bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing vocabulary card base on Augmented Reality (AR) for learning English
Authors: Rozi I F , Larasati E , Lestari V A
Published: February 2021

The application of technology in the world of education can affect the quality of students in understanding learning material because technology is one of the important elements to assist in the teaching and learning process. One educational application that can be developed is Augmented Reality (AR) technology. AR is one of the learning media that can be used to combine virtual objects or objects into a real environment with the virtual world to be able to interact in realtime. English is a foreign language that must be mastered. In learning English, students need to be given adequate vocabulary learning that can be provided through reading or listening activities. The importance of adequate vocabulary in English to support the smoothness of oral communication, where there are 2 things that inhibit the ineffectiveness of oral communication namely students do not have adequate vocabulary mastery and do not know how to use vocabulary in their spoken language. Effective and interactive learning media will facilitate students in learning vocabulary. This research an instructional media based on augmented reality vocabulary cards will be made for learning English vocabulary. Learning English vocabulary developed is a basic vocabulary consisting of object recognition and greeting. The learning media will display real objects and sounds, so that the object looks real and the ease of pronunciation of English consonants correctly.
Designing real-time research data portal of JTI POLINEMA
Authors: Budiprasetyo G , Yunhasnawa Y , Mentari M , Pratama D C
Published: February 2021

Research culture in Department of Information Technology Polinema is always developed continuously every time. In addition, many research titles that are critical of environmental problems are beginning to be seen. The Information Technology Department has an outstanding focus on issues, namely smart systems which include smart education, smart living, smart healthy, smart city and smart tourism. Research resources in the Information Technology Department Polinema are competing to realize that research focus. For this reason, efforts are needed to increase the availability of facilities that are not only material. The fast response can be solved by providing Real-Time Research Data Portal. The contribution of this paper is to design of middle ware on web portals that have high scalability capabilities for providing large data facilities and fast access. Real-time big data processing is the path that will be described in the architecture that will be created and adapted to the needs of the Department of Information Technology Polinema. The results of the latency evaluation test indicated that RabbitMQ allowed us to enforce concurrency, thereby reducing overall RTC by up to 50% compared to traditional HTTP.
Enhancing student interest in learning through the development of serious mathematics games
Authors: Rawansyah R , Pramudhita A N , Pramitarini Y
Published: February 2021

Learning mathematics in Indonesia today has not given the expected results, it is not better when compared to other countries. If a student is active and able to count quickly and carefully in mathematics, then the student will indirectly be easier to solve problems either in mathematics itself or applied in everyday life. The average educator has not used interesting media to improve children’s numeracy skills, there are often difficulties in students’ mathematics learning. Many research contributions are directed at taking advantage of the success of video games and using them to benefit from the education domain. The concept of learning in this research is carried out by utilizing the development of information and communication technology through interesting mathematical game applications through web-based learning. The game is developed using scratch by MIT so that the development process is less time consuming and easier. The game is in form of the quiz that makes students utilize their mathematical skills to get rewards. In that way, the competitiveness of the students also increases at the same time with their numeric ability. This learning has an appropriate value to be a breakthrough to balance current technological developments and improve students’ numerical ability. Because of the mathematics games has a rewards system, it is interesting for students to learn math.
3D reconstruction using Structure From Motion (SFM) algorithm and Multi View Stereo (MVS) based on computer vision
Authors: Kholil M , Ismanto I , Fu’ad M N
Published: February 2021

The development of the Information and Computer Technology (ICT) sector, three-dimensional (3D) technology is also growing rapidly. Currently, the need to visualize 3D objects is widely used in animation and graphic applications, architecture, education, cultural recognition and Virtual Reality. 3D modeling of historic buildings has become a concern in recent years. 3D reconstruction is an attempt to document reconstruction or restoration if the building is destroyed. By using the 3D model reconstruction using Structure from Motion (SFM) and Multi View Stereo (MVS) algorithm based on Computer Vision, it is hoped that the results of this 3D modeling can be utilized as an effort to preserve 3D objects in the Penataran Temple cultural heritage area. This research was conducted by taking as many as 61 images of objects in the Blitar Penataran Temple area. The photos obtained were reconstructed into a 3D model using the Structure From Motion algorithm in the meshroom. This research a trial of the original image with a compressed image for reconstruction is used to compare the 3D reconstruction process from the two input data. From 61 images processed using the Structure Form Motion algorithm, 33 poses of camera pose and 3D points were improved, both original and compressed images. The number of iterations compresses 1.4% less than the original image and takes 43.53% faster than the original image.
Building Information Modelling for clean water and wastewater system in the medium rise school building
Authors: Rofi K A , Hapsari R I , Riskijah S S , Harsanti W , Dharmawan M A , Rahman T
Published: February 2021

Human activities in a building are closely related to the use of clean water. At this time, the concept of a water installation is demanded to be as effective as possible by minimizing the possibility of water waste caused by leakage problems, broken pipes, blocked pipes, and others. However, some concepts still use traditional techniques by depicting 2D models and manual calculations. The method used is a hydraulic analysis with SNI 03-7065-2005 as standard and using Autodesk Revit MEP as modeling and calculating tools. Necessary data are technical specifications, design drawings, and designed plumbing facilities. The system comprises of Domestic Cold Water, Sanitary Grey Water, Sanitary Black Water, and Vent. Building Information Modelling (BIM) is applied to evaluate the effectiveness of the existing design and design its modification as well as to provide the working drawing. The analysis includes analysis of piping system completeness, system crash, flow, velocity, pressure loss. Based on the report, it can be concluded the flow velocity of Domestic Cold Water is 0.7-6.4 m/s which is out the range of standard value. For the pressure, a manual calculation is performed to validate the report results using the Darcy Weisbach equation that has the same value as the report by Building Information Modeling. The cost of the design conditions has a difference of Rp 8.363.608 or eight million three hundred sixty-three thousand rupiah or 0.4% more efficient.
Micro-expression recognition based on motion detection method
Authors: Rosiani U D , Choirina P , Shoumi M N
Published: February 2021

Micro-expressions are emotional representations that occur spontaneously and cannot be controlled by humans. The micro-expression movements are temporary with fast duration and have subtle movements with little intensity. This is difficult to detect with the human eye. Previous studies have shown that micro-expression movements occur in several areas of the face. This study aims to determine the subtle movements in several areas of the face using the motion detection method. We compared the performance of two motion detection methods: the optical flow method and the Block Matching Algorithm (BMA) method. The optical flow method uses the Kanade-Lucas Tomasi (KLT) method and the BMA method uses the Phase Only Correlation (POC) algorithm. Observations were carried out based on region, where the face area was divided into several observation areas: eyebrows, eyes and mouth. Both methods perform motion detection between frames. The KLT method tracks the movement of the observation points on the frame movement. Meanwhile, the POC method matches the blocks between frames. If the two blocks are the same, no motion vector is generated. However, if the two blocks are different, it is assumed that there is a translational motion and a motion vector is generated. Experiments were conducted using a dataset from CASME II with emotional classes of disgust, happiness, surprise, and sadness. The classification accuracy of the POC method is 94% higher than the KLT method of 84.8% which uses the SVM classification.
Object recognition using cognitive artificial intelligence’s knowledge growing system: A preliminary study
Authors: Sumari A D W , Syamsiana I N , Putra D R H , Asmara R A
Published: February 2021

Object recognition has been a challenge for an intelligent system. There have been various approaches to develop such a system by utilizing machine learning especially which are based on neuron that is, neural network and deep learning. Common problems when using those approaches are the first one is dataset availability and the second one is the number of data. Lack of data causes neural-based approaches cannot be well operated, while a small number of data causes low accuracy results on the system. From another point of view, a considered-new technology from Cognitive Artificial Intelligence (CAI) perspective called as Knowledge Growing System (KGS) which may cope with such problems. With the capability to build its own knowledge from nothing, KGS is able to carry out recognition while developing its knowledge regarding the phenomenon it is trying to recognize. In this research, we showed KGS capability to perform object recognition as it is developing knowledge when interacting with such object directly. We did a benchmark on face recognition use-case with some common machine learning methods to show their performance on a small number of data, and KGS showed good results. With 100 feature-set from 5 persons’ face images, KGS achieves Degree of Certainty (DoC) as much as 80% which is the system’s prediction accuracy that enables it recognizing the person based on that-moment data. Even though it is still lower compared to machine learning methods, but KGS shows advantages that it does not require high computational cost because it requires no training and no model development. In this research, we also showed that KGS enables the fast-deployment light-operated object recognition system.
Testing of black chromium coating on low carbon steel
Authors: Hadi S , Buwono H P , Setiawan A , Valeria S S M , Setyawan H
Published: February 2021

The problem lies in the colour resistance of the black chromium electroplating results that are not yet known for its effective test methods. The purpose of this research is to find an effective method for testing colour resistance. The methods used include preparing the specimen from motorcycle rim, black chromium electroplating, heating the specimens in Muffle furnace for different temperatures, cooling the specimen to room temperature, measure and calculate the colour state in order to decide the fading level, corroding the specimen in several acidity solutions from chloric acid, calculate the corrosion rate and analysis of the results. The results of the study in the form of (1) measurements with Colourflex showed that heating the low carbon steel rim for 2 hours at 200°C the colour remained dark black, at 250, 300, 350, and 400°C the colours faded 20, 40, 45, and 60% respectively. (2) corrosion test shows that at pH 3, 4, 5, and 6 the corrosion rate is 0.166, 0.136, 0.122 and 0.107 mm/year which the colour begins to fade at pH 4 and increase for more acid.
Quality by design of optimum parameter to minimize the weight of plastic products
Authors: Hartono M , Gumono G , Wirawan W , Qosim N , Wicaksono H
Published: February 2021

The research aims to control product quality off-line (quality by design) in producing lightweight plastic products for food packaging. The lightweight product is very profitable for the producers due to the efficient production costs. Quality by design was done by designing controlled parameters in the plastic molding process which includes clamping force, holding time, injection pressure, injection speed and injection temperature and their interactions. The plastic material that is made observable is Polypropylene (PP). The response parameter in this study is the weight of the product. The parameter design method used was Factorial Experiment Design with 5 independent variables with different levels of variation. The results showed that the combination of injection temperature and holding time had a significant effect on product weight. To produce heavier products that are in the area above the holding time of 0.4 seconds at any injection temperature. Meanwhile, to produce a lightweight product (under 43 grams) is at a combination of injection temperature below 263 C and holding time of about 0.3 seconds. The standard weight of the product on the plain bucket food packaging for this yogurt container is 42.5 gram.
Control of rotation speed using angle change or effective cross-sectional area of blades on wind turbines
Authors: Yulianto Y , Pracoyo A , Priyadi B
Published: February 2021

To cope with wind changes when the speed is excessive, the turbine needs to be equipped with a speed regulator. To control the turbine rotational speed can be done by changing the variable angle or blade cross-sectional area. Changes in blade angle are automatically followed by changes in blade cross-sectional area. To get a more stable rotational speed, the cross-sectional area of the blades is changed inversely with the wind speed. Experiments were carried out to obtain an effective blade cross-sectional area, in order to obtain a characteristic curve of the change in angle or the effective cross-sectional area of the blade when wind speed changes. Based on the results of this test, a wind turbine prototype has been designed using 3 blades with a maximum area of 0.22 m2 each. The change in angle is automatically carried out by adding a centrifugal pendulum at a position half the radius of the blade length. Control of rotational speed using a centrifugal pendulum is quite effective as a control for wind turbine rotational speed. Although the rotating speed is not very constant, it shows better stability.
HYBRID SHUFFLED FROG LEAPING ALGORITHM WITH PROBABILITY DISPERSAL METHOD FOR TUMOR DETECTION IN 3D MRI BRAINTUMOR IMAGES
Authors: Sirisha P.G.K. , Haritha D.
Published: February 2021

In the medical image study, the brain tumor classification using MRIs is difficult due to the brain’s complicated structure and the high variance in tumor tissues’ position. So, the requirement for useful and specific tumor identification methods is developing for medical recognition and regular medical applications. The conventional brain tumor identification performs anatomical knowledge of irregular tissues in the brain, helping the doctor design approach. The research proposes several techniques for brain tumor identification. This work aims to present brain tumor identification methods based on evolutional intelligence and segmentation. Unusual areas in the brain are identified by using the Expectation-Maximization (EM) algorithm. For segmenting the 3D brain MRI data, this work presents a novel hybrid optimization meta-heuristic called the Shuffled Frog Leaping Algorithm (SFLA) with probability dispersal (i.e., SFLA - Stochastic Diffusion Search (SDS)). The efficacy of the suggested 3D SFLA probability dispersal EM in enhancing the performance of the 3D SFLA tabu EM has been proven by empirical outcomes.
A Review on Detection of Land Use and Land Cover from an Optical Remote Sensing Image.
Authors: Kavitha A.V. , Srikrishna A. , Satyanarayana Ch.
Published: February 2021

Detection of land use and land cover from an optical remote sensing image is an essential research area from the inception of a remote sensing image. Land use land cover maps have numerous applications in agriculture, environment monitoring, urban planning, etc, along with managing various catastrophic events like floods, tsunamis, forest fires, etc. This paper reviewed major techniques for detection of land use and land cover from an optical remote sensing image. Many techniques based on only spectral information, spatio-contextual information and knowledge based methods have been discussed, finally arguing the importance of the techniques based on spatio-contextual information and Mathematical Morphology.
Sentiment Analysis using Neural Network and LSTM
Authors: Srinivas Akana Chandra Mouli Venkata , Satyanarayana Ch. , Divakar Ch. , Sirisha Katikireddy Phani
Published: February 2021

People put their opinions or views on various events happening in the society or world. Twitter is one of the best social networking sites where a huge amount of data generates on the daily basis. These data can be used to classify their tweets based on various sentiments attached to them. Numerous technologies are applied to analyse the sentiments of users. Sentiment analysis needs a very efficient method to manage long arrangement data and their drawn-out dependencies. In this paper, we have applied a deep learning technique to perform Twitter sentiment analysis. Simple Neural Network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) methods are applied for the sentiment analysis and their performances are evaluated. The LSTM is the best among all proposed techniques with the highest accuracy of 87%. We have collected a Twitter dataset from Kaggle to perform our experiment. The future improvement of the proposed research should include REST APIs and web crawling-based solutions to get live tweets to perform real-time analytics. We have analysed 1.6 million tweets in our research work.
Classification of Breast Cancer using Histology images: Handcrafted and Pre-Trained Features Based Approach
Authors: Kundale Jyoti , Dhage Sudhir
Published: February 2021

Breast cancer has become a critical disease in women. The number of patients with breast cancer is quite high in India. It is of paramount importance to detect the disease in advance. Digital histopathology is one of the most advanced techniques for detection using machine learning. Artificial intelligence is going to be like a sunrise in the field of medicine. Deep neural networks have been successfully applied to the problem under consideration in the past. As, we know the feature extraction is one of the essential and crucial steps in case of classification. In this paper, we compare two approaches, first is feature extraction using traditional Handcrafted based and other is Transfer Learning based model (Pre-trained) for multiclass classification of Breast Cancer using Convolutional Neural Network (CNN) as a classifier. The models are trained using handcrafted features like Seeped Up Robust Features (SURF) and Dense Scale Invariant Feature Transform (DSIFT) techniques, later these extracted features are encoded by Locality Constrained Linear Coding method (LLC). In pre-trained model we have used VGG16, VGG19, ResNet50, GoogLeNet for feature extraction. The maximum accuracy for “SURF+CNN” is 92.88% for Handcrafted feature and in case of Pre-trained “GoogLeNet+ CNN” model gives 94%, both for 400X magnification factor.
Ensemble Deep Learning for Brazil Currency Coin Prediction
Authors: Muppalaneni Naresh Babu , Prathima Ch. , Sekhar Akula Chandra
Published: February 2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In 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

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.
Research of the damage accumulation and destruction processes of perforated composite plates
Authors: Strungar E M , Feklistova E V , Wildemann V E , Lobanov D S
Published: February 2021

The main purpose of the research is to comprehensively study the regularities of deformation processes damage accumulation and destruction of modern composite materials in stress concentration zones. The object of research is fiberglass samples with four round holes. As a result of experimental studies loading diagrams were obtained for samples with different length of the working area. The paper presents graphs of the load dependence on the longitudinal deformation. According to the data obtained it can be noted that the longer the length of the working area of the sample the earlier the failure occurs. The deformation was obtained using the optional Vic-3D “virtual extensometer” non-contact optical video system. In the experiments process curves of longitudinal deformations were plotted. From these data it is possible to estimate the maximum longitudinal deformations occurring near the hole. Also in the work numerical modeling of the process of deformation and destruction of perforated plates was carried out in the software engineering complex Ansys. The calculated data obtained confirm the experimental results of the research.
Modeling the structure of the TiO2(rutile)/SrTiO3 heterointerface
Authors: Tikhonov S A , Zavjalov A P , Kosyanov D Yu
Published: February 2021

The structure of the TiO2(rutile)/SrTiO3 interface is modeled using the density functional theory method. Based on calculations, the most stable structure of the heterointerface is determined, for which there are noticeable differences in the structure of the bordering TiO2 and SrTiO3 layers. This determines mainly the ionic nature of interfacial bonding and the metallic nature of the interface, which can contribute to an increase in the number of charge carriers and the formation of a 2D electron gas. In addition, oxygen vacancies and under-coordinated O and Ti atoms are observed on the bordering TiO2 layer, indicating excess energy of the heterointerface. The results obtained serve as a basis for using theoretical methods to evaluate the effect of dopants on the thermoelectric properties of TiO2–SrTiO3 biphasic ceramics.
Structure, morphology and mechanical properties of arrays of zirconia nanofibers at different heat treatment modes
Authors: Tyurin A I , Rodaev V V , Korenkov V V , Razlivalova S S , Zhigachev A O , Vasyukov V M , Pirozhkova T S
Published: February 2021

Mats of yttria-stabilized tetragonal zirconia nanofibers were prepared in the present research. The effect of the mats calcination temperatures (600, 900, and 1200 °C) on their structure and morphology was investigated. It was found that phase composition of the fibers did not change in all range of the calcination temperatures, while the average grain size in fiber increased from 8 to 39 nm. Multi-cyclic and single loading-unloading nanoindentation testing of the ceramic mats showed that the hysteresis loop energy in samples decreases with higher calcination temperature. Hardness and the elastic modulus measured by spherical (with 10 and 250 μm radii) and sharp Berkovich indenters were the highest in the mats calcined at 900 °C. This calcination temperature can be considered an optimal one for preparation of nanofibers mats which mechanical properties are of importance for their application.
Vis-NIR photodetector with microsecond response enabled by 2D bismuth/Si(111) heterojunction
Authors: Dang Zhaoying , Wang Wenhui , Chen Jiayi , Walker Emily S , Bank Seth R , Akinwande Deji , Ni Zhenhua , Tao Li
Published: February 2021

Atomic sheets of bismuth (Bi) have been expected to yield exotic optoelectronic properties, holding great promise for photodetector devices. However, existing Bi thin film photodetectors have limited performance in terms of photoresponsivity or response time, hindering its practical application. Herein, we report an experimental research progress on optoelectronic properties of epitaxial 2D Bi grown on Si(111) substrate. Our 2D Bi/Si(111) heterolayer exhibits inspiring photodetection performance, including a Vis-NIR broadband response with a responsivity up to 80 A W−1 and response time ∼3 μs, which is attributed to promoted generation and transportation of charge carriers in the heterojunction. 2D Bi/Si(111) here also demonstrates stable and reproducible photo switching behavior. This work paves an avenue to develop photodetectors based on heterointerface between group VA Xene and Si(111) with rapid switching behavior and adequate photoresponsivity.