bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heart disease prediction using machine learning techniques
Authors: Garg Apurv , Sharma Bhartendu , Khan Rijwan 
Published: January 2021

Abstract
Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. Algorithms like K-Nearest Neighbor (KNN), Random Forest are used to classify people who have a heart disease from people who do not. Two supervised machine learning algorithms are used in this paper which are, K-Nearest Neighbor (K-NN) and Random Forest. The prediction accuracy obtained by K-Nearest Neighbor (K-NN) is 86.885% and the prediction accuracy obtained by Random Forest algorithm is 81.967%.
Difference computation using change identification techniques for structured web documents
Authors: Arora J , Ramkumar K R 
Published: January 2021

Abstract
In this era of the competitive world, one needs to stay updated with all the information that is required for their professional and personal growth. But due to vast information, it is difficult to cope up with the ever-changing data. WWW is the main source of data. It consists of thousands and millions of web pages, out of which some are static pages while the other is dynamic pages. The contents of the dynamic web pages such as news websites, stock prices, weather broadcast change frequently. The changes include the insertion of some new data, deletion of data, or modification of existing data. To detect the changes that are variedly occurring in these web pages, various change detection tools and algorithms have been developed. This paper will discuss the categories of changes occurring on the structured document and their corresponding algorithms and tools which can be applied to retrieve the relevant changes.
A Survey on Emerging Software-Defined Networking and Blockchain in Smart Health Care
Authors: Wadhwa Shivani , Babbar Himanshi , Rani Shalli 
Published: January 2021

Abstract
With assistance from Software Defined Networking (SDN) and Blockchain, networks have become more creative to build and maintain over the last few years. The inflexibility of modern network architecture is presenting researchers with a tough achievement. SDN replaces existing inescapable and complicated networks with a creative way of separating the control plane from the data plane and fixing those constraints, namely configurations done manually, monitoring, protection, usability, and functionality. This paper provides an overview of blockchain and SDN in healthcare. Various existing projects of their integration have been studied the benefits of the Blockchain provides security, privacy, integrity to the health care data has been discussed. The decentralization feature of blockchain provides access to data within and across the nation. Different use cases are discussed in the paper that provides the acceptability of blockchain and SDN in healthcare. This paper provides the overview and results that integration of blockchain and SDN in healthcare is a good research area and various initiatives have been taken to explore its integration.
Application of IoT in Current Pandemic of COVID-19
Authors: Kumar Mukesh , Nayar Nandini , Mehta Gaurav , Sharma Abha 
Published: January 2021

Abstract
As we know that Internet, a rebellion innovation has transformed everything, Internet of Things has made a hope for excellent future of Internet with Machine-Machine (M2M) type of communication. This review paper has also shown that it is possible and affordable to construct these smart systems based on Internet of Things (IoT). IoT delivers exceptional advancement in the healthcare domain. This paper explores the roles of IoT that revolutionize the healthcare domain by providing enormous healthcare benefits to the mankind by offering affordable and practical healthcare solutions. The main focus of this paper is to discuss the role of IoT in smart hospitals and its significance to deal with pandemics is also highlighted. For serving the community-specific needs during spread of pandemic, various smart devices can be utilized that can provide varied functionalities including proper monitoring of high-risk patients, tracking their bio-metric measurements and capturing real-time data. We have also studied various blueprints which can sense unforeseen happenings using numerous sensors and reveal the facts accumulated on LED display. Observational outcomes have shown good agreement with the hypothetical statements.
IoT based Forest Fire Detection System in Cloud Paradigm
Authors: Singh H , Shukla A , Kumar S 
Published: January 2021

Abstract
The most common hazard in forest fires are accident as the forest themselves destroys the forests and can be great threat to wild life and peoples. The Internet of Things (IoT) is the physical device which is used to connect, store and enable the objects to collect information for exchanging the data through the internet based system. The main objective of paper is to predict the developing trend of the fire by monitoring temperature, humidity, etc. There are several types of sensors which can be used for the collections of temperature and humidity data as an input for IoT based system which increases the efficiency of process of fire detection. The proposed mechanism can be used a threshold value of each sensor to validate the data and take initiative to avoid the prerequisite of achieving high reliability prevention using this mechanism.
Enhanced Bee Colony Approach for reducing the energy consumption during VM migration in cloud computing environment
Authors: Talwani Suruchi , Singla Jimmy 
Published: January 2021

Abstract
To achieve virtualization in a cloud environment, resource utilization and energy need to be handled carefully. For this one should have to manage the workload, by distributing the load equally among the node. So that, the resources should be distributed equally among the cloud user and access data anytime from anywhere with minimum energy. In this paper, an enhanced Artificial Bee Colony (E-ABC) approach is presented to minimize overall energy consumption with minimum number of migrations. E-ABC approach migrates the VM from the overloaded host to underloaded hosts and hence save energy. The enhancement of the proposed work is exhibited by showing comparison with the Enhanced Cuckoo Search (E-CS) approach and Ant Colony Optimization technique using MATLAB simulator. Enhancement in the reduction of energy consumption of about 15.45 %, and 17.03 % is observed against E-CS, and existing work.
Efficient Intrusion detection of malicious node using Bayesian Hybrid Detection in MANET
Authors: Sangeetha V , Vaneeta M , Swapna Kumar S , Pareek Piyush Kumar , Dixit Sunanda 
Published: January 2021

Abstract
In the past several years there have been considerable interest developed towards study on distributed networks. The key underlying application under such technology is mobile ad hoc networks (MANETs), which have been exploiting the range of research opportunity. In MANET due to infrastructure less network and dynamic topology changes, security becomes one of the important issues. The defense strategies such as intrusion detection system (IDS) impose a method to build efficient detection of malicious nodes. Game theory is mainly used to study security problems identification in MANET. The Bayesian Hybrid Detection (BHD) is applied to detect the malicious nodes. A BHD allows the defender to adjust based on opponent observation. The simulation is carried out using the MATLAB for malicious nodes detection. The security degree is measured by the payoff index and system stability index (SSI). Also the processing vs. accuracy index level is measured to identify reliability of detection. The proposed system enables for enhancing security in MANET’s by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET.
Improved Safety of Self-Driving Car using Voice Recognition through CNN
Authors: Totakura Varun , Vuribindi Bhargava Reddy , Madhusudhana Reddy E. 
Published: January 2021

Abstract
Deployment of Artificial Intelligence (AI) in daily life will show a new way to the future, the complex task will become easy. One of such evolution of AI technology is the creation of the Self-Driving cars. But according to the statistics, there are some cases where the Self-Driving cars killed people due to the failure in the prediction or hardware problem. So, to overcome those kinds of situations the AI models should be accurate and also humans should take part in making them perfect. For example, when there are some passengers in a self-driving car and the car has lost its control due to the fault in the hardware of the computer installed in the car then an authorized person in the car should be able to instruct it using voice commands to control the car at that moment. The main focus of this paper relies on the development of Self-Driving Cars using the Convolutional Neural Networks (CNN), drawbacks and solutions for the drawbacks. The Self-Driving Car CNN model was trained using the Asphalt-8 game data and the Voice command prediction CNN model was trained with 3 different persons (1-Kid, 1-Man, 1-Woman) voices. The accuracy of both the CNN models were 99% and was tested on the same game where they have produced their best results.
Detection of Diabetic Retinopathy using Deep Mining
Authors: Pradeep Kumar KG , Karunakara K , Thyagaraju GS , Dixit Sunanda 
Published: January 2021

Abstract
In this paper, we propose a procedure for diagnosing diabetic retinopathy and detecting other suspected areas by using Convolution Neural Network (CNN). The paper is divided into two research areas: First is to identify and predict the highly suspicious region in the high-resolution suspicious patches using Deep-in-Net which can generate disease detection accurately. Second is four bounding boxes generated using machine learning technique by automatically reading maps that gives 80% of information of the eyes labelled by experienced ophthalmologist. This technique has good ability to seek attention mapping. The experiment has two datasets, EyePACS and Messidor.
Growth of Digitization and its Impact on Big Data Analytics
Authors: Garg A , Popli R , Sarao B S 
Published: January 2021

Abstract
The world is changing in every aspect with innovations and technologies. In the current era, everyone needs to use technology smartly. The Internet, web and smartphones have become a necessity of today’s life. The developed countries have already migrated their work from manual mode to digital mode. The developing countries are also striving for the overall national development using digitisation. In the last few years, India, as a developing country, also focused its efforts and took concrete steps in this regard, e.g., the Jan-Dhan Yojna, demonetisation, Digital India, etc. In the last five years, the usage of digital transaction increased in every sector. During the COVID-19 pandemic, the digitisation proved to be the optimal solution. During the lockdown period, only those sectors could survive, which had deployed digital services. Everyone needs to move to digitisation for the overall development of one society and country.
New path trajectory followed by Single Anchor by using Salp Swarm Algorithm in Wireless Sensor Network
Authors: Kaur Pardeep , Rani Shalli 
Published: January 2021

Abstract
In the various applications of wireless sensor networks (WSN), the event occurrence information or data sensed by the sensor node has no significance until the location of an event/sensor node is not known. Therefore, localization is an important parameter in WSN for tracking these deployed sensor nodes in the sensing area. To localize these sensor nodes, there are various existing localization models in available detailed literature. One solution to this problem is to deploy a set of static anchor nodes (GPS equipped) in the sensing area, that broadcast their coordination information to help unknown nodes to localize themselves. Another way is to use a single mobile anchor node that will follow a specific path and localize unknown nodes. But a mobile anchor node must follow an optimal path that helps to achieve high accuracy and able to localize a large number of unknown nodes in less computational time. In this paper, the author design and developed a localization algorithm in which a GPS equipped single mobile anchor node follows a new path trajectory i.e. spiral pentagon. Once the target node falls under the communication diameter of the virtual node, six virtual anchor nodes are projected in the shape of a ring around the anchor node and localize all the unknown nodes in the sensing area by using the application of Salp Swarm Algorithm (SSA) for optimization. The hypothesis of six virtual anchor nodes is to avoid the problem of Line of Sight. This proposed method assured that each target node can estimate their node coordinates at least once with a low localization error.
Depression Detection from Social Media Posts Using Multinomial Naive Theorem
Authors: Chatterjee Rinki , Gupta Rajeev Kumar , Gupta Bhavana 
Published: January 2021

Abstract
All 350+ million people worldwide are suffering from mental disorder called Depression. An individual who is suffering from depression functions below average in life, is vulnerable to other diseases and in the worst-case, depression leads to suicide. There are many restraints preventing expert care from reaching people suffering from depression in time. Impediments such as social stigma associated with mental disorders, lack of trained health-care professionals and ignorance of the signs of depression owing to a lack of awareness of the disease. The World Health Organization (WHO) suggests that people who are depressed are regularly not correctly diagnosed and others s who are misdiagnosed are prescribed antidepressants. Thus, there is a strong need to automatically assess the risk of depression. Social media platforms increasingly come closer to become a true digitization of the human social experience. In many cases people would in fact prefer to express themselves online than offline. In this paper, we are using Facebook comments as data set, and based on it categorizing the users as depressed or non-depressed.
Analysing search engine optimization techniques for a full stack web application
Authors: Shivani G , Mutreja Parth , Nagrath Preeti 
Published: January 2021

Abstract
In this paper, we firstly discuss the step by step process to build a fully functioning full stack app and the technologies used while doing so. Following that, a research on various search engine optimization techniques is done for the deployed site of the app. Upon completion of the project and research, we arrived at the algorithm which showed the best results within a given time frame. It was concluded that Google page rank algorithm works the best in this case and also yielded positive results. With that we improved the ranking of our MERN stack application. We also implemented other common techniques for the search engine optimization of a site and have shown results for the same. The factor which significantly improved the ranking of our site was found to be generating more traffic. As the site traffic increased, the cPR score of our site also went up, which was also visible from the increase in the number of clicks.
Movie Recommender System using Single Value Decomposition and K-means Clustering
Authors: Rahul Mayur , Kumar Vinod , Yadav Vikash , Rishabh  
Published: January 2021

Abstract
The recommender system is technique and tool to filter the massive overloaded information for suggesting most useful information to the user in a personalized manner. In the period of “Big Data”, the researcher experiences many problems to process big data accurately and efficiently. In this work, we introduce an efficient model using Single Value Decomposition (SVD) as a method for dimensions reduction and K-means clustering as classification method. Our proposed method and its corresponding results have been evaluated and compare results with other existing methods using metrics like standard deviation (SD), mean absolute error (MAE) root mean square error (RMSE), t-value, dunn index, average similarity and computational time using two publicly available datasets using Flixter dataset and MovieLens dataset. The result demonstrates that our proposed method is able to outperform other existing methods.
Predicting defects in object-oriented software using cost-sensitive classification
Authors: Malhotra R , Jain J 
Published: January 2021

Abstract
In this software era, it is vital to produce reliable and good quality software. Early detection of defects aids in building accurate software with reduced cost and other resources. Researchers have a keen interest in producing machine learning models for effective and accurate software defect prediction in the early stages of software development. Object-oriented metrics of the software are used in developing these models. These models may result in biased predictions owing to the class imbalance problem existing in most of the software datasets. This paper provides an effective defect prediction framework for imbalanced data by employing cost-sensitive classifiers and stable performance measures like GMean, Balance, and AUC. Four decision tree-based classifiers with different cost ratios are investigated to predict defects in three Apache projects. The empirical results are statistically validated using the nonparametric Friedman test and Wilcoxon signed-rank test. The results state with 99% confidence that the predictive capability of J48, AdaBoostM1, Bagging, and RandomSubSpace improved after employing cost-sensitive learning for the four classifiers used in this study.
Technological stability of processing the machine part surface by methods of surface plastic deformation with elastic action tools
Authors: Nagorkin M N , Fyodorov V P , Kovalyova E V 
Published: January 2021

Abstract
The article presents the results of physical modelling of the processes of diamond burnishing of machine part surfaces with a complex differentiable profile and the concept of technological stability of treatment processes by methods of surface plastic deformation of surfaces with elastic action tools. The criteria for technological stability of the treatment process under the specified conditions are determined.
Design embedded control system based controller of the quasi time optimization approach for a magnetic levitation system
Authors: Nguyen C X , Pham T D , Lukynov A D , Tran P C , Truong Q D 
Published: January 2021

Abstract
The magnetic levitation system is a typical system with many successful applications in practice. Due to the inherent instability and strong open-loop nonlinearity of the MLS, a controller is used to control the stability of the magnetic levitation system. With the rapid development of embedded systems, the intelligent digital control has begun to replace conventional analog control technology creating a new approach to the control MLS. This paper proposes a hardware module for the MLS based on a digital signal processor combined with a fast acting controller to ensure system stability even with incomplete mathematical models. The simulation and experimental results are compared with the linearized feedback control law. Finally, experiments are carried out to test the practical feasibility of the proposed control laws in the MLS embedded control system. The system, with the recommended controller, well responds to the tolerances allowing for stable system working. Both simulation and test results are included in this paper to show that the fast acting suboptimal controller has the advantage of being more durable and less complicated to perform in MLS control applications.
Physical and mathematic N-option modeling and optimization of dynamically loaded nonlinear technical systems
Authors: Kolesnikov I V , Shapovalov V V , Ozyabkin A L , Kharlamov P V , Burakova M A , Kornienko R A 
Published: January 2021

Abstract
The development of modern industries significantly depends on the effective usage of innovative research methods and optimization of technological processes. Modeling methods are one of the science-intensive methods put into practice in research and diagnostics of both the current state of mechanical systems and predicting changes in the state of technological equipment during operation. The authors have developed and successfully adapted fundamental theoretical foundations of physical and mathematical modeling for solving problems of optimization and simulation of dynamically loaded nonlinear technical systems. A method for diagnosing tribo-couplings of helicopter tail drive couplings is proposed.
Thermo-metal-cladding of working surfaces of closed friction units of mobile systems
Authors: Shapovalov V V , Kolesnikov I V , Burakova M A , Kharlamov P V , Scherbak P N 
Published: January 2021

Abstract
Modeling methods play a significant role in the operation, diagnostics, and prediction of the current state of various mechanical systems. The authors have developed fundamentally new approaches to solving problems of optimization, increasing the efficiency and competitiveness of nonlinear technical systems. Test facility investigations of closed friction units of mobile systems using a lubricant with the aluminum powder insertion were carried out. Test bench allows simulating a reciprocating motion. The analysis of research demonstrates the stabilizing effect of such a lubricant on the tribothermodynamics of the friction unit.
Improving the efficiency of technical means and methods for diagnostics of isolation of high-voltage distribution networks
Authors: Yurov A A , Voronov A S 
Published: January 2021

Abstract
This work is aimed at developing and improving diagnostic equipment used in high-voltage distribution networks by modeling electromagnetic transients and expanding the functional part of devices. Presented are models of a three - phase distribution network and a high-voltage part of a test installation forming a technological system “diagnostic device-distribution network”. Examples are shown of wave processes in the simulated system during insulation breakdowns, load characteristics of the test installation during operation of the device are shown, which makes it possible to optimize insulation control methods and technical diagnostic tools for electrical laboratories. Disclosed are examples of calculating the location of damage in case of insulation failures in different sections of the controlled network based on the analysis of wave processes in the line.
Obtaining and physicochemical properties of cobalt-containing thermolysis products complex compounds
Authors: Burlakova V E , Drogan E G , Plekhanova E V , Plekhanova T G , Karyukov E V 
Published: January 2021

Abstract
Thermal decomposition of complex compounds of cobalt ataconate phenanthroline and cobalt itaconate dipyridine in order to obtain nanoparticles. The size and shape of the particles of the substance was studied by atomic force microscopy with using a PHYWE Compact scanning probe microscope. The presence of functional groups, as well as the qualitative presence of nanoscale cobalt particles were investigated by IR spectroscopy. As a result of research, it was revealed that as a result thermal decomposition of complex cobalt compounds it is possible to obtain nanosized powder of cobalt oxide. It was found that the particle diameter of the obtained powder does not exceed 15 nm, and the particle size along the Z axis does not exceed 24 nm.