bluuebunny/crossref_metadata_2025_split huggingface dataset:
Indian agriculture GDP and non performing assets: A regression model
Authors: Jethwani Bhoomika , Dave Devasya , Ali Tamanna , Phansalkar Shraddha , Ahhirao Swati 
Published: February 2021

Abstract
Agriculture is one of India’s crucial sectors in terms of its contribution to employment and the country’s (Gross Domestic Product) GDP. It has primarily emerged as an essential - growing sector in the global economy since independence. [21]However, the non-realization of the reasonable price for agricultural crop production leads to the introduction of loan waivers, which impact the credit culture and weaken the farming economy and growth. The presented work aims to perform exploratory data analytics on the GDP data in agriculture public domain by performing feature engineering on the factors affecting the agricultural GDP using the data for the period 1961 to 2019. It further builds a multi-linear prediction model to forecast the Agriculture Sector’s economic performance in terms of GDP and NPAs generated by the Agricultural Sector using Machine Learning Techniques. Keywords: Multiple Linear Regression, Ordinary least Squared, Agricultural GDP, Non-Performing assets, Indian Economy.
A survey on Pose Estimation using Deep Convolutional Neural Networks
Authors: Patel Manisha , Kalani Nilesh 
Published: February 2021

Abstract
Human pose estimation is a technique, which identifies the human body’s landmarks in images and videos. Human pose estimation can be divided into single person pose estimation and multi-person pose estimation, also an estimated human poses in crowded places as well as in videos. Depends upon the application such as activity recognition, Animation, Sports, Augmented reality, etc., Pose estimation output can be in 2D or 3D coordinate format. 3D pose estimation is estimated considering joint angles in 2D. Some challenges like small and barely visible joints, strong articulations, occlusions, clothing, and lighting changes increase difficulty in estimating pose. Remarkable progress has gained in the field of human pose estimation using Deep learning-based CNN models. In this paper, we compare and summarized various deep learning models for pose estimation of a single person and multi-person.
Reconstruction of Gene Regulatory Network using Bayesian Network
Authors: Bhola Abhishek , Mittal Sandeep 
Published: February 2021

Abstract
Gene Regulatory Networks (GRN) are complicated govern systems, that refers to the intercommunication of genes and it also helps to understand and visualize all the biological interconnections. Reconstruction of GRN helps in finding out various hidden facts that can be helpful in solving various complexities related to living life, curing many diseases and inventing new drugs for the humans. There are number of computational methods available in literature for reconstructing GRNs. In this paper, a method is proposed for reconstructing GRN i.e. based upon Bayesian network. The mentioned method in this paper is considered as one of the most capable method for reconstructing GRN. The proposed method functions in two stages. In its first stage, there is two level clustering is employed to diminish the size of given dataset. In its second stage, Bayesian Network will be utilized to reconstruct the network for the sub-clusters that are gained from the first stage. The proposed method is also validated and evaluated using different parameters.
Intelligent Travel Route Suggestion System Based on Pattern of Travel And Difficulties
Authors: Pulmamidi Nikhitha , Aluvalu Rajanikanth , Uma Maheswari V 
Published: February 2021

Abstract
People travel a lot to new places for career, business, education, travel, health and for various other reasons. Travellers always search to find shortest and safest route to reach the destination. They majorly consider distance, modes of transport and safety as major parameters. Various algorithms, mechanisms are proposed earlier to serve the purpose. However existing algorithms, mechanisms suffer from various shortcomings like unable to analyze the suggestions and filtering the database. The existing models use collaborative or content filtering algorithms. These methods do not collect the past user experience. The main objective of this paper is to help the travellers from their source to target from a database with best possible way. Proposed method provides the information from the travellers and problems faced by them during their travel. Based on the reported issues and experiences, new path will be enrooted to the destination. In the proposed model C4.5 decision tree algorithm is used to collect data from previous user experience, minimum redundancy maximum relevance (MRMR) and features selection algorithm is used to reduce execution time and increase system accuracy. The experimental outcome of this paper is to improve the system accuracy and performance.
VMATE – Intelligent E-Learning Management System for Educational Institutions
Authors: Anjali Pudiyadatha , Harshita Nunnabhatla , Akash M.D.N. , Vinith Patlolla , Prasada Reddy G.S 
Published: February 2021

Abstract
The world is facing a huge health crisis which is demanding to reorganize the way it functions from square one. The spread of the pandemic, the shutdown of education institutions, and the shift to online learning were so fast that it barely gave any time to consider the difficulties faced by students. This is a great opportunity where educational institutions will shift to blended learning; hence it will become a new way of learning where there will be a need to find new ways to deliver the best content by using learning management systems. Therefore, the virtual learning world will create a great impact on the current education system due to the diverse effect of the epidemic. Hence different organizations are tremendously advancing their technology, and it will become more crucial over time. VMATE –Intelligent E-Learning Management System provides an interface for learners, which is of great significance and practical value for the improvement of learning and teaching quality. The use of Computers and the Internet has introduced technological conditions for teachers and students who can make good use of online information provided and communicating with others. This can be used by institutions and for different environments, where they can have computer-based instructions, blended-learning, and increased quality and quantity learning. Besides some basic management functions, the system focuses on assignment grading, assessment for various learning courses which makes it suitable to provide personalized grading and detailed feedback.
A Multifarious Diagnosis of Breast Cancer Using Mammogram Images – Systematic Review
Authors: Swapna M. , Hegde Nagaratna 
Published: February 2021

Abstract
Breast Cancer is most common disease in worldwide leads to high rate in mortality. Detection of symptoms at early stage is difficult to identify the breast cancer. Diagnosis through General Medical Examination cannot detect the disease. This paper represents various common methods to detect the Breast Cancer and detection of tumors using mammogram images with Artificial Intelligence (AI). Algorithms helps Images in preprocessed using various feature extraction methods and classification algorithms to predict the class of tumors is benign or malignant. This article insight the recent approaches used in detection of tumors and stage of breast cancer.
A Neural Network trained to perform as an Imprecise 4:2 Compressor using Machine Learning Algorithm
Authors: Maddisetti Lavanya , Senapati Ranjan K. , Ravindra JVR 
Published: February 2021

Abstract
In many fields, such as speech recognition, image processing, Internet of Things (IoT) systems, arithmetic circuits, etc., artificial neural networks have become a popular solution for a wide range of problems. In this research work, a 4:2 compressor-based arithmetic circuit is considered and an approximation technique called probabilistic pruning is applied to it. This proposed compressor is compared with various existing approximate 4:2 compressors. Training of a neural network as proposed inexact 4:2 compressor is performed using a supervised machine learning algorithm. The accuracy values of the proposed compressor are acceptable at architectural and neural network levels. The absolute difference between the train set and the predicted test set accuracy values obtained is very less (1.73%) when compared with the other (inexact 4:2 compressors) trained deep neural network models.
WSN Smart Irrigation System and Weather Report System
Authors: Srinidhi J. Akhila , Aasish A. , Kumar N. Kishore , Ramakrishnaiah T. 
Published: February 2021

Abstract
This paper proposes a cost-effective system that can be used by farmers easily. In these modern times also, we find farmers going to their fields to check the water level and to see that no cattle are present near the field. Even if there are cattle he can’t do anything at that moment. So, we proposed a system where he can be notified if there is any animal or if the water content is low on his registered mobile number. In this system, an irrigation-based Wireless Sensor Network (WSN) using Raspberry Pi and the solar panel is implemented, Raspberry Pi being used as Coordinator node and Arduino as an end node, nRF24L01 as a transceiver. The sensor input values such as soil moisture level, PIR; the temperature is collected by the Arduino and is wirelessly sent to the Raspberry Pi. The water pump can be turned on based on sensor values using the Raspberry Pi web server and those values are also posted on the webpage. For temperature and humidity detection, we used the DHT11 sensor and the solar panel is used as an additional source of power. A PIR sensor is used to check if there are any animals present near the field.
Visualization of Real World Enterprise Data using Python Django Framework
Authors: Vamsi K. Manikanta , Lokesh P. , Reddy K. Neha , Swetha P. 
Published: February 2021

Abstract
In the meantime, startup companies are rising more and more. Companies want to track their prominence and their advancement. Basically, the whole data need to be found and stored in database. They need to check their company’s turnout-catastrophe ratio and their expansion. The company obliged to know how compact it is, with different companies. Since the data is in huge amount, estimating the data is almost difficult. Soon these terms, physically reckoning may become annoyed. Considering the plot, we have a proposal termed visualization. Visualization is the graphical depiction of data in the pattern of graphs, tabulations, charts by which we could know about company widening. Using data visualization, we could form an opinion on company’s stand/rate. So to evolve and to develop web pages, the automations we used are html, css for the data analysis, python modules are used. Few of them are pandas, seaborn, matplotlib, plotly. Implementing the data visualization using IDE’s like jupyter, Sublime Text makes the effort transparent. But integrating web pages with python modules will be the challenging load. So to carry out this, we use DJANGO Framework to visualize the data. DJANGO affords frontend, back-end and database This Data visualization is intended to evolve web pages and scrutinize the data.
Improved accuracy of pest detection using augmentation approach with Faster R-CNN
Authors: Patel Deven , Bhatt Nirav 
Published: February 2021

Abstract
In agriculture, Pests are decreasing agricultural productivity. Identifying a pest is a challenging process and subject to expert opinion. Nowadays, lots of work carried out for automatic pest detection. It becomes possible because of emerging Deep Learning’s object detection architectures. This paper shows the multi-class pest detection using Faster R-CNN architecture and compared the performance results of image augmentation with focused on the accuracy performance along with small dataset. We have used Horizontal Flip and 90 Degree Rotation augmentation parameters for solving class imbalance problem. We found that trained pest detection model with augmentation options can perform better with an accuracy of 91.02% using Faster R-CNN architecture.
Usage of time series forecasting model in Supply chain sales prediction
Authors: Raiyani A , Lathigara A , Mehta H 
Published: February 2021

Abstract
This paper representing a study of supply chain operation data that was used on 100 different store items from 10 stores using 5 years history of sales through open sources contest to compare the performance of time-series forecasting model mainly, decomposition, Auto-Regressive Integrated Moving Average(ARIMA), Prophet, Box-Cox transformation. Here data is collected from 2013 to 2018 were used in real-time transaction at different store, initially model was applied on 2013 to 2017 data and based on the that predicted for 2018 then again cross checked with actual 2018 with proceed predicted data of 2018. To improve the performance and evaluation of the supply chain management system, scrutiny 3 metrices that will help to make decision on the model selection. The accuracy of the Machine learning model in forecasting future sales of supply chain store. Although the result on comparison indicates that there is no single method gives better and superior result. But present study indicates that prophet and ARIMA hybrid model gives better result compare to individual model.
Tyre Pressure Monitoring System
Authors: Abbi Amogha , Ramakrishnaiah T. 
Published: February 2021

Abstract
The Tyre Pressure Monitoring System which is used today indicates only whether the air pressure in any of the tyres is below a particular level of safety. This avoidable signal has made it possible for the statistical death rate to come up to 650 deaths per year. This project displays values through continuous monitoring. Therefore, this research proposed a method to cumulate the various factors of the tyre and to display it to the driver for a safer experience. The proposed TPMS has an electronic device unit that is attached to the tyre and transmits the values of the pressure, temperature, and the wheel alignment to the unit to be placed inside the vehicle, observable to the driver. This unit includes pressure sensors, microcontrollers, RF transmission, and batteries. An alert is introduced which indicates if the pressure exceeds the maximum or minimum safe pressure level. Several experiments have been carried out to analyze the proposed system. The integrated TPMS has proven to be an effective alternative to maintain the tyres and aims to improve the comfort and safety of the driver.
Multi Variant Handwritten Telugu Character Recognition Using Transfer Learning
Authors: Ganji Tejasree , Velpuru Muni Sekhar , Dugyala Raman 
Published: February 2021

Abstract
Optical Character Recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain information from various images. However, existing OCR techniques cannot recognition Telugu literature characters (Handwritten Golusu Kattu writing) due to a lack of datasets and trained deep Convolutional Neural Networks (CNN). Since the Kakatiya Empire (12th to 14th century) the glorious era of Telugu literature spread across the region. Thereupon, several handwritten documents consist of ancient knowledge, health care tips, wealth information, and several land records written in Telugu Golusukattu writing. Therefore, getting that information has become a major problem because of a lack of expertise in Golusukattu writing skills in skills. In order to solve the above problem, we are proposing deep learning aided-OCR for Telugu literature.
A literature survey on recurrent attention learning for text classification
Authors: Mariyam Ayesha , Basha SK Althaf Hussain , Raju S Viswanadha 
Published: February 2021

Abstract
Witha rapid rise of complex data every year needs more enrichment in machine learning methods to provide vigorous and accurate data classification. Deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory(LSTM) have accomplished to obtain better results in the domain of computer vision, object recognition, speech recognition and natural language processing compared to traditional machine learning algorithms. This paper mainly discusses about the blending of attention mechanism with various deep learning models for text classification which improves the performance of text classification task.
An Approach to detect fault text in articles
Authors: Reddy Pakala Prahasit , Prasheela Yempati , Reddy Avula Uday Kumar , Aluvalu Rajanikanth 
Published: February 2021

Abstract
The problem of Fault text (that contains fake news) has spread enormously in recent years. Social media is the major source for this type of news because it is easily and freely accessible to everyone and this type of news can be shared easily and rapidly, this enabled the widespread of news, so there is a need to detect this type of news. This news will mislead the users creating chaos and unrest. So the detection has become mandatory to avoid chaos and to maintain the pleasant news. We use Machine Learning Algorithms and basic Natural Language Process techniques to detect this type of news. In this work, Machine Learning Algorithms are used to parse the article and extract the summary from the article, this summary is used to predict the credibility. A passive-aggressive classifier and multinomial naive Bayes algorithms are used to train the model. A web app is created using the Flask API. One can put the URL in the web page to detect the news as “FAKE” or “REAL”.
5:3 compressor based neural network with LM Algorithm in Multiplier as Application
Authors: Charan Lanka Sai , Reddy M Vaishnavi , Reddy N Pooja , Ravindra J V R 
Published: February 2021

Abstract
In conventional approach for VLSI, implementing large numbers of operations in parallel is possible with NN. The fundamental task of a neural network hardware is not dependent on the implementation technology and are quite constructive in simulating the digital circuits. These NN representations can be incorporated in various applications where in the behaviour of these circuits are essential to get a solution for discrete problems. Therefore, supervised learning algorithms are used to train the computer software. One such algorithm is the L-M method which is one of the powerful and widely used approaches. This paper accounts to the designing and training of a neural network model as computing technique in 5:3 compressor with Lavenberg-Marquardt Algorithm. The performance parameters are significantly remarkable for real time implementation, are presented in the paper.
Green synthesis of Co-doped ZnO via the accumulation of cobalt ion onto Eichhornia crassipes plant tissue and the photocatalytic degradation efficiency under visible light
Authors: Zelekew Osman Ahmed , Aragaw Setegn Geta , Sabir Fedlu Kedir , Andoshe Dinsefa Mensur , Duma Alemayehu Dubale , Kuo Dong-Hau , Chen Xiaoyun , Desissa Temesgen D. , Tesfamariam Belay Berehane , Feyisa Gebisa Bekele , Abdullah Hairus , Bekele Eneyew Tilahun , Aga Fekadu Gochole 
Published: February 2021

Abstract
Nowadays, water pollution is a major concern to the globe. For this reason, various research works has been done to access pure water thereby minimizing the effect of pollutants. In this work, the cobalt doped ZnO (Co-doped ZnO) via the accumulation of cobalt ion onto Eichhornia crassipes plant tissue for different days and combined with zinc precursor was synthesized. The resulting catalyst powder samples were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-Ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and Ultraviolet–vis (UV–vis) spectroscopy, and microwave plasma atomic emission spectrometer (MP-AES). The catalysts were also tested for the photocatalytic degradation of methylene blue (MB) in the presence of H2O2 under visible light irradiation. The best catalytic activity was gained by the 8th-days accumulation of cobalt ion onto the Eichhornia crassipes plant tissue and 99.6% of the dye was degraded within 45 min. However, 69.6, 65.7, 73.6, and 94.8% of MB dye was degraded by 1, 2, 4, and 6 days accumulations. Hence, removal of toxic heavy metal by using Eichhornia crassipes plant and recycling in the wastewater treatment gain is highly appreciated. Moreover, the Co-doped ZnO photocatalysts could enhance the photocatalytic activities due to suppressing of the electron and hole recombination and the porosity of the catalysts resulted from the Eichhornia crassipes plant after calcination.
Storage and separation of methane and carbon dioxide using platinum- decorated activated carbons treated with ammonia
Authors: Aboud Mohamed F Aly , ALOthman Zeid A , Bagabas Abdulaziz A 
Published: February 2021

Abstract
Activated carbon (AC) was treated with ammonia for nitrogen doping and then was further anchored with platinum nanoparticles through ultrasound-assisted impregnation method. Methane (CH4) and carbon dioxide (CO2) storage of the pristine and modified activated carbon were determined at ambient temperature. All the samples showed high storage capacities with preference towards carbon dioxide storage, which would be favorable for both energy and environmental applications. Ammonia treatment slightly improved the storage of both CH4 and CO2 for all the samples, which could be attributed to the little improvement in the micropore properties upon nitrogen doping. Platinum decoration had an opposite influence on both CH4 and CO2 storage, which might be due to the absence of metal encouraging storage mechanism such as gas dissociation or reconstruction on platinum’s surface in addition to the harmful effect of metal pore-blocking and higher density of metal particles.
Electro-brush plating Ni-Graphene oxide composite coating enhanced corrosion resistance of fluoride ion
Authors: Xu Pengyuan , Shen Xiaochao , Xin Gang 
Published: February 2021

Abstract
Ni-Graphene Oxide(GO) composite coating was prepared on Q235 steel plate by electro-brush plating technique to reduce the corrosion of fluoride ion. In this study, SEM, XRD and Raman tests were performed to analyze the morphologies and structures of the coatings, then electrochemical characterization was evaluated to determine the corrosion resistance. The results showed that GO provided more nucleation sites, and the surface of coatings looked more uniform and much denser because GO and Ni atoms were co-deposited on coating surface. The composite coating showed a better corrosion resistance than the pure nickel and there was an optimal addition amount of GO (Ni-GO/0.75 coating). In detail, the corrosion voltage increased from −0.61187 V to −0.52542 V, and the corrosion current correspondingly decreased from 26.6 μA·cm−2 to 6.8 μA·cm−2, correspondingly. The charge transfer resistance was also increased from 558.6 Ω to 1769.0 Ω. Moreover, the immersion testing demonstrated that the weight loss of Ni-GO/0.75 composite coating was only 22.2% of the pure nickel. In addition, XPS spectra indicated that the fluoride ions in the corrosive medium preferentially bonded with nickel atoms, preventing the medium from entering the active area. In conclusion, Ni-GO composite coating exhibited excellent corrosion resistance to fluoride ion due to the impermeability and high conductivity of GO.