bluuebunny/crossref_metadata_2025 huggingface dataset:
Deciphering organization of GOES-16 green cumulus through the empirical orthogonal function (EOF) lens
Authors: Dror Tom , Chekroun Mickaël D. , Altaratz Orit , Koren Ilan
Published: August 2021

Abstract. A subset of continental shallow convective cumulus (Cu) cloud fields has been
shown to have distinct spatial properties and to form mostly over forests and
vegetated areas, thus referred to as “green Cu”
(Dror et al., 2020). Green Cu fields are known to form organized mesoscale
patterns, yet the underlying mechanisms, as well as the time variability of
these patterns, are still lacking understanding. Here, we characterize the
organization of green Cu in space and time, by using data-driven organization
metrics and by applying an empirical orthogonal function (EOF) analysis to a
high-resolution GOES-16 dataset. We extract, quantify, and reveal modes of
organization present in a green Cu field, during the course of a day. The EOF
decomposition is able to show the field's key organization features such as
cloud streets, and it also delineates the less visible ones, as the
propagation of gravity waves (GWs) and the emergence of a highly organized
grid on a spatial scale of hundreds of kilometers, over a time period that
scales with the field's lifetime. Using cloud fields that were reconstructed
from different subgroups of modes, we quantify the cloud street's wavelength
and aspect ratio, as well as the GW-dominant period.
Extension of a gaseous dry deposition algorithm to oxidized volatile organic compounds and hydrogen cyanide for application in chemistry transport models
Authors: Wu Zhiyong , Zhang Leiming , Walker John T. , Makar Paul A. , Perlinger Judith A. , Wang Xuemei
Published: August 2021

Abstract. The dry deposition process refers to flux loss of an atmospheric
pollutant due to uptake of the pollutant by the Earth's surfaces, including
vegetation, underlying soil, and any other surface types. In chemistry
transport models (CTMs), the dry deposition flux of a chemical species is
typically calculated as the product of its surface layer concentration and
its dry deposition velocity (Vd); the latter is a variable that needs to
be highly empirically parameterized due to too many meteorological,
biological, and chemical factors affecting this process. The gaseous dry
deposition scheme of Zhang et al. (2003) parameterizes Vd for 31
inorganic and organic gaseous species. The present study extends the scheme
of Zhang et al. (2003) to include an additional 12 oxidized volatile organic
compounds (oVOCs) and hydrogen cyanide (HCN), while keeping the original
model structure and formulas, to meet the demand of CTMs with increasing
complexity. Model parameters for these additional chemical species are
empirically chosen based on their physicochemical properties, namely the
effective Henry's law constants and oxidizing capacities. Modeled Vd
values are compared against field flux measurements over a mixed forest in
the southeastern US during June 2013. The model captures the basic
features of the diel cycles of the observed Vd. Modeled Vd values
are comparable to the measurements for most of the oVOCs at night. However,
modeled Vd values are mostly around 1 cm s−1 during daytime, which
is much smaller than the observed daytime maxima of 2–5 cm s−1.
Analysis of the individual resistance terms and uptake pathways suggests that
flux divergence due to fast atmospheric chemical reactions near the canopy
was likely the main cause of the large model–measurement discrepancies
during daytime. The extended dry deposition scheme likely provides
conservative Vd values for many oVOCs. While higher Vd values and
bidirectional fluxes can be simulated by coupling key atmospheric chemical
processes into the dry deposition scheme, we suggest that more experimental
evidence of high oVOC Vd values at additional sites is required to
confirm the broader applicability of the high values studied here. The
underlying processes leading to high measured oVOC Vd values require
further investigation.
Characteristics and robustness of Agulhas leakage estimates: an inter-comparison study of Lagrangian methods
Authors: Schmidt Christina , Schwarzkopf Franziska U. , Rühs Siren , Biastoch Arne
Published: August 2021

Abstract. The inflow of relatively warm and salty water from the Indian Ocean into the South Atlantic via Agulhas leakage is important for the global overturning circulation and the global climate. In this study, we analyse the robustness of Agulhas leakage estimates as well as the thermohaline property modifications of Agulhas leakage south of Africa. Lagrangian experiments with both the newly developed tool Parcels and the well established tool Ariane were performed to simulate Agulhas leakage in the eddy-rich ocean–sea-ice model INALT20 (1/20∘ horizontal resolution) forced by the JRA55-do atmospheric boundary conditions.
The average transport, its variability, trend and the transit time from the Agulhas Current to the Cape Basin of Agulhas leakage is simulated comparably with both Lagrangian tools, emphasizing the robustness of our method. Different designs of the Lagrangian experiment alter in particular the total transport of Agulhas leakage by up to 2 Sv, but the variability and trend of the transport are similar across these estimates. During the transit from the Agulhas Current at 32∘ S to the Cape Basin, a cooling and freshening of Agulhas leakage waters occurs especially at the location of the Agulhas Retroflection, resulting in a density increase as the thermal effect dominates. Beyond the strong air–sea exchange around South Africa, Agulhas leakage warms and salinifies the water masses below the thermocline in the South Atlantic.
Cross-diffusion waves resulting from multiscale, multiphysics instabilities: application to earthquakes
Authors: Regenauer-Lieb Klaus , Hu Manman , Schrank Christoph , Chen Xiao , Clavijo Santiago Peña , Kelka Ulrich , Karrech Ali , Gaede Oliver , Blach Tomasz , Roshan Hamid , Jacquey Antoine B. , Szymczak Piotr , Sun Qingpei
Published: August 2021

Abstract. Theoretical approaches to earthquake instabilities propose shear-dominated source mechanisms. Here we take a fresh look at the role of possible volumetric instabilities preceding a shear instability. We investigate the phenomena that may prepare earthquake instabilities using the coupling of thermo-hydro-mechano-chemical reaction–diffusion equations in a THMC diffusion matrix. We show that the off-diagonal cross-diffusivities can give rise to a new class of waves known as cross-diffusion or quasi-soliton waves. Their unique property is that for critical conditions cross-diffusion waves can funnel wave energy into a stationary wave focus from large to small scale. We show that the rich solution space of the reaction–cross-diffusion approach to earthquake instabilities can recover classical Turing instabilities (periodic in space instabilities), Hopf bifurcations (spring-slider-like earthquake models), and a new class of quasi-soliton waves. Only the quasi-soliton waves can lead to extreme focussing of the wave energy into short-wavelength instabilities of short duration. The equivalent extreme event in ocean waves and optical fibres leads to the appearance of “rogue waves” and high energy pulses of light in photonics. In the context of hydromechanical coupling, a rogue wave would appear as a sudden fluid pressure spike. This spike is likely to cause unstable slip on a pre-existing (near-critically stressed) fault acting as a trigger for the ultimate (shear) seismic moment release.
An Implementation of Text and Color Image Steganography Technique Using Cryptographic Algorithm
Authors: Agrawal Prakhar , Upadhyay Arvind
Published: August 2021

The craft of data stowing away has gotten much consideration in the ongoing years as security of data has turned into a major worry in this web time. As sharing of delicate data by means of a typical correspondence channel have become inevitable, Steganography techniques aimed at secretly hiding data in a multimedia carrier such as text, audio, image or video, without raising any suspicion of alteration to its contents. The original carrier is referred to as the cover object. In this paper, we proposed a mechanism of end user data security by means of image steganography technique using ECDH (Elliptic Curve Diffie–Hellman) algorithm for improving image quality while we hide data in image. Our proposed approach is simplified yet efficient algorithm that can be implemented for end user application that strictly enforces the data integrity over the communication channel. The performance of the proposed approach is measured in terms of time, memory, MSE and PSNR which was better improved from the previous approach in the similar parameters.
Implementation and Comparison of Image Enhancement Techniques Using Low Resolution IRS-1C LISS III Image
Authors: Parveen Rubina , Kulkarni Subhash , Mytri V. D.
Published: August 2021

Image enhancement is the primary step in image processing. Image enhancement improves the interpretation and makes the image visually clear. In this process pixels of input image were fine-tuned, so that the results are more suitable for display or further image analysis. Numerical manipulation of digital image includes pre-processing as the preliminary step of analysis. Contrast manipulation, spatial filtering, noise suppression and color processing are different methods of image enhancement. Choosing suitable method for satellite image enhancement depends on the application. This paper aims to compare results of various image enhancement techniques using an IRS-1C LISS III satellite image. It attempts to assess enhancement techniques. Shortcomings and general requirements in enhancement techniques were also discussed. This study gives promising directions on research using IRS-1C LISS III image enhancement for future research.
A Novel Approach on Focused Crawling With Anchor Text
Authors: Subatra Devi S.
Published: August 2021

A novel approach with focused crawling for various anchor texts is discussed in this paper. Most of the search engines search the web with the anchor text to retrieve the relevant pages and answer the queries given by the users. The crawler usually searches the web pages and filters the unnecessary pages which can be done through focused crawling. A focused crawler generates its boundary to crawl the relevant pages based on the link and ignores the irrelevant pages on the web. In this paper, an effective focused crawling method is implemented to improve the quality of the search. Here, three learning phases are considered namely, content-based, link-based and sibling-based learning are undergone to improve the navigation of the search. In this approach, the crawler crawls through the relevant pages efficiently and more relevant pages are retrieved in an effective way. It is proved experimentally that more number of relevant pages are retrieved for different anchor texts with three learning phases using focused crawling.
A Sound Assessment of Test Suite Minimization Techniques
Authors: Khan Fayaz Ahmad
Published: August 2021

During software development, testing and re-testing occurs frequently to ensure that the software is working correctly before and after modifications. To carry out an effective testing process a test suite is created and executed to detect the faults in the existing code as well as in the modified code. The manual approach of test suite creation and execution is time consuming and labour intensive task as compared to automatically generated test data or test suite. The automatic test data generation is supposed to be an effective way, but a lot of redundant test cases are generated that increase the time, effort and cost of testing. Therefore, test suite minimization techniques are used to further minimize or reduce the number of test cases by selecting a subset from an initially random and large test suite to test the code before as well as after modification. In this study, a comprehensive analysis of the different test suite minimization techniques is presented in order to extend the existing studies and to propose new ideas in this direction.
Using Predictive Modeling System and Ensemble Method to Ameliorate Classification Accuracy in EDM
Authors: Ashraf Mudasir , Zaman Majid , Ahmed Muheet
Published: August 2021

Educational data mining has illustrated an increasing demand for extracting and maneuvering data from academic backdrop, to generate prolific information which is indispensible for decision making. Therefore in this paper, an attempt has been made to deploy various data mining techniques including base and meta learning classifiers across our pedagogical dataset to foretell the performance of students. Among several contemporary ensemble approaches, researchers have practiced widespread learning classifiers viz. boosting to predict the performance of students. As exploitation of ensemble methods is considered to be significant phenomenon in classification and prediction mechanisms, therefore analogous method (boosting) has been applied across our pedagogical dataset. The entire results have been evaluated with 10-fold cross validation, once pedagogical dataset has been subjected to base classifiers including j48, random tree, naive bayes and knn. In addition, techniques such as oversampling (SMOTE) and undersampling (Spread subsampling) have been employed to further draw a comparison among ensemble classifiers and base classifiers. These methods were exploited with the key objective to observe any improvement in prediction accuracy of students.
Structural Equation Modeling for Investigating the Factors Affecting the Faculty Members Adoption and Use of E-Learning Platform in Academic Purposes: An Empirical Validation in Higher Educational Context
Authors: Choudhury Hitesh , Khataniar Guruprasad
Published: August 2021

E-learning Platform is one of the most flexible and important innovations for delivering education in modern educational system. However successful implementation of the E-learning Platform (ELP) depends on the users behavioral intension of adoption. Most of the Higher educational institutions in North-East Indian region is using Information and Communication Technology in teaching and learning process and moving towards the adoption of ELP. In this paper the main focus is to examine the factors affecting the faculty members perception and adoption of E-learning platform in academic purposes. In the theoretical framework of this study Technology Acceptance Model (TAM) is used to analyze the perception and behavioral intension of faculty members adoption and use of E-learning platform in academic purposes. The theoretical framework is proposed that includes the core construct of TAM namely Perceived Usefulness, Perceived Ease of Use, Attitudes towards using together with two external variables namely E-learning Self Efficacy and Job Relevance. The data were collected from 81 full time/part time faculty members of randomly selected University/Colleges in North East Indian region through a self-designed questionnaire comprising 21 items that represent the above mentioned six constructs. The data were used to validate and hypothesized research model. The data analysis was performed through structural equation modeling (SEM) by using the software package SPSS version 16 together with STATA. The study reveals that the adoption and using E-learning platform mainly depends on the behavioral intention and attitude towards using the platform. Perceived usefulness and job relevance are the most strongest and important predictors of behavioral intention and attitude towards using E-learning platform in academic purposes.
Smart Prediction Method of Software Defect Using Neuro-Fuzzy Approach
Authors: Singh Sunil Kumar , Shree Raj
Published: August 2021

Faults in software program structures continue to be a primary problem. A software fault is a disorder that reasons software failure in an executable product. A form of software fault predictions techniques were proposed, however none has proven to be continually correct. So, on this examine the overall performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting software program defects and software program reliability has been reviewed. The datasets are taken from NASA Metrics Data Program (MDP) statistics repository. In the existing work a synthetic intelligence technique viz. Adaptive Neuro Fuzzy Inference System (ANFIS) goes for use for software disorder prediction.
Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques
Authors: Anusha M.
Published: August 2021

Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. This paper uses criterion knowledge ranking algorithm solving multi-objective optimization problems. The aim of this research paper is to solve a multi-objective optimization algorithm with close reference point learning method to identify high quality data clusters. A Simple crossover measure is used to quantify the diversity of the whole set, by considering all patterns as a complete entity. In this paper, the task of identifying high quality data clusters using close reference points is proposed to solve multi-objective optimization problem using evolutionary clustering techniques. The proposed algorithm finds the closest feature from the selected features of the data sets that also minimizes the cost while maintains the quality of the solution by producing better convergence. The resultant clusters were analysed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets. The experimental results substantiates that the algorithm is efficient and robust.
Multi Objective Vehicle Routing Problem: A Survey
Authors: . Sandhya , Goel Rajeev
Published: August 2021

In the last decades, a lot of research has been done on Multi Objective Vehicle Routing due to its rich real life applications. However, the related literature is yet not being summarized anywhere. This paper presents a broad survey on the considered problem. This paper broadly presents the objectives considered, approaches used to solve multi objective vehicle routing problem. Finally, the survey classifies the main features of recently published literature and also provides some future directions in the considered field.
A Detailed Study on Advanced Persistent Threats: A Sophisticated Threat
Authors: Sarkunavathi A. , Srinivasan V.
Published: August 2021

In the present world computer networks are used to store sensitive information and to provide services for organizations and society. The growth of internet and the increased use of computers in society along with smart devices lead to the increase in cyber crimes and persistent attacks. The most complex and advanced attacks are targeted attacks which are specifically aimed at companies or governments to accomplish the predetermined goals such as economic advantages, strategic benefits, getting control of sensitive information. Hackers try to access sensitive data from cyber space and there by become as advanced malware developers for the security systems. One type of such attack is Advanced Persistent Threats (APT) which targets the governmental institutions, military, multinational enterprises, financial industry, manufacturing and banks. The approach that is followed by the attackers are repeated attempts using different methods such as , stealth approach, adapting to the existing defense mechanisms, stealthily infiltrating the network to avoid any suspicions like involving in sleep modes before commencing any attack. The effects of these attacks are ex-filtration of key intelligence property, stoppage of fundamental services, and destruction of critical infrastructure. This paper is about the detailed study of Advanced Persistent threats to provide an idea about the advanced attacks.
Big Data Analytics to Increase the Agricultural Yield by Using Machine Learning Approaches
Authors: Sudha V. , Mohan S. , Arivalagan S.
Published: August 2021

Agriculture is the backbone of Indian economy. Big data are emerging précised and viable analytical tool in agricultural research field. This review paper facilitates the farmers in selecting the right crops and appropriate cropping pattern for a particular locality. A modern trend in the Agriculture domain has made people realize the importance of big data. It provides a methodology for facing challenges in agricultural production, by applying this Algorithm, using machine learning techniques. The different machine learning techniques survey is presented in this paper to realize enhanced monitory benefits in a particular area. A study of machine learning algorithms for big data Analytic is also done and presented in this paper.
Video Watermarking and Encryption Scheme for Online Multimedia Copyright Protection to Using Chaotic Maps Cryptography
Authors: Udayakumar J. , Prabakaran G. , Madhan Mohan R.
Published: August 2021

Nowadays, there is an explosive growth in the digital multimedia creation, capturing, processing and distribution. Protecting the multimedia contents from copyright in fingerprint has become a major concern. Encryption and watermarking are two complementary techniques that are used for protecting the multimedia data. In this paper, a proposed hybrid encryption-watermarking algorithm for copyright protection is proposed. The watermarking phase of this proposed algorithm is based on, the discrete cosine transform (DCT), while the encryption phase is based on using four chaotic maps with different dimensions. The proposed watermarking scheme uses a new PN-codes embedding strategy of the watermark into the cover image. This strategy allows decreasing the embedding strength factor of the scheme to a value that maximizes imperceptibility performance while maintaining acceptable robustness of the watermarking scheme. On the other hand, the proposed chaos-based encryption algorithm used four chaotic maps of different dimensions and it has two diffusion stages rather than one to improve the algorithm efficiency. The proposed encryption algorithm is tested using different experiments. The experimental results demonstrate that the proposed encryption algorithm shows advantages of large key space, high resistance against differential attacks and high security analysis such as statistical analysis, and sensitivity analysis.
Real Time Health Monitoring System Using Arduino with Cloud Technology
Authors: Anandh R. , Indirani G.
Published: August 2021

Internet of Things (IoT) is a new technology in which smart devices are connected to the internet. IoT serves and motivates for health monitoring systems where sensors that are either worn inside or outside the human body. The sensors collect the information of physical and logical conditions and the movements of the patient. This research paper aims to develop a system which gives body temperature and heart rate using sensors LM35 and AD8232 heart rate monitor sensor respectively. These sensors are incorporated and interfaced with Arduino UNO board, ESP8266 for wireless data transmission to Thing Speak. The outcome of result on Thing speak is Data visualization. So that record of data can be stored and accessed over a period of time.
Routing Methods in MANET with Secured Key Management and Packet Drop Reductions: A Survey
Authors: Alapati Yaswanth Kumar , Ravichandran Suban
Published: August 2021

MANET routing is complex task and selecting secured route ought to be made sooner before the node leaves the network. Quick choices dependably redress network execution. Most MANET routing conventions are intended to work in a cordial and agreeable condition which makes them powerless against different assaults. Further, nodes need to forward data packets to different nodes to empower information correspondence between the nodes that are not in the radio scope of each other. Be that as it may, a node may decline to forward data packets or halfway do the sending or even endeavor to intrude on the system tasks. This is for the most part thought to be a sort of bad conduct which demonstrates the presence of malicious node in a system. Trust and Reputation would fill in as a noteworthy answer for these issues. Secured data transmission is a complex task in MANET as there is a maximum chance for attackers to crash the network. Taking in the system qualities and picking right routing choices at right occasions would be a noteworthy arrangement. In this work, we have completed a broad overview of fault tolerant methods connected to routing in MANETs. Because of the proximity of attacker nodes, the procedure of administration disclosure is extraordinarily influenced, which may prompt poorer execution of the network. This may prompt less packet delivery proportion, throughput, expanded control overhead;add up to overhead and packet drops. Further, anchoring administration revelation task is exceptionally troublesome since, it includes the notoriety of the extensive number of middle of the road nodes present in the system[3]. This paper presents a survey on different techniques for routing,key management and reductions method on packer droppings.
SQL Injection Attack on Web Application
Authors: Parameswari S. , Kavitha K.
Published: August 2021

SQL injection attacks are one of the highest dangers for applications composed for the Web. These attacks are dispatched through uncommonly made client information on web applications that utilizes low level string operations to build SQL queries. An SQL injection weakness permits an assailant to stream summons straightforwardly to a web application’s hidden database and annihilate usefulness or privacy. In this paper we proposed a simplified algorithm which works on the basic features of the SQL Injection attacks and will successfully detect almost all types of SQL Injection attacks. In the paper we have also presented the experiment results in order to acknowledge the proficiency of our algorithm.
A Novel HABF Technique for 5G in VANET
Authors: Suresh Kumar Reddy K. , Tamizhazhagan V. , Senthil Murugan V. , Rajaveerappa D.
Published: August 2021

VANET is the standout amongst the most rising Technology in current situation. Step by step street mishaps are rising. So as to keep mind these mishaps knowledge VANET came into picture. Yet, still greater upgrade is required to stop mishaps for that discovering area of Vehicle is vital thing. To discover the area of a vehicle in VANET we take the assistance of cell organizes. Still with 1G, 2G, 3G, 4G there are a few issues like blurring, Handoff, and so forth.., .To stay away from mishaps in VANET, finding careful area of vehicle and getting speedy reaction is imperative. This can be conceivable with development innovation 5G. Thus, to locate the correct area of vehicle in VANET, we executed a novel strategy utilizing AOA-MUSIC (Angle of Arrival – for Multiple Signal Classification), Godara, and LMS (Least Mean Square) Algorithms and this is an ideal system among straightforward and complex methods. We have planned and executed the proposed technique utilizing MATLAB reproduction and results demonstrate that our strategy gives progressively exact estimations of area of versatile vehicles, and exact scratch-off of obstruction signals contrasted with other existing strategies.
Comparative Analysis of Hash Authentication Algorithms and ECC Based Security Algorithms in Cloud Data
Authors: Hendry Leo Kanickam S. , Jayasimman L.
Published: August 2021

Cloud computing is ensuring the security of stored data in cloud computing servers is one of the mainly demanding issues. In Cloud numerous security issues arises such as authentication, integrity and confidentiality. Different encryption techniques attempt to overcome these data security issues to an enormous extent. Hashing algorithm plays an important role in data integrity, message authentication, and digital signature in modern information security. For security purpose using encryption algorithm like ECC (Elliptic Curve Cryptography) and Authentication of data integrity using hashing algorithms like MD5, and SHA-512. This combination method provides data security, authentication and verification for secure cloud computing.
Prediction of Secondary Structures of Human Prion Proteins using Miscellaneous Methods
Authors: Khan Ahmad Aftab , Sharma Kalpana
Published: August 2021

The contemporary study is an attempt to predict the secondary structures of proteins, from the dataset of human prion proteins which has been acquired from NCBI repository. In this context, we have exploited PSIPRED server which is considered to be proficient and impulsive method to protein structure prediction where users can submit the query sequence of their desire and receive the results of prediction both textually and eloquently. Furthermore, Phyre2 was applied across the amino acid sequence which is among the most widely server deployed for generating consistent protein models characterizing the prediction of protein structures. Moreover, two feed forward neural networks with a sole hidden layer which was tested and trained on a 10 fold cross validation mechanism in MATLAB, and subsequently significant prediction accuracy of 71.73% and minimum mean absolute error of 12.3% was achieved.
A Novel Technique for Automatic Image Enhancement using HTHET Approach
Authors: Murali V. , Venkateswarlu T.
Published: August 2021

Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.
Score Based Network Selection in Heterogeneous Wireless Networks to Enhance Quality of Service for End User
Authors: Ingole Piyush K. , Sarode M. V. , Arya Meenakshi S.
Published: August 2021

Abrupt node movements is mobile networks directly affects the network connectivity of the node. Abruptly moving nodes generally face the issue of low network quality, and call drops. An effective handoff mechanism is needed in order to address this issue. In this research, we are proposing a network score based handoff mechanism, which follows the make-before-break principle. An improvement in terms of handoff delay, and packet loss is reported by the extensive analysis and simulations performed on the proposed system. Average handoff delay of less than 1ms, and packet loss ratio of less than 1% are obtained by our research. We plan to extend the research further for multiple networks to extend the performance analysis.
Data Mining for the Prediction of Heart Disease: A Literature Survey
Authors: Umasankar P. , Thiagarasu V.
Published: August 2021

The health care environment is found to be rich in information, but poor in extracting knowledge from the information. This is because of the lack of effective analysis tool to discover hidden relationships and trends in them. By applying the data mining techniques, valuable knowledge can be extracted from the health care system. Heart disease is a group of condition affecting the structure and functions of heart and has many root causes. Heart disease is the leading cause of death in the world over past ten years. Researches have been made with many hybrid techniques for diagnosing heart disease. This paper deals with an overall review of application of data mining in heart disease prediction.
Intrusion Detection System Using Deep Learning
Authors: Solomon Irin Anna , Jatain Aman , Bajaj Shalini Bhaskar
Published: August 2021

Intrusion detection system (IDS) plays a very critical part in identifying threats and monitoring malicious activities in networking system. The system administrators can use IDS to detect unauthorized access by intruders in different organizations. It has become an inevitable element to the security administration of every organization. IDSs can be generally categorized into two categories. The first group focuses on patterns/signatures of network packets/traffic and they identify network intrusions using rule-based matching. The second group uses machine learning (ML) based approaches such as supervised and/or semi-supervised learning and train IDS models on a collection of labeled and/or unlabeled network data. This method has obtained better detection compared to the previous method. This project paper’s scope involves implementing an intrusion detection system using deep learning technology for efficient detection of intrusion and intrusive activities that can cause disruption in the networking system. We use a Feed-forward Neural Network, a deep learning based technique, on KDD99 CUP – a commonly used dataset for network intrusion. In this paper the performance of the proposed system is compared with the existing previous work.
Improved Heart Disease Prediction Using Deep Neural Network
Authors: Ashraf Mohd , Rizv M. A. , Sharma Himanshu
Published: August 2021

Heart disease is biggest challenge for medical professionals. Modern life style made it an epidemic; according to a survey conducted by WHO heart attack is leading cause of death all over the world especially in the western world. It is surveyed that 23% of the death in US is due to Heart related disease [1]. It has been observed assistance is needed for helping medical professionals in detecting the chance of heart attack in the human. In recent times a lot of work related to providing an automated support system for predicting chance of Heart attack in human has been done. After advancement of computer science, researchers felt that they can help in some of the key interdisciplinary areas like medical science. Machine learning techniques are compared on the single data set which does not reflect true potential of any algorithms. They also suffer from some of the key anomalies such as accuracy and manual data set pre-processing. In this paper, we propose Deep Neural Network methods for creating an automated system for heart attack prediction. It is tested on multiple dataset to find out true potential and providing certainty in the accuracy. Method also promises to remove all the mentioned anomalies from the system like lack of accuracy and automated approach in pre- processing of the data set. In result analysis, it has been observed that prediction is much more efficient and minimum accuracy achieved through this proposed method is 87.64% on any of the data set taken under consideration.
A New Approach of Functional Dependency in a Neutrosophic Relational Database Model
Authors: De Soumitra , Mishra Jaydev
Published: August 2021

In order to model the imprecise and uncertain information, different classical relational data model have been studied in literature using vague set theory. However, neutrosophic set, as a generalized vague set, has more powerful ability to process fuzzy information than vague set. In this paper, we have proposed a neutrosophic relational database model and have defined a new kind of neutrosophic functional dependency (called  -nfd) based on the -equality of tuples and the similarity measure of neutrosophic sets. Next, we present a set of sound neutrosophic inference rules which are similar to Armstrong’s axioms for the classical case. Finally, partial  -nfdand neutrosophic key have been studied with the new notion of  -nfdand also tested.
IDSFS: A Signature Based Intrusion Detection System with High Pertinent Feature Selection Method
Authors: Latha S. , Prakash Sinthu Janita
Published: August 2021

Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFS – a signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection method (HPFSM), Artificial Neural Network for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics like number of features, classification accuracy, False Positive Rate (FPR), Precision, Number of rules, Running Time and Memory consumption are checked and proved the proposed frame work’s efficiency.
Classification of Pests for Rice Crop Using Big Data Analytics
Authors: Durgabai R. P. L. , Bhargavi P. , Jyothi S.
Published: August 2021

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.
Monitoring the Wind Turbine Condition Using Big Data Technique
Authors: Poorima N. V. , Srinivasan B. , Karthikeyan S.
Published: August 2021

The desire to cut back the price of energy from turbine generation has seen a rise within the analysis applied to the sphere of turbine condition observation. Wind turbine condition observation has the potential to cut back operation and maintenance prices through optimized maintenance programming and also the rejection of major breakdowns. To aid this analysis, increasing volumes of knowledge are being captured and keep. These massive volumes of knowledge could also be deemed ‘Big Data’, and need improved handling techniques so as to figure with the information with efficiency. It introduces a turbine condition observation system that has been put in in AN operational Vestas V47 turbine for the aim of developing algorithms to sight machine deterioration. The system’s ability to capture massive volumes of knowledge (approx.2TB per month) has LED to the need of victimization increased knowledge handling techniques. This paper can discuss these ‘Big Data’ techniques and recommend however they will ultimately be used for condition observation of multiple wind turbines or wind farms.
Data Analytics in Banking and Financial Services
Authors: Palaniammal S. , Thangamani V. S.
Published: August 2021

In Journal of Banking and Finance [1] we are living in the era of the big data. The rapid development of scientific and data technology over the past decade has brought not only new and sophisticated analytical tools into Financial and Banking services, but also introduced the power of data science application in everyday strategic and operational management. Data analytics and science developments have been particularly valuable to financial organizations that heavily depend on financial information in their decision making processes. The article presents the research that focuses on the impact of the data and technology trends on decision making, particularly in Finance and Banking services. It covers an overview of the benefits associated with the decision analytics and the use of big data by financial organizations. The aim of the research is to highlight the areas of impact where the big data trends are creating disruptive changes to the way the Finance and banking industry traditionally operates. For example, we can see rapid changes to organisation structures, approach to competition and customer as well as the recognition of the importance of data analytics in strategic and tactical decision making. Investment in data analytics is no longer considered a luxury, but necessity, especially for the financial organizations in developing countries. Technology and data science are both forcing and enabling the financial and banking industry to respond to transformative demands and adapt to rapidly changing market conditions in order to survive and thrive in highly competitive global environment. Financial companies operating in developing countries must develop strong understanding of data-related trends and impacts as well as opportunities. This knowledge should not only be utilized for survival efforts, but also seen as the opportunity to engage at global level through innovation, flexibility, and early adoption of data science benefits. The paper also recommends further studies in related areas, which would provide additional value and awareness to the organizations that are considering their participation in the global data and analytical trends.
Advanced Region Detection Technique to Enhance Accurate Abnormality Isolation of Cancer in Digital Images
Authors: Mallika N. M. , Janarthanam S. , Aruljoth A.
Published: August 2021

In recent years, extensive research is carried out in computer assisted interpretation carried out for cancer classification. Computer aided Interpretations are involves with pre-processing, contrast enhancement, segmentation, appropriate feature extraction and classification. Though considerable research is carried out in developing contrast enhancement and image segmentation techniques, cancer regions could not be isolated and extracted efficiently. Hence this work focuses on developing efficient image segmentation techniques for isolating the cancer region and also identifying suitable descriptors for describing the cancer region. Hence this work focuses to introduce a simple and easy approach for detection of cancerous tissues in mammals. Detection phase is followed by segmentation of the region in an image. Our approach uses simple image processing techniques such as averaging and thresholding along with a Max-Mean and Least-Variance technique for cancer detection. Experimental results demonstrate the effectiveness of our approach.
Sentiment Analysis on Myocardial Infarction Using Tweets Data
Authors: Monicka M. B. , Krishnaveni A.
Published: August 2021

In 2016, the survey reports that 1.7 Million people die of Myocardial Infarction (MI), due to less medication facilities, less prevention care and treatment planning is top most analysis of effective disease risk assessment, through this we have take prevention using sentiment analysis of recent advancements, the text analytics have opened up new potential of using the rich information of tweet analysis, to identify the relevant risk factors in MI. To tackle the MI risk factors tweet analysis gives more remedy and care factors by users, also this leads to decrease of MI in India. Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar conditions and experiences. It provides the knowledge sharing about MI, plays a vital role through Opinion Mining system.
Airline Traffic Analysis Using Clustering Method in R Language
Authors: Thanganila S. , Keerthana T. , Tamilzhchelvi P.
Published: August 2021

In this paper, we analyze the airlines traffic in R using k-means clustering algorithm to avoid traffic in airlines. Clustering is the process of grouping data, where grouping is recognized by discovering similarities between data based on their features. K-Means algorithm is applied to get results and to predict before any problem arise in airline traffic. R takes too much time to load huge amount of data and sometimes does not support to upload huge volume of datasets. So, to avoid this space and time complexity, we have used Google Cloud.
A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis
Authors: Bonta Venkateswarlu , Kumaresh Nandhini , Janardhan N.
Published: August 2021

In recent years, it is seen that the opinion-based postings in social media are helping to reshape business and public sentiments, and emotions have an impact on our social and political systems. Opinions are central to mostly all human activities as they are the key influencers of our behaviour. Whenever we need to make a decision, we generally want to know others opinion. Every organization and business always wants to find customer or public opinion about their products and services. Thus, it is necessary to grab and study the opinions on the Web. However, finding and monitoring sites on the web and distilling the reviews remains a big task because each site typically contains a huge volume of opinion text and the average human reader will have difficulty in identifying the polarity of each review and summarizing the opinions in them. Hence, it needs the automated sentiment analysis to find the polarity score and classify the reviews as positive or negative. This article uses NLTK, Text blob and VADER Sentiment analysis tool to classify the movie reviews which are downloaded from the website www.rottentomatoes.com that is provided by the Cornell University, and makes a comparison on these tools to find the efficient one for sentiment classification. The experimental results of this work confirm that VADER outperforms the Text blob.
Big Data Analytics and Security in Healthcare Industry
Authors: Rohitha K. , Bhagyasree V. , Kusuma K. , Kokila S.
Published: August 2021

Big data analytics plays a major role in today’s industry which insisted to use big data analytics for the analysis of previous data. Patient record keeping is very much important to track the history of the patient. According to the patient previous records, decision is made. Large volumes of data are created on a daily basis and this data is used in decision making process. But, health care industry has not sensed the potential benefits from big data analytics. To address this need, four big data analytics capabilities were identified. In addition to four, five capabilities were proposed which provides practical insights for administrator. On the other way, data security plays a key role in health care industry. In order to overcome this, a new architecture is proposed for the implementation to IOT and process scalable sensor data for health care systems. This paper focuses on data security so that we can make use of potential capabilities and benefits of big data analytics in a better way.
A Robust Watermarking Scheme Using DWT and SVD
Authors: Gopi B. , Sudha T.
Published: August 2021

Singular value decomposition is in use quite recently in many applications. The SVD is being used for many applications along with other techniques. In this paper a robust watermarking scheme was proposed by using SVD along with wavelet transform. In addition to normal hiding scheme a random signature was used to improve its robustness against spurious intruders. The unitary matrices are used to generate a signature which is going to be embedded into the fourth level decomposition of cover image. After extracting the watermark at the other end, it will be checked with the signature embedded. If these signatures are matched the unitary matrices will be used to extract watermark from watermarked image. Different attacks are considered and the simulation results have shown that the extraction of watermark after attacks have shown minor effect only.