bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ensemble Deep Learning for Brazil Currency Coin Prediction
Authors: Muppalaneni Naresh Babu , Prathima Ch. , Sekhar Akula Chandra 
Published: February 2021

Abstract
In this present fast growing environment the automatic coin reorganization and identification machines has a vital role in all financial allied fields. At present most of coin recognition techniques are depends on physical properties of the coin like length, width, weight etc. Whereas image processing techniques are based on extraction of colour of the coin, edge features of the coin and shape of the coin. For recognition and detection of Brazil currency we have designed Machine Learning, Deep Learning (DL) models in this paper. We have designed various a Machine Learning (ML) models with Convolutional Neural Network for identification and reorganization of Brazil currency. Brazil currency consisting of 5 Centavos, 10 Centavos, 25 Centavos etc. Each of which has different shapes and designs. Different deep learning models are designed and applied ensemble to find the better accuracy. The trained ensemble model is tested on various the datasets which consists of shifting of images, rotation and translated images.
An Efficient Machine Learning Techniques Based on IoT for Effective Load Forecasting
Authors: Rameshwaraiah K. , Kumar Sankarpu Sateesh , Srinivasa Babu K. , Madhu T. 
Published: February 2021

Abstract
Internet of Things (IoT) networks (IoT) are computer networks which have an acute IT protection problem and in particular a computer attack detection problem. In order to solve this issue, the paper recommends the combination of machine learning approaches and concurrent data processing. The framework is developed and a new approach to the combination of the main classifiers intended for attacks on IoT networks. In which the accuracy ratio to the training time is the integral measure of efficacy, the problem classification statement is developed. We recommend the use of the data processing and multithreaded mode provided by Spark to accelerate the speed of training and testing. In addition, a technique is suggested for preprocessing data set, which results in a large reduction in the sample length. An experimental examination of the proposed approach reveals that the precision of IoT networks’ attack detection is 100 percent and the processing speed of data sets increases proportionally to the number of parallel threads.
SAMS: Smart Agriculture Management System Using Emerging Technologies IoT, AI -A Study
Authors: Srinivas Kumar B. , Santhi S.G. , Kranthi Kumar K. 
Published: February 2021

Abstract
Farming or Agriculture is the science and art of cultivating plants. It is the vital enrichment in the growth of human living. It creates food and the other related things by farming with domestic animals and will enable people to live long and healthy life. There are two agricultural categories, one is traditional Agriculture and the second one is Modern Agriculture. In Traditional Agriculture Farmers had faced many difficulties to Farm, but with the modern Agriculture Farmers can be able to see many improvements in Framing due to the agronomy, use of agricultural chemicals such as pesticides and fertilizers, Plant breeding and technological growth, these enrichments in Farming would yield good crop.(IoT)Internet of Things very often called as a system it can able to connect real physical Things, Implanted sensors, Embedded software’s along with different technologies to connect and to transfer data amongst various diverse devices over the Internet. It is the hopeful expertise or technology which gives accurate and most reliable solutions on the way to too many domains. There is an extensive set of wide range of real time applications of IoT devices such as Industrial, Consumer, Commercial, Infrastructure may made IoT is the one of the best Technology to see enhancements in many domains. Solutions which are given by IoT would reduce human efforts in Farming and may provide sound of crop. In this paper a small survey will be presented to see how Internet of Things and its equivalent or other represent technologies AI, Cloud Computing would be useful to provide SMART AGRICULTURE along with Modern agricultural improvements will also be presented.
Timely and Accurately Predict Rainfall by using Ensemble Predictive Models
Authors: Vuyyuru Veera Ankalu. , Apparao Giduturi. , Anuradha S. 
Published: February 2021

Abstract
Weather and rainfall are important factors for human life. By depends upon the rainfall agriculture, horticulture harvesting, and goods transportation. These all are goods and supply chain processes. If in supply chain process breaks any point, Ultimately, the farmer will get the loss. Timely predicting rainfall helps the farmers and agriculture and horticulture stock manage to maintain people require India’s coastal area. For these reasons, this paper proposes the Ensemble Models (Catboost, Boost). Most of the authors are working on rainfall prediction using statistical models. Using statistical models to analyze and predict a huge amount of data is very difficult, depending upon the features. But using Ensemble models is likely to boost up the elements, and apply the classification to prediction makes it easy. This paper discusses and Compares the statistical decision tree model with ensemble models to find out the difference between the characteristics of algorithms and how they impact the timely predict the rainfall.
Unsupervised Learning Approach for Clustering Leaf Images
Authors: Chamundeswari G 
Published: February 2021

Abstract
With appropriate representation methods, the clustering techniques are found to be efficient with neural networks. The present work aims to propose various feature representation techniques for efficient clustering. The methods used for feature representation in this paper are, a method using random closed set, a method using edge information of input entity, a method that uses Huff transformation and a method that uses boundary moments. A comparative study of these representation methods for clustering the input objects using artificial neural networks, specifically Self-Organizing Map (SOM) is focused.
Epoch interrogation for skin cancer detection using convolutional neural network models
Authors: Poornima A , Shyamala Devi M , Sumithra M , Bharath Mullaguri Venkata , Swathi  , Sathishkumar S , Yogesh K , Upadhyay Shubham Santosh , Sah Nitesh Kumar 
Published: February 2021

Abstract
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.
Keyphrase Extraction And Source Code Similarity Detection-A Survey
Authors: Sharma Nakul , Yalla Prasanth 
Published: February 2021

Abstract
Keyphrase extraction is the starting phase of feature extraction and many other NLP tasks. Hence, keyphrase extraction ensures the separation of essential and relevant information from the rest of the document or corpus. A similarity score is used for detecting similarities between two or more documents. In this paper, a summary of existing research conducted in the field of keyphrase extraction and source code similarity detection is presented. The literature focuses on more concept-based research instead of going towards the domain level. There exist a promising potential for the creation of recommendation systems by employing similarly situated research techniques.
ICT-based Learner-Centric Evolutionary Learning Model: An effective solution to Teaching-Learning process
Authors: Bhoir Smita Vinit , Patil Sunita 
Published: February 2021

Abstract
In India around in the second week of March 2020, in order to avoid spread of COVID-19 virus, the Central Governments have declared LOCKDOWN in the country. It results in temporary shutdown of schools and colleges. By looking around, at severity of situation due to spread of the novel corona virus, there is no certainty when schools and colleges will reopen. This is a crucial time for the education field, including board tests, kindergarten admissions, entry tests of various universities and competitive exams. It has greater impact on Education field. It has affected continuous teaching-learning process, student counselling operation, assessment methods of teachers, students’ interest towards higher studies in abroad got lower. Information Communication Technology (ICT) may play important role in the lockdown period by providing online learning platforms. Its need in education is discussed in this paper. With the help of power supply, basic digital skills to teachers and students and internet connectivity digital learning can be made possible. Similarly, students those are from low-income groups or presence of disability, etc. for them distance learning program can be included. To mitigate such hazardous situations, ICT-based learning model is essential. In this paper, ICT-based Learner-Centric Blended Education Model is proposed which blends traditional classroom teaching with e-learning where students and teachers both are active, students can interact with teachers; also, teachers can monitor progress of students, etc. After surveying different research papers on Education Models, it has been observed that in learning based on ICT, the Learner-centric environment need to be implemented. In online platform instructors provide course material to students but personalization of these materials is missing. The proposed work presented an ICT-based Learner-Centric Evolutionary Learning Model which suggests appropriate course material to learner, according to learner’s profile built by our profile builder module which includes details like learning style, demographic details, recommendations, expectations, likes and dislikes. Our proposed work presented is seamless integration of traditional teaching-learning methods with online teaching-learning methods. The equal importance to teachers is given here to design and recommend study material to different types of learners, their preference is at highest priority in proposed recommendation model. Active engagement of students in learning process increases education graph using technologies and finally it will be helpful in development of our country.
Activation Layers Implication of CNN Sequential Models for Facial Expression Recognition
Authors: Shyamala Devi M. , Sagar Ankita , Thapa Karan , Hazarika Mreenmoy , Swathi P. 
Published: February 2021

Abstract
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 evaluation.Firstly, the facial expression dataset is extracted from the website http://www.consortium.ri.cmu.edu/ckagree/, http://app.visgraf.impa.br/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%.
Predicting User’s Web Navigation behaviour using AMD and HMM Approaches
Authors: Roy Rita , p p a R a o G A 
Published: February 2021

Abstract
The academics were introducing various techniques in web usage mining to reduce user latency time to improve their web performance. The server-side web log data helps to identify the most appropriate pages, based on the user’s request. Analyzing web log data creates difficulties since it comprises exhaustive web page data. This paper proposes a new technique in which the web log data we can preprocess in order to extract sequence and navigation patterns useful to predict. In this paper, we cluster users into communities labelled by the websites most frequently visited, to discover their preferences and to describe website reorganisation. The URL sequence navigated by a user during a session. Session denotes a user’s browsing pattern. Multiple users’ sessions are clustered using the hierarchical clustering technique to analyse the sequence in their navigation patterns. The two model predictions are Adaptive Mahanabolis Distance (AMD) and Hidden Markov model (HMM), providing a list of web pages of interest to the user. We validate the proposed framework on NASA, Clarknet and serc datasets web log files.
RETRACTED: Multi Facial Expression Recognition (MFER) for Identifying Customer Satisfaction on Products using Deep CNN and Haar Cascade Classifier
Authors: Indira DNVSLS , Sumalatha L , Markapudi Babu Rao 
Published: February 2021

Abstract
Face Expression is one of the most normal, remarkable and a general sign for individuals to convey on their enthusiastic states and it is not restricted to national borders, linguistics and gender. This article presents the modeling of a framework that plans to foresee the fulfillment of a customer through his facial feelings. The cutting edge innovation of Facial Expression Recognition framework is the consumer satisfaction estimation. MFER, a Novel procedure is proposed in this paper for identifying consumer satisfaction levels. This sound methodology of client satisfaction estimation is an alternative option of the ordinary method of gathering clients’ reaction. This model must anticipate client’s behavior in the dynamic cycle. To expect consumer trustworthiness, we have characterized mathematical highlights of the face by utilizing Deep CNN and Haar Cascade Classifier. The kinds of consumer fulfillment are classified as satisfied, not-satisfied and neutral. Our framework shows a decent exhibition, testing it on the FER2013 dataset. Our MFER –Multi Facial Expression Recognition procedure identifies multiple objects in the same image which consists of same and different expressions.
A New Feature Based Online Rating System for E-Commerce Websites
Authors: Kumar Raja D. R. , Ramu M. , Sekhar AKULA CHANDRA 
Published: February 2021

Abstract
Online reviews and ratings are considered extremely important in any business. Any customer who wants to purchase a particular product online will have a look on the ratings before placing an order, any person who wants to book a hotel or restaurant in a particular location will have a glance on online ratings, any person who wants to choose a best hospital will check ratings. The question which arises here is how far the ratings can be reliable? The ratings cannot be trusted completely as they are not feature specific. The overall rating leaves the people in chaos and they cannot conclude that on which basis the ratings have been awarded. To overcome this problem feature based online rating is introduced. Where ratings for different features have been awarded separately. It helps people to take decisions easily and can purchase a product of their requirement. It helps in the growth of the business as customer satisfaction is equally important and it increases the quality of service.
Numerical study of the influence of relative parameters of the metamaterial structure on its mechanical behavior
Authors: Akhmetshin L R 
Published: February 2021

Abstract
In this paper, attention is focused on the influence of changes in the variable parameters of the structure of mechanical tetrachiral metamaterial on its linear elastic behavior, in particular, on its twist. The parameters characterizing the structure of the metamaterial were chosen in a relative form with respect to the unit cell size and changed independently of each other in the investigation. The results of the mechanical behavior of the tetrachiral metamaterial in the event of changes in the structural parameters were obtained. The dependencies of the rotation angle when the relative parameters change were established and analyzed. The parameters of the chiral structure, the most affecting the unusual behavior of mechanical metamaterial—a twist under uniaxial loading, were determined.
Influence of the angle interaction of the projectile with the wire mesh
Authors: Akhmetshin L R , Kushnarev A G , Pashkov S V 
Published: February 2021

Abstract
The problem of interaction of the high-velocity solid spherical shock element simulating a micrometeoroid and the combined protective screen including the mesh barrier is considered. The mesh layer is made of steel wire by weaving method. As a result of numerical modeling by SPH method the results of protective wire mesh and projectile destruction behavior are obtained. The effectiveness of several variants of multilayer screen of equal specific mass with different angles of mesh layer orientation is compared.
Numerical study of the stress strain state during tests of polymer composite materials samples
Authors: Feklistova E V , Babushkin A V , Babushkina A V 
Published: February 2021

Abstract
To date, a large number of experimental methods for determining the mechanical properties of composite materials is known. However, the results of these methods. i.e. their comparison features, are relative and give good repeatability in strict compliance with the sample production and experiment technology. In this case, the stress-strain state in the working area of sample structures should be numerically evaluated to determine the generally accepted characteristics. This paper constructs and develops the simplest models of the mechanical behavior of polymer composite material samples. The stress-strain state is numerically simulated in the working areas of samples with various geometric parameters and external loading features. Modeling is made using the ANSYS engineering analysis system. According to the results of calculations for each of the samples, the fields of stresses, deformations, and displacements under compression and tension are obtained for medium models: effective isotropic medium, effective anisotropic medium, and layered composite with effective anisotropic medium texture layers.
Phase formation, structure, and properties of the solid solutions in a system based on K0.5Na0.5NbO3 and Ca2Nb2O7
Authors: Glazunova E V , Verbenko I A , Shilkina l A , Nagaenko A V , Reznichenko L A 
Published: February 2021

Abstract
This study reports the creation of the solid solutions (1-x-y)KNbO3 – yNaNbO3 – xCa2Nb2O7 system, with y=0.50, 0.0≤x≤0.1, Δx=0.025 using solid-state reaction and sintering by conventional ceramic technology. The mechanisms and patterns of formation of the microstructure and dielectric properties of received solid solutions are established. The most probable mechanism of the formation of defects in the structure of (1-x-y)KNbO3 – yNaNbO3 – xCa2Nb2O7 solid solutions was determined.
Deformation analysis of cylindrical lattice body of spacecraft in zone of high-pressure tank attachment
Authors: Khakhlenkova A A , Burov A E , Lopatin A V 
Published: February 2021

Abstract
Finite element analysis of the strain-stress state in a load-bearing shell of the spacecraft body under longitudinal and lateral inertia loads was carried out. The shell is a composite anisogrid structure constituted by circumferential and helical ribs made of carbon fiber composite. A massive fuel storage tank is fixed inside the shell by means of a cable-stayed system. The shell structural behaviour in the zone of fuel tank installation was particularly addressed. A local reinforcement with additional circumferential ribs was shown to remarkably reduce the stress level and deflection in the tank attachment zone.
Using an Electromechanical Analogy to Describe the Damping Characteristics of an MFC Actuator
Authors: Pan’kov A A , Anoshkin A N , Pisarev P V , Bayandin S R 
Published: February 2021

AbstractIn this article, a mathematical model was formulated that describes the dynamic behavior of MFC actuators. The model was developed on the basis of numerical-analytical methods for solving coupled boundary value problems of electromagnetoelasticity for inhomogeneous media equipped with piezoactive structural elements and loaded with coherent electromechanical disturbances. Within the framework of the performed numerical experiments, stationary harmonic axisymmetric oscillations of an elementary composite two-phase MFC actuator cell loaded with coherent electromechanical disturbances were considered. The calculation of the MFC actuators complex impedance and conductivity values, taking into account the energy losses and phase shift at different levels of the control electric voltage, was carried out.
Formation of the CoCrCuFeNi high entropy cladding layer by non-vacuum electron beam treatment
Authors: Ruktuev A A , Lazurenko D V , Kiseleva N A , Petrov I Y , Golkovski M G 
Published: February 2021

Abstract
Non-vacuum electron beam cladding was applied to obtain CoCrCuFeNi cladding layers with thicknesses of 0.89 and 1.24 mm on a surface of mild steel. The cladding layers possessed the dendritic structure. Interdendritic space was filled with a Cu-rich phase. It was found by X-ray diffraction analysis that FCC solid solution phases were formed during crystallization. The lattice parameter of the dendritic phase was a=3.59 Å, while that of the interdendritic phase was equal to a=3.60 Å. The average microhardness values of the cladding layers were lower compared to the base material and equaled 156 HV and 190 HV.