bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Undergraduate Mathematic Students’ Understanding on Microscopic Representation of General Chemistry Lecture based on their Scientific Reasoning Ability
Authors: Asih F E , Mahdiannur M A , Aulia E V 
Published: February 2021

Abstract
Undergraduate mathematic students (UMS)’ scientific reasoning can affect their ability to do abstraction of microscopic phenomenon. Based on Piaget’s theory of cognitive development of UMS are in operational formal category, but this research was found that only 2 students of UMS whose have scientific reasoning as formal category. The aim of this research was analysing UMS’ achievement on understanding microscopic representation based on their scientific reasoning (concrete, transition, or formal category). Mixed method was used in this research which integrated quantitative data (test score) and qualitative data (analysing of understanding and scientific reasoning ability). The research funding that mean score test of UMS as formal reasoned (F) were the highest. UMS as transition reasoner and concrete reasoner were difficult to solve stoichiometry calculation which contains microscopic representation. Question about limiting reactant based on molecule representation, that 10,7% of UMS as concrete reasoner (C) answered there was no limiting reactant. The characteristic of concrete reasoner (C) is understanding based on observable object. They difficult to transform it as microscopic representation (number of molecules as reactant and product) which was written on net reaction as symbolic representation. They did not consider stoichiometric chemical equation by using representation of X as a variable in mathematical calculations. Another response that 14,3% of UMS as transition reasoner (T) determined limiting reactant by using logical mathematic equation of (mole=mass/molar mass) without considered coefficient in net reaction. In general chemistry learning, requires innovation to visualize microscopic aspects, so that UMS at concrete reasoning and transitions reasoning have the ability to interpret microscopic phenomena which is not observed by naked eyes.
Diabetes Mellitus Prediction System Using Hybrid KPCA-GA-SVM Feature Selection Techniques
Authors: Dinesh M G , Prabha D 
Published: February 2021

AbstractDiabetes mellitus is a serious health issue in healthcare industry, which is a type of uncontrolled level of sugar. It is a chronic disease happened to the person who are having low insulin production and increase level of blood glucose because glucose is not properly utilized by body. In the medical field, predicting the correct diabetes is an important area that is under research to define a good predictive system to help the doctors to diagnose the disease. In the predictive system, feature selection plays on vital role to select the relevant feature for classification. There are several algorithms were applied on classification of diabetes data. In this proposed work, the features are transformed into high dimensional space before selection. So that the transformation of the features will give the better selection of attributes. With this effort, the proposed work implements the Kernel Principal Component Analysis for dimensionality reduction. KPCA will reduce the features space better than PCA. Once the features are transformed, the proposed work uses Genetic Algorithm to select the relevant and optimal features from the dataset. Then at the last Support Vector Machine is used as a classifier to classify the diabetes mellitus data. The proposed research on applying feature reduction before feature selection will reduce the irrelevant features that will improve the accuracy of the classification based on the selected relevant features. This proposed algorithm on diabetes mellitus data will compare with the existing algorithms to prove the effectives of the algorithm.
SEIR Model for COVID-19 Epidemic Using Delay Differential Equation
Authors: Devipriya R. , Dhamodharavadhani S. , Selvi. S 
Published: February 2021

Abstract
SEIR compartmental model for COVID-19 pandemic (SEIR_COVID) with exposed group is proposed and analysed in this paper. The steady state of this model is determined to control the pandemic. Zero Disease equilibrium and pandemic equilibrium are presented in SEIR_COVID model. In each case, the equilibrium prints are locally asymptotically stable under certain conditions. The Routh-Hurwitz criteria are used to study the stability of the equilibrium for COVID-19. This study showed that proposed SEIR_COVID model using Routh-Hurwitz criteria will be more effective for the COVID-19 pandemic prediction.
Data Visualization in Splunk and Tableau: A Case Study Demonstration
Authors: Balaji N , Karthik Pai B H , Bhat Bhaskar , Praveen Barmavatu 
Published: February 2021

Abstract
This article mainly deals with data analytics and visualization techniques. Data visualization techniques helps user to understand the data in a faster and easier way. Any data which is in the form of text is difficult to analyse in a short period of time, so using some colours or visualization techniques like bar chart, scattered plots will give a better understanding of any data in a glance. Using this technique we can analyse the data in a faster way and also represent a data in a batter way. Apart from data visualization, this internship focused on handling machine generated log data. Most of the time machines will generate some kind of log data in response to our actions, which is very difficult to understand. A study says that in the present world, machines will generate large amount of data, which are difficult to understand and analysing this data can be used for the betterment of the business or to invent new things. So to understand this machine generated data there is requirement of some experience with the process and also using of the tools, we can make the machine generated data divide into events and represent it in an easy and understandable manner.
Deep Learning Techniques in Tomato Plant – A Review
Authors: Mohana Saranya S , Rajalaxmi R R , Prabavathi R , Suganya T , Mohanapriya S , Tamilselvi T 
Published: February 2021

Abstract
Deep learning establishes an ongoing, modern technique for image processing with large potential and promising results. After proving its efficiency in various applications DL has also entered into the domain of agriculture. Here, we surveyed 38 research works that applied deep learning techniques to various research problems in tomato plant. We examine the areas of tomato plant research where deep learning is applied, data preprocessing techniques applied, transfer learning and augmentation techniques used. Studied dataset information like data sources used, number of images, classes and train test validation ratio applied. In addition, we study comparisons done on various deep learning architectures and discussed the outcome. The finding showed that DL techniques outperformed all other image processing techniques but DL performs mainly depends on the dataset used.
Cloud and Big Data: A Mutual Benefit for Organization Development
Authors: Abiodun Moses Kazeem , Awotunde Joseph Bamidele , Ogundokun Roseline Oluwaseun , Misra Sanjay , Adeniyi Emmanuel Abidemi , Arowolo Micheal Olaoluwa , Jaglan Vivek 
Published: February 2021

Abstract
The emergence of big data as a novel model gives unparalleled composition and value for an organization. The growth of big data is posing challenges in areas such as processing, analytics, sharing, and visualization. Interestingly, the cloud computing technology also emanated to specify important required infrastructure to tackle these disputes. Cloud computing is an effective interface that’s used to manipulate enormous-scale and complicated calculations. It eliminated the requirement to sustain lavish computing hardware, dedicated infrastructure, and software. Big data and cloud computing (BDCC) are considered to become the source of productivity that drives today’s electronic business transformations. Nonetheless, the ratio of the companies that adopt and use BDCC isn’t significant. There are also obstacles to facilitating BDCC’s market proliferation. Hence, this paper aims to recognize and emphasize the factors that affect the adoption and use of BDCC in technology-organization-environment (TOE) viewpoints in enterprises. Data were obtained from a sample of 100 companies to scientifically check our system. The understanding of BDCC’s potential advantages and technical capabilities are defined as the BDCC’s important elements. Consistency with current infrastructure, data reliability and functionality, and protection and privacy are highly rated within the sense of technologies. Managerial expertise and financial expenditure capacity for the application and use of BDCC and governmental assistance and regulation are defined as organizational and environmental considerations for acceptance and use, simultaneously.
Kannada word sense disambiguation using semantic relations
Authors: Manjunatha Kumar B.H. , Siddappa M 
Published: February 2021

Abstract
In this paper, a method is proposed to use the semantic relations of words to solve Word Sense Disambiguation (WSD) for the Kannada language. Semantic relationships like hypernymy and hyponymy are used in finding the overlaps between two concepts. Integrating the idea proposed in this paper with original Lesk’s technique improves the accuracy of the Lesk’s algorithm. Indo-WordNet is used as the lexical resource to gain the knowledge about the semantic relations of words and this knowledge is exploited to disambiguate the target word of the Kannada language. Since the approach used in this paper is unsupervised, so it can be scalable to other languages also.
Review on Crop Prediction Using Deep Learning Techniques
Authors: Dharani M K , Thamilselvan R , Natesan P , Kalaivaani PCD , Santhoshkumar S 
Published: February 2021

Abstract
Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.
Comparative analysis between theoretical and simulatory learning methods by data science methodology approach
Authors: Sharadkumar Patani Rucha , Shankar Narayan S 
Published: February 2021

Abstract
Data Science is the most trending interdisciplinary science that integrates the steps of data collection, preprocessing data, transforming data, storing data, data visualization, and hence extracting the insights from the data to serve stakeholder’s purposes. Python is commonly used and, along with being a versatile and open-source language, is a favorite tool in Data science studies. The vast libraries are being used to manipulate data and are very simple for even a beginner data scientist to understand. In the present work, we intend to apply the data science methodology to decision making and predictive analysis using the python programming language. We consider the problem of selecting the better mode of study concerning some of the impractical phenomena from physics for the exact understanding of the process. Data collection has been from an educational institute and the comparison has been made between theoretical learning and simulatory learning for selected topics from the vast fields like mechanics, thermodynamics, fluid dynamics, and radioactivity. The steps of data science methodology are germinated to achieve the insights into the data procured and the results are wangled concerning the teaching methodology that could be employed. In the present work, we undertake a comparative study between the theoretical and simulatory modes of teaching by exploring the modes individually through evaluating the responses imparted by a class of high school students. The analysis reported the more inclination of the student’s responses towards the simulatory methods when compared to the theoretical method of learning.
Variant component principal linear reduction for prediction of hypothyroid disease using machine learning
Authors: Shyamala Devi M. , Swathi P. , Jahangir Ayesha , Kumar A. Dhanunjai Sai , Naidu A. Naga Bhushan , Manas M. Raghuram 
Published: February 2021

Abstract
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.
Transfer Learning approach for grading of Diabetic Retinopathy
Authors: Aswathi T , Swapna T R , Padmavathi S 
Published: February 2021

Abstract
There has been a wide interest in applying Deep Learning (DL) algorithms for automated binary and multi class classification of colour fundus images affected with Diabetic Retinopathy (DR). These algorithms have shown high sensitivity and specificity for detecting DR in non-clinical setup. Transfer learning has been successfully tested in many medical imaging applications like skin cancer detection, pulmonary nodule detection, Alzheimer’s disease etc. This paper experiments with the different DL architectures such as VGG19, InceptionV3, ResNet50, MobileNet and NASNet for automated DR classification (binary and multi class) on Messidor dataset. The dataset is publicly available, and comprises of 1200 retinal fundus images. The images belong to four different classes of DR namely, normal (class 0), mild (class 1), moderate (class 2) and severe (class 3), graded based on the severity level of DR. In our experiment, we have enhanced the quality of input images by applying algorithms like CLAHE (Contrast Limited Adaptive Histogram Equalisation) algorithm and Powerlaw transformation as pre-processing techniques, which work on the small image patches with high accuracy, contrast limiting and image sharpening. Hyper parameter tuning on pretrained InceptionV3 architecture, resulted in enhancing the accuracy of the model. Both binary and multi class results were analysed considering inter class (one class with another class) accuracies. We achieved an accuracy of 78% between class 0 and class 1, the accuracy between class 0 and class 2 further reduced to 69%, while class 1 and class 2 showed an accuracy of 61%. Moreover, the interclass class accuracy between class 1 and class 3 was 62%, class 2 and class 3 further reduced to 49%. The accuracy further diminished between class 0 and class 3 to 32%. These experiments suggest that the pretrained models provided better results in classifying normal and mild, but they were not that much efficient in classifying moderate-severe and normal-severe binary classifications.
Comparative analysis of glioma tumor in brain using machine learning and deep learning techniques
Authors: Sangeetha J. , Vaishnavi D. , Premalatha J. 
Published: February 2021

Abstract
Brain tumour is one of the major causes of the increasing mortality rate. There is an emerging need for precise diagnosis from the pathology, as human prediction is prone to errors. Brain pathology is captured using Magnetic Resonance Imaging (MRI) which gives high quality images of the blood vessels. These MRI images can be used to extract features and predict the tumor. The extracted features can be classified using Machine leaning (ML) algorithms. In this advanced and emerging deep learning era, images can be fed directly to the prediction system and the system itself extracts features at higher level of abstraction and classifies the images. This article presents a comparative analysis for classifying the MRI images of Glioma (a type of brain tumor) and healthy brain using convolutional neural networks (CNN) and ML algorithms like support vector machines (SVM), random forests, and bagging. CNN and the ML algorithms were implemented on BRATS 2013 challenge dataset and it is found that CNN has achieved highest accuracy of 95%.
Optical Chaos KEY generator for Cryptosystems
Authors: Baby H T , Sujatha B R 
Published: February 2021

AbstractChaotic structures are ideally appropriate for the construction of cryptographic schemes, because random numbers are an inseparable part of every encryption scheme as a safety mechanism. The optical chaotic circuit is an ultra-fast physical Random number generator, which is illustrated using the CW laser that generates optical signal at 193.1 THz and M-Z modulator that works on an interferometric principle. The circuit generates truly high random bit sequences at different higher bit rates of 320Gb/s and 640 Gb/s. These generated random bits have passed the NIST statistical tests and Diehard battery tests, indicating their randomness for different practical applications. Further, the random number generated by optical chaos circuit is used to secure the image data using RC4 ciphering method.
A responsiveness model for privacy conservation against antagonist and fake user in cyber- physical and online social networks
Authors: Vanitha A , Balakrishnan N 
Published: February 2021

Abstract
The enormous numbers of users are interacted with Social networking sites throughout the world. They are consistently engaging with Social Medias are Twitter, Instagram and Facebook have an incredible impression and rarely objectionable consequences for daily life. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). This paper proposes to perform a review of techniques used for detecting spammers on social media like Twitter, Facebook, Instagram, etc. Furthermore, a taxonomy of alerting spam message methodologies is presented that classifies the techniques based on their ability to detect: fake content, spam message or junk mail depends on either url or trendy news, and identifying unauthenticated users. The proposed techniques are also compared based on historical features that comprise the characteristics of user profile, wish-list, content usage, methodology used, and physical context, logging activities. This research paper contributes the study on identifying appropriate users against fake users and spam or junk mail detection using Adversary-Privacy evaluation model that helpful to preserving private data in Cyber-Physical Social Systems.
Resolving ownership rights of video data using visible and invisible watermarking in DWT-SVD domain
Authors: Ponni alias sathya S , Ramakrishnan S , Sumathi T , Sasidharan S 
Published: February 2021

Abstract
This paper addresses the concern of video piracy and also resolves ownership rights. Piracy entails utilizing, modifying and sharing the data without owner concern. Identifying the owner of the piracy and also the legal owner is difficult. For resolving these problems, this paper employs the prominent technique like watermarking with legal data base creation and SVD in wavelet domain. In addition to that, master key frame selection concept is used for identifying the key frames. Here the problem is resolved by two approaches. That is visible and invisible watermark to the authorized data. The watermark may be legal information about the owner like photo, thumb impression, signature of the owner and legal id. The legal id is the visible watermark and photo, thumb impression or signature of the owner is inserted as an invisible watermark. This methodology resists several video and image processing attacks. The experimental results show that the algorithm produces good perceptual quality and robustness to the watermarked video as well as watermark.
Design of Compact S Box for Resource Constrained Applications
Authors: Sherine Jenny R. , Sudhakar R. , Karthikpriya M. 
Published: February 2021

Abstract
The era of IoT has brought huge impact in the lives of people. It changed the way the people interact with the outside world and led to the development of many smart devices. The important factor to be considered in smart devices is security as the sensor nodes are prone to vulnerable attacks. To incorporate security in IoT lightweight block ciphers are preferred. Lightweight block ciphers can be tuned according to applications. This can be motivated by optimizing the S-box since S-box has significant impact on area. Reducing the area produces an impact on power, delay and memory. Here area optimized S-box of lightweight ciphers that serves for IoT devices is proposed. The S-box of PRESENT, GIFT and LED are optimized using karnaugh mapping. Further factorization is carried out to utilize the redundant terms which in turn reduces the gate count. The proposed method is found to be optimum when compared with previous works and it also consumes less area and power.