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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rohit Srivastava R.P. Mishra Vivek Kumar Himanshu Kumar Shukla Neha Goyal Chandrabhan Singh Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (数字)9781728189086

ISBN: (纸本)9781728189093

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.

关键词： COVID-19 XML Static analysis Machine learning Feature extraction Malware Smart phones

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Grain refinement process of AZ61 alloy by 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 attributes 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 by transforming larger grains into much finer ones, thereby 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 been 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 subsequent SPD processes. This study aims to meticulously examine the impact of various initial treatments on the grain refinement of AZ61 alloy through SPD. by 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 substantial implications for industries such as aerospace and automotive, where optimizing the balance between 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 bhushan 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 by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based 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 contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected 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 before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before 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 bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

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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 based on the power of expression detection of idle image or videos. The Social communication with any object can be done by verbal or non-verbal format. Expression and Emotion detection completely rely on the non-verbal 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 website http://***/ckagree/, http://***/database/faces/ is subjected with the data processing. Secondly, the data analysis is done for the distribution 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 bottleneck 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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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 bharath Swathi S Sathishkumar K Yogesh Shubham 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 notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms 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 feasible 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 by dividing all the RGb values by 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, by using the kernel filter matrix. MaxPool reduce the computational cost by 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 number 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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Extracting Facial Features in Filter Modified Images using Neural Networks

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

作者： M Sumithra M Shyamala Devi A Poornima Shatadru Sarkar Harish Patel Preethi Mahankali boggula Navya 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 Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam's razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.

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Annotation and Retrieval of Retinal Images using Support Vector Machine with Active Learning

Annotation and Retrieval of Retinal Images using Support Vec...

引用

2019 IEEE International Conference on Intelligent techniques in Control, Optimization and Signal Processing, INCOS 2019

作者： Hephzi Punithavathi, I.S. Ganeshkumar, P. Duraipand, P. Shadan College of Engg. Tech Department of Computer Science Engineering Hyderabad India Anna University Regional Campus Department of Information Technology Coimbatore India J.B.Institute of Engg. Technology Department of Electrical Electronics Engineering Hyderabad India

ISBN: (纸本)9781538695425

in the medical field, many images are acquired using different modalities like X-ray, Computed Tomography and Magnetic Resonance Imaging. Organizing these images and retrieving them is a critical task. in this paper, a novel retinal image retrieval and automatic annotation of ten different retinal diseases is presented and is comprised of three steps namely, feature extraction, automatic annotation and retrieval of similar images. in feature extraction, visual features like texture, color and shape were derived from the images and stored in a database. The retinal images are affected by various diseases and have to be identified easily and quickly for better treatment. For automatic annotation, we have considered ten different retinal diseases and annotated the retinal image with its corresponding disease using Support Vector Machine (SVM) with Active Learning (AL) and discriminative based automatic image annotation. in retrieval of images, bray Curtis distance measure was used to retrieve the similar images by comparing the feature values of images stored in the database. Our proposed system has achieved a precision of 82% and specificity of 96.3%. © 2019 IEEE.

关键词： Support vector machines

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Scalability of high-performance PDE solvers

arXiv

引用

arXiv 2020年

作者： Fischer, Paul Min, Misun Rathnayake, Thilina Dutta, Som Kolev, Tzanio Dobrev, Veselin Camier, Jean-Sylvain Kronbichler, Martin Warburton, Tim Swirydowicz, Kasia brown, Jed Mathematics and Computer Science Argonne National Laboratory LemontIL60439 Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 Institute for Computational Mechanics Technical University of Munich Garching b. Muenchen85748 Germany Department of Computer Science University of Colorado BoulderCO80309 National Renewable Energy Laboratory LakewoodCO80401 Department of Mathematics Virginia Tech BlacksburgVA24061 Mechanical & Aerospace Engineering Utah State University UT84322

Performance tests and analyses are critical to effective HPC software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this paper, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for PDEs that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite *** Codes 35-04 Copyright © 2020, The Authors. All rights reserved.

关键词： computer architecture

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Secured payment system using face recognition technique

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AIP Conference Proceedings 2020年第1期2282卷

作者： Arun Misra Rahul Kumar Dev Mr. M. Rajasekaran 1B. Tech Computer Science and Engineering SRM Institute of Science and Technology Chennai India 2Assistant Professor Computer Science and Engineering SRM Institute of Science and Technology Chennai India

This paper describes the process through which “Secured payment system using face recognition technique”- project has been made. It describes the main objective of the project which is to be able to develop a secured and robust payment system using face recognition in python programming language with the help of OpenCV. It uses confidence value which can be described as the value generated by the system informing us about the accuracy of prediction. This project was made by combining various detection modules and pattern recognizer. In this project various existing methods are combined together to give us the result. Pin and one time password (OTP) are used for security measures to make safe and successful transaction. OTP will be sent to the registered phone number of the user.

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iot based saline level monitoring system

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IOP Conference Series: Materials science and engineering 2020年第3期981卷

作者： G. Sunil Srinivas Aluvala G. Ranadheer Reddy Vedantham Sreeharika Parupally Sindhu Sriramula Keerthana Department of Computer Science and Engineering S R Engineering College Warangal Telangana India School of Computer Science and Artificial Intelligence S R University Warangal Telangana India Assistant Professor Department of Computer Science and Engineering Sumathi Reddy Institute of Technology for Women Warangal Telangana India B. Tech Student Department of Electronics and Communication Engineering S R Engineering College Warangal Telangana India B. Tech Student Department of Computer Science and Engineering S R Engineering College Warangal Telangana India

In the process of medication, it is a common practice to treat patients with saline for dehydration and other medical ailments to improve the health condition of the patients. When fed with saline continuous observation of nurses is mandatory in monitoring the level of the saline. There are many cases where patients are being harmed due to the staff inattentiveness, as their absence does not notice the completion of saline level in the container. This arise the problem of back flow of blood immediately after the completion of saline in container. Hence to protect the patient from getting harmed an iot based saline level monitoring system has been developed. The proposed model incorporates a sensor which continuously detects the saline drops. Whenever the sensor does not detect the drops for a certain interval it alerts the staff of the hospital with the buzzer, helping to monitor the safety of the patients.

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