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Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Computer Systems Science & Engineering 2022年第7期42卷 183-199页

作者： S.Karthik m.santhosh m.S.Kavitha A.Christopher Paul Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India Department of Information Technology Karpagam Institute of TechnologyCoimbatore641105India

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased *** the same time,latest develop-ments of artiﬁcial intelligence(AI)techniques have the ability to manage and ana-lyzing massive amounts of biomedical datasets results in clinical decisions and real time *** can be employed for medical imaging;however,the 1D biomedical signal recognition process is still needing to be ***(ECG)is one of the widely used 1-dimensional biomedical sig-nals,which is used to diagnose cardiovascular *** assisted diag-nostic modelsﬁnd it difﬁcult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse proﬁles of ECG *** resolve these issues,this study designs automated deep learning based 1D biomedical ECG sig-nal recognition for cardiovascular disease diagnosis(DLECG-CVD)*** DLECG-CVD model involves different stages of operations such as pre-proces-sing,feature extraction,hyperparameter tuning,and classiﬁ*** the initial stage,data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further *** addition,deep belief network(DBN)model is applied to derive a set of feature ***,improved swallow swarm optimization(ISSO)algorithm is used for the hyper-parameter tuning of the DBN ***,extreme gradient boosting(XGBoost)classiﬁer is employed to allocate proper class labels to the test ECG *** order to verify the improved diagnostic performance of the DLECG-CVD model,a set of simulations is carried out on the benchmark PTB-XL dataset.A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy,sensitivity,speciﬁcity,kappa,mathew correlation coefﬁcient,and Hamming loss.

关键词： Biomedical signals 1-dimensional signal electrocardiogram artiﬁcial intelligence deep learning cardiovascular disease decision making

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Advancing Li-ion storage performance with hybrid vertical carbon/Ni_(3)S_(2)-based electrodes

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Journal of Energy Chemistry 2022年第4期31卷 8-18页

作者： Neelakandan m.santhosh Nitheesha Shaji d Petra Stražar Gregor Filipič Janez Zavašnik Chang Won Ho murugan Nanthagopal Chang Woo Lee UrošCvelbar Department of Gaseous Electronics(F6) Jožef Stefan InstituteJamova cesta 39SI-1000 LjubljanaEUSlovenia Jožef Stefan International Postgraduate School Jamova cesta 39SI-1000 LjubljanaEUSlovenia Faculty of mathematics and Physics University of LjubljanaJadranska cesta 19SI-1000 LjubljanaEUSlovenia Department of Chemical Engineering(Integrated Engineering) College of EngineeringKyung Hee University1732 Deogyeong-daeroGiheungYonginGyeonggi 17104South Korea Center for the SmART Energy Platform College of EngineeringKyung Hee University1732 Deogyeong-daeroGiheungYonginGyeonggi 17104South Korea

Conversion-reaction induced charge storage mechanisms of transition metal sulphides have received considerable interest in designing high-capacity electrodes for electrochemical energy storage ***,their low conductivity and structural degradation during cycling limit their applications as energy storage devices.A combination of different nickel sulphide phases tailored with carbon nanostructures is suggested to address these ***,a facile,two-step approach is demonstrated for fabricating a hybrid electrode,consisting of trinickel disulphide(Ni_(3)S_(2))formed on a metallic Ni nanoparticle supported by vertical carbon nanotubes(VCN)backbone in the form Ni_(3)S_(2)/Ni@***_(3)S_(2)/Ni@VCN electrodes were tested as anode for lithium-ion batteries,and the electrode featured outstanding lithiumstorage capabilities with a high reversible capacity(1113 m Ah g^(-1) after 100 cycles at 100 m A g^(-1)),excellent long-term cycling stability(770 m Ah g^(-1) after 500 cycles at 200 m A g^(-1)),and good rate *** resulting electrode performance is one of the best Li-ion storage capabilities in the Ni_(3)S_(2)-type anode materials described.A unique “broccoli-like”structure of polycrystalline Ni_(3)S_(2)capped on conductive VCN backbone helps the interface storage process and boosts lithium storage performance.

关键词： Ni_(3)S_(2) Vertical carbon nanostructures Hierarchical structures Binder-free electrode Lithium-ion batteries

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Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

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Computers, materials & Continua 2024年第7期80卷 695-713页

作者： G.Sunil santhosh Kumar m.Rudra Kumar Department of CSE Jawaharlal Nehru Technological UniversityAnantapur515002India Department of CSE Marri Laxman Reddy Institute of Technology and ManagementHyderabad500043India Department of Information Technology Mahatma Gandhi Institute of TechnologyHyderabad500075India

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and *** framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern *** goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied ***,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the *** sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant *** core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature *** article presents a detailed overview of the mathematical model and algorithmic steps behind this ***,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data *** employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation *** research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.

关键词： XGBoost business rule engine machine learning categorical query language humanitarian computing environment

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Design of a multi-Stage Ensemble model for Thyroid Prediction Using Learning Approaches

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Intelligent Automation & Soft Computing 2024年第1期39卷 1-13页

作者： m.L.maruthi Prasad R.santhosh Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTamil NaduIndia

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women *** major research problem is the lack of automated model to attain earlier *** existing model fails to give better prediction ***,a novel clinical decision support system is framed to make the proper decision during a time of *** stages are followed in the proposed framework,which plays a substantial role in thyroid *** steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(mS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier *** classifiers aremerged to handle the issues identified in others and intend to enhance the prediction *** proposed model provides superior outcomes and gives good quality prediction *** simulation is done in the mATLAB 2020a environment and establishes a better trade-off than various existing *** model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

关键词： Thyroid machine learning pre-processing classification prediction rate

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modelling a Fused Deep Network model for Pneumonia Prediction

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Computer Systems Science & Engineering 2023年第9期46卷 2725-2739页

作者： m.A.Ramitha N.mohanasundaram R.santhosh Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreIndia

Deep Learning(DL)is known for its golden standard computing paradigm in the learning ***,it turns out to be an extensively utilized computing approach in the mL ***,attaining superior outcomes over cognitive tasks based on human *** primary benefit of DL is its competency in learning massive *** DL-based technologies have grown faster and are widely adopted to handle the conventional approaches ***,various DL approaches outperform the conventional mL approaches in real-time ***,various research works are reviewed to understand the significance of the individual DL models and some computational complexity is *** may be due to the broader expertise and knowledge required for handling these models during the prediction *** research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification *** work incorporates a novel fused Squeeze and Excitation(SE)block with the ResNet model for pneumonia prediction and better *** expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is *** experimentation is carried out in Keras,and the model’s superiority is compared with various advanced *** proposed model gives 90%prediction accuracy,93%precision,90%recall and 89%*** proposed model shows a better trade-off compared to other *** evaluation is done with the existing standard ResNet model,GoogleNet+ResNet+DenseNet,and different variants of ResNet models.

关键词： Disease prediction pneumonia deep learning SE ResNet fused network model

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multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC motor Design Problem

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Computers, materials & Continua 2022年第2期70卷 2435-2452页

作者： m.Premkumar Pradeep Jangir B.santhosh Kumar mohammad A.Alqudah Kottakkaran Sooppy Nisar Department of Electrical and Electronics Engineering GMR Institute of TechnologyRajam532127Andhra PradeshIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Department of Computer Science and Engineering GMR Institute of TechnologyRajam532127Andhra PradeshIndia German Jordanian University Amman11180Jordan Department of mathematics College of Arts and SciencesPrince Sattam Bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

The first step in the design phase of the Brushless Direct Current(BLDC)motor is the formulation of the mathematical framework and is often used due to its analytical ***,the BLDC motor design problem is considered to be an optimization *** this paper,the analytical model of the BLDC motor is presented,and it is considered to be a basis for emphasizing the optimization *** analytical model used for the experimentation has 78 non-linear equations,two objective functions,five design variables,and six non-linear constraints,so the BLDC motor design problem is considered as highly non-linear in electromagnetic ***-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic *** bio-inspired multi-objective grey wolf optimizer(mOGWO)is presented in this paper,and it is formulated based on Pareto optimality,dominance,and archiving *** performance of themOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design *** results proved that the proposedmOGWO algorithm could handle nonlinear constraints in electromagnetic optimization *** performance comparison in terms of Generational Distance,inversion GD,Hypervolume-matrix,scattered-matrix,and coverage metrics proves that the mOGWO algorithm can provide the best solution compared to other selected *** source code of this paper is backed up with extra online support at https://***/mysite and https://***/matlabcentral/fileexchange/75259-multiobjective-non-sorted-grey-wolf-mogwo-nsgwo.

关键词： BLDC motor electromagnetics metaheuristic multi-objective grey wolf optimizer

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Experimental and numerical analysis on suitability of S-Glass-Carbon fiber reinforced polymer composites for submarine hull

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Defence Technology（防务技术） 2023年第1期19卷 1-11页

作者： Elango Natarajan Lídio Inacio Freitas m.S.santhosh Kalaimani markandan Ammar Abdulaziz majeed Al-Talib C.S.Hassan Faculty of Engineering Technology and Built EnvironmentUCSI UniversityKuala LumpurMalaysia Dili Institute of Technology DiliTimor-Leste Selvam Composite materials Research Laboratory Department of Mechanical EngineeringSelvam College of TechnologyTamil NaduIndia

Suitability of S-Glass/carbon fiber reinforced polymer composite for submarine hull subjected to hydrostatic pressure has been investigated in the present *** materials have raised concerns owing to their decomposition due to low resistance towards salinity and hence polymer composites have been explored to showcase their mechanical stability to withstand transverse and impact *** this end,the mechanical properties of S-Glass/carbon fiber reinforced polymer composite were experimentally investigated and higher specific strength and stiffness of the composite in comparison to many metallic materials used for submarine hull were *** obtained experimental values were used for the static and dynamic crash analysis of the bow,stern and foil through Finite Element Analysis(FEA);where depth of travel was varied from sea surface level of 0-7000 *** assembly was later developed with the optimum shape and thickness of each *** also report the nonlinear crash analysis upon impact at velocity ranging from 3 to 21 m/***,kinetic energy,acceleration peak and internal energy in struck submarine revealed that travel depth 1750 m and 3500 m is recommendable,more particularly,crash safety factor of the submarine is found to be within limit when submarine encounters crash at 1750 m.

关键词： Crash Impact Design Submarine Hull Bow Stern Foil

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BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

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Computers, materials & Continua 2022年第1期70卷 557-579页

作者： R.manjula Devi m.Premkumar Pradeep Jangir B.santhosh Kumar Dalal Alrowaili Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical and Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Department of Computer Science and Engineering Guru Nanak Institute of TechnologyHyderabad501506TelanganaIndia mathematics Department College of ScienceJouf UniversitySakakaP.O.Box:2014Saudi Arabia Department of mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization *** the run time increases exponentially,FS is treated as an NP-hard *** researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization *** paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large *** proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary *** findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art *** results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification *** proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 *** low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

关键词： Binary optimization feature selection machine learning hunger games search optimization

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ITERATIVE REGULARIZATION mETHODS FOR NONLINEAR ILL-POSED OPERATOR EQUATIONS WITH m-ACCRETIVE mAPPINGS IN BANACH SPACES

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Acta mathematica Scientia 2015年第6期35卷 1318-1324页

作者： Ioannis K.ARGYROS santhosh GEORGE Department of mathematical Sciences Cameron University Department of mathematical and Computational Sciences National Institute of Technology Karnataka

In this paper, a modified Newton type iterative method is considered for ap- proximately solving ill-posed nonlinear operator equations involving m-accretive mappings in Banach space. Convergence rate of the method is obtained based on an a priori choice of the regularization parameter. Our analysis is not based on the sequential continuity of the normalized duality mapping.

关键词： nonlinear ill-posed equations iterative regularization m-accretive operator Newton type method

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LAVRENTIEV'S REGULARIZATION mETHOD FOR NONLINEAR ILL-POSED EQUATIONS IN BANACH SPACES

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Acta mathematica Scientia 2018年第1期38卷 303-314页

作者： santhosh GEORGE C.D.SREEDEEP Department of mathematical and Computational Sciences NIT Karnataka

In this paper, we deal with nonlinear ill-posed problems involving m-accretive mappings in Banach spaces. We consider a derivative and inverse free method for the imple- mentation of Lavrentiev regularization method. Using general HSlder type source condition we obtain an optimal order error estimate. Also we consider the adaptive parameter choice strategy proposed by Pereverzev and Schock （2005） for choosing the regularization parameter.

关键词： nonlinear ill-posed problem Banach space Lavrentiev regularization m-accretive mappings adaptive parameter choice strategy

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