bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Temperature and Relative Humidity towards the Dispersion of CoVid-19 in Indonesia
Authors: Hariyono E , Rahmadhani E A , Kusumawardhani K D 
Published: February 2021

Abstract
The research purpose is to analyze how the temperature and humidity affect the development of CoVid-19 in Indonesia. The data used is secondary data from BMKG and official CoVid-19 data based taken from May 2-8, 2020. The data analysis techniques use the violin plots and the jitter plots with the assistance of software R. Based on the research can conclude that the temperature has a positive correlation of 0.3 and that humidity has a positive correlation of 0.2. That means the temperature and humidity are not the main factors in the development of CoVid-19 in Indonesia. High temperatures and humidity do not cause the virus to grow slower.
The Effectiveness of Blended Learning in Chemistry Creative Media Course
Authors: Yonata B , Novita D 
Published: February 2021

Abstract
This study has purpose to figure out the effectiveness blended learning activity in creative media course through blended learning for education chemistry students year 2017. The effectiveness declared from learning outcome and students’ response. The study was held in odd semester 2019-2020 for 15 meetings. After the instrument of blended learning (Students worksheets, visual and video/audio media) showed validity then learning activity was conducted from August to December 2019. There were combination of online and offline in teaching-learning activity for 15 meetings. Discussion, Assignments and its submission activities was using vi learn for 4 meetings, and the rest of meetings were used offline. As final project, students in team presented chemistry creative media in internal exhibition. The theme of exhibition is physical chemistry creative media. The media assessed by lecturers and students (other years or other study programs) who attended the exhibition. The data of learning outcome were obtained from final project score and assignments. While the data of students’ response were gained after the exhibition was held. The result of this study showed 100% students could achieve mastery learning outcome and student responded this course in very good criteria. It can be concluded that chemistry creative media course effective to be conducted by using blended learning activity.
The Understanding and Awareness of First-Year Undergraduate Physics Students in Astronomical Phenomena: A Response of Penumbral Lunar Eclipses on June 5th, 2020
Authors: Deta U A , Fadila D , Lestari N A , Yantidewi M , Nurlailiyah A , Prahani B K 
Published: February 2021

Abstract
A penumbral lunar eclipse phenomenon occurs when Earth’s outer shadow falls on the Moon’s face. This astronomical phenomenon is easy to miss because sunlight still illuminates most of the Moon. In this research, we want to describe the first-year undergraduate physics students’ understanding and awareness of Penumbral Lunar Eclipses, especially on June 5th, 2020. The study using qualitative research with a questionnaire and an interview. The subject of the course is 100 participants from first-year undergraduate physics students. Physics students’ answers are categorized into four levels, namely Dissatisfaction, Intelligible, Plausibility, and Fruitfulness. From the results, it can be concluded that physics students know with the Dissatisfaction category related to the phenomenon of the penumbra lunar eclipse. Otherwise, for knowledge about the strawberry full moon eclipse, students learn the intelligible and dissatisfaction types. It recommended that physics students increase their understanding of the occurrence of a lunar eclipse by understanding scientific processing skills to reinforce their opinion.
Constructive metacognition activities through students’ communication skills in solving PISA based on personality
Authors: Santoso H A , Istiqomah N R 
Published: February 2021

Abstract
This study aims to describe the constructive metacognition activity through students’ communication skills in solving PISA’s problems. The subjects in this study were 4 junior high school students. Three types of metacognitive activity functions used in this study are awareness, regulation, and evaluation. Data collection instruments used were adaptations of PISA problems with space and shape content. Data were analyzed through the communication skills of students verbally and in writing, after that they got three descriptions about the constructive metacognition activity patterns of each student. It was found that the constructive metacognition activity pattern of each student was very unique based on their personality. From this study, we suggest further studies to conduct a deeper assessment of the metacognitive components that arise in the communication skills of the subject.
Ultrasound assisted interesterification for biodiesel production from palm oil and methyl acetate: Optimization using RSM
Authors: Ansori Ansori , Mahfud Mahfud 
Published: February 2021

Abstract
High energy demand in the industrial world and pollution problems caused by the use of fossil fuels causes the need for the latest innovations that will replace the use of non-renewable energy. One of them is the replacement of diesel fuel with biodiesel. At present biodiesel production uses a transesterification reaction which produces a side product in the form of glycerol. However, the presence of glycerol is considered waste and has no economic value so a separation process is needed. So to eliminate the by-product separation process, a new route is used. This route is called interesterification, using methyl acetate instead of methanol which later produces triacetin as a by-product. Triacetin is recognized as an additive in biodiesel which functions as an anti- knocking in diesel engines. In this research, an interesterification study was carried out by ultrasound and a potassium methoxide catalyst was used to increase the reaction rate and the yield of biodiesel. The operating parameters used include the reactant molar ratio of 1:3; 1:6 and 1:9, catalyst concentration 0.5%; 1.0%; and 1.5%, and reaction times 5, 10, and 15 minutes. The parameter optimization is carried out by the central composite design (CCD) method to reduce the number of experiments needed and also to evaluate various variables and their interactions. The optimal operating conditions are the molar ratio of methyl acetate to palm oil of 8.95, catalyst concentration of 1.44%, and interesterification time of 10.03 minutes can produce the yield of biodiesel of 99.66%.
Alleviating NB conditional independence using Multi-stage variable selection(MSVS): Banking customer dataset application
Authors: Siva Subramanian R , Prabha D , Aswini J , Maheswari B , Anita M 
Published: February 2021

Abstract
Customer research is one of the important aspects of understanding customer behavior patterns with business enterprises and predicate how consumer satisfaction is achieved. Customer analysis brings out various underlying information about the customer patterns with enterprises and analysis decision helps to make better marketing strategies to improve the customer lifetime and also enhance the business profit. To perform effective customer analysis in this research Naive Bayes an ML algorithm is applied. The efficiency of NB comes from its conditional independence assumption and the violation of NB assumption results in poor prediction. But in most real-time customer datasets, the NB assumption is violated due to the presence of correlated, irrelevant, and noisy variables. To improve NB prediction with these customer customers, in this research Multi-Stage Variable Selection(MSVS) is proposed to select the relevant variables from the customer dataset which helps to predicate the customer patterns wisely. The proposed approach consists of two stages in selecting the relevant variable subset from the customer datasets. Further variable subset obtained from the proposed MSVS approach is experimented with using the NB algorithm and the results obtained are compared using the wrapper and filter approaches. From the results, it clearly shows the proposed MSVS approach performs better in selecting the variable subset and improves the NB prediction in customer analysis efficiency compare to wrapper and filter approaches. Further, the proposed approach works efficiently in time and less computational compare to wrapper and filter approaches.
Survey on Forecasting the vulnerability of Covid 19 in Tamil Nadu
Authors: Ananthi P. , Jabeen Begum S. , Latha Jothi V. , Kayalvili S. , Gokulraj S. 
Published: February 2021

Abstract
Predictive and analytic models for forecasting the vulnerability and recovery rate of patients who are affected by COVID 19 are made in this project for good analysis and better decision-making. In this project, linear regression (LR) a Machine Learning model is used to forecast the number of patients will get the infection in near future. By simulating SIRD model, the infection spread and recovery rate of the disease in a geographic region can be predicted. The vulnerability of the disease is checked by observing the transmission of disease over a period. In addition to this many info graphic models and graphs are created for easy understanding of data to get more insights about the disease. However, these prediction models enable us to make quick response of pandemic and to bring a conclusion to the disease. INDEX TERMS: Covid 19, data science, machine learning, prediction, analysis, pandemic, recovery and infection.
A Context-free Grammar based Association Rule Mining Technique for Network Dataset
Authors: Saranyadevi S , Murugeswari R , Bathrinath S 
Published: February 2021

Abstract
Among various data mining concepts like prediction, clustering, classification, association and outlier discovery, association is a useful technique to extract the interesting relations among data items effectively. Association technique is applied in a number of applications like marketing, education, chemical, bioinformatics, computational linguistics and etc. The important purpose of association is to provide useful information of buying preferences of customers in supermarket in order to increase the sales opportunity, which is called as market- basket analysis. Till now there are many algorithms were developed, but the usage of formal grammars in association rule mining (ARM) is a latest technique to mine required data by means of grammars. In this paper ARM is performed using Context –free Grammar (CFG) – (ARM – Grammar) and the experiments are conducted on MATLAB 2017 software using network dataset, KDDCUP’99. Experimental outcomes prove that the proposed ARM – Grammar is effective than the traditional ARM approach.
Analyzing ELearning platform reviews using Sentimental Evaluation with SVM Classifier
Authors: Muralidharan R , Kanagasabapathy T , Vijai Ganesh R P 
Published: February 2021

Abstract
In today’s scenario, the participation of eLearning courses is rapidly increasing among users, especially during post-pandemic situation. Due to a hike in the need of eLearning, there is a sharp increase in emerging of online courses. Not only the academic people (students and teachers), but others also interested in the online courses, irrespective of their ages. Since numerous eLearning platforms are mushrooming, it is mandatory to follow various standard techniques to evaluate the quality of these courses. It would be more adaptable, if the evaluation techniques are made based on the Course users feedback. These feedback of the users contain more valuable information which helps in constructive decision making. The satisfactions and criticisms of the courses are shared through opinion of the registered user. Due to enormous amount of web reviews available for a course, it is extremely time-consuming and difficult to manually analyze the review and come to a conclusive decision. Reviews contain course feature specific factual information along with the opinion statements which may be positive or negative. The text reviews obtained from most of the webpages of the online courses feedback or comments section, are found to be unstructured and inconclusive. Analyzing and extracting the actual opinion throughout the reviews manually is very difficult. So, an automated technique is needed to evaluate the course based on the features of the courses, which results in attaining a decision. The Aspect based opinion mining is such a methodology which describes the important aspects of each opinion and classify them on their polarity.
Covid-19 Pandemic Spread as Growth Factor using Forecasting and SIR Models
Authors: Vennela Gudimella Sai , Kumar Priyanka 
Published: February 2021

Abstract
The novel Coronavirus outbreak began in Wuhan which is located in China in December 2019 and after the outbreak immediately the virus has spread across the globe. Millions of people across the globe are infected and many more have died. World Health Organization (WHO) declared this virus as global emergency. In this work, main goal is to analyse the spread of virus in India that to particularly for Guntur District within India in order to settle the situation quickly. In order to analyse the spread of virus three types of forecasting methods have been compared and the SIR model is used to predict the number of Susceptible, Infected and Recovered persons for the Guntur district which is located in Andhra State.
Enhancing the performance of social spider optimization with neighbourhood attraction algorithm
Authors: Tamilarasi K , Gogulkumar M , Velusamy K 
Published: February 2021

Abstract
Data clustering is a well-known problem in order to identify the inherent structures and extracting the useful information. Recently, social spider optimization (SSO) algorithm is applied to solve a clustering problem. But, it may fall into premature convergence due to find improper nearest spiders in order to achieve the global solution. In this research paper, the neighbourhood attraction (NA) method is used to enhance the performance of the SSO clustering algorithm. In the experimental results, the proposed NA+SSO clustering method is producing better performance when compared with other conventional clustering algorithm.
IoT based detection of bore-well unclosed holes using automated drone operated cameras in a remote area
Authors: RenugaDevi S , Dharshini M , Gayathri K , Gopalakrishnan B 
Published: February 2021

Abstract
In the current era, most of the accidents occur due to the borewells that are left unclosed and many children got trapped in them. Those borewells remain as a hell to many children. These borewells have played endlessly in most of the innocent lives. This process is very difficult to rescue the children away from the borewells. To avoid this critical situation, we must prevent the children from falling in borewells. Here, we came up with an idea to take precaution in order to save many infant lives. The main objective of the system is to fix a Drone camera in a remote area to find the borewells that remain uncovered. After finding the borewells, we have to find whether it is a normal hole or a depth pothole. So to detect the size of a hole, we attached an ultrasonic sensor on the bottom of the drone for identifying the potholes and also to measure the height and depth of a bore well respectively. If the depth and height of the bore well is large, the information will be send to the respected officials to properly close the bore well accordingly. These details will be updated to the cloud for maintaining the information in timeline. Hence, we can save number of lives of children from falling in borewells.
Hybrid Genetic Algorithm and Simulated Annealing for Clustering Microarray Gene Expression data
Authors: Pandi M , Sivakumar T , Senthil Madasamy N , Sadhasivam N 
Published: February 2021

Abstract
Gene expression is the process by which information in gene is used to create proteins. The gene expression studies generate large amount of data. These data, referred to as the gene expression matrix, represent the expression levels for thousands of genes recorded at a few time instances. A typical microarray experiment involves the hybridization of an mRNA molecule to the DNA template from which it is originated. Many DNA samples are used to construct an array. The amount of mRNA bound to each site on the array indicates the expression level of the various genes. This number may run in thousands. All the data is collected and a profile is generated for gene expression in the cell. Clustering is a process of partitioning a set of meaningful subclasses called clusters. Clustering is a key step in the analysis of gene expression data. Genetic Algorithms are a family of computational models inspired by evolution. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster center in feature space such that a similarity metric of resulting clusters is optimized. The chromosome which are represented as strings of real numbers, encode the centers of fixed number of clusters. The experiment results are demonstrated on real data sets and the performance of GA is evaluated in comparison with the state-of-the art algorithm K-Means with use of internal validation criteria.
A Cluster-based Approach for Finding Domain wise Experts in Community Question Answering System
Authors: Menaha R , Jayanthi VE , Krishnaraj N , Praveen sundra kumar N 
Published: February 2021

Abstract
Community Question Answering (CQA) systems is an emerging web-based information service. CQA enables web users to get precise answers to questions from experts of the specific domain.CQA is used in wide areas such as biomedicine, information technology, tourism, etc. This paper focused on finding experts in community question answering system using unsupervised machine learning technique. Our Proposed system consists of three phases namely i) Clustering tags ii) Determining Experts for the unanswered questions iii) Finding experts for the given Question. By doing the tag analysis process, identified similar tags in a particular domain, and formed a cluster. By doing question analysis, we found the unanswered questions in each domain and identified the experts. For the given question, suggest the expert by doing pattern matching technique. The results section proves that our system predicts the experts for the given question with good accuracy.
Design of an expert system architecture: An overview
Authors: Janjanam Durgaprasad , Ganesh Bharathi , Manjunatha L 
Published: February 2021

Abstract
An area of Artificial intelligence (AI) that can claim a large measure of responsibility for the current heightened AI awareness is Knowledge Based Expert System (KBES), computer programs that embody human expertise. KBES specifically enable the computer to assist in solving ill-structured and non-deterministic problems such as design activities including synthesis, evaluation, modeling and decision-making process. The paper explains the role of Backward Chaining method, Forward Chaining method, Rule-Value method as three major methods involved in solving these problems to differentiate the Knowledge Based Expert System programming from normal programming. The development of an expert system requires a tool that will provide for better means of knowledge acquisition, inference mechanism and user interface. This paper brings out an overview of Expert System, a systematic study involving a critical analysis of the Expert System (ES) development, specifying its requirements. Further it highlights the features of Expert System and its implementation.
Smart maintenance of continuous duty cycle motors
Authors: Benin Pratap C , Samson Kenny , Shine Let G , Chandrasekaran G 
Published: February 2021

Abstract
Maintenance is a considerable part in the efficient working of the continuous duty cycle motor, especially in the motors used in day-to-day usage. It is practically impossible to do routine manual checkups to ensure the motor is safe and working conditions. The only relevant solution is to automate the routine checkups of the motor. This work’s primary objective is to develop an automated solution to the task of regular maintenance of the continuous duty motor. Here a wireless module using Arduino is integrated with the motor to wirelessly oil the motor. The proposed system can be used for regular oiling and machine maintenance without having the hassle of going to the site. Using this machine, the employee can check the working condition of the machine and turn ON and OFF the oiling system for the required duration. The proposed system prototype working has been tested and verified.
An Image Encryption Algorithm with Hermite Chaotic Polynomials and Scan Pattern
Authors: Sivakumar T. , Pandi M. , Senthil Madasamy N. , Bharathi R. 
Published: February 2021

Abstract
In today’s digital world, massive amount of sensitive information is being generated in the form of image and further it is shared via Internet. Maintaining the confidentiality of these data has become a major issue due to security vulnerability present in the network and transmission media. Image encryption algorithms are developed by various researchers to permit only authorized users to access and view sensitive images. This paper presents a novel image encryption scheme based on Hermite polynomials chaos function. First, random key stream is generated by using Hermite polynomial with different initial and incremental values of the variables. Next, the original image is scrambled with scan pattern to rearrange the pixels position. The scrambled image is XORed with the random key stream to get the encrypted image. The proposed method is experimented and observed that it is efficient and confirms the confusion and diffusion properties of a secure image encryption method. Large key space, low relation between the pixels of encrypted image, and sensitivity to initial seed value of the key are the strength of the proposed image encryption method.
Next Generation Digital Forensic Investigation Model (NGDFIM) - Enhanced, Time Reducing and Comprehensive Framework
Authors: Thakar Akash A , Kumar Kapil , Patel Baldev 
Published: February 2021

Abstract
Rapid technological advancement can have a substantial impact on the process of digital forensic investigation and presents numerous challenges to the investigator. With these challenges, it is imperative to have a standard framework for the digital forensic investigation to be implemented within most incidents. This induces a great stride to formulate a nonspecific framework that may be applied to most digital investigation procedures. The Next Generation Digital Forensic Investigation Model (NGDFIM) formalizes the framework to facilitates the practitioners in the investigation process. This framework could potentially generate more evidence during the incidence response through on-site triage as compared to conventional investigations process. Moreover, the framework diminishes the analysis time and provides the suspect with privacy protection by incorporating custom content imaging.
An element-wise generalized singular value decomposition (GSVD) based image encryption algorithm
Authors: Suresh Babu V , Vijaykumar V R , Gopalakrishnan T 
Published: February 2021

Abstract
The image encryption algorithm aids a secure data transmission. In our proposed method, we use the generalized singular value decomposition (GSVD) as the encryption tool. Here, the secret image will be decomposed with respect to the key and generates the ciphered text. Further, to improve its security, the Arnold transforms and the XOR operation is used respectively. The region-wise applied GSVD reduce the time consumption of the conventional image encryption methods. Here, the GSVD decomposition is element-wise, so, the algorithm is more robust. The generated cipher text is transmitted and the key is generated with the combination of the key used to decompose the secret image pixel in GSVD module, the Arnold transform key and the XOR operator key. In receiver end, the ciphered image is synthesized with the key received in a separate channel. This proposed model is lossless encryption method.