bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effectiveness of The Animation Video Learning Earth’s Layer Media to Improve Students’ Concept Understanding
Authors: Rosdiana L , Ulya R M 
Published: May 2021

Abstract
Science learning of the earth’s layers subject is not optimal. One of the factors causing the less optimal learning is the lack of media that support this subject. Students want a learning media that can motivate them to learn and also make it easier for them to understand the subject matter. This study aims to analyze the effectiveness of instructional video animation media on the earth’s layers subject in terms of the results of understanding the concepts of junior high school students. This study used a research design “One group pretest-posttest design”. The subjects of this study were 28 students of class VII-D SMP Negeri 3 Sidoarjo. The data collection technique used the test method. The conceptual understanding indicator used is to give examples, categorize, compare and explain. The results of this study are that the indicators give an example of obtaining an n-gain of 0.84 in the high category, the indicators categorizing obtaining an n-gain of 0.67 in the moderate category, the indicators compare getting an n-gain of 0.69 with the medium category and the indicator. explained that obtaining an n-gain of 0.68 in the medium category. The indicator compares getting the lowest posttest score and the indicator gives an example of getting the highest posttest score. So it can be concluded that the animated video media as a learning medium on earth layer material is very effective in increasing students’ understanding of concepts 0.69 in the medium category and the indicator explains that it gets an n-gain of 0.68 in the moderate category. The indicator compares getting the lowest posttest score and the indicator gives an example of getting the highest posttest score. So it can be concluded that the animated video media as a learning medium of earth’s layers subject is very effective in increasing students’ understanding of concepts 0.69 in the medium category and the indicator explains that it gets an n-gain of 0.68 in the moderate category. The indicator compares getting the lowest posttest score and the indicator gives an example of getting the highest posttest score. So it can be concluded that the animated video media as a learning medium of earth’s layers subject is very effective in increasing students’ understanding of concepts.
Characteristics and practicality of problem-based project learning to improve process and product performances of prospective physics teachers
Authors: Dwikoranto  , Munasir  , Setiani Rahyu , Suyidno  , Rohman A , Suprianto  , Zainudin  
Published: May 2021

Abstract
This study aims to describe the characteristics and practicality of a problem-based project learning model to improve process performance and product performance of prospective physics teachers. The problem-based project learning (PBPL) model uses the design of the Plomp. The trial subjects are the PBPL model and the Unesa laboratory physics course students. The trial used a one-group pretest-posttest design. The data were collected using theoretical and empirical study sheets, observation sheets for the implementation of the Lecture Program Unit (LPU) PBPL Model, an instrument for student learning material readability assessment sheets, an instrument for Student Activity Sheet (SAS) readability assessment sheets, an instrument for observing field constraints during learning process. The data were analyzed descriptively by calculating the percentage and proportion then looking at the category group. Data analysis used quantitative descriptive statistics. The results showed: 1) The characteristics of the developed model have: syntax consisting of 5 steps, supported by a solid theoretical foundation, specific learning environment, and specific learning objectives. 2) The model developed is included in the practical category in terms of: (a) The implementation of the model with implemented criteria; (b) Students are active in learning physics using the PBPL model; (c) small and insurmountable learning constraints. It is concluded that the PBPL model developed has certain characteristics and is practical to improve process performance and product performance of prospective teachers.
Google Classroom as a Distance Learning Tool during a Pandemic
Authors: Maman  , Baharun Hasan , Witarsa Ramadhan , Ainin Dewi Tumatul , Hodaili Zafrul , Mushorfan  , Wiranata Majid Afnani 
Published: May 2021

Abstract
This research was conducted to analyze the assessment strategy using Google Classroom at Nurul Jadid University, Paiton, Probolinggo, East Java, Indonesia. Learning through Google Classrooms is an alternative in the distance learning system during the Covid-19 pandemic. Data collection techniques are carried out through interviews, observation and documentation. The data analysis was carried out in a circular manner, starting from presenting the data, reducing data to drawing conclusions. It uses a case study type qualitative approach. The results of this study indicate that the Google Class room based assessment strategy is carried out through; assessment of student discipline, assessment of student performance / portfolio assessment, self-assessment and assessment through forms.
Intelligent defaulter Prediction using Data Science Process
Authors: Raviteja Chilagani , Santosh D Teja 
Published: May 2021

Abstract
The machine learning classifiers employed are not good enough to clearly classify the loan defaulters. In order to alleviate this problem, the data science process is adopted. During the machine learning phase, the random forest classifier with tuned hyperparameters is trained on the loan lending dataset for obtaining the bank loan defaulter model. This classifier is learned by using a smaller number of features as predictor variables obtained after the Exploratory Data Analysis phase. The ability of the model to correctly classify the unseen loan applicant’s data is evaluated in terms of the classifier accuracy and attending the false positives during the model diagnosis from the confusion matrix which unattended will prove fatal for the loan lending banks. The conclusion drawn from this analysis is that the performance metric of the classifier namely the classifier accuracy for Random Forest has outperformed the state-ofart statistical classifiers.
Review on Radiation Hardness Assurance by Design, Process and NextGen Devices
Authors: Rao Ch S V Maruthi , Chavan Ameet 
Published: May 2021

Abstract
Aggressive scaling, shrinking device size and exponential rise in device densityover the past two decades has contributed significantly in improving the power and performance metrics of Integrated Circuit (IC) technology. For specific applications like Space electronics, Implanted Biomedical, Sensor devices etc. drastic scaling has resulted in increasing the susceptibility of the nanoscale circuits to radiation effects. The objective of the paper is to provide an overview of the radiation hardening techniques at various levels of IC design and development, i.e. at Device Structure-Fabrication process (Radiation Hardening by Process) and Circuit and layout design (Radiation Hardening by Design), to mitigate the effects of radiation and improve reliability. The work also presents comparison of next generation device structure that utilize Carbon Nano Tubes (CNTs) and Semiconductor NanoWires (GAA-Si, GaAs, InN) against traditional MOSFET based device structures for operation under the radiation environment.
Patang Abhidhani - Convolution Neural Network based Butterfly Research Survey
Authors: Priya R L , Mate Yash , Tirodkar Gaurav , Somai Neelam , Patil Gayatri , Bhopale Nikhil 
Published: May 2021

Abstract
There has been a great loss of biodiversity worldwide and efforts have to be taken in order to restore it. To restore biodiversity, it’s crucial to know the cause for its decline and the role played by the insects in it. In India, butterflies are not given much importance in the conservation species. Since there are no names for butterflies in regional languages, the only access for comprehensive information for people’s awareness is really nothing. This paper aims at providing details of research done on different butterfly species, their existence once in different parts of the world, and technologies employed for solving the problems like identification of different species, distribution, restoration, and rejuvenation of diversity.
Classification of abnormalities in breast ultrasound images using ANN, FIS and ANFIS classifier: A comparison
Authors: Precious J Glory , Selvan Shirley , Avudaiammal R 
Published: May 2021

Abstract
When diagnosed early, cancer can be cured. The leading cause of death for women is breast cancer, the most common form of cancer in women. However, there are several ways to think about breast cancer, the US is widely used as it is not aggressive and painless. The appearance of dots on ultrasound images reduces image clarity, affecting image quality. Dots are drawn to accurately define distorted images. This is accompanied by the separation of the wound using a level setting method, after which the features of the lesions are removed. Only the most distinct traits are considered. Using the division of ANN, FIS and ANFIS, with a accuracy of 92.3 percent, 88 percent and 96 percent, the inconsistencies in the 41 images of Breast Ultrasound are identified and classified.
An Intelligent System for Waste Materials Segregation Using IoT and Deep Learning
Authors: Azhaguramyaa V R , Janet J , Narayanan Vijay Varshini Lakshmi , Sabari R S , Santhosh Kumar K 
Published: May 2021

Abstract
Disposing of Garbage is still a hurdle in today’s world and not many waste materials are disposed of correctly as per the requisite procedure. They are rather dumped in landfills and other sources like water bodies causing Pollution. The proposed system amalgamates the Internet of Things with Deep Learning and Image Processing which replaces the conventional waste management system with a smart segregation process. The proposed model consists of a Raspberry Pi camera to scan and detect the object. This detected object is then exported to the Deep learning Model as the input using the ‘Histogram of Oriented Gradients’ algorithm. This is invariable to the geometrical and photometrical changes of the object. The image classification is accomplished using the combination of Faster Regions with the CNN algorithm (RCNN) with contextual information and incremental learning which improves the detection performance under special and extreme conditions, accurately classifying the objects into bio and non-biodegradable waste. Using the Faster R-CNN algorithm, explicit information can be obtained from the different layers of the neural network of Faster R-CNN, thereby proving that the proposed system has a higher accuracy rate.
Convoluted fashion classifier prediction using Neural Networks
Authors: Kumar T Rajesh , Kishor S , Kishore R U , Kumar C Naveen 
Published: May 2021

Abstract
The conventional way of choosing a fashion catalogue has a lot of drawbacks and is yet to be resolved. Struck in a dilemma of what to choose and what not to, not wearing fashion items that are appropriate, and lured by terms that are misleading such as 50 percent off limited period offer, when in reality the prize was never higher in the first place. These are the flaws that go under the radar for humans, but that is not the case with machines. The proposed system when given an image of a fashion item, by the user makes use of a Convolution Neural Network to find the type of item, then suggest items based on occasion. The system also gives you the prize of that particular item over a period of time in various e commerce sites, and if there is any chance of drop in prize in the future. This eliminates the human part of errors while choosing a fashion catalogue and makes everything automated. The next advantage of our site is to resell our old clothes at best prize so our used clothes are not wasted and it can be used by others.
Yolov3 Supervised Machine Learning Framework for Real-Time Object Detection and Localization
Authors: Srithar S , Priyadharsini M , Margret Sharmila F , Rajan R 
Published: May 2021

Abstract
Nowadays to detect and classify the objects from the sequence of image frames various machine learning models are used. The performance of the object recognition model is purely depending on the number of trained images. The image processing and pattern extraction focus on object recognition, localization, and classification. To classify the objects from the external events or to detect multiply objects from an image the confident level of the trained weights should be maximum. The existing object detection methods look at the specific region to detect and classify the objects in an image. The proposed object detection system takes an improved YOLOv3 model for object classification. The improved model will look at the entire image to detect and recognize the objects. The YOLOv3 model uses the neural network on the image which split the image into a region and map the confidence probability. The proposed model will detect multiple objects by exploiting the contextual information using a single CNN. The model can process 45 frames in a second and it is suitable for object detection in real-time.
AI enabled smart surveillance system
Authors: Keerthana T , Kaviya K , Priya S Deepthi , Kumar A Suresh 
Published: May 2021

Abstract
The conventional household door locking system has lot of drawbacks and it is still yet not resolved. Most of the security systems so far in our markets includes video surveillance or vigilance system. In order to improve security level facial recognition and object detection technique using CNN algorithms can be used which is also provides remote proctoring facilities to owners. The proposed system detects the object and identifies the anomalous activity near the door by applying Convolutional Neural Network. Electric door lock solenoid is used to unlock the door. An ultrasonic sensor is utilized to measure the distance between a person and door through the facial recognition when it reaches a certain threshold value that has been kept to detect the person reaching the doors and it tries to capture the human image only if it is mismatched from database. When a stranger try to access the door an alert message might be triggered to registered mobile number and he proprietor would be able to control the door locking system and inspect the image of person which has been mailed.
Detect fake identities using improved Machine Learning Algorithm
Authors: Suganya R , Muthulakshmi S , Venmuhilan B , Varun Kumar K , Vignesh G 
Published: May 2021

Abstract
Nowadays, Social networks are controlling the world. Number of users using the social media platforms are continuously increasing every year. The main advantage of Online social media is anyone from anywhere in the world can connect to any users easily and able to communicate with them. This paved the way of malicious attacks like fake identities and spams. The Survey of Facebook and Twitter states that the actual users present in their platforms is lesser than the number of accounts created. It means there is an increase in fake profiles over the year. With these accounts, their creators can influence the users and distribute false information. They exploit the users in order to achieve their hateful aims, for example, creating hateful posts/tweets. The increasing number of fake accounts in social media platforms is becoming a major risk for the internet users. So, there is a need for identifying these fake identities. The existing machine learning algorithms are SVM linear, naïve bayes, random forest and Decision tree algorithm which are used to detect fake identities provides less accuracy and has become inefficient. We implement Ada-boosting algorithm with SVM to detect and identify bots. The proposed model is Ada-boost SVM classifier provides higher accuracy in detecting the fake accounts and show better results in the detection of identity bots.
Automated Railway gate control system using Arduino and Ultrasonic sensors
Authors: Ilampiray P , Deepak K , Santhosh M G Deepak , kishore S 
Published: May 2021

Abstract
In a country like India, which has a population of about 1.39 Billion depends on its Transportation for its daily living. Transportation plays a main role in India’s Economic Development and more than 35% of the lives of people in India depends on its transportation.
Railways is the most popular and highly used transportation in India. It is the most effective mode of transportation not only in India, but also all over the World. As like its usage it also leads to a high number of accidents. Like road accidents, there are more accidents happening at railway crossings due to the unmanned level crossings and the carelessness of the road users. According to the National Crime Records Bureau [NCRB], the rate of level crossing accidents is 20% up in 2019 than 2018. However there will be an increase in the percentage of accidents every year but the percentage in the last two years has increased drastically. There were 1788 level crossing accidents in 2019, which include 1762 deaths. “Necessity is the mother of Invention” as like this saying the is a simple system, which automatically closes the level crossing gates during the train’s arrival and then the gates are opened automatically when the train passes by. In our system we have used ultrasonic sensors for detecting the departure and arrival of the trains. As soon as the sensor senses the train’s arrival it sends a message to the Arduino which will switch on the buzzer, so that the road users will know that the train is nearer, and after that the servo motor which is attached to the gates will close them and it will be opened after the train passes by. And then the buzzer will also be turned off. This is automated, highly effective and cheap. Our system will eliminate the manpower used at the level crossings.
The Face Mask Detection Technology for Image Analysis in the Covid-19 Surveillance System
Authors: Hussain G K Jakir , Priya R , Rajarajeswari S , Prasanth P , Niyazuddeen N 
Published: May 2021

Abstract
Face mask recognition has been growing rapidly after corona insistent last years for its multiple uses in the areas of Law Enforcement Security purposes and other commercial uses Face appears spreading others to corona a novel approach to perform face new line detection and face mask recognition is proposed. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network. Extensive experimentation on the datasets and the performance evaluation of the proposed methods are exhibited. Further, we made a successful attempt to preserve inter and intra class variations of face mask detection using symbolic approach. We studied the different classifiers like Support Vector Machine and a Symbolic Classifier. The project is developed as a prototype to monitor temperature measurement and to detect mask for the people. The first method is performed using temperature sensor used to detect the present temperature of the body and automatically spray the sanitizer. In the second method, the work is designed to provide a safety system for the people in order to avoid COVID-19. We proposed continuous monitoring of the people conditions and store the people’s data in the server using the Deep learning concept. In order to investigate the performance the proposed method an extensive experimentation is conducted on 50 various Image dataset. We conducted experimentation under varying of training and testing percentage for 10 random trails. From the results we could observe that, the results obtained for symbolic approach is better than the conventional approach.
A Smart Helmet for the Mining Industry using LoRaWAN
Authors: Pradeepkumar G , Rahul S Sanjay , Sudharsanaa N , Suvetha S , Ponnusamy Dineshkumar 
Published: May 2021

Abstract
Air Quality becomes an important factor in mining areas where the health condition of the workers is prominently considered. The composition of many toxic gases under the mining area causes many fatalities that keep on increasing day by day. The Total Volatile Organic Compounds (TVOC) and Carbon-di-oxide creating a significant role in the health system of the workers. The unstable levels of these TVOC’s cause many health issues like nausea, emesis, fatigue, epitasis and dyspnoea. Exceeding the standard levels of these toxic compounds in the air causes many problems like severe breathing trouble and headache. In this proposed work, Smart Helmet has been developed for mining workers using LoRaWAN to overcome the above mentioned problems. This device helps to alert user on the air quality. When the air quality changes, the device inform the user, so that they can take necessary actions. The effectiveness and performance of this smart helmet using LoRaWAN can be analyzed with the help of experimental results.
Mixture Weibull probabilistic model in Wind Turbine Power Analysis
Authors: Indhumathy D , Narmatha D , Meenambika K 
Published: May 2021

Abstract
Wind energy is renewable and environment friendly. It is pollution free source of energy when compared with fossil fuels that pollute the lower layer of atmosphere. A new method depending on the parameters of three parameter mixture Weibull distribution is presented in this paper for calculating the output energy. Parameter estimation is much critical for the application of statistical model and is a challenging problem precisely for a Weibull distribution with more than two parameters. A study on estimation of parameters using maximum likelihood method is discussed. An expression to estimate the Capacity factor is formulated and the average power produced from a turbine is computed using the three parameter mixture Weibull distribution.
Intelligent Analysis for Drowsiness Alert using Conventional Neural Networks
Authors: Jeyabharathi D , Jeevanantham K , Kavinmukhil M , Mohamed Quanith hasan J B 
Published: May 2021

Abstract
When a job is assigned to drivers who travel through the roads and highways riding a CAB or NATIONAL PERMIT TRUCKS face the risk of tiredness especially during night travels and early morning. This project is to develop a driver drowsiness detection system by using Deep learning. It is known that a driver is under drowsiness influences by looking at the eyes for a moment of duration. Based on the previous research, there is none added tome counter that may exclude driver drowsiness from other activities of eyelid movement. The result can be accurate because histogram analysis analyzed the whole image upto a certain duration giving alarm with battery connection to be disabled for mentioned span of time. Therefore, he can start the vehicle only when the driver completes his rest time.
Ensemble Modeling on Job Scam Detection
Authors: Asmitha Shree R , Nirmala D , Sweatha S , Sneha S 
Published: May 2021

Abstract
The new advancement of online enrollment and occupation enlistment systems has made another media for web fraudsters. The improvement of the update of ongoing innovation has restricted a few organizations to move into electronic board structures. The benefits of such electronic edges are liberal. From one perspective, they are the most ideal approach to lead numerous organizations’ applicants, and afterward once more, the contenders while following a task will be more involved. Community with bogus motives research these structures for sensitive information to be optimized by techniques for fake counteractive action advancements. In this article we are utilizing open informational indexes and AI computations to arrange phony or real posts. The target of this audit is to fuse both content, put together information and meta-information with respect to the situations in the work. The informational collection can be utilized to make characterization models that can acclimate themselves with the deceptive arrangements of duties. Gathering information models utilizing request computations like Logistic Regression, K Nearest Neighbors and RandomForest.