bluuebunny/crossref_metadata_2025_split huggingface dataset:
Applying SWOT for B2B Decisions, Extension to larger data with Machine Learning Regression
Authors: Akella Suresh 
Published: August 2021

Abstract
In this research Akella Systems company, AS, wants to locate its operation in another company either Sreyas an institute or C1 a company. A Business to Business, B2B association decision was required. The Strengths, Weaknesses, Opportunities and Threats, SWOT, concerning defined fields are defined and points allotted for AS, Sreyas, and C1. SWOT is used individually for decision-making on one parameter. In this analysis, a relative SWOT of the company’s capabilities is used for decision-making of location. Further, each company is evaluated relative to the other by SWOT polynomial regression. Standard regression using Cramer’s rule was used. A Business unit to Business unit, B2B decision was taken by locating the Akella Systems company, within the Sreyas Institute after relatively evaluating the SWOT. Linear equation fit of the data was also obtained from MS Excel and fits perfectly with R2 = 1 with the regression model. The relative evaluation of the two companies can be inferred from the coefficients of the equation. The constant-coefficient gives the average difference of all SWOT parameter differences of the company and the coefficient of the linear variable gives the progressive influence of one company over the other. Sreyas college had an average more SWOT value of 6.12 compared to AS and AS had a progressive influence in the relationship over Sreyas, as the coefficient was 0.1845. Sreyas college was selected to be the partner by AS. The polynomial fit metric developed for relative SWOT can be used as a training set, for large ML data regression.
Comparative Assessment of Existing Meaningful Image Encryption Techniques
Authors: Himthani V , Dhaka V S , Kaur M 
Published: August 2021

Abstract
In recent trends, digital images are commonly used for communication, storage, medical imaging, etc. These images may contain confidential data. Image encryption and steganography are popular techniques to provide information security. In image encryption, a secret image is encoded using an encryption key, and in image steganography, a secret image is embedded in some cover media. Encrypted images are visually recognizable as these are noise-like, but stego images are visually unrecognizable. In this concern, some visually secure encryption techniques are developed, known as Meaningful Image Encryption (MIE). It includes the advantages of both image encryption and steganography. This paper provides a thorough review of existing MIE techniques. A comparative assessment based on various performance parameters is presented. Further, this paper provides the main application areas of MIE techniques. Moreover, the future research perspective to enhance the existing MIE techniques are discussed in detail.
A blockchain based approach for improving transparency and traceability in silk production and marketing
Authors: Sharma Abhilash , Kalra Mala 
Published: August 2021

Abstract
Blockchain technology may be described as a distributed digital ledger database for transparent and immutable recording of transactions occurring between a groups of nontrusting parties. Transactions can be implemented without any intermediaries. Given this potential, it can play a great role in enhancing Supply chain management. Silk production and marketing has exponentially increased and spanned across the world in recent years. To redefine current silk production and marketing industry, there is a need to strengthen its supply chain management process. With Blockchain, government, farmers, weavers, and sellers can collaborate on a single system without any inconvenience. They create an immutable chain of transactions which can be verified by any of the parties. There is an immutable shared ledger that no one can modify. This research presents a private and permissioned application that uses Blockchain and aims to automate the shipping processes among different participants in the supply chain ecosystem. Data in this private ledger is governed with the participants’ invocation of their smart contracts. The performance of the proposed work is evaluated in terms of the transaction throughput, transaction average latency, resource utilization with a fixed and variable rate of transactions.
Gender Prediction using Deep Learning Algorithms and Model on Images of an Individual
Authors: Rajendra G , Sumanth Kodali , Anjali Chandragiri , Pardhasai Gudavalli , Supraja Madhumanchi 
Published: August 2021

Abstract
The classification of gender based on the biometric is an old and traditional approach which are treated as a sub-branch of “Soft Computing”. But in this proposed work, works on the celebrity images dataset obtained from the Kaggle repository. The major focus of this to extract the important features from the images by training the neural network by using an autoencoder and decoder. The system extracts the feature maps to visualize the extracted features and makes some sort of interpretations to analyze the data. Gender classification using iris information is a relatively old technique, and most gender classification methods classify all iris texture features. These features are fed to the classifier, this data might be proper or improper, and if it is improper it leads to more generalization results in less accuracy. The proposed algorithm to create increase the size of the dataset implements the Data Augmentation Technique using basic image operations. Then it applies a convolution neural network for predicting the gender of the user. The model has proved practically that increasing the size of the dataset and reducing the features improves the classifiers learning performance.
A Secure Framework for Health Record Management Using Blockchain in Cloud Environment
Authors: Verma G , Pathak N , Sharma N. 
Published: August 2021

Abstract
Health records of any type should be confidential, but simultaneously they should be accessible to an authorized user. In the current conventional health care system, health records are processed in the local database. Only authorized hospital staff can have access to the patient health records kept in health centers. The flow of information in this kind of system is center-restricted. One technology that can potentially overcome this problem is Blockchain. Blockchain technology allows sharing of data in a distributed way with benefits like privacy-preserving and secure access control. To achieve secure access control and privacy of electronic healthcare data (EHD), a secure framework for distributed sharing of the health record is proposed. The use of blockchain will remove the requirement of a trusted 3rd party for data storage. The framework uses searchable key attribute-based encryption (ABE) for achieving fine-grain access control. The simulation results indicate that our scheme is quite effective and secure.
Novel cryptographic approach to enhance cloud data security
Authors: Tyagi Kanika , Yadav S.K , Singh Mayank 
Published: August 2021

Abstract
In today’s era, cloud computing has become the more promising business concept which impacted almost every section of our lives and business. Cloud computing for sure has grown rapidly to become one of the major areas of research. As it provides an on demand access to a shared pool of resources and makes easier for the organizations to use their data at any place and at anytime without considering hardware devices along them but cloud security is still the most crucial and considerable issue in each organization. So there is need for some secure authentication so that data on clouds remain safe and secure. In this context passwords are the basic form of authentication. so there is need of some mechanism which provides password security. Weak and poor passwords management leads to breach in cloud data. Passwords to gain access to secret data should be so as strong to prevent dictionary attacks and brute force attacks. In our proposed system a mechanism is presented to secure the data on cloud using combination of some algorithms viz: PBKDF2, Argon 2, AEs-256 and IDA. In this paper we proposed a method to generate the most secured cryptographic keys using the blend of two key derivation functions PBKDF2 and Argon 2. In our proposed model there would be no need to store and send key for encryption and decryption. Advanced Encryption Standard (AES) is used for encryption for encryption. Information Dispersal algorithm is used to prevent data breaching situations on clouds in financial institutions and provide better confidentiality, availability and integrity of data.
Plant Leaves Disease Classification Using Bayesian Regularization Back propagation Deep Neural Network
Authors: Trivedi Vijay Kumar , Shukla Piyush , Pandey Anjana 
Published: August 2021

Abstract
Automatic leaf disease segmentation and detection from plant images has recently become a major research area around the world. The proposed method uses plant images to automatically segment and classify different leaf disease regions. The proposed procedure consists of four steps: Pre-processing is used in the first step to reduce the amount of background noise in the plant image using the Wiener filter. The disease spot is then detected using the hue histogram on the HIS model and these disease spots are then segmented using the K-means algorithm applied on the L*a*b* colour model and highest hue value calculation on HSV colour model. Finally, seventeen colour and texture features are extracted from the disease segment and these features are fed to a forward-propagation deep neural network (FPDNN) classifier which classifies the diseases. We have used the Bayesian regularization back propagation algorithm to fine-tune the results. We have applied FPDNN on varying hidden layers ranging from 1 to 40 and achieved highest accuracy i.e. 97.18% at 19 hidden layers which is larger than other state of art classifiers. Proposed method is implemented in matlab 14a.
Credit Score Prediction System using Deep Learning and K-Means Algorithms
Authors: Kumar Ashwani , Shanthi D. , Bhattacharya Pronaya 
Published: August 2021

Abstract
In financial markets, credit rating and risk assessment tools are used to minimize potential risk up to some extent for credit score. Nowadays, the banking and financial industry has experienced rapid expansion. Therefore, with this growth, the numbers of credit card applications with various credit products are increasing day by day because many people want to avail these services for their personal interest. The challenge here is to identify insights on the performance of a finance industry by using deep learning algorithms as they directly affect the viability of that industry. These industries have a limited number of resources and capital, which can be used to deliver the services among the customers. In this research work, we proposed prediction of credit scoring system using deep learning and K-Means algorithm for the financial industry. The scheme contains a predictive model which uses feature selection (FS) classification and deep learning applications simultaneously to train the proposed model to perform effectively. The scheme 1) pre-processing credit card data 2) uses a feature selection technique to minimize the dimension of data in order to obtain the finest training data 3) applies a deep learning algorithm to map the input weight with hidden biases to achieve excellent performance 4) Decision support system is used to enable the deep learning algorithm to provide a more accurate and intelligent decision. Furthermore, the proposed model is validated on different credit scoring dataset in real-world scenarios and is capable of improving the effectiveness and accuracy. The studies indicate that our predictive model performs well for credit scoring of existing customer and helps lenders to allocate funds in finance industry.
EaGWO: Extended algorithm of Grey Wolf Optimizer
Authors: Kumar Alok , Lekhraj  , Singh Avjeet , Kumar Anoj 
Published: August 2021

Abstract
The Grey Wolf Optimizer (GWO) and their very recent improved algorithms have some limitations such as stagnation in local optima, slow convergence rate, and so forth. In order to overcome these limitations, we have proposed an algorithm of GWO, named Extended algorithm of GWO (EaGWO), that modifies the hunting equation and encircling equation of the original GWO. The proposed algorithm has been examined on twenty-three very renowned test functions and compared the results with basic GWO and other very recent variants of GWO. This research work also considers the Friedman ANOVA test to verify the effectiveness of the proposed methodology. Consequently, the findings of this work justify quite competitive performance compared to other meta-heuristic algorithms.
Impact of Intermediate Bottleneck Nodes on the QoS Provision in Wireless Infrastructure less Networks
Authors: Sultana Ijteba , Bari Mohd Abdul , Sanjay  
Published: August 2021

Abstract
Wireless Infrastructure less networks consist of wireless mobile nodes with heterogeneous and constrained resources, to establish internet connectivity regardless of the location. The applications the networks are disaster relief, health care and military. These applications are sensitive, and demands the reliable, and best quality of communication. Routing is one of the resources to enable the communication. Routing is acceptable in an infrastructure less networks if it satisfies the quality of service requirements. Thus the paper aims to investigate the existing QoS routing protocol performance in various conditions. The aim is to achieve a broad understanding of QoS routing protocol designed for Wireless Infrastructure less networks, and further pave the pathway for future research. The paper analyses the QoS routing protocol performance in presence of bottleneck intermediate node, and it is the novel aspect of the work.
Influence of an efficient Hierarchical Clustering Algorithm in analyzing Cancer affected DNA Dataset
Authors: Kiruba Nesamalar E. , Satheesh Kumar J. , Amudha T. 
Published: August 2021

Abstract
This research work presents an influence of hierarchical clustering approach in anlyzing cancer affected DNA data set. The primary objective of this research work is to identify the best clustering algorithms to group cancer-affected DNA datasets. Data analysis shows an important role in bioinformatics. Data analysis technique used for grouping the data objects is based on unsupervised learning. Clustering is an unsupervised learning technique in data mining. It groups a set of clusters from the entire dataset. In this research work, 700 cancer-affected DNA datasets are considered for analysis. This research work compares three types of Clustering techniques, K-Means (KM), K-Medoids (KMS), and Hierarchical Clustering (HC), to group cancer-affected DNA. Each algorithm has some strengths and weaknesses. These clustering algorithms are compared in detail based on various parameters. Results prove that the hierarchical clustering algorithms show lesser execution time and increased accuracy than other KM and KMS algorithms.
Driver’s Drowsiness Detection Based on Facial Multi-Feature Fusion
Authors: Venkata Sai Vardhan V. , Ritish Kumar Reddy N. , Jaya Surya K. , Uday Kiran J. , Kumar Ashwani 
Published: August 2021

Abstract
Among the leading causes of traffic accidents and deaths is drowsy driving. As a result, detecting and indicating driver drowsiness is an important research subject. The majority of existing approaches are based on automobiles, behavioral-based, or physiologically based. A few approaches are invasive and so the driver been interrupted, while others necessitate the use of pricey sensors and data processing. As a result, real time driver sleepiness detection system with adequate consistency and minimized cost has been established in this work. A webcam captures the video in the proposed system, and image processing techniques are used. Histogram of oriented gradients (HOG) which is available in Dlib toolkit, is used to recognize the driver’s face in every frame. The landmarks on the face are identified by State Vector Machine, and the eye aspect ratio, mouth aspect ratio, are computed, and drowsiness is recognized using created adaptive thresholding based on their values. Offline implementations of machine learning techniques were also been made. In terms of accuracy and speed, we show that our algorithm outperforms existing fatigue methods. In Support, a sensitivity of 95.28 percent and a response rate of 100 percent were attained in Support Vector Machine.
Algorithms for detecting network attacks in an enterprise industrial network based on data mining algorithms
Authors: Vulfin A M , Vasilyev V I , Kuharev S N , Homutov E V , Kirillova A D 
Published: August 2021

Abstract
The article proposes the structure of a network traffic analysis system using machine learning models. The analysis of the specialized dataset WUSTL-IIOT-2018 was carried out, the main stages of data preprocessing, construction and testing of classifiers for detecting network attacks were performed. The possibility of embedding models as modules of specialized network equipment is proposed, which makes it possible to increase the efficiency of the analysis of network traffic, including specialized industrial protocols.
Randomized C/C++ dynamic memory allocator
Authors: Aleksandrovna Astrakhantseva Irina , Gennadevich Astrakhantsev Roman , Viktorovich Mitin Arseny 
Published: August 2021

Abstract
Dynamic memory management requires special attention in programming. It should be fast and secure at the same time. This paper proposes a new randomized dynamic memory management algorithm designed to meet these requirements. Randomization is a key feature intended to protect applications from “use-after-free” or similar attacks. At the same time, the state in the algorithm consists only of one pointer, so it does not consume extra memory for itself. However, our algorithm is not a universal solution. It does not solve the memory fragmentation problem and it needs further development and testing.
Data transfer in the mathematical broken symmetry group model
Authors: Gorshkov Kirill , Nikitin Oleg , Kuznetsova Ekaterina 
Published: August 2021

Abstract
The article shows a mathematical description of data transfer using a directed graph, as well as an algorithm for data transport in a network based on the multiplication tables of substitutions – elements of the broken symmetry group. It was analyzed how many steps are necessary when data transmission in systems with alternate sending of data packets. Using multiplication tables of substitutions the possibility of calculating the graph trees with nested nodes was demonstrated, including PQ-trees. The article provides examples of models for collecting information in two main network nodes, as well as networks with a more complex topology, for example the tree with its “rootstock”.
Modeling of the belt conveyor control system using artificial intelligence methods
Authors: Druzhinina O V , Masina O N , Petrov A A 
Published: August 2021

Abstract
The paper is devoted to the development of instrumental and methodological support for the study of conveyor transport systems with intelligent control mathematical models. An optimal control model is constructed for a belt conveyor with a dynamic change in the angle between the horizontal plane and the belt plane. Methods for studying the belt conveyor model based on the design of PID controllers and neural network controllers are proposed. The results of computational experiments using the training of a feedforward neural network and the use of reinforcement learning are presented. A comparative analysis of computational experiments with the use of control based on the synthesis of a fuzzy controller, control using artificial neural networks, and control based on a PID controller is performed. The obtained results can be used in neural network modeling of complex systems and in solving problems of automation of technological and production processes.
Mathematical modeling of the thermally stressed state of elliptical-section concrete structures
Authors: Ivanovich Kanareykin Aleksandr 
Published: August 2021

Abstract
At present, there is an urgent need to determine the level of temperature stresses in super-large concrete structural elements that occur during the manufacture of such products and contain a huge amount of concrete at the same time, which is in fact a heat source that is inaccessible to the control of the exothermic process of the entire mass of concrete. The main task of this work is the mathematical modeling of temperature stresses in concrete structures of elliptical cross-section in order to reduce them. Based on mathematical modeling, it is established that the level of thermal stresses that occur during the transition to an elliptical cross-section decreases.
Digital transformation of expert systems design within geoinformation management in Arctic
Authors: Abramov V M , Sikarev I A , Chusov A N , Tatarnikova T M , Shilin M B 
Published: August 2021

Abstract
The article contains the digitalization results for expert systems design in the framework of geoinformation management within Arctic while coronavirus and change of climate. Currently, the methods of designing expert systems within the framework of geoinformation management in the Arctic demonstrate the use of improved concepts of data collection and visualization. The article gives preference to the use of web constructors in distributed networks. An example of a working layout of an expert system for geoinformation support of the activities of a subarctic port in the conditions of ice waters is given. The results of the research can be useful in operational environmental activity and for university learning purposes, including Master’s programs.
Artificial intelligence as an instrument against social engineering
Authors: Britvina V V , Musatova D A , Altoukhov A V , Zueva A S , Sorokin A Y 
Published: August 2021

Abstract
Annotation. This article discusses the use of artificial intelligence technologies in the fight against social engineering. The purpose of this study is to analyze the technologies of artificial intelligence, its main features and the possibilities of its application. The article also examines the issue of improving the legislation of the Russian Federation, taking into account the use of artificial intelligence technologies. This problem is extremely relevant today due to the growth of crimes involving social engineering methods.
Formation of the cyber-physical platform “Digital Region”
Authors: Ermakova A M , Oznobikhina L A 
Published: August 2021

Abstract
The article considers the cyber-physical platform “Digital Region”, which will allow managing technical, technological, industrial and infrastructure facilities of the region. The authors describe the concept of ensuring the digital and physical functioning of this platform; argue for the positive impact of the new digital platform on the sustainable development of the regions.
Progressive digital and information technologies in natural and technical education
Authors: Sergeenko Yu S , Scherbakova O V , Permykov A I , Kombarova E V , Ukraintseva V S , Rybkina M V , Ermolovich G P 
Published: August 2021

Abstract
In the context of a pandemic in 2020, a serious transformation of natural and technical education took place, in this connection; the authors consider modern approaches to the introduction of educational activities in conditions of restrictions. The paper proposes a concept that allows for a high-quality transition in natural and technical education, which allows students to assimilate material in a virtual learning environment. The work formulates a step-by-step system that allows transforming natural and technical education.