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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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Computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

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Computer Systems Science & Engineering 2024年第5期48卷 1085-1112页

作者： Varada rajkumar Kukkala Surapaneni Phani Praveen Naga Satya Koti Mani Kumar Tirumanadham Parvathaneni Naga srinivasu Department of Computer Science&Engineering(AIML) MLR Institute of TechnologyHyderabad500043India Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science&Engineering Sir C.R.Reddy College of EngineeringEluru534001India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile range(IQr)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQr and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular ***,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)*** findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning *** work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization *** studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

关键词： Grey wolf optimization ant colony optimization Z-Score interquartile range(IQr) AdaBoost outlier

Artificial neural network approach for prediction of stress–strain curve of near b titanium alloy

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rare Metals 2014年第3期33卷 249-257页

作者： srinivasu Gangi Setti r.N.rao Mechanical Engineering National Institute of Technology

In the present study, artificial neural network（ANN） approach was used to predict the stress-strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network（NN） to predict the stress-strain curve. In order to achieve this, three different NN architectures（feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network）, three different transfer functions（purelin, Log-Sigmoid, and Tan-Sigmoid）, number of hidden layers（1 and 2）, number of neurons in the hidden layer（s）,and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an *** system was trained using the prepared training set（a,16 % a, 40 % a, and b stress-strain curves）. After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress-strain curve of near b titanium alloy.

关键词： Artificial neural network (ANN) Stressstrain curve Titanium alloys

Surface integrity of ball burnished bioresorbable magnesium alloy

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Advances in Manufacturing 2023年第2期11卷 342-362页

作者： G.V.Jagadeesh srinivasu Gangi Setti Department of Mechanical Engineering National Institute of Technology RaipurChhattisgarh492010India

Magnesium alloys are potential biodegradable and biocompatible implant materials because of their excellent biological properties. recently, interest in these alloys as a promising alternative for temporary orthopedic implants has grown owing to their desirable biological, mechanical, and physical properties. However, the application of magnesium alloys is hindered by their rapid degradation and low corrosion resistance in physiological fluids, leading to the failure of implants. Thus, the current challenge is to enhance the corrosion resistance and control the degradation rate of magnesium under physiological conditions. The rapid degradation of magnesium alloys can be controlled by improving their surface integrity, such as surface roughness and microhardness. The present study aims to improve the surface integrity of the Mg Ze41A alloy by the ball burnishing technique. The surface roughness improved by 94.90% from 0.941 μm to 0.048 μm with a burnishing force of 50 N, burnishing speed of 1 300 r/min, burnishing feed of 130 mm/min, and three passes. Similarly, the microhardness improved by 50.62% from 75.2 HV to 113.27 HV with a burnishing force of 60 N, burnishing speed of 1 100 r/min, burnishing feed of 100 mm/min, and five passes. The variations in microhardness, which were observed up to 400 μm beneath the surface, exhibited a linear nature. These variations may be attributed to the movement of dislocations, formation of new dislocations, nanocrystal structures, metastable phases and subgrains, and lattice distortion or grain refinement. The surface features obtained from optical images demonstrated the fundamental mechanisms involved in the ball burnishing process. The concept of burnishing maps and zones will assist in the design of the ball burnishing parameters of a material with an equivalent yield strength of 140 MPa. The significant improvement in the surface integrity of the Mg Ze41A alloy by the ball burnishing technique is expected to improve its func

关键词： Magnesium alloy Bioresorbable Orthopedic implants Surface integrity Ball burnishing

Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites

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Friction 2021年第2期9卷 250-272页

作者： rajesh EGALA G V JAGADEESH srinivasu Gangi SETTI Department of Mechanical Engineering National Institute of Technology RaipurChhattisgarh 492010India Department of Mechanical Engineering Gudlavalleru Engineering CollegeGudlavalleruAndhra Pradesh 521356India

The present study aims at introducing a newly developed natural fiber called castor oil fiber,termed ricinus communis,as a possible reinforcement in *** short castor oil fiber reinforced epoxy resin composites of different fiber lengths with 40%volume fraction were fabricated using hand layup *** sliding wear tests were performed on a pin-on-disc tribometer based on full factorial design of experiments(DoE)at four fiber lengths(5,10,15,and 20 mm),three normal loads(15,30,and 45 N),and three sliding distances(1,000,2,000,and 3,000 m).The effect of individual parameters on the amount of wear,interfacial temperature,and coefficient of friction was studied using analysis of variance(ANOVA).The composite with 5 mm fiber length provided the best tribological properties than 10,15,and 20 mm fiber length *** worn surfaces were analyzed under scanning electron ***,the tribological behavior of the composites was predicted using regression,artificial neural network(ANN)-single hidden layer,and ANN-multi hidden layer *** confirmatory test results show the reliability of predicted *** with multi hidden layers are found to predict the tribological performance accurately and then followed by ANN with single hidden layer and regression model.

关键词： natural fiber castor oil fiber epoxy composite full factorial design of experiments(DoE) analysis of variance(ANOVA) prediction regression artificial neural network(ANN)

Novel design and composition optimization of self-lubricating functionally graded cemented tungsten carbide cutting tool material for dry machining

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Advances in Manufacturing 2021年第1期9卷 34-46页

作者： rityuj Singh Parihar raj Kumar Sahu srinivasu Gangi Setti Department of Mechanical Engineering National Institute of Technology RaipurRaipurIndia

The functionally graded cemented tungsten carbide (FGCC) is a suitable material choice for cutting tool applications due to balanced hardness and fracture *** presence of cobalt and CaF2 composition gradient in FGCC may enhance mechanical as well as antifriction ***,structural design of selflubricating FGCC was proposed using Power law composition gradient model and thermal residual stresses (TrSs) as a key ***,*** and *** model was adopted for estimation of TrS,and optimum composition gradient was identified at Power law exponent n =*** designed material displayed compressive and tensile TrS at surface and core respectively;subsequently fabricated by spark plasma sintering and characterized via scanning electron microscope (SEM),indentation *** agreement between experimental and analytical values of TrS demonstrated the effectiveness of intended design model in the composition optimization of self-lubricating *** work will be helpful in implementation of dry machining for clean and green manufacturing.

关键词： Functionally graded cemented tungsten carbide(FGCC) Thermal residual stress(TrS) Solid lubricant Power law

Optimal harvesting for a single-species population governed by Gompertz law:Influence of environmental fluctuation and limited harvesting capacity

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International Journal of Biomathematics 2019年第2期12卷 115-142页

作者： P.D.N.srinivasu Simon D.Zawka Sri Sathya Sai University for Human Excellence Navanihala Village Okali PostKamalapur Taluk Gulbarga DistrictKarnataka 585313India Department of Mathematics Andhra University Visakhapatnam 530003India Department of Mathematics Ar ba Minch University Arba MinchEthiopia

This work presents an optimal harvesting problem associated with a single-species population governed by Gompertz law in a seasonally fluctuating *** influence of environmental fluctuation is accommodated by choosing the coefficients in the differential equation to be periodic functions with the same period and restriction on the harvesting effort is accommodated by considering binding constraints on the control ***,a linear optimal control problem has been considered where the state dynamics is governed by Gompertz equation and the control variable is subject to the binding *** the help of maximum principle and the concept of blocked intervals,an optimal periodic solution has been obtained which is followed by the construe tion of optimal solution using the theory of most rapid *** results of the study are demonstrated through numerical simulations.

关键词： Optimal harvesting Gompertz equation seasonality binding constraint blocked intervals

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

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Computer Systems Science & Engineering 2023年第4期45卷 869-886页

作者： Sirisati ranga Swamy SPhani Praveen Shakeel Ahmed Parvathaneni Naga srinivasu Abdulaziz Alhumam Department of CSE Vignan’s Institute of Management and Technology for Women GhatkesarTelanganaIndia Department of CSE PVPSITVijayawadaAndhra Pradesh520007India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science and Engineering-AIML VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at *** pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already *** established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the *** problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical *** models help predict the effects of the disease outbreak and help detect the effects in the coming *** this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan *** are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from *** current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better *** model’s performance is assessed using receiver Operating Characteristic(rOC)curve,the root Mean Square Error(rMSE),and the Confusion *** is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

关键词： Chest CT COVID-19 classification rOC curves multi-feature disease analysis

An AW-HArIS Based Automated Segmentation of Human Liver Using CT Images

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Computers, Materials & Continua 2021年第12期69卷 3303-3319页

作者： P.Naga srinivasu Shakeel Ahmed Abdulaziz Alhumam Akash Bhoi Kumar Muhammad Fazal Ijaz Department of Computer Science and Engineering GITAM Institute of TechnologyGITAM Deemed to be UniversityVisakhapatnam530045India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical and Electronics Engineering Sikkim Manipal Institute of TechnologySikkim Manipal UniversityMajitar737136India Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be *** disease may impact the hormonal and nutritional balance in the human *** earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HArIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human *** proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for *** the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better ***,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its *** is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.

关键词： CT image automated segmentation HArIS anisotropic weighted social group optimization