bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sentiment Analysis, Tweet Analysis and Visualization on Big Data Using Apache Spark and Hadoop
Authors: Shetty Sujala D 
Published: March 2021

Abstract
Big Data lets companies generate new growth opportunities and whole new types of industries that can continually change and mushroom data from the market that can merge and evaluate. In today’s world, Apache Spark and Hadoop are the most popular and effective tools used to conduct big data analysis. This paper aims to perform two types of analysis-Sentiment Analysis of tweets and Tweet Analysis. For conducting sentiment analysis, we used a dataset created by Stanford University which contains 4 fields – ID of the tweet, Sentiment of the tweet, Sentiment Source and the Tweet itself. Two machine learning algorithms performance are compared these are Logistic Regression and Random Forest Classifier and observe which algorithm provides better accuracy and why. A data visualization tool -Tableau is used to view the sentiments of tweets by filtering the text of tweets by words and hashtags. For tweet analysis, we use Python and a library called Tweepy to download tweets from the Twitter API and perform analysis using another data visualization tool called Power BI. We conduct analysis on the basis of three parameters – user followers count, retweet count and favourite count. Finally, we draw a comparison on the performance and user-friendly nature of both data visualization tools – Power BI and Tableau.
Applications of Flying Ad-hoc Network During COVID-19 Pandemic
Authors: Devi Manisha , Maakar Sunil Kumar , Sinwar Deepak , Jangid Mahesh , Sangwan Poonam 
Published: March 2021

Abstract
The 2019 novel coronavirus (2019-nCoV), with a beginning stage in Wuhan (China), has spread quickly among people living in different nations and has affected more than 6 million lives. Researchers around the globe are trying to device a solution in the form of the vaccine but unfortunately, till date, there is no full-proof vaccine for 2019-nCoV. Most of the countries are adopting prolonged lockdown and social distancing strategies to counter this pandemic. Flying Ad-hoc Network (FANET) on the other hand, can provide several services such as the delivery of essential items, disinfecting common areas, surveillance, traffic monitoring, communication, temperature monitoring, etc. Availing such services using FANETs during Corona Virus Disease (COVID-19) outbreak is like a boon that can minimize the general problems of mankind to some extent during lockdowns. Nowadays, it is foremost required to deploy solutions that can automate the process of availing several services using FANETs and the same is attracting researchers in great demand. This paper has presented a brief survey of several applications of FANETs (especially UAVs and Drones) that can facilitate mankind in coping with the general problems during COVDI-19 outbreak and subsequent lockdowns. We hope that this survey has covered most of the applications of UAVs and can provide new insights into the research community.
Computing and Analysis of Multi Server Queueing Model Using Pentagonal Fuzzy Numbers
Authors: Jain Mridula , Jain Anamika 
Published: March 2021

AbstractIn the present study, analysed the multiple server queue miniature using Pentagonal Fuzzy number. To cope with a real-world scenario, the input parameters are converted from crisp to fuzzy type by taking into account. The system parameters, rate of arrival and rate of service are taken fuzzy. The system characteristics like expected number of clients waiting in queue and expected number of clients waiting in system are calculated using DSW algorithm and using alpha-cut at various levels, in interval [0,1]. Using pentagonal number arithmetic, the performance measures are calculated directly as PFNs. Different numerical examples with arrival rate and service rate as PFN are demonstrated to show the authenticity implementation of the performance measures. Finally effect of different parameters are shown for validation purpose.
An Analytical Approach to Predict Employability Status of Students
Authors: Saini Bhavna , Mahajan Ginika , Sharma Harish , Ziniya  
Published: March 2021

Abstract
One of the major concerns of the students after graduation is the job opportunities offered to them. Not only students, but also the universities are inclined towards maximizing the job offers for their students through campus recruitment drives. Against this background, the scope of this study is to gauge the performance of top four known classification techniques of data mining, which are, Decision tree, Random forest, Naive Bayes & KNN. These machine learning algorithms are applied on students’ data, collected from the university database of Manipal University Jaipur and student models are created which will predict the employability status of students in future and discover factors which will significantly contribute to their employability. After applying and studying the ac- curacies of these algorithms, we have found that Random forest behaves better than the rest of the algorithms with 89% accuracy.
Performance Study of Snort and Suricata for Intrusion Detection System
Authors: Sharma Neha V , Kavita  , Aggarwal Gaurav , Sharma Saurabh 
Published: March 2021

Abstract
As of late, move to multitasking processors and thus applications using multithreaded structure has increased in an abrupt manner. There is a constant thought of using Network Intrusion Detection and Anticipation Systems (NIDPS) for multithreading. Suricata is an open source NIDPS that works on multithreading and is created by means of the (OISF) Open Information Security Forum. The paper depicts an analysis, including a progression of inventive tests to set up regardless of whether Suricata shows an expansion in precision and framework execution over the true norm, single strung Snort. Conclusions demonstrate that Snort has a lesser framework overhead than Suricata and this deciphers to less bogus rejections using a solitary center, focused condition. Be that as it may, Suricata is demonstrated to have more precision in conditions where many centers are accessible. Suricata is demonstrated to be adaptable through expanded execution when running on four centers; be that as it may, in any event, when working on four centers its capacity to process a 2Mb PCAP record is still not as much as Snort. With respect to this, no advantage is there to using multi-centers when working with a solitary condition of Snort.
A Review on Energy-Efficient Mechanisms for Cluster-Head Selection in WSNs for IoT Application
Authors: Goyal Ankur , Mudgal Shivam , Kumar Sandeep 
Published: March 2021

AbstractInternet of things (IoT) incorporates sensing, connectivity, networking, or cloud computing technology into a wide variety of control fields. The wireless sensor networks (WSNs) are better placed for IoT applications. Developing an energy-efficient (EE) cluster head (CH) system is of great importance to enhance the network life of WSNs. WSNs are now becoming an emerging research field through technological growth. Sensors are integrated to gather the data from which this is known in a collective of clusters. CH maintains a process of transmission that requires high energy. This review paper discusses various techniques, advantages, disadvantages, objectives, and different methods for EE clustering techniques in WSN on IoT-related research. This study is beneficial to identify the problems associated with EE-CHS in WSN and how to resolve it.
A Framework for Multi-Sensor Data fusion in the Context of IoT Smart City Parking Data
Authors: Saxena Monika , Vyas Vaibhav , Jha C.K. 
Published: March 2021

Abstract
Now a day, things are communicating using the Internet, In the context of a smart city all things are interconnected and they are transferring the data to each other. Smart city subapplications like parking, pollution, traffic, transportation are some examples. By using the internet, we can combine with communication, the big challenge in a multi-sensory environment to fetch meaning full data from sensors. There are many of the methods are used to analyse and mine IoT data like classification, clustering, association rule, and regression, etc. The data is collected on the Internet of Things (IoT) from heterogeneous sources in different formats. Data Fusion is a method to operate on fused data that are collected from different sources. In the case of IoT, the concept of distributed data fusion work well. One of the popular IoT applications is Smart City, where we can collect data and analysis them according to need. In this research paper, the researcher introduced the new framework of data fusion where the data is collected from a different type of smart city sub-application. and framework deployment is done by parking data set of smart cities.
Machine Learning Based Risk Assessment and Early Diagnostic Model of Inflammatory Bowel Disease Associated Arthropathy
Authors: Verma A , Rathi B , Sharda S 
Published: March 2021

Abstract
Inflammatory bowel disease (IBD) is a persistent idiopathic disorder responsible for intestinal inflammatory conditions. IBD exhibits several extraintestinal manifestations, the most common being arthropathy, that are important triggers and risk factors for adverse progression of the disease. Due to a lack of definite diagnostic criteria and treatment regime, a need arises for early diagnosis and management of IBD associated arthropathy to reduce its morbidity. The present study utilizes a machine learning approach for development and validation of a risk prediction and early diagnostic disease model for IBD associated arthropathy. A publically available IBD cases vs control dataset from University of Massachusetts Medical School’s institutional repository was taken and the data was filtered into 54 IBD individuals along with their demographic and clinical characteristics. The arthropathy characteristics were incorporated from literature in the IBD dataset. Data was randomly split into training (50%; n=27) and testing (50%; n=27) for development and validation of model using logistic regression. Out of 54 IBD cases, 21 had a higher risk of developing arthropathy. The area under the receiver operator curve for the validated model was 0.90 (95% CI 0.80–0.99; accuracy 96%). This disease model can aid in identification of high-risk individuals and for early diagnosis of arthropathy in IBD cases before reaching the imaging and invasive diagnostic stage. This model warrants for prospective case-control trials validation.
Analysis of Exertion with Relative Grading Mechanism in Academics Using Computational Technique
Authors: Dasari Nagaraju , Parihar Mahendra , Shaik Mohiddin Shaw 
Published: March 2021

Abstract
Evaluation process is an integral part of academic system in order to monitor the performance of students from many perspectives. Since, recent past lots of methodological changes has been taken places in evaluation process from traditional one. Many experts from concerned domain claims the superiority of one method over other but still dilemma continue with reference to optimum or appropriate method. In this connection even very recently being used to a maximum extent in Indian context too is the “Grade Method”. However, even “Grade Method” is also not completely free from certain controversies. That is, though grades were initially meant to serve various pedagogical purposes, grading systems remain controversial and hotly debated when those doesn’t give recompensing results. Some argued that grades are psychologically harmful. Thus, given certain limitations of existing methods in some respect, the current study has been undertaken in order to address few issues associated with minimum passing grade and cut-off mark for highest grade - resulting out of relative grading, Therefore, in this article, through mathematical computation we propose acceptable ranges for the parameters mean and standard deviation-respectively to have appropriate mechanism and hence can make evaluation process more effective and applicable in various conditions.
Multi Algorithmic Approach to Galactic Swarm Optimization (MAGSO)
Authors: Kathirvelu Vasanthakumar , Muthiah-Nakarajan Venkataraman 
Published: March 2021

Abstract
Multi population algorithms have noted advantages over single population algorithms when optimizing over multimodal function space. The existence of multiple attractors allows exploration on multiple optimality regions which in turn provide more exploration compared with single population algorithms. This paper is a novel utilization of Galactic Swarm Optimization (GSO) framework in which the subpopulations are evolved with multiple variants of Particle Swarm Optimizers (PSOs) rather than single PSO. The proposed algorithm is called Multi Algorithmic Galactic Swarm Optimization (MAGSO). MAGSO utilizing multivariant PSOs in subswarm level combined with the unique superswarm tailored for exploitation has shown significant improvement of performance over GSO. Experiments done on Congress on Evolutionary Computation 2013 (CEC 2013) and other standard general benchmark suite indicate that the proposed MAGSO algorithm outperforms the previously proposed state-of-the-art swarm algorithms such as Bollinger bands approach on boosting ABC (ABCBB) and Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNLPSO) besides GSO. Detailed statistical evidence over 51 independent trials on the benchmark functions indicate the best performance of MAGSO. The main research goal is to propose a superior performing algorithm by exploiting the GSO framework. In order to show its efficacy, the algorithm is compared with state-of-the-art swarm algorithms such as ABCBB, DNLPSO and unmodified GSO. The method proposed here is quite general and demonstrates that more ability and extension work possible from the basic GSO framework.
Methodological Approaches to Data Pre-Processing Formalization for Statistical Analysis
Authors: Pisareva D A , Pakhomova E A , Rozhkova O V 
Published: March 2021

Abstract
The topic of data processing is not accidental for us. Initially, we were engaged in the development of mathematical tools for assessing the impact of the University of Science city on the effectiveness of Regional development (USR). The theme turned out to be similar to the Triple Helix (TH) model of H. Etzkowitz. The main difference of the TH from the USR: the TH gives deep processes interpretations, the USR – the economic-mathematical modeling tools. In our opinion both models supplement each other: the USR interpretation results become brighter by means of the TH formulations. Then we began to develop a toolkit for the TH model with its adaptation to the conditions of Russia. Our articles are available on the Official Website of the Triple Helix Association Russian Chapter. It was important to adapt the apparatus of continuous functions to discrete functions, since official statistics are discrete in nature. Working with statistics revealed methodological problems. The paper offers methodological approaches to formalizing data pre-processing (the method of time series linking) in order to unify them and to further process them by modeling and forecasting. It seems that this formalization eliminates the appropriate methodological flaw in the literature.
Social Welfare Maximization in Smart Grid: Review
Authors: Ramnath Gaikwad Sachin , Harikrishnan R. 
Published: March 2021

Abstract
The modernized power grid system intelligently integrates various advanced Information and Communication Technologies (ICT’s) with various renewable energy sources. Moreover, recently huge developments are happening in renewable sources, with enhancement in technologies eras, the role of consumers is shifting towards producers so-called “prosumers”. Furthermore, main challenges are enlarged energy demands, uncertainty in weather conditions and varied occupant’s behavioural interventions in energy consumption. So, residential user-end is having potential for energy optimization by energy efficiency, conservation, and active participation. Besides, the dynamic fair energy pricing with respect to generation cost and load demands is motivational parameters to users. On the same, this paper focuses on analysing a novel, attractive business model that can actively participate prosumers through dynamic pricing and incentive based demand response programs for the maximization of social welfare. Further this review paper focusses on various optimization models to achieve social welfare maximization. And also bibliometric analysis has been done through the Scopus database.
A Comparative Study of Laplace Transform and Sumudu Transform on

H¯

-Function
Authors: Bhargava Alok , Jain Ravi Kumar , Agarwal Garima 
Published: March 2021

Abstract
In this work, we have done the comparative study of Laplace Transform and Sumudu Transform on

H
¯

-function and derived certain new results. Some corollaries are also mentioned to illustrate the main results.
Machine Learning and Prophecy of Behavior: A Breakthrough in Artificial Intelligence
Authors: Saini G , Seema  , Mor K 
Published: March 2021

Abstract
This paper addresses how the cognitive theory and machine learning methods are combined for predicting human behavior. Prediction of human behavior is a complex task. Researchers addressing this task proposes behavior models using statistical methods and rationality theory such as machine learning algorithms. This paper addresses how the potential cognitive theory and machine learning methods are combined in using by providing and predicting human behavior. We discussed two theory focal points theory and aspiration adaptation theory can be used in combination with machine learning algorithm to simplify the task and understanding the behavior. These two approaches are combined and compared in two domains. In simple domain, both machine learning models and psychological models give clear predictions and results. While in complex domains, the results states that psychological models’ results are not cleared and in learning models are also inaccurate. To overcome this problem, the hybrid model is introduced which helps in understanding of how people behave and at the same time how they wouldn’t behave reducing the space helping machine learning algorithms in searching the accurate behavior models. Hybrid model with cognitive models and machine learning have contributed in models which predicted behavior accurately as compared to machine learning models/cognitive models alone. The future researches must work in these directions using hybrid models for successful prediction of behavior.
On the treatment of zero returns in the estimation of log-GARCH model : Empirical study
Authors: Settar Abdeljalil , Badaoui Mohammed 
Published: March 2021

AbstractThis paper proposes a QML estimation of log-GARCH model in the presence of zero returns. Our approach consists in treating zeros within the data as missing values which are objects of imputation based on the EM algorithm and information filter. Empirical applications to the Crude Oil WTI, CAC40 and the FTSE100 return series are presented and show that through the proposed imputation, the log-GARCH model fits better the data compared to the traditional approach whereby the estimation is performed after removing the zeros from the data.
Data Stream Clustering for Big Data Sets: A comparative Analysis
Authors: Dubey Ankit Kumar , Gupta Rajendra , Mishra Satanand 
Published: March 2021

Abstract
The world is growing rapidly with constantly increasing the data. There are innumerable people around the world who use different types of applications, whether it is for reservation, marketing, shopping or knowledge in the form of text, image, audio and video. Only data is being generated everywhere and this growing data which is large and high dimensional is in nature is generally known as “big data”. For the organizations, it is a big task to cluster streaming big data successfully. In this paper, we are presenting a survey of data stream clustering algorithms applied over big data and big datasets. The paper shows the comparative analysis of all the studied methods and also review the evolution and progression of data stream clustering algorithm for big datasets. The paper also analyses the proposed and implemented algorithms in recent years.
Role of Machine Learning in Sustainable Engineering: A Review
Authors: Bhatnagar Vaibhav , Sharma Shefali , Bhatnagar Anurag , Kumar Lov 
Published: March 2021

Abstract
Sustainable engineering is the method of modelling or running systems that allow efficient use of products and resources, that is, at a scale which would not endanger the natural habitat or the potential of subsequent generations to reach their own needs. Machine learning has large number of applications such as healthcare, agriculture, security and even in day to day life. In sustainable computing, machine learning also plays a crucial role. The emergence of Machine learning and even its exponentially greater implications on many markets require an evaluation of its implications on sustainable innovation achievement. In this paper, a review of supervised and unsupervised machine algorithms is done which are used in sustainable engineering. Different engineering disciplines such as mechanical, civil, chemical engineering are covered that working on sustainability. As the interpretation of the review it can be stated that there is a wide scope of working on sustainable development though machine learning. Specific machine learning algorithms are required to work on sustainable engineering. This review is helpful for engineers which are working in the field of sustainable development.
Mathematical Analysis of Radiating Viscoelastic Unsteady MHD Fluid Flow through an Absorbent Media between Upstanding Equidistant plates with Joule Heating Impact
Authors: Mehta T , Mehta R , Kumar M 
Published: March 2021

Abstract
Focus of the present study is to explore Joule heating effect on incompressible, radiating, electrically conducting viscoelastic unsteady magnetohydrodynamic fluid and heat transfer through an absorbent media between upstanding equidistant plates with constant suction and slip condition. Lorentz force and Joule heating is discussed by momentum and energy equation respectively. Electric conductivity and the electromagnetic force both are in very small case in fluid flow. A consistent magnetic field is taking to perpendicular to the plane of the plates. The oscillatory time-dependent coupled equations (non-linear) are simplified to provide result for the fluid velocity and heat transfer by using structured perturbation execution. It has been noticed that velocity and heat transfer rise due to rise in Grashof number, Eckert number, heat source parameter or slip parameter while decrease as permeability parameter, radiation parameter or wavelength rises. As Hartmann number rises the fluid velocity diminishes, while opposite behaviour is noted in case of heat transfer effect. The velocity and heat transfer impact of the fluid rise with cross flow Reynolds parameter or frequency of the oscillation. Numerical outcomes for the motion and heat transpose effect profiles for multiples physical numbers also the local skin friction coefficient and spot Nusselt number are argued numerically, presented graphically along with.