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Roles of nanoparticles and heat generation/ab.orption on MHD flow of Ag–H2O nanofluid via porous stretching/shrinking convergent/divergent channel

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Journal of the Egyptian Mathematical Society 2020年第1期28卷 1-18页

作者： Mishra, Ashish Pandey, Alok Kumar Chamkha, Ali J. Kumar, Manoj Department of Mathematics Statistics and Computer Science G. B. Pant University of Agriculture and Technology Pantnagar India Department of Mathematics Graphic Era Deemed to be University Dehradun India Mechanical Engineering Department Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

This article unveils the comb.ned impact of heat generation/ab.orption and Joule heating on MHD flow of Ag-H2O nanofluid into a porous stretching/shrinking divergent/convergent channel with viscous dissipation and solid volume fraction. The mathematical modeling is presented for the existing equations of continuity, momentum, and energy fraction. The reduced b.undary value prob.em is solved numerically employing Runge-Kutta-Fehlb.rg (RKF) method via shooting scheme and then the outcomes are sketched and interpreted. The results explore that the thermal b.undary layer thickness of the stretching divergent and the shrinking divergent channels enhance b. increasing the value of the Eckert numb.r, while the opposite tendency is scrutinized on the momentum b.undary layer thickness b. increasing the value of the porosity parameter for the stretching divergent and the shrinking convergent channels.

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Expression of Concern for: Deep Learning Model to Predict Pneumonia Disease b.sed on Ob.erved Patterns in Lung X-rays

Expression of Concern for: Deep Learning Model to Predict Pn...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Utkarsh Singh Aditi Totla Dr. Prakash Kumar Dept. of Computer Science and Engineering R. V. College of Engineering Bengaluru Karnataka Dept. of Electronics and Electrical Engineering R. V. College of Engineering Bengaluru Karnataka Dept. of Production Engineering B. I. T. Sindri Dhanbad Jharkhand

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Due to the recent technological advancements, healthcare industry is also undergoing some major changes. The volume of medical data continues to increase at a staggering rate and artificial intelligence is poised to b. the engine that drives improvements across the care continuum. Pneumonia is a serious disease, which can range from mild to life-threatening as it causes inflammation and fluid accumulation in the lungs and making it hard to b.eathe. It remains as the most serious for infants, children, and people older than 65 years. It is also considered to b. a potential complication of the COVID-19 disease. Chest X-ray is an important pneumonia diagnosis worldwide. To help detect patients, who require intense medical care, artificial intelligence can b. used to analyse lung imaging as a part of the clinical research study. The primary goal is to reduce the complex task of understanding scans through experts and provides easy prediction b. developing a model that can predict the possib.lity of the disease in a patient through their lung X-ray images.

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Structural and Optical Properties of b.0.7Dy0.3Fe11.4Cr0.6O19 /Polyaniline (PANI) Nanocomposites for Optoelectronics 2

Structural and Optical Properties of Ba0.7Dy0.3Fe11.4Cr0.6O1...

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2nd International Conference on Recent Advances in Fundamental and Applied sciences, RAFAS 2019

作者： Tchouank Tekou, Carol T. Mohammed, J. Mukhtar, G. Halilu, N. Getso, R.S. Ab.ullahi, Z.H. b.sandrai, D. Srivastava, A.K. Department of Physics Lovely Professional University PhagwaraPunjab144411 India Department of Physics Faculty of Science Federal University Dutse DutseJigawa state7156 Nigeria Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University PhagwaraPunjab144411 India Department of Science Education Faculty of Arts and Education Bauchi State University Gadau P.M.B. 65 Itas GadauBauchi State Nigeria Department of Physics Sa'adatu Rimi College of Education P.M.B. 3218 KumbotsoKano State Nigeria Department of Computer Engineering Hussaini Adamu Federal Polytechnic P. M. B. 5004 KazaureJigawa State Nigeria

M-type hexaferrites have found considerab.e application in microwave ab.orption and magnetic recording. Sol-gel auto-comb.stion technique was utilized to prepare b.0.7Dy0.3Fe11.4Cr0.6O19 nano-sized particles which were sintered at 1000 C for 6 hours. XRD patterns of the prepared sample show magnetoplumb.te phase of M-type hexaferrite along with the presence of hematite (-Fe2O3) and polyaniline. Two specific b.nds in the range 400-600 cm-1 were ob.erved in the FTIR spectra signifying the formation of hexaferrites structure. The peaks corresponding to polyaniline are also present in the FTIR spectra in the range 800-1600 cm-1. FESEM analysis shows grains with shape similar to hexagonal plate emb.dded on the surface of polyaniline. The b.nd gap was found to decrease from 1.96 eV to 1.59 eV. © 2019 Pub.ished under licence b. IOP Pub.ishing Ltd.

关键词： Fourier transform infrared spectroscopy

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Grain refinement process of AZ61 alloy b. plastic deformation

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Materials Today: Proceedings 2024年

作者： Rajalaxmi Hegde S. Mohanasundaram S. Krishnaraj S. Arunkumar M. Udayakumar D. Suresh Department of Computer Science and Engineering NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karnataka India Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Mechanical Engineering Sri Sai Ram Engineering College West Tambaram Chennai India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sankunthala Engineering College Avadi-Veltech Road Chennai India University of Technology and Applied Sciences Salalah Oman Department of Mechanical Engineering V.S.B Engineering College Karur Tamil Nadu India

Grain refinement is a pivotal technique for enhancing the mechanical attrib.tes of magnesium alloys, offering improvements in strength, ductility, and fatigue resistance. Among the methods for achieving grain refinement, Severe Plastic Deformation (SPD) stands out due to its effectiveness in imposing extreme strains on materials, either at room temperature or under moderately elevated thermal conditions. This process significantly alters the microstructure b. transforming larger grains into much finer ones, thereb. augmenting the alloy’s mechanical performance. The preparatory treatment of an alloy prior to SPD plays a critical role in determining the resultant microstructure and its associated mechanical properties. Although various preliminary treatments have b.en investigated for their impact on magnesium alloys, the specific influence of these treatments on the grain refinement of the AZ61 alloy necessitates further exploration. This gap in knowledge highlights the need for rigorous research to comprehend how different initial treatments, such as pre-annealing, influence the effectiveness of sub.equent SPD processes. This study aims to meticulously examine the impact of various initial treatments on the grain refinement of AZ61 alloy through SPD. b. systematically investigating these effects, the research seeks to deepen our understanding of microstructural evolution in AZ61 alloy and to derive strategies for optimizing processing routes that enhance its mechanical properties. The findings from this investigation are expected to significantly advance the application of AZ61 alloy in critical engineering domains where the demand for lightweight yet high-performance materials is crucial. This research could therefore have sub.tantial implications for industries such as aerospace and automotive, where optimizing the b.lance b.tween weight and strength is paramount.

关键词： AZ61 alloy Magnesium alloys Grain refinement Severe plastic deformation Initial treatment

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Variant component principal linear reduction for prediction of hypothyroid disease using machine learning

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： M. Shyamala Devi P. Swathi Ayesha Jahangir A. Dhanunjai Sai Kumar A. Naga b.ushan Naidu M. Raghuram Manas Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Third Year B.Tech Students Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

With the tremendous technological growth, the world is shifted to adapt the different food and life style b. the people that results in the improper working of the b.dy organs. The change in the food hab.ts leads to a major prob.ems that we face in the current scenario is the presence of hypothyroid in the b.dy. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this b.ckground, the machine learning can b. used towards health care scenarios for the prediction of disease b.sed on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients' medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient's records is used for the experimentation of hypothyroid with the following contrib.tions. Firstly, the hypothyroid dataset from UCI machine repository is sub.ected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics b.fore and after feature scaling. Thirdly, the data is processed to PCA with various comb.nation of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics b.fore and after feature scaling. Fourth, the data is processed to LDA with various comb.nation of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics b.fore and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve b.yes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2022年 820卷 v页

作者： Chanda, Chandan Kumar Szymanski, Jerzy R. Sikander, Afzal Mondal, Pranab.Kumar Acharjee, Dulal Department of Electrical Engineering Indian Institute of Engineering Science and Technology West Bengal Shibpur India Department of Electrical Drives Kazimierz Pulaski University of Technology and Humanity Radom Poland Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Mechanical Engineering Indian Institute of Technology Guwahati Guwahati India Computer Science Applied Computer Technology West Bengal Kolkata India

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Optimal correlation order in super-resolution optical fluctuation microscopy

arXiv

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arXiv 2020年

作者： Vlasenko, S. Mikhalychev, A.b. Karuseichyk, I.L. Lyakhov, D.A. Michels, D.L. Mogilevtsev, Dmitri B. I. Stepanov Institute of Physics National Academy of Sciences of Belarus Nezavisimosti Ave. 68 Minsk220072 Belarus Computer Electrical and Mathematical Science and Engineering Division 4700 King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Here, we show that, contrary to the common opinion, the super-resolution optical fluctuation microscopy might not lead to ideally infinite super-resolution enhancement with increasing of the order of measured cumulants. Using information analysis for estimating error b.unds on the determination of point sources positions, we show that reachab.e precision per measurement might b. saturated with increasing of the order of the measured cumulants in the super-resolution regime. In fact, there is an optimal correlation order b.yond which there is practically no improvement for ob.ects of three and more point sources. However, for ob.ects of just two sources, one still has an intuitively expected resolution increase with the cumulant order. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Activation Layers Implication of CNN Sequential Models for Facial Expression Recognition

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： M. Shyamala Devi Ankita Sagar Karan Thapa Mreenmoy Hazarika P. Swathi Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial Expression Recognition is the critical part in the human emotional detection in the field of image processing. The application tends to soft or hard real time application b.sed on the power of expression detection of idle image or videos. The Social communication with any ob.ect can b. done b. verb.l or non-verb.l format. Expression and Emotion detection completely rely on the non-verb.l communication and facial expression. Machine Learning play an important role in the recognition of facial expression. An attempt is made in this paper to analyse the performance of Convolutional neural network models with diverse activation layer for the performance ***, the facial expression dataset is extracted from the web.ite http://***/ckagree/, http://***/datab.se/faces/ is sub.ected with the data processing. Secondly, the data analysis is done for the distrib.tion of expression image in the training and testing dataset. Thirdly, the facial expression images are detected with HAAR cascade and then the images are cropped with (350, 350). Fourth, the facial expression images is applied with normalized and the b.ttleneck features are created for training and testing data. Fifth, the training dataset is fitted with convolutional sequential neural network models with various activation layers like Sigmoid, Elu, Relu, Selu, Tanh, Softsign and Softplus. Sixth, the performance analysis is done with loss and accuracy for all the epoch of all CNN models for all the activation layers. Experimental results show that CNN sequential model with Relu activation layer is found to have the accuracy of 100%.

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Agile network simulator framework

Agile network simulator framework

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2018 International Conference on Networking, Emb.dded and Wireless Systems, ICNEWS 2018

作者： Shivram, Raghuram Prasad, b.G. B. M. S. College of Engineering Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781538679494

Advances in networking technology is rapidly ab.tracting underlying network level protocol details from application layer. Often application layer is concerned ab.ut top level network parameters like b.ndwidth, latency, quality of service, etc or b.oadly network b.haviour to achieve its primary ob.ects than contemplating details of underlying network infrastructure. Similar expectations are set while simulating new applications. There are numerous simulators with speciality features to simulate b.th application and network capab.lity. b.t such simulators pose hardship in quick simulations. To circumvent such limitations this work proposes simplified discrete event simulation framework for network applications using SimPy. Self contained WiFi P2P Group Owner selection process simulation is attempted in this concept proving work, and the results are encouraging. © 2018 IEEE.

关键词： Python

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Epoch interrogation for skin cancer detection using convolutional neural network models

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： A Poornima M Shyamala Devi M Sumithra Mullaguri Venkata b.arath Swathi S Sathishkumar K Yogesh Shub.am Santosh Upadhyay Nitesh Kumar Sah Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Skin cancer is very important notab.e disease and it is prob.b.e to everyone nowadays, it flourishes on the area of b.dy where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of b.dy like face, hand and b.ttoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasib.e challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized b. dividing all the RGb.values b. 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, b. using the kernel filter matrix. MaxPool reduce the computational cost b. down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less numb.r of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.

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