bluuebunny/crossref_metadata_2025 huggingface dataset:
Loan default prediction using decision trees and random forest: A comparative study
Authors: Madaan Mehul , Kumar Aniket , Keshri Chirag , Jain Rachna , Nagrath Preeti
Published: January 2021

With the improving banking sector in recent times and the increasing trend of taking loans, a large population applies for bank loans. But one of the major problem banking sectors face in this ever-changing economy is the increasing rate of loan defaults, and the banking authorities are finding it more difficult to correctly assess loan requests and tackle the risks of people defaulting on loans. The two most critical questions in the banking industry are (i) How risky is the borrower? and (ii) Given the borrower’s risk, should we lend him/her? In light of the given problems, this paper proposes two machine learning models to predict whether an individual should be given a loan by assessing certain attributes and therefore help the banking authorities by easing their process of selecting suitable people from a given list of candidates who applied for a loan. This paper does a comprehensive and comparative analysis between two algorithms (i) Random Forest, and (ii) Decision Trees. Both the algorithms have been used on the same dataset and the conclusions have been made with results showing that the Random Forest algorithm outperformed the Decision Tree algorithm with much higher accuracy.
Comparison between attendance system implemented through haar cascade classifier and face recognition library
Authors: Malhotra Samiksha , Aggarwal Vaibhav , Mangal Himanshu , Nagrath Preeti , Jain Rachna
Published: January 2021

Face detection and face recognition are the most widely used features of machine learning(ML) and deep learning(DL). These features are slowly gaining popularity in fields like surveillance through CCTV cameras, mobile phone security (biometric locks), etc. This paper presents a college attendance system based on the above mentioned feature which automatically marks the attendance of the students through the live feed by the CCTV cameras in the classroom. This system saves time, works more efficiently, then manually marking the attendance by the roll call. System was built with the haar cascade features, open CV and face recognition library. These are discussed in the proposed paper. We have presented a comparison between the two models discussed and concluded that haar cascade features work more efficiently than the inbuilt face recognition library for the college attendance system.
Heart disease prediction using machine learning techniques
Authors: Garg Apurv , Sharma Bhartendu , Khan Rijwan
Published: January 2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Magnetic-pulse of assembly of details one-piece compositions
Authors: Strizhakov E L , Nescoromniy S V , Inasaridze G I , Stroganov A N
Published: January 2021

The article discusses the technological processes of magnetic-pulse assembly for obtaining one-piece metal, metal-glass and cermet units. The analysis of connection of stranded wires with lugs of on-board cable networks of aviation and space technology, assembly of metal-glass fuses and sintered assemblies of electrovacuum devices is carried out.

The use of a uniform, radially remote electromagnetic effect on the assemblies to be assembled makes it possible to carry out a denser assembly of conductive conductors with lugs, to refuse soldering and to provide heat removal in the insulating assemblies of microwave devices.
Modelling of the stress-strain state of the lower jaw
Authors: Avilov A V , Avilova N V , Tananakina E S , Sadyrin E V
Published: January 2021

Mathematical modelling is being used in modern dentistry to solve a wide range of scientific problems. The models allow one to predict and estimate the medical intervention impact during the calculations of stress-strain state of prostheses and medical devices. In the present research a mathematical model of the lower jaw, considering the individual characteristics of the geometry, properties of bone tissue and activity of the patient’s mastication muscles using the finite element method is proposed. The work provides an example of determining the operational characteristics of dentoalveolar structures. The analysis of displacements, tensions, and deformations for a particular tooth as well as the selection of the implant diameter were presented as results.
Indicator polymer coating with built-in fiber optic piezosensor
Authors: Pan’kov A A
Published: January 2021

A mathematical model of the functioning of the indicator polymer coating with a continuous optical fiber piezoelectroluminescent sensor built in the form of a flat spiral has been developed. The informative light signal occurs due to the mechanical-luminescent effect caused by the interaction of the piezoelectric and electroluminescent elements in the sensor. Informative characteristics are described and algorithms are given for numerical processing of sequence of output (recorded) light pulses from optical fiber of sensor for indication, location and identification of external mechanical impact and tactile effects. The results of numerical simulation of the distribution of intensity values of light outputs and locations along the length of the sensor of activated (due to external mechanical applied load and “mechanical-luminescent effect” in the sensor) sections of the electroluminescent layer of the sensor are presented; this distribution corresponds to the distribution of intensity values of light pulses at the output from the optical fiber over various time points for model geometric shapes of bodies contacting the tactile surface.
Features of the database implementation for materials for industry and medicine
Authors: Litvinenko A , Karotkiyan R
Published: January 2021

The paper discusses issues related to the creation of a database of materials for industrial and medicine applications. This database is developed to accumulate information about the mechanical and other properties of materials under study and the corresponding sources of information. The paper mainly focused on organizing the change log of the described database. The change log allows one to store, display and analyse all changes made in the database. The technical solutions are presented allowing to implement universal models for logging changes that are invariant with respect to the composition and structure of the tables used.
Calculation of the Energy of Permanent Magnets by the Method of Point Magnetic Moments
Authors: Balaban A L , Bakhvalov Y A , Grechikhin V V , Yufanova J V
Published: January 2021

The article proposes to use the gridless method of point magnetic moments to calculate the energy of devices with permanent magnets. The method makes it possible to exclude the operations of integration over the volume and surfaces, replacing them by summing the contributions of the moments, and significantly reducing the total number of unknowns. The method allows the use of well-known sources, namely charges. The stages of the method implementation are given and examples of its implementation are considered. The calculation results coincide with the known analytical solutions for bodies in the form of a ball and a cylinder. Numerical analysis showed that the total field of a permanent magnet, including the field of the permanent magnet itself and the field of the surrounding space, consisting of piecewise homogeneous subdomains, is equal to zero. An example of the placement of point magnetic moments in a permanent magnet in the form of a parallelepiped is shown. The calculation of its field showed the effectiveness of the method in comparison with the finite element method. The results obtained make it possible to recommend the use of the method of point magnetic moments for calculating the energy of permanent magnets.
The method of spatial-temporal reconstruction of dynamic images based on a geometric model with contour and texture analysis
Authors: Ibadov R R , Fedosov V P , Ibadov S R
Published: January 2021

The article presents a method for image restoration based on a geometric model. The object of research is methods of image removal and restoration. The research is aimed at creating a high-speed and highly efficient system for recovering the lost zones of the underlying surface map image. An algorithm for removing objects and restoring images of lost areas is investigated and its software implementation is developed. The efficiency of the developed algorithm was evaluated on a test example using a statistical criterion.
Additively manufactured Ti-6-4 ELI customized subperiosteal dental exo-implant
Authors: Mukanov G Z , Stepanov S I , Loginov Yu N
Published: January 2021

The role of digital technologies in the manufacture of metal products with complex geometry used in medicine is discussed. A sequence of actions is proposed to obtain a dental implant from biocompatible titanium alloy powder using the additive manufacturing based on features of human jaw anatomy. The boundary conditions for the problem are stated based on the literature data for jaw loading. The strength calculations by finite element method enabled the topological optimization of the implant. The production features of subperiosteal dental exo-implant using electron beam (EBM) melting are presented. Microstructure implant produced from Ti-6-4 ELI powder by EBM was examined.
Development of an algorithm for the calibration of optical and thermal cameras into a unified machine vision system and its software implementation
Authors: Khamidullin I V , Voronina S V , Balabaeva O S , Gavlicky A I , Semenishchev E A
Published: January 2021

The analysis of thermal images is an urgent task for many areas of modern technology and technology, since thermal images are used in many areas of life. Disjointed systems do not allow you to quickly form a general idea of the ongoing processes, since they do not allow an analysis of all parameters. The combination of data spaces obtained in different ranges can increase the productivity and speed of decision making by automated systems. Most often, images obtained with only a thermal imaging camera are a set of spots that characterize the gradient of temperature change in space. These images do not have clear boundaries, which is associated with the physics of thermal energy transfer processes in space. Combination of data obtained by thermal imaging and a camera recording data in the visible range, which are familiar to perception and more understandable to humans.

Areas of application of combined images are: medicine, when making a diagnosis, identifying patients in a crowd (for example, a pandemic), searching for oncology or tissue death processes (as a result of hypothermia or burns); security systems and access control systems; chemistry, in the analysis of mixing processes and reactions; automated and autonomous control systems when building automatic driving systems; and etc.

The development of machine vision systems is based on the analysis of information received by light-sensitive matrices in the form of two-dimensional signals (images). Formation of images is associated with the imposition of distortions on them, which can affect the final result. It is possible to reduce the amount of distortion in different ways or a combination of them. One of the possible methods of primary data processing can be the use of an algorithm for combining data by means of their joint calibration, which will make it possible to achieve a relationship between a 3D point in the real world and the corresponding 2D image, a projection (each pixel) in the image taken by a calibrated camera. In this article, we will consider a thermal camera calibration algorithm based on the analysis of a test pattern.
Rule extraction from the Artificial Neural Network
Authors: Marshakov D V
Published: January 2021

Applying machine learning techniques to various critical decision-making domains implies the need to explain why a machine learning algorithm makes certain conclusions. Artificial neural networks (ANNs) have achieved high classification accuracy for many classification problems, but their results can’t be interpreted, which is why ANNs are often considered as «black box». To interpret the results of neural network classification, the paper presents a method for extracting rules from ANN. The proposed method is based on the structuring of information flows processed in the ANN information field through transforming complex multidimensional data into a simpler structure of a lower dimension, followed by a trivial transformation of the results into a set of fuzzy rules of a certain type. The implementation of the proposed method provides for the construction of the hybrid ANN configuration based on self-organizing and multilayer ANNs. The results of an experimental research of the proposed method are presented on the example of solving the well-known problem of multiparameter classification. The results obtained confirm the adequacy of the proposed method, which can be used both in independent neural network pattern recognition systems and within decision support systems.
Performance of Type-4 Direct Driven PMSG Based Wind Energy Conversion System under Nonlinear Load Demand
Authors: Singh Surinder , Kumar Deepak
Published: January 2021

The wind power installed capacity in wind power generation increasing day by day and having a substantial amount of share in total electrical power generation. As a result, there is lot of research going on in wind power generation system for the improvement in its steady-state and transient operation by choosing better controllers. In this paper, the control schemes have been developed for the switching of power converters which interfaced with common back-to-back dc-link in Type-4 PMSG based WECS configuration. The proposed system has been tested under non-linear reactive load demand, the controllers of generator-side and grid-side converters operates independently to fulfill the active and reactive power demand, by maintaining the DC link voltage constant. The performance of proposed Type-4 PMSG based WECS system has been evaluated in MATLAB/SIMULINK software under dynamic operation and simulation results verify the proposed control scheme.
Wear characteristics of aluminium matrix nanocomposites (AMNCs) – A review
Authors: Singh Mandeep , Bhandari Deepak , Goyal Khushdeep
Published: January 2021

Aluminium matrix nanocomposites (AMNCs) have the ability to meet the needs of innovative applications of engineering materials. These materials are on expanding utilization in industries like ground transportation, aeronautical, sports, and foundation due to the enhanced properties, for example, high strength to weight ratio, corrosion, fatigue, and wear resistance. A few applications including dynamic contact stresses require incredible wear and frictional execution for an improved life. In any case, the integration of these nanoparticles can diminish the wear execution of AMNCs under serious conditions. In several laboratory studies carried out so far in the most recent decade, it has been identified that the wear properties of Aluminium (Al) based nanocomposites are much better than micro composites and its alloys. Within this paper, the publication findings from the year 2016 to 2020 are focussed on the nano-sized reinforcements to analyse the wear performance which is affected by extrinsic variables such as loads, sliding speeds, sliding distance along with intrinsic aspects as microstructure and production method of AMNCs. The reviewed literature found that nano-particle reinforcements substantially enhanced the wear of Al and its alloy nanocomposites.
Short Term Load Forecasting using Metaheuristic Techniques
Authors: Panda Saroj Kumar , Ray Papia , Mishra Debani Prasad
Published: January 2021

The power systems are important by using short term load forecasting (STLF) because it predicts the load in 24 hours ahead or a week ahead. The artificial neural network (ANN) using short term load forecasting brings good result in the predicted load because of its accurateness, easiness in the processing of data, construction of the model as well as excellent performances. The optimization value of ANN is found by different methods which consist of some weights. This manuscript explains the work of ANN with back propagation (BP), genetic algorithm (GA) as well as particle swarm optimization (PSO) for the STLF. The detailed work of the GA and PSO based BP is presenting in this paper which helps for its utilization in the STLF and also able to find the good result in the predicted load. Finally, the result of GA and PSO are compared by simulation and after that, it concluded, the PSO-BP is a good method for STLF using ANN.
DFT Study of Effect of Hydrostatic Pressure on Structural, Electronic and Magnetic Properties of In0.875Cr0.125P
Authors: Kaur Kirandish , Sharma Suresh
Published: January 2021

In this paper, the effect of hydrostatic pressure on structural, electronic and magnetic properties of In0.875Cr0.125P Diluted Magnetic Semiconductor (DMS) in Zinc Blende (B3) phase has been studied at 0 GPa to 26 GPa. The calculations have been performed using Density Functional Theory as implemented in the Spanish Initiative for Electronic Simulations with Thousands of Atoms code (SIESTA) using LDA+U (U=3) as exchange-correlation (XC) potential. The study of electronic structures and magnetic properties show that In0.875Cr0.125P is half metallic in nature and this behaviour is maintained at given pressure range. It is found that forbidden energy band gap increases with increase in pressure due to shifting of valence band maximum and conduction band minimum of energy levels. The investigation of structural properties shows that lattice constant and volume of compound decreases along with increase in total energy of compound with applied pressure. The study of induced local magnetic moment values show their continuous increase on non-magnetic indium and phosphorus atoms with increasing pressure.
Effect of band to band tunnelling (BTBT) on multi-gate Tunnel field effect transistors (TFETs)-A Review
Authors: Shilla P , Verma V , Kumar R , Kumar A
Published: January 2021

TFETs (tunnel field effect transistor) are providing solution to affairs associated with conventional MOSFET devices such as short-channel effects (SCEs) and limitation of minimum (60 mV/decade) subthreshold slope (SS). TFET is a p-i-n diode which conducts in reverse bias and behaves like a transistor due to tunnelling mechanism of the charge carriers across the barrier called band-to-band tunnelling (BTBT). TFETs face some critical problems like lower ON-state currents and ambipolar behaviour of conduction currents. The purpose of this review is to study a highly efficient TFET which provides significant improvements in ION/IOFF ratio with improved ON state current and ambipolar current suppression to enhance the performance of the device. TFET with multigate structure will be studied by using different dielectric and substrate materials. TFET may be considered as promising candidate over MOSFETs in low-power and high-speed switching circuits.
Performance Analysis of FDSOI based Gate Diffusion Input Logic Gates at 28nm Technology Node
Authors: Singh Rana Udayveer , Madhu Charu , Joshi Garima , Godara Mehak
Published: January 2021

In this paper, Fully Depleted Silicon on Insulator (FDSOI) MOSFET at 28nm channel length are simulated in COGENDA Visual TCAD tool. Buried Oxide layer (BOX) between channel and substrate in FDSOI improves the electric field inside the device. The device characteristics are compared in terms of threshold voltage, transconductance parameter, Ion/Ioff ratio and Short Channel Effects (SCEs). To implement VLSI circuits, Complementary Metal Oxide Semiconductor (CMOS) logic is a common choice. However, in the case of CMOS, the logic implementation requires a transistor count equal to twice the number of logic inputs. Therefore, the area is always an issue in the case of the CMOS logic family. To address this issue, Gate Diffusion Input (GDI) based logic has been investigated because it uses less number of transistors. Hence FDSOI MOSFET based GDI logic gates are proposed in this article. FDSOI MOSFET exhibits improved electrical characteristics which can reduce the power consumption considerably and help in expanding the battery life of devices while the GDI logic helps in designing the compact circuit for portable equipment.
A Survey on Prevention of the Falsification Attacks on Cognitive Radio Networks
Authors: Pratibha  , Thangjam Sharmelee , Kumar Naresh , Kumar Sanjiv
Published: January 2021

Today is the era of intelligent cognitive radio network technology that allows the utilization of available spectrum efficiently by allocation of the spectrum dynamically to unlicensed users. The technology utilizes the free spectrum bands which are not being used by the authorized users without causing any obstruction to the existing transmission. Cognitive radio technology promises interference free spectrum access by users. However, there exist many security attacks on spectrum impacting on spectrum access probability and network services. This will result in significant degradation of the performance of a cognitive radio network. This paper discusses the several attacks on different layers, their motives and various mechanisms to prevent them. The specific threats based on issues with configurability are explored on the basis of the related work. In this paper the role of authentication mechanism to prevent the attacks for hassle free spectrum utilization and its performance in resolving the cognitive network security issues are discussed. The need for effective sensing and decision mechanisms to enhance the security of cognitive networks is demonstrated in the paper. Hence, the presented paper gives an overview of existing research efforts at first and secondly, the research challenges are identified that can address the ways to secure the cognitive radio network and lastly the countermeasures in cognitive radio network security strategies are demonstrated.
Design and development of low cost Nano drop UV-Vis spectrometer
Authors: Jothiraj S , Sameera Fathimal M , Jobin Christ MC
Published: January 2021

The ultimate aim of the project is to develop a low-cost spectrometer that analyses the samples in the Nanoscale range and to minimise the usage of samples for diagnostic application. The reflection, absorption or transmission phenomenon alters the incident light during the interaction with the sample. A spectrometer measures this change over a range of incident wavelengths of electromagnetic radiation that has interacted with a sample. The light that acts as a source is passed through the sample. The prepared test sample is analysed in order to validate the developed system. Following the test samples, urine is analysed. 10 volunteers were involved as subjects. The light from the sample passes through a slit and then reflected by a collimating mirror. The wavelength of the light from the sample is analysed by Thermino software in the UV and visible range. The input to this software is provided by the webcam. A 1000 lines/mm diffraction grating is utilized to split the light into its constituent wavelength. This is a low-cost system than the available commercial spectrometers used in the laboratory.
Switching function based inverter modeling for a grid-connected SOFC system in real time
Authors: Gupta Preeti , Pahwa Vivek , Verma Yajvender Pal
Published: January 2021

This paper presents a switching function based modeling of a three-phase voltage source inverter along with hysteresis current control loop, which uses two-axis synchronously rotating reference frame for a grid-connected solid oxide fuel cell, SOFC system. In comparison to detailed inverter model, this inverter model gives the reduced simulation time while keeping the total harmonic distortion within limit as per the latest grid code and at the same time it maintains the required accuracy. All this is accomplished using MATLAB/SIMULINK environment. Further, a one stage ahead has been developed for the researchers by transforming this inverter model into real time model using OPAL-RT’s simulator with hardware-in-loop technology for its implementation in actual power system.