design of distinct levels of pre-processing.Moreover,the feature extraction of the images is carried out by the use of EfficientNet model.Furthermore,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as decoder.At last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models.The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Epilepsy Radiology Reports Classification Using Deep Learning Networks

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Computers, Materials & Continua 2022年第2期70卷 3589-3607页

作者： Sengul Bayrak Eylem Yucel Hidayet Takci department of Computer engineering Halic UniversityIstanbul34445Turkey department of Computer engineering Istanbul University–CerrahpasaIstanbul34320Turkey department of Computer engineering Sivas Cumhuriyet UniversitySivas58140Turkey

The automatic and accurate classification of Magnetic Resonance Imaging(MRI)radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy.Since the majority of MRI radiology reports are unstructured,the manual information extraction is time-consuming and requires specific expertise.In this paper,a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically.This method combines the Natural Language Processing technique and statisticalMachine Learning methods.122 realMRI radiology text reports(97 epilepsy,25 non-epilepsy)are studied by our proposed method which consists of the following steps:(i)for a given text report our systems first cleans HTML/XML tags,tokenize,erase punctuation,normalize text,(ii)then it converts into MRI text reports numeric sequences by using indexbased word encoding,(iii)then we applied the deep learning models that are uni-directional long short-term memory(LSTM)network,bidirectional long short-term memory(BiLSTM)network and convolutional neural network(CNN)for the classifying comparison of the data,(iv)finally,we used 70%of used for training,15%for validation,and 15%for test observations.Unlike previous methods,this study encompasses the following objectives:(a)to extract significant text features from radiologic reports of epilepsy disease;(b)to ensure successful classifying accuracy performance to enhance epilepsy data attributes.Therefore,our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models.The traditionalmethod is numeric sequences by using index-based word encoding which has been made for the first time in the literature,is successful feature descriptor in the epilepsy data set.The BiLSTM network has shown a promising performance regarding the accuracy rates.We show that the larger sizedmedical text reports can be analyzed by our proposed method.

关键词： Epilepsy radiology text report analysis natural language processing feature engineering index-based word encoding deep learning networks-based text classification

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