bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Formalism and Political-Economic Content
Authors: Foley D.
Published: April 2019

Mathematical methods are only one moment in a layered process of theory generation in political economy, which starts from Schumpeterian vision, progresses to the identification of relevant abstractions, the development of mathematical and quantitative models, and the confrontation of theories with empirical data through statistical methods. But today the relevant abstract problems of political economy are modified to fit available mathematical tools. The role of empirical research in disciplining theoretical speculation, on which the scientific traditions integrity rests, was undermined by specific limitations of nascent econometric methods, and usurped by ex cathedra methodological fiats of theorists. These developmentssystematically favored certain ideological predispositions of economicsas a discipline. There is abundant room for New Thinking in political economy starting from the vision of the capitalist economy as a complex, adaptive system far from equilibrium, including the development of the theory of statistical fluctuations for economic interactions, redirection of macroeconomics and financial economics from path prediction toward an understanding of the qualitative properties of the system, introduction of constructive and computable methods into economic modeling, and the critical reconstruction of econometric statistical methods.
Money Demand in Structural Modelsof Exchange Rate Determination
Authors: Kadochnikov D.
Published: April 2019

The demand for money is the key element uniting major structural models of exchange rate determination. The author analyzes the history of money demand theorizing in connection with the development of structural models of exchange rate determination. The author demonstrates that the evolution of the theory of money demand shaped the evolution of the theory of exchange rate. The paper also contains suggestions regarding the approach to criticizing structural exchange rate determination models on methodological grounds and the manner of teaching the economics of exchange rates.
Institutional Matrix for Russias Modernization
Authors: Bessonova O.
Published: April 2019

This article analyzes the formation of the Russian institutional matrix with regard to its previous development, but in line with the laws of economic evolution. The structure and development of an razdatok-type institutional matrix on three institutional cycles is considered. It is shown that in transformationphases the market institutional matrix masking the quasi-market is used. The author concludes that system crises could be overcome due to the mutual substitution of the two types of institutional matrices, and the cyclic path of Russias development became a consequence of that. The paper claims that the current institutional matrix will provide transition to a new level of high-quality growth only in case of synthesis of market and razdatok mechanisms.
Kornai, Schumpeter, and Economic Theory
Authors: Maevsky V.
Published: April 2019

The author claims that J. Kornai in his paper Innovation and Dynamism (Voprosy Ekonomiki. 2012. No 4) ignored the understanding of socialism as a specific type of culture and not just as an economic system. He also shows profound differences between Schumpeters theory and mainstream economic models. Evolutionary theory, he claims, may itself become mainstream if Schumpeters legacy is not interpreted straightforwardly and if evolutionary economists consider not only micro-, but also macro-level of analysis in studying macrogenerations of capital of a different age.
Macroprudential Policy Instruments of Central Banks
Authors: Andryushin S. , Kuznetsova V.
Published: April 2019

The paper analyzes central banks macroprudencial policy and its instruments. The issues of their classification, option, design and adjustment are connected with financial stability of overall financial system and its specific institutions. The macroprudencial instruments effectiveness is evaluated from the two points: how they mitigate temporal and intersectoral systemic risk development (market, credit, and operational). The future macroprudentional policy studies directions are noted to identify the instruments, which can be used to limit the financial systemdevelopment procyclicality, mitigate the credit and financial cycles volatility.
The Systemic Effects of Prudential Regulation Toughening:The Results of a Stress-test for Russian Banks
Authors: Mamonov M. , Pestova A. , Solntsev O.
Published: April 2019

The stability of Russian banking sector is threatened by three negative tendencies - overheating of the credit market, significant decrease of banks capital adequacy ratios, and growing problems associated with banks lending to affiliated non-financial corporations. The co-existence of these processes reflects the crisis of the model of private investments in Russian banking sector, which was observed during the last 20 years. This paper analyzes the measures of the Bank of Russia undertaken to maintain the stability of the banking sector using the methodology of credit risk stress-testing. Based on this methodology we conclude that the Bank of Russias actions can prevent the overheating of the credit market, but they can also lead to undesirable effects: further expansion of the government ownership in Russian banking sector and substitution of domestic credit supply by cross-border corporate borrowings. The later weakens the competitive positions of Russian banks. We propose a set of measures to harmonize the prudential regulation of banks. Our suggestions rely on design and further implementation of the programs aimed at developing new markets for financial services provided by Russian banks to their corporate and retail customers. The estimated effects of proposed policy measures are both the increase in profitability and capitalization of Russian banks and the decrease of banks demand for government support.
Economic Way of Thought and Legislationon Nonprofit Organizations
Authors: Yakobson L.
Published: April 2019

The article considers proper legislation as an essential prerequisite for actualization of NPOs comparative advantages. Restrictions imposed on NPOs are reasonable if they are compensated by benefits from greater trust. The rigidity of constrains and requirements should be optimized while accounting for peculiarities of a social medium, the state of the nonprofit sector, and the governments readiness to encourage the development of the latter. As empirical data suggests, Russian NPOs being on different stages of maturity need separate legal treatment. In the meanwhile, interests that prevail in the NPOs community are not always conducive to rapid changes.
Culture Transformation and Changing Consumption Patterns
Authors: Lipsits I.
Published: April 2019

The paper analyzes the main trends of consumer markets in the post-crisis period. Particular attention is paid to the manifestation of global trends in Russia. The increasing value systems and lifestyle fragmentation leads to changes in even such fundamental elements of the human society as gender differentiation and models of family life. The growth of popularity of the economical consumption model is accompanied by a drop in loyalty to brands, as well as by increasing popularity of downshifting and minimalism. In these circumstances, those productsthat are able to take immediately into account several consumption trends and offer complete solutions have the best commercial prospects.
Human Capital in the European Union:Public and Supranational Context
Authors: Klinova M. , Sidorova Y.
Published: April 2019

The article examines the process of shaping and employment of human capital as a key factor in modern economic development. The interconnection between human capital and economic growth in the European countries is analyzed, as well as the role of human capital for realization of the Strategy Europe 2020. The significance of European experience for managing human capital in Russia is reviewed.
What Did the Study of Transition EconomiesContribute to Mainstream Economics?
Authors: Ellman M.
Published: April 2019

Basing on examples this paper shows that the experience of the postcommunist transition, and the policy-related and academic discussion about it, have made a useful contribution to mainstream economics. In particular, such issues as the role of the state and social policy, institutions and banking system in economic development were reconsidered. The new perspectives on the issue of the efficiency of privatization, path dependence, and the role of political elites in institutional reforms were developed. The author shows which of the recommendations provided by international organizations proved to be inadequate and why.
Automatic Authorized Access and Unauthorized Denied Security Data Maintenance for Organization Using RFID Technology
Authors: Din Qutubud , Sayed Akhtar , Yousafzai Fida Husain , Yu Jiong
Published: April 2019

Using of this latest RFID (Radio Frequency Identification) technology for an organization to improve the efficiency of organization security and maintain the record automatically. Security is the biggest challenge for everything and everywhere it’s the first priority of organization responsible authorities and they must provide the best way to deal these problems with full confidence. With help of RFID Technology it’s more easy and faster to identify the object is authorized or unauthorized to access or denied from the restricted premises. RFID Technology based on two components RFID Reader (Interrogator) & RFID Tags (Transponder). The tag contains an integrated circuit a unique number that number is assign to the object and it’s become the identification I’D of the object. This is used for processing data, modulating and demodulating the radio frequency signal that is being transmitted to RFID Reader, RFID reader read the tag and send the query to database for comparison of information which are exist in the existing database. The tag information is stored in the database the database is pass the query to micro controller the micro controller is perform operation on the base of object requirement and send back the information to database. The developed software performing functionality automatic and authentic. RFID Tags used as ID (identification number) registered in database is presenting the organization concerned object to allow access to the restricted premises otherwise denied the access inside the organization and saved all movement which pass through at the entrance and provide easily understandable, user-friendly and flexible interface for user. Maintain and update all information automatically. To minimize manually checking and record maintenance headache.
Multi-genre Movie Data Analysis Using Pearson's Correlation
Authors: Mounika Maturu Shalini , Eekshitha Tamma , Ramya Talla , Thanmai Durga Tulluru
Published: April 2019

The success of a movie plays an important role because it usually involves huge amounts of investments. Thus it becomes important to know beforehand whether the movie will be successful or not. The aim of our work is to prove that various attributes or factors related to a movie could prove useful in predicting the success or failure of a movie. Since one single attribute is not sufficient to predict the success of a movie, we’ve used multiple attributes and the comparisons between various attributes and their correlation for the success prediction. Therefore, in this paper we are considering a statistical technique called Pearson’s correlation coefficient in finding out which factors are highly correlated with a movie’s ratings.
Quadcoptor Monitoring
Authors: Ganesan M. , Gayathri S. S. , Krishnakumari S. , Nivedithaa S.
Published: April 2019

UAV technology has been an open research topic for many years. This is because of their potential huge benefits at an affordable cost in a wide range of tasks. UAV are commonly used in public and private places, yet also with few serious limitations. In this paper, we are going to design a drone that monitors the road traffic system as well as measures the pollution level emitted by the vehicle into the air. Most of the traffic monitoring systems based on UAV use a fixed trajectory to extract information about the vehicles, but we monitor the vehicle using the IP camera. During peak hours, the level of pollution will be higher so with the help of the drone we can measure the pollution level and take the required precautions.
Chicago Crime Analysis using R Programming
Authors: N Monish
Published: April 2019

In recent years law enforcement have improved by taking better strategies, computer aided technology, efficient use of resource, etc. As a result of these over the couple of years there has been a steep decline in crime rate in the US (United States). Law enforcement have turned to data science for insights (ranging from reports, corrective analysis and behavior modelling). There has been an overall drop in crime rates in Chicago in recent years. In fact, these rates are at the lowest when compared to the previous decades. This paper uses the criminal dataset found at “data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2” to describe historical trends, insights, etc. in Chicago from 1965 to 2018 and not to assign any casual interpretation of the vanguards of crime rates during this period. Here K-Nearest Neighbor (KNN) classification is used for training and crime predication. Discussions on future investigation can also be found. The proposed model has an accuracy of 83.2%.
Comparative Study of Artificial Intelligence Techniques for Image Classification
Authors: Udawant Prashant , Patidar Atul , Singh Abhijeet , Yadav Atyant
Published: April 2019

There are various applications of image processing in the field of engineering, agriculture, graphic design, commerce, historical research and architecture. This paper studies and compares most of the research works done in the field of image processing and machine learning for the purpose of image classification based on the features extracted from the image through different feature extraction techniques. The machine learning techniques studied in this paper are Convolution Neural Network (CNN), Support Vector Machine (SVM) and Fuzzy logic. The paper studies and compares these methods for their implementation in classification of digital images. Color based segmentation models are used to segment the specific features from image and categories them into different classes. First image preprocessing is done on the image to reduce the noise from the image. Then image segmentation and edge detection techniques are used to identify the objects in the image and extract the features through which the image can be labeled with a specific class.
Energy Trading through Blockchain
Authors: Uday M. , Prasad P. Devi , Sai N. Jagadeesh
Published: April 2019

Energy Trading through Blockchain is an innovative way to trade solar energy across different places. It enables the owners of solar plants to trade the solar energy that is been produced by their plants in an easy and utmost secure manner using Blockchain technology. The core point of the project is to pay for what is being served. The source and destination stations will be abiding to a smart contract which allows them to pay for exactly the served amount of energy. Each and every transaction is stored in a ledger. Operating the entire system in Ethereum platform provides transparency, security and credibility to the process. The process entirely runs on the transfer of cryptocurrencies and not any other mode of money.
Elixir for Medicine
Authors: B Thiyagarajan. , I Jamuna. , G Keerthana. , B Krithika.
Published: April 2019

The Drug formulation is a proactive part of research in the existing Biomedical Industry is highly money consuming and the general pipeline involved in terms of time is huge that it takes around 10-12 years. People generally get typically skeptical as the time progresses. This is an essential need that needs to be addressed. The process of getting a drug from Initial Discovery to market can take years or decades. Experiments, clinical studies, and clinical trials are necessary.90% of all clinical trials in humans fail even after the molecule compounds have been successfully tested in animals.
Location Based Agricultural Product Recommendation System Using Novel KNN Algorithm
Authors: J Sachin , P Geethatharani , M K Surya , K V Kavin
Published: April 2019

It is evident that the need for personalized product recommendation is much needed these days. Generally, product recommender systems are implemented in web servers that make use of data, implicitly obtained as results of the collection of Web browsing patterns of the users. Here, the project's motive is to provide location-based agricultural product recommendation system using a novel KNN algorithm by ensuring effective communication and transparency in agriculture trade marketing among buyers and sellers (farmers). It helps the farmer to fix up the market price by preventing the rue pricing of their products. The farmer can post their products into the application with price and other details like a timestamp of harvesting, color, size, the absence of pest, freshness, ripeness etc. Based on the location, the distance between the seller and buyer is calculated using great circle distance. An improved Novel KNN algorithm is used to find the K Nearest Seller by calculating the distance between the sellers and buyer using a Euclidean distance metric. The details posted by the farmers and buyers are stored and updated in a database dynamically. The recommender system recommends nearest sellers and their agricultural products based on buyer interest. The performance of the system is analyzed in terms of accuracy and mean absolute error.
A Review on Various Algorithms used in Machine Learning
Authors: Chaudhary Divya , Vasuja Er. Richa
Published: April 2019

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.
A Study on Peer-to-Peer Allocation for File Sharing Systems
Authors: Ravikumar K. , Ambika S.
Published: April 2019

Many Internet renovate supplier (ISPs) have organize peer-to-peer (P2P) hoard in their system in order to decrease valuable inter-ISP transfer. Capable heading question is important to the in general presentation of peer-to-peer (P2P) file contribution systems. Come together aristocracy by their extensive soothe can significantly get better the good quality association of file hesitation. Clustering aristocracy by their corporeal nearness can also get better file enquiry recital. P2P rose to successful profitable system in the internet. It represent the best embodiment of the end to end quarrel, the regularly doubtful plan philosophy that guide the intend of the internet. A work of fiction P2P architecture is planned which attaches ratings to the uploaded text directly. These ratings then develop into as score constituent in its XML statement which has quite a few youngster basics for in twist classification. Eventually, we communicate possible attack on the secrecy of these system and provide examples of demanding attack and martial protector second-hand by the organization we elucidate.
Reception Secure Authentication
Authors: Jain Abheeraj , Khandelwal Roshan , Khandelwal Sahaj , Barhate Deepti
Published: April 2019

Today the technological advancements have offered us opportunities to think farther beyond to what we used to. Now that we have the tools we must also develop a payment system that justifies the world we live in. Our system tries to do the same. Our system is a payment system which works solely on the biometrics. It is an efficient system which makes transaction of money possible just by a simple touch. Along with its efficiency it also brings effective security and reliability to the table. Matters which deal with money must be safeguarded and to do the same we make use of encryption algorithm to send and receive consumer information.
Using Transfer Learning in an Ad Hoc Team
Authors: Sandheer Sannidhya , Sangtiani Tanya , Singh Aditya , Varshney Niharika , Soni Piyush
Published: April 2019

In a practical scenario, we have a myriad of robotic systems; single and multi agent, not operating under any standard communication protocols. This is eminent from a point of view where independent robots can come together to achieve goals as a team. This problem is well defined in the domain of Ad Hoc Teamwork(AHT) which strives for a MAS wherein agents are heterogeneous, independent in their own respect and, as a whole accomplish goals that may be above any individual's capability. A key aspect is transfer learning which allows on the fly addition of team members. This paper shares development in transfer learning in the field of Ad Hoc Teamwork.
Clinical Decision Support Systems
Authors: Tiwari Pratibha , Shah Nishant , Samuel Evanglelin , Shah Poojan , Patel Yask , Tiwari Pratibha
Published: April 2019

Nowadays, every field is digitizing their data for easy access at anytime and anywhere or even for enclosed cabinet servers, especially the health care sector. But, that is not the only reason health care sector is computerizing its data. These huge chucks of records are used for research purposes. Many hospitals are working with education institutes with research departments (Damian Borbolla et.al 2010).CDSS performs Knowledge-based analyses on these EHRs and running disease prediction models on these data is done. There may be many complications. We have reviewed the problems faced by such system from previous researches and implemented systems.
A Gesture Controlled and Cost Effective - Home Automation System
Authors: Malhotra Mohit , Mittal Raghav , Jain Madhur
Published: April 2019

This paper presents a secure and reliable home automation system in order to make everyone’s life simpler and fulfil the needs of the disabled or the elderly. The paper proposes a system based on efficient smart watch that uses arduino microcontroller which is embedded with a WIFI module and enables us to control a number of home appliances such as lights, fans and others using on/off relay. In our work, the appliances need not to be smart beforehand as arduino would be externally connected to them such that the system designed would be controlled by gestures i.e. users can control the working of the appliances by moving their hand in upward or downward direction over the screen. This system is comparatively efficient and cost effective than other proposed systems in terms of scalability and reliability. Also, the proposed system has been thoroughly tested and verified in various environmental conditions.
Nearby Product Recommendation System Based on Users Rating
Authors: Jyoti Akanksha , Roy Abhijeet , Singh Suraj , Shaikh Nabab , Desai Payal
Published: April 2019

The recommendation system is very popular nowadays. Recommendation system emerged over the last decade for better findings of things over the internet. Most websites use a recommendation system for tracking and finding items by the user's behavior and preferences. Netflix, Amazon, LinkedIn, Pandora etc. platform gets 60%-70% views results from recommendation. The purpose of this paper is to introduce a recommendation system for local stores where the user gets a nearby relevant recommended item based on the rating of other local users. There are various types of recommendation systems one is User-based collaborative filtering by which the system built upon and uses user's past behavior like ratings and gives similar results made by another user. In collaborative filtering uses Euclidean distance algorithm is used to find the user's rate score to make relations with other users and Euclidean distance similarity score distinguish similarity between users. K-nearest neighbor algorithm is used to implement and find the number of users like new user where K is several similar users. Integrate with map interface to find shortest distances among stores whose product are recommended. The dataset of JSON is used to parse through the algorithm. The result shows a better approach towards the recommendation of products among local stores within a region.
le of Technology In Education : A 21st Century Approach
Authors: Tamhankar T. K. , Pujari V. I. , Patil R. B.
Published: April 2019

The future of India will be formed in the lecture hall. Whereas India has made great steps in refining the education system but much still remains to be complete. If you see the current situation then you will find that there is a torrent of advanced technology all over the world but our education system is not helping due to the absence of information and information of teachers, students, and the organization. The students have their own limitations, teachers have their owned and the organization is also confessing the fact that the education system is really in a poor form today. The aim of this paper is to skeleton how information technology can help to make an education system that is based on the ideologies of helping teachers, students, and management to be effective in what they do, improving the superiority and significance of teaching-learning process.
A Comparative Study on Load-Frequency Controllers of a Five-Area Interconnected Power System
Authors: Mai Phuong Dao Thi
Published: April 2019

The crucial objectives of load-frequency control (LFC) to a multi-area interconnected power system are to maintain the system frequency at a nominal value (50 Hz or 60 Hz) and the tie-line power flows at predetermined values. Based on tie-line bias control strategy, conventional regulators, such as I, PI and PID, were initially used for solving the LFC problem. Due to the complexity, nonlinearity and uncertainty of a multi-area power system in practice, the conventional regulators may not obtain the control performances good enough to bring the network back to the steady state as soon as possible. Meanwhile, intelligent controllers, such as fuzzy logic (FL)-based controllers, are able to completely replace these conventional counterparts. The superiority of the FL-based LFC controllers over the conventional ones for a typical case study of five-area interconnected power grids is validated in this paper through numerical simulations implemented in Matlab/Simulink package. It should be apparent from this comparative study that the LFC controller based on FL technique is a feasible selection in dealing with the LFC problem of a multi-area power network.
Networking Android App for Sociality
Authors: Rajadurai R. , Bhalaji S. V. , Puvanesh M. , Raagul S. , Kumar R. Aravind
Published: April 2019

This paper we described about the technologies and working of the real time android application that is completely based on the networking protocols. The application that we are constructing is based on the modules, databases and a real time server. The application that we have built must enraged in the easiest methods and strategies for the benefit in society.
Content-Based Image Retrieval : A Comprehensive Study
Authors: Aman Er , Rawat Amit , Giri Ashwin , Gothwal Hardik
Published: April 2019

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.
Phone Addiction Monitoring : An android Application for Smartphones
Authors: Gokhru Rishabh , Gelot Kautilya , Kotecha Darshit , Hankare Piyush , Kansagra Zalak
Published: April 2019

Phone Addiction Monitor is application which is based on android platform. The “Phone Addiction Monitor” is to developed for manage the time of phone usage and overcome the spending time on social media. Using an advanced algorithm, the app studies your phone usage and calculates addiction score in real time. Show the graph on daily, weekly & monthly basis, so it is easy to analyse the data and manage time. Application show the Analysis application wise so you can see how much time you spend on each application. An adult checks his phone a taping 110 times a day. That makes it once every 14 minutes. And for most people this peak to once every 6 seconds in the evenings. So, people easily track the phone usage
Breast Cancer Prediction using SVM with PCA Feature Selection Method
Authors: Yadav Akshya , Jamir Imlikumla , Jain Raj Rajeshwari , Sohani Mayank
Published: April 2019

Cancer has been characterized as one of the leading diseases that cause death in humans. Breast cancer, being a subtype of cancer, causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. Also, the medical tests conducted in hospitals for detecting cancer is expensive and is difficult for any common man to afford. To counter these problems, in this paper, we use the concept of applying Support Vector machine a Machine Learning algorithm to predict whether a person is prone to breast cancer. We evaluate the performance of this algorithm by calculating its accuracy and apply a min-max scaling method so as to counter and overcome the problem of overfitting and outliers. After scaling of the dataset, we apply a feature selection method called Principle component analysis to improve the algorithms accuracy by decreasing the number of parameters. The final algorithm has improved accuracy with the absence of overfitting and outliers, thus this algorithm can be used to develop and build systems that can be deployed in clinics, hospitals and medical centers for early and quick diagnosis of breast cancer. The training dataset is from the University of Wisconsin (UCI) Machine Learning Repository which is used to evaluate the performance of the Support vector machine by calculating its accuracy.
Comparative Study of Machine Learning Algorithms for Breast Cancer Prediction - A Review
Authors: Yadav Akshya , Jamir Imlikumla , Jain Raj Rajeshwari , Sohani Mayank
Published: April 2019

Cancer has been characterized as one of the leading diseases that causes death in humans. Breast cancer being a subtype of cancer causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. In this paper, different machine learning algorithms which can be used as tools by physicians for early and effective detection and prediction of cancerous cells have been studied and introduced. The different algorithms introduced here are ANN, DT, Random Forest (RF), Naive Bayes Classifier (NBC), SVM and KNN. These algorithms are trained with a dataset that contain parameters describing the tumor of a person having breast cancer and are then used to classify and predict whether the cell is cancerous.
Automatic Number Plate Recognition System Using Optical Character Recognition
Authors: Arora Monika , Jain Anubha , Rustagi Shubham , Yadav Tushar
Published: April 2019

In the last few decades, the number of active vehicle population has increased drastically which has made it difficult for the authorities to keep a track of them as well as to identify the vehicle owner in case of any traffic violation. Automatic Number Plate Recognition System (ANPR) is a real-time machine-intelligent and embedded system which identifies the characters directly from the image of the number plate. Due to crucial research and development of technology and the increasing use of vehicles, the need for a machine-oriented recognition and monitoring system is of immense importance. The technology has become a major requirement and is playing a crucial role in a vast sea of applications related to automated transport monitoring and control system such as traffic monitoring, challan management, detection of stolen vehicles, electronic payment of tolls on highways or bridges, parking lots access control, etc. This technology requires extensive mobility and station flexibility which causes it to be installed on such hardware that is very mobile enough so that the operator can use it very efficiently. ANPR System through the use of Optical Character Recognition (OCR) makes the system to be used as an application on smartphones. This provides the operator to use the system and identify number plates by just capturing the image and processing by neural networks working in the background of OCR. The ANPR system as a whole will result in easy and safe monitoring of the traffic and to keep an easy record in case of any violation. Also, it will save individuals to save their time in standing at long queues at toll taxes and paying cash which will be done with the ANPR system and using E-wallet.
Convenient Electricity Billing System through QR Code Android Application : A Review
Authors: Desai Abhay , Bhalani Mital , Chaudhary Ved Prakash
Published: April 2019

The electrical meter reading and bill generation process is very complex and exhaustive as it is done manually. So this project is developed for the betterment of the Electric meter reading and payment process. This solution would provide better performance, integrity, consistency and many more. We have tried to achieve the solution for convenient electric meter reading through QR code. In this project, we have tried to remove the drawbacks. It was found that the human effort is too much in the traditional electric meter reading and efficiency of traditional process is much less than it could be with this new process. We would provide an improved system for this project. For which we would be converting the traditional manual meter reading process which is done by the company worker removing the human error through modernizing it in some way So, there is no chance of any kind of fraud happening with customers. We would use QR code scanning for smart meter reading and android base application that will provide support to this system by providing previous data of that particular user. We would create an application for user interface. So the user doesn't get into a complex situation to use this system.
To Alleviate Depression by Interactive Artificial Conversation Entity
Authors: Jha Uditesh , Khant Keyur , Kotadiya Milan , Gamdha Kirti , Kansagra Zalak
Published: April 2019

This work is developed to alleviate depression. We provide transparency and privacy between user and chatbot by using the best approach which proves to be more reassuring, empathetic and non-judgmental. This new technology is interactive artificial conversation entity - chatbot. In this project, we are trying to relieve sufferer from depression. It is found that depression is the leading cause of disability globally. Projections indicates that after heart disease, depression is expected to become the second leading cause of disease burden by the year 2020. We will provide psychiatric treatment to users. After understanding the symptoms, causes and treatments of depression, user will be judged and treated according to defined and well-suited treatment. It will be helpful to those having depression, fear of sharing and fear of being judged. Also, it will help at those places where therapists are not easily available and to people who cannot afford therapist. A mobile application will be created for user interface.
Progressive Web App (PWA) - One Stop Solution for All Application Development Across All Platforms
Authors: Sharma Varsha , Verma Rajat , Pathak Vaishali , Paliwal Muskan , Jain Priya
Published: April 2019

Previously specific features were found in native apps or natively developed apps. There were several various and distinct platforms for development of those features. This was known as cross platform approach; today we have a new approach which is known as progressive web application which can be implemented through a set of latest technologies. This application can then be used on all or almost all platforms. In this research paper we suggest that progressive web applications will suffice the need for native applications. First, we introduce the topic then we will scrutinize the performance and compare the recent specifications provided by each of the technologies for web application development across platforms.
Stock Market Prediction using Machine Learning Techniques
Authors: Agrawal Saurav , Thakkar Dev , Soni Dhruvil , Bhimani Krunal , Patel Chirag
Published: April 2019

Prediction of Stock Market has been an area of interest for investors as well as researchers from a long time due to its intrinsic volatility, complex and regularly changing in nature makes it difficult to make reliable prediction. So, predicting daily behaviour of stock market is a serious challenge for investors and corporate stockholders. The objective of this paper is to predict the market performance by using Artificial Neural Network. These techniques are used to classify the stock in 3 categories – Buy, Hold and Sell, based on historical data while providing an in-depth understanding of the models being used. The Study shows that logistic regression model compared to Linear Regression can be used by the investors, individual as well as fund managers to predict “good or poor” stock. Because of the data being Non-Linear we will be using artificial neural network to classify Non-linear data using hidden layers.
Generic Disease Prediction using Symptoms with Supervised Machine Learning
Authors: Pal Ashish Kailash , Rawal Pritam , Ruwala Rahil , Patel Vaibhavi
Published: April 2019

Data Mining and Machine Learning plays most inspiring area of research that become most popular in health organization. It also plays a vital part to uncover new patterns in medicinal science and services association which thusly accommodating for all the parties associated with this field. This project intend to form a diagnostic model of the common diseases based on the symptoms by using data mining technique such as classification in health domain. In this project, we are going to use algorithms like Random forest, Naive Bayes which can be utilized for health care diagnosis. Performances of the classifiers are compared to each other to find out highest accuracy. This also helps us to find out persons who are affected by the infection. The test based on the outcomes of the diseases.
Student Future Prediction Using Machine Learning
Authors: Chaudhary Dileep , Prajapati Harsh , Rathod Rajan , Patel Parth , Gurjwar Rajiv Kumar
Published: April 2019

Selecting an appropriate career is one of the most important decisions and with the increase in the number of career paths and opportunities, making this decision have become quite difficult for the students. According to the survey conducted by the Council of Scientific and Industrial Research's (CSIR), about 40% of students are confused about their career options. This may lead to wrong career selection and then working in a field which was not meant for them, thus reducing the productivity of human resource. Therefore, it is quite important to take a right decision regarding the career at an appropriate age to prevent the consequences that results due to wrong career selection. This system is a web application that would help students studying in high schools to select a course for their career. The system would recommend the student, a career option based on their personality trait, interest and their capacity to take up the course.
Logistic Network Scheme using Block Chain and Internet of Things (IoT)
Authors: Padmini S.
Published: April 2019

The older method for the management of the flow of goods and services is constantly a website which is presented on a computer, the business organization has established or presented from the supplier. The logistic chain structure information is held at this place, parting the website as the single entryway to the information, thus decreasing accessibility of information, making the risk factor high which leads to an obstinate computer where all renewals must direct to accommodate computer. All bookings, renewals, conversions, pursuing information, client’s information, suppliers, and producers of resources, constructors and all others must be dispersed. This is aim of presenting conventional management of flow of goods and services to the block chain. This devolution of this information through various stages varying between resources producer to seller would tend to a very supple as well as clear computer. These qualities will then result into a computer where the information streams quicker than conventional unified method to management of flow of goods and services. Usage of IoT makes sure which there is no discrepancy present in procedures which are in transport, mechanizing the modernizing procedures which need more individual interference.
Identity-Based Encryption Anonymous Communication on the Internet
Authors: Ravikumar K. , Kathiravan T.
Published: April 2019

Inscrutability is an significant characteristic in a lot of two gathering communication systems. In the era of large data, data confidentiality has happen to significant as extra individual and managerial information is involved. Furthermore, the globe has turn into extra diversify and independent than constantly facing. some peer-to-peer approach have been future in direct to improve the scalability concern of the mount system, excluding they are just capable to provide heuristic protection; in reality, the protection community has been fairly victorious at betrayal the condition of the ability systems using together inert and dynamic attacks. We explain the follow attack as fine as known protections next to these attacks: precursor attacks, crossroads attacks, timing attacks, and Sybil attacks. Earlier occupation find it complicated to implement encryption with network coding not including revealing in total routing information, consequently losing time alone and inscrutability. We compare our system with other existing move towards for various networks. Thus RIOMO securely communicates linking nodes not including disclosing node identities; it also provides dissimilar attractive unidentified properties such as identity privacy, location privacy, direction inscrutability, and sturdiness next to more than a few attacks.
Heart Disease Prediction System Using K- Nearest Neighbour Classification Technique
Authors: V. S Sowbarnica , V Vismaya , M Vidhyapoonthalir , Bhuvana S.
Published: April 2019

The heart is an operating system of the human body .If it does not function properly it will affect other parts also. Heart disease problem describes a range of conditions that affect your heart. The existing system uses Support Vector Machine (SVM), it propose a system for heart disease prediction. The method will help doctor to explore their data and predict heart disease accurately. The Hospitals do not provide the same quality of service even though they provide the same type of service. The Proposed system includes the following phases: Pre-Processing of the input data with Min-Max scalar and Normalization ,Feature extraction by PSO algorithm, Classification of data by K-Nearest Neighbour. In comparison with the existing approach ,the proposed approach significantly improves the accuracy from 51% to 76.66%.
An Overview of Apache Pig and Apache Hive
Authors: Arora Saiyam , Verma Abinesh , Vasuja Richa , Vasuja Richa
Published: April 2019

Ever since the enhancement of technology has taken place, the data is growing at an alarming rate. The most prominent factor of data growth is the “Social Media”, leads to the origination of a tremendous amount of data called Big Data. Big Data is a term used for data sets that are extremely large in size as well as complicated to store and process using traditional database processing applications. A saviour to deal with Big Data is “Hadoop” and two major components of Hadoop which are HDFS (Distributed Storage) and Map Reduce(Parallel Processing). Apache Pig and Hive is an essential part of the Hadoop Ecosystem. This paper covers an overview of both Apache Pig and Hive with their architecture. As Hadoop, no doubt is doing tremendously great work by storing and processing the huge volume of data but there are more frameworks now a days to increase the efficiency of Hadoop framework which are basically seen as the layers of Hadoop or a part of Apache Hadoop project. And that is why this paper includes the two most important layers namely Apache Pig and Apache Hive.
Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments
Authors: Nithya R Anitha , A Saran , R Vinoth
Published: April 2019

Minimizing the energy consumption and resource usage in cloud computing environment is one of the key research issues. Energy aware resource allocation is used to optimize the power consuming by computer resources and storage in cloud. The proposed system is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. Using migration for minimizing the number of active physical nodes the dynamic single threshold VM consolidation leverages fine-grained fluctuations in the workloads and continuously reallocates VMs . A genetic algorithm based power-aware scheduling of resource allocation (G-PARS) has been proposed to solve the dynamic virtual machine allocation policy problem. The experiment results show that strategy that has been proposed has a better performance than other strategies, not only in high Quality Of Service(QoS) but also in less energy consumption.