bluuebunny/crossref_metadata_2025 huggingface dataset:
Importance of MapReduce for Big Data Applications: A Survey
Authors: Durairaj M. , Poornappriya T. S.
Published: August 2021

Significant regard for MapReduce framework has been trapped by a wide range of areas. It is presently a practical model for data-focused applications because of its basic interface of programming, high elasticity, and capacity to withstand the subjection to defects. Additionally, it is fit for preparing a high extent of data in Distributed Computing environments (DCE). MapReduce, on various events, has turned out to be material to a wide scope of areas. MapReduce is a parallel programming model and a related usage presented by Google. In the programming model, a client determines the calculation by two capacities, Map and Reduce. The basic MapReduce library consequently parallelizes the calculation and handles muddled issues like data dispersion, load adjusting, and adaptation to non-critical failure. Huge data spread crosswise over numerous machines, need to parallelize. Moves the data, and gives booking, adaptation to non-critical failure. A writing survey on the MapReduce programming in different areas has completed in this paper. An examination course has been distinguished by utilizing a writing audit.
An Efficient Cryptosystem to Perform Encryption and Decryption of Data
Authors: Vishwakarma Manila , Jain Sourabh
Published: August 2021

The backbone of the modern world is electronic communication. Data is transferred from one place to another in almost no time using the electronic medium. But it also exposes the confidential data to the intruder. RSA is the most common and efficient cryptography technique that is used for the purpose of encrypting the content and then sending it over the channel, then than at receivers end the content is decrypted and converted in to original form. Although there are many security mechanisms are available. But there is a continuous need to improve the existing methods. Cryptography is a security mechanism which caters the security services of world in perfect manner.
A Technique for Regression Testing of Object Oriented Software
Authors: . Vedpal , Chauhan Naresh
Published: August 2021

The main challenge in testing an OOS is that there are large number of test cases which are not feasible to execute all of them within constrained time and budget. This paper proposes some factors which can be used to prioritize the test cases in order to have an effective testing. The considered factors are based on the testing history and structured analysis of the software. These factors may be nature of bug, capability of a test case, execution time, business impact, coverage of code in terms of classes(old and new classes), etc. Every factor has been assigned a positive weight which shows the criticality of the factor and ability to introduce the errors in the software. The prioritized set of test cases thus obtained is helpful to discover maximum bugs as early as possible.
MRI Image Enhancement: Optimized Filtering Mechanism for Achieving High Accuracy in Diagnose Process
Authors: Andotra Abhinav Singh , Sharma Sandeep
Published: August 2021

Segmentation plays an important role in separating data from medicinal images and also helps in clinical findings. Segmentation is the way toward apportioning the image into different regions. MRI is utilized to extract images of delicate tissues of human body. It is utilized in analyzing the human organs without the requirement of surgery. For the most part MRI images contain a lot of noise caused by operator performance, equipment and the environment, which prompts genuine errors. MRI is a productive way in giving data in regards to the area of tumors and even the volume. The noise present in the MRI image can be evacuated by utilizing different de-noising procedures whichever is most appropriate method depending on the type of image obtained and afterward can be handled by any of the segmentation techniques. The noise in MRI images might be because of field strength, RF pulses, RF coil, voxel volume, or receiver bandwidth. In our proposed paper a review of different noise handling and filtering mechanism is conducted in order to enhance the quality of image. In this paper we modify the adaptive median filter by applying redundancy handling mechanism and enhance the contrast of image by applying histogram equivalence method.
Discovering Efficient Association Rule Mining via Correlation Analysis
Authors: Anuradha C. , Anandavally R.
Published: August 2021

A Discovery of Association rule mining is an essential task in Data Mining. Traditional approaches employ a support confidence framework for finding association rule. This leads to the exploration of a number of uninteresting rules, such rules are not interesting to the users. To tackle this weakness, this paper examines the correlation measures to augment with support and confidence framework, which resulting in the mining of correlation rules. We then added an additional interesting measure based on statistical significance and correlation analysis. This paper reveals an overview of interesting measures and gives an insight into the discovery of more meaningful rules from large applications than traditional approach. Also it covers a theoretical issues associated with correlations that have yet to be explored.
An Improved Security Threat Model for Big Data Life Cycle
Authors: . Kanika , . Alka , Khan R. A.
Published: August 2021

Big data is a huge amount of data created by individuals related to their medical, internet activity, social networking sites, energy usage communication patterns etc. From these sources, data is being collected and processed by various survey organizations, national statistical agencies, medical centres, and other companies etc. There are many security challenges which occur during data transactions, such as un-authentication, phishing, Vishing, data mining based attacks, etc. From a security point of view the biggest challenge for big data is the protection of user’s privacy. Yazan et.al, have presented big data lifecycle threat model. This paper does a critical review of the work. An Improved Security Threat Model for Big Data Life Cycle has been proposed as a main contribution of the paper. A new phase i.e. data creation phase has been added to the life cycle and it is claimed that the phase is very important one with respect to security and privacy. To justify the claim theoretical and statistical evidences have been provided.
M.Phil. Programs in IT and Computing into New Heights: A Case Study of Srinivas University, Karnataka
Authors: Paul P. K. , Aithal P. S. , Shivraj K. S.
Published: August 2021

Computing programs are internationally available at different levels and nomenclatures. The popular levels in this regard are Bachelors and Master’s Degree. Internationally the Bachelors programs are commonly available as BS and MS program. Though UK and its follower countries the level and nomenclature are also called and popular as BSc and MSc programs. Worldwide Computing and IT programs are available only in science platform (except few countries that offers Master of Engineering/ Technology program). As far as Research levels are concerned most common are PhD and M.Phil. Though, among these, PhD is most common. However, it is worthy to note that M.Phil. Program is not offered and not so much popular in some countries. M.Tech by Research is considered as another program of research level offered to the B.Tech. / BE degree holders and in some cases MSc degree holders. In India, M.Phil. and PhD are available in IT and Computing fields for the science candidates, though engineering post graduate in related fields may also join the program. M.Phil. program in India is offered only at universities. In recent past, the number of private universities has been significantly increased and thus many of them are offer M.Phil. And many of those private universities offer M.Phil.in the field of IT and Computing. The traditional M.Phil. Programs are concentrated on broad areas viz. Computer Science/ Applications. Though, a significant move was undertaken by the Srinivas University, Karnataka for offering M.Phil.in subfields of IT. The paper discusses in detail of M.Phil. Program available in India with a special focus on specialized M.Phil.in Cloud Computing, Big Data Analytics etc. Paper also tries to move into healthy educational policy related work for future potentialities.
Big Data Security and Privacy Issues
Authors: Kapil Gayatri , Agrawal Alka , Khan R. A.
Published: August 2021

Big data gradually become a hot topic of research and business and has been growing at exponential rate. It is a combination of structured, semi-structured & unstructured data which is generated constantly through various sources from different platforms like web servers, mobile devices, social network, private and public cloud etc. Big data is used in many organisations and enterprises, big data security and privacy have been increasingly concerned. However, there is a clear contradiction between the large data security and privacy and the widespread use of big data. In this paper, we have indicated challenges of security and privacy in big data. Then, we have presented some possible methods and techniques to ensure big data security and privacy.
Improving Quality of Service for Mobility Aware of Multi-path Routing Protocol for Load Balancing in Mobile Ad-Hoc Network
Authors: Manju V. , Vadivel R.
Published: August 2021

In Mobile Ad hoc Network (MANET), the single path QoS routing is disadvantageous as it may cause interference, fading, collision and link failures. For QoS routing, the load balancing is necessary, because it allows a router to take advantage of best of multiple shortest paths to a given destination. It also avoids congestion within the network using multipath. While using the MQRLB, the Load balancing may cause some of the major issues such as Link failure, Out of range, Limited Bandwidth, due to this the load balancing in multipath function is not efficient.To overcome these issues, in this paper, MMQRLBMultipath Mobility Aware QoS Routing Load Balancingwith AODV(Ad-hoc on demand distance vector)protocol is proposed to improve the QoS in Load balancing.
Fruit Disease Detection Using Convolution Neural Network Approach
Authors: . Shivani , Singh Sharanjit
Published: August 2021

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.
Optimization of Fruit Disease Detection Process: Using Gaussian Filtering Along With Enhanced SVM
Authors: Singh Hardeep , Sharma Sandeep
Published: August 2021

Fruit disease detection becomes critical since economic and related issues are influenced through the healthy and non-healthy fruits. Technology has advanced and is used to primarily detect and abnormality which is not visible through the naked eye. This paper proposes a new technique of fruit disease detection at early stage for which Gaussian smoothening is used at pre-processing stage along with weighted kernel function within SVM for achieving higher classification accuracy. Feature extraction and selection mechanism uses rank based mechanism that allocates ranks on the basis of predictive significance. The result is obtained in terms of prediction accuracy and mean or average error. Result is optimized by the factor of 10%.
Reliable and Efficient Routing Mechanisms for Vehicular ad-hoc Network
Authors: Nampally Venkatamangarao , Raghavender Sharma M.
Published: August 2021

In order to provide stable connections among nodes, a routing protocol is necessary in VANET system. Dynamic topology and frequent disconnection makes difficult to design an efficient routing protocol for VANET to route information among nodes. The survey of routing protocols in VANET system helps in understanding the concepts of smart intelligent transport system (ITS). It is observed that carry-and-forward is the key consideration and main function of an efficient routing protocol in VANET system. This paper explains various routing protocols for VANET system.
Performance Evaluation of Routing Protocols for Voice and Video Traffics
Authors: Vasundra S. , Venkatesh D.
Published: August 2021

Wireless networks are comprised of nodes which are high in mobility and are adhoc natured where centralized access point is not required. Each node will have particular transmission range in which the data transmits from source to destination in that transmission range. Route construction for transmission of data from source to destination is tough due to high node mobility and dynamic nature of network. Many routing protocols are proposed and implemented in networks to reduce such mobility and dynamic difficulties for route construction. These routing protocols construct a route from source to destination based on availability of network nodes. Adhoc On demand Distance vector (AODV) is a self-starting dynamic network, Dynamic source routing (DSR) and Temporally Ordered Routing Algorithm (TORA) are most frequent used ones for dynamic route construction. Voice and Video are two mostly used applications nowadays as the users are immensely using them. In this paper, a comparison is made in performances of AODV, DSR and TORA for the traffics of voice and video. The performance is evaluated in terms of delay, throughput and packet delivery ratio. Simulations are conducted using network simulator NS 2.35.
Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation
Authors: Singh Satyaveer , Aswal Mahendra Singh
Published: August 2021

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.
Comparative Performance Evaluation of On-Demand Multicast Routing Protocols for MANET
Authors: Kavitha K.
Published: August 2021

A Mobile Ad hoc NETwork (MANET) is a collection of wireless nodes communicating with each other in the absence of any infrastructure. Each device in a MANET is free to move independently in any direction, and will therefore change its links to other devices frequently. Each must forward traffic unrelated to its own use, and therefore be a router. The primary challenge in building a MANET is equipping each device to continuously maintain the information required to properly route traffic. Such networks may operate by themselves or may be connected to the larger Internet. In this paper, we compare the performance of On-Demand Multicast Routing Protocol (ODMRP), Adaptive demand driven Multicast Routing Protocol (ADMR) With Efficient Geographic Multicast Routing Protocol (EGMP) under different mobility models such as Random Way Point Model, Manhattan Model and Random Drunken Model. Using these models, performance metrics such as Packet Delivery Ratio, End-to-End Delay and Control Overhead are evaluated. ODMRP dynamically builds the route and manages the group membership. In ADMR, Multicast routing state is dynamically established and maintained only for active groups and only in nodes located between multicast senders and receivers. ADMR detects the high mobility without the use of GPS or other positioning system. EGMP supports a zone-based scheme to efficiently handle the two-tier membership management, and takes advantage of the membership management structure to efficiently track the locations of all the group members. The simulation result shows that the throughput of ADMR is higher than that of ODMRP and EGMP at high mobility and EGMP is high at low mobility. End to end delay and control overhead of EGMP is higher than that of ODMRP and ADMR.
A Relative Study of Various Routing Protocols in Mobile Ad Hoc Network
Authors: Anandha Lakshmi R. , Suresh T.
Published: August 2021

Mobile Ad hoc Network (MANET) is an infrastructure fewer networks built on a multihop conveying idea. It transmits data from base node to end node. Because MANETS don’t have any fixed infrastructure and it works in a distributed manner. For efficient communication, many routing protocols are used in MANET. Routing protocols are to find a new route to transmit data from base to accurate end through the midway nodes. Every in-between node is act as a router. So, this paper is covers the routing protocols AODV, DSR, DSDV and OLSR to keep current routing information from every node to each other nodes. The purpose of this paper to offers a relative study of routing protocols in MANET and energy aware performance metrics are analyzed on two routing protocols of AODV and DSDV.
A Food Management Based on Smart Refrigerator System
Authors: Sudha R. , Indirani G.
Published: August 2021

Internet of Things (IoT) conceptualizes the idea of remotely connecting and monitoring real world objects (things) through the Internet [1]. When it comes to our house, this concept can be aptly incorporated to make it smarter, safer and automated. This IoT paper focuses on smart refrigerator system which sends alerts to the user when the milk packets count to be decreased because the user refilling he packet purpose. The advantage of smart system is to maintaining the performance and avoids unavailability of milk. Besides, the same can also be utilized for smart fridge by making use of the sensors. The leverage obtained by preferring this system over the similar kinds of existing systems is that the alerts and the status sent by the wifi connected microcontroller managed system can be received by the user on his phone from any distance irrespective of whether his mobile phone . The microcontroller used in the current prototype which comes with an embedded micro-controller and an onboard Wi-Fi shield making use of which all the electrical appliances inside the home can be controlled and managed.
Content Based Video Retrieval Systems with Local Features: A Survey
Authors: Kalakoti Gowrisankar , Prabhakaran G. , Sudhakar P.
Published: August 2021

With the improvement of mixed media information composes and accessible transfer speed there is immense interest of video retrieving frameworks, as clients move from content based recovery frameworks to content based retrieval frameworks. Determination of removed features assume an imperative job in substance based video retrieving paying little mind to video qualities being under thought. This work assists the up and coming analysts in the field of video retrieving with getting the thought regarding distinctive procedures and strategies accessible for the video recovery. These highlights are proposed for choosing, ordering and positioning as indicated by their potential enthusiasm to the client. Great feature determination likewise permits the time and space expenses of the recovery procedure to be lessened. This overview surveys the fascinating highlights that can be separated from video information for ordering and retrieving alongside likeness estimation techniques. We likewise recognize present research issues in territory of content based video retrieving frameworks.
A Literature Survey on the Network Security and Intrusion Detection System Using Data Mining Techniques
Authors: Dharmarajan R. , Thiagarasu V.
Published: August 2021

Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern and the history of security allows a better understanding of the emergence of security technology. The entire field of network security is vast and in an evolutionary stage. The range of study encompasses a brief history dating back to internet’s beginnings and the current development in network security. In order to understand the research being performed today, background knowledge of the importance of security, types of attacks in the networks. This paper elaborates the literature study on network security in various domains. Finally, it summarizes the research directions by literature survey.
Grouping of E Learners Using Fuzzy K-Medoid Clustering
Authors: . Vidyaathulasiraman , Anthony Philomen Raj S. , George Louis Raja A.
Published: August 2021

The process of clustering in the general perspective is limited to the grouping of data into clusters and finds its applications in the fields of information retrieval, text ranking and classification and more. The dimension of e-Learning is to improve learning with various tools and technologies. Grouping of learners based on their learning levels is found to improve the learning abilities. Scientific method to cluster the learners is not available in literature, which can further simplify the amalgamation of learning complemented through clustering. This paper is an attempt to examine the aspects of implementing clustering to group the learners according to their learning abilities.
A Study on a Multiplicity of Load Balancing Algorithms
Authors: . Vidyaathulasiraman , Manikandan G.
Published: August 2021

Past days rulers and publics need to convey message their relatives or one place to somewhere else with the assistance of some correspondence channels like birds or individuals like present mailmen or post ladies and days are passing and development of data and innovation the communication quick and less expansive and present day interchanges totally through on the web. Day by day populaces development and innovation utilizing people groups heterogeneously expanding. So present days network is a critical part and significant assuming jobs in day by day life its outcomes more system issues like less throughput, insufficient network resources, less flags, resource not similarly shared for a given time frame, time complicity because of this troubles the communication mostly or here and there totally interfered with now daily’s a fundamental piece of information is an expected portion to social and economic related change and it is logically expanding worldwide condition. In this exploration essentially an undertaking has been made to classify secured sharing models, architecture, sharing arrangements and load adjusting algorithms in w3. At present need to configure modern load adjusting algorithms on account of our central and state Government focusing propelled correspondences and modernized classrooms through distributed computing.
Retinal Image Vessel Width Analysis in Retinopathy of Prematurity
Authors: Manjunatha R. , Sheshadri H. S.
Published: August 2021

This paper aims at development of Oscillating Pendulum Based Algorithm (OPBA) for retinal image width computation and analysis for the investigation of Retinopathy of prematurity (ROP). The algorithms have been explained in detail with theoretical investigations and simulations, images from local hospital data base were considered in the investigation. Further this paper also presents an overview of direct retinal vessel width computation method along with the comparative analysis. The results obtained are found to be encouraging for analysis of plus disease under ROP.
Research Key Techniques of Data Security in Public Cloud
Authors: Saidhbi Sheik
Published: August 2021

Internet working security has become one of the biggest uprising points of concern now days. People are getting attached more and more to the internet in order to fulfill their demands. Not only customers but also the IT based companies are also getting themselves relying on up growing technology called as cloud computing. Cloud is a branch of computer science that provides the services on lease. In this paper we will make a comparative analysis of various technical security issues towards cloud computing, cloud deployment based security and model based security issues. A comparative analysis has been made at the end of the paper.
A Survey on Online Social Networks: Applications, Threats and Solutions
Authors: Jayabalan K. , Subramani B.
Published: August 2021

Online social networks (OSNs) are playing crucial role in digital world today. OSNs are web applications that allow users to share their personal information, digital images and videos, and to inform others about online or real time world activities and events with people in their network. OSN’s are decentralized and distributed computer networks where users communicate with their friends and relatives through Internet services. The Smartphone has increase the situation for hacker’s to attacks end user data from OSNs. Behaviors of OSN users has been monitored for data analytics[1]. In this paper, we represent full review of the applications, different security level threats and privacy issues, which threaten the well-being of OSN users in general, and children in particular. In addition, we present an overview of existing solutions that can provide better protection, security, and privacy for OSN users[2]. We also offer few recommendations for OSN users, which can improve their security and privacy when using the online social networks.
Evaluation of Random Number Generator Functions Using Statistical Analysis
Authors: Chaurasia Rajashree
Published: August 2021

Most programming languages have in-built functions for the sole purpose of generating pseudo-random numbers. This manuscript is aimed at analyzing the appropriateness of some of these in-built functions for some basic goodness-of-fit statistical tests for random number generators. The document is divided into four sections. The first section gives a broad introduction about randomness and the methods of generation of pseudo-random numbers. Section two discusses the statistical tests that were employed for testing the built-in library functions for random number generation. This section is followed by an analysis of the data collected for the various statistics in the third section, and lastly, the fourth section presents the results of the data analysis.
Performance Analysis of Median Filter With Respect to Different Padding Methods in the Context of Removing Salt and Pepper Noise
Authors: Sudha T. , Nagendra Kumar P.
Published: August 2021

Image Processing is one of the major areas of research. Images are often corrupted with different types of noise such as Gaussian noise, Poisson noise, Salt and Pepper noise, Speckle noise etc.The present work analyses the performance of the median filter with respect to different padding methods in the context of removing salt and pepper noise.Peak Signal-to-Noise ratio and Mean Squared Error have been considered as parameters for performance evaluation. The results obtained show thatthe Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with symmetric padding method on the image corrupted with salt and pepper noise is same as the Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with replicate padding method on the image corrupted with salt and pepper noise respectively.
Enhancement of Security for Cloud Based IoT Using XHE Scheme
Authors: Sailaja K. , Rohitha M.
Published: August 2021

Security is a million dollar issue for all computer systems. Every week there is news of another major breakin to a commercial or government system. Also it is well known that many governments are actively engaged in cyber-warfare, trying to break into the systems of other governments and other groups. The Internet of Things is increasingly changing into an omnipresent computing service, requiring vast volumes of knowledge storage and process. Unfortunately, due to the unique characteristics of resource constraints, self-organization and short range communication in IoT, it always resorts to the cloud for outsourced storage and computation. Security is one of the major challenges faced by cloud based IoT. The standard file protection technique relies on password based encryption schemes and they are vulnerable to brute force attacks. The reason is that, for a wrongly guessed key, the decryption process yields an invalid-looking plaintext message, confirming the invalidity of the key, while for the correct key it outputs a valid-looking plaintext message, confirming the correctness of the guessed key. Honey encryption helps to minimise this vulnerability. Hence, this paper proposed an extended Honey Encryption (XHE) scheme for enhancing the security of the cloud based IoT.
Big Data Analytics for Brain Tumour Detection Using Subspace Clustering
Authors: Vanka Padmavathi , Sudha T.
Published: August 2021

In healthcare industry data mining holds a great prospective to enable health systems steadily use big data and analytics to identify inefficiencies which improves care and reduces cost. Due to the slower rate of technological adoption in health care, software industry lags in implementing effective data mining and analytic strategies. The segmentation, detection, and extraction of infected area of tumours from magnetic resonance images (MRI) are a primary distress but a tiresome task performed by radiologists or clinical experts. To improve the performance and reduce the complexities in segmenting the MRI data new methods need to be introduced. A 3D magnetic resonance images can give better accuracy in detecting the tumour area. In this paper we proposed subspace clustering techniques to detect brain tumours using 3D images.
A Study of Viral Marketing on Social Network in Stream Line Business Model of Data Mining in Management
Authors: Muthumari S.
Published: August 2021

Viral marketing and viral advertising refer to marketing techniques that use pre-existing social networks to produce exponential increases in brand awareness, through self-replicating viral processes, analogous to the spread of a computer virus. It can be often word-of-mouth delivered and enhanced online harness the network effect of the Internet and is very useful in reaching a large number of people very fast. The ultimate goal of marketers interested in creating successful viral marketing programs is to create viral messages that appeal to individuals with high social networking potential (SNP) and that have a high probability of being presented and spread by these individuals and their competitors in their communications with others in a short period of time. There are several sources of social networks where our model can be applied to, and a few are: Facebook, Twitter, LinkedIn, YouTube, Google+, Instagrams, online forums, email mailing lists.
Security Threats in Cloud Computing
Authors: VimalRosy J. , Britto Ramesh Kumar S.
Published: August 2021

Day by day witnesses a wide development of information technology in the use of cloud computing, and its services shape its architecture. It is indeed, that replaces the former attempts to enrich the security strategy in cloud computing. Security is one of the major issues in the cloud computing environment. In this paper we investigate about security threads and possible solution for cloud. The paper is categorized as follows: Section I describes the cloud computing overview. Section II describes the security threats and challenges in cloud computing. Section III describes the Obstacles and vulnerabilities that can be carried out to cloud environments. Section IV describes the possible solution of the issues. Section V concludes the paper with a cloud computing security.
Review on Security in Wireless Sensor Networks
Authors: Divya K. , Srinivasan B.
Published: August 2021

Wireless Sensor Networks (WSN) is a rising innovation and step by step it is pulling in the consideration of scientists with its testing qualities and differentiated application space. The more scientists attempt to grow encourage cost and vitality proficient figuring gadgets and calculations for WSN, the all the more difficult it gets to be to fit the security of WSN into that obliged environment. Be that as it may, security is vital to the accomplishment of applying WSN. Along these lines, commonality with the security parts of WSN is basic before planning WSN framework. In this paper, we review the condition of craftsmanship in securing remote sensor systems. We audit a few conventions that give security in sensor systems. Additionally, this study records the notable assaults at the Network layer of WSN.
Sentiment Analysis about Smart Phones Using Twitter Corpus by Deep Learning Approach
Authors: Pavithra R. , Mohamed Shanavas A. R.
Published: August 2021

Micro blogging websites are nothing but social media website to which user makes quick and frequent posts. Twitter is one of the well-known micro blog sites which offer the space for person which can read and put up messages that are 148 characters in duration. Twitter messages also are referred to as Tweets. And will use these tweets as raw facts. Then use a way that automatically extracts tweets into advantageous, bad or neutral sentiments. By the usage of the sentiment evaluation the consumer can recognize the feedback about the product or services before make a purchase. The organization can use sentiment evaluation to know the opinion of clients about their products, so can examine customer pleasure and in line with that they could improve their product. Now-a-days social networking sites are at the growth, so massive amount of data is generated. Millions of human beings are sharing their views each day on micro blogging sites, since it includes short and simple expressions. In this thesis, able to discuss approximately a paradigm to extract the sentiment from a famous micro running a blog carrier, Twitter, wherein customers submit their opinions for the whole thing. And can use the deep mastering algorithm to categories the twitters which incorporates Convolutional Neural Networks. The experimental end result is presented to demonstrate the use and effectiveness of the proposed system.
Precision Data Acquisition and Analysis for Nutrient Management of Tomatoes
Authors: Yogeswari G. , Padmapriya A.
Published: August 2021

Agriculture is the soul of every nation, where it considers some factors such as uneven rainfall, changing climate and weather conditions, monsoon for soil and nutrient during the crop growth. Agriculture is predominantly essential and the main source of our livelihood. Nutrient management is a major thirst area and to be the focus in the field of agriculture. Due to the paucity of nutrients in plants, the human is forced to face many challenges in day-to-day life. Restoration of nutrient is crucial, in this view there is need to espouse the precision agriculture system which alters crop related plan and policies. The main aim of this research is to collect some of the factors influencing nutrients in plant growth and analyze them. The data collection is done by both manual and precision methods. The plant chosen for the analysis is Tomato – a horticulture crop. This is an attempt towards developing an expert system based on precision data.
Diabetics Prediction Based on Multi-Linear Regression Using R Language
Authors: Madhubala V. , Porkodi P. , Selvapriya R. , Tamilzhchelvi P.
Published: August 2021

Classification is an important technique in data mining which is applied in many fields including medical diagnosis to find diseases. In this research work, the multi-linear regression algorithm is used to find the possibilities of occurrence of diabetes. This research work would help the developers to identify the characteristics and flow of algorithms. This implementation helps the diabetalogist to make decision quickly. The explict outcome of the performance of the algorithm is reported for the chosen data.
Encrypted Mobile Cloud Data Searching With Efficient Traffic and Energy Saving Method
Authors: Jamuna M. , Supriya A.
Published: August 2021

Cloud storage provides a convenient, massive, and scalable storage at low value; however information privacy could be a major concern that prevents users from storing files on the cloud confidingly. a technique of enhancing privacy from information owner purpose of read is to cipher the files before outsourcing them onto the cloud and decode the files when downloading them. However, encryption could be a heavy overhead for the mobile devices, and information retrieval method incurs an advanced communication between the information user and cloud. Commonly with restricted bandwidth capability and restricted battery life, these problems introduce significant overhead to computing and communication as well as the next power consumption for mobile device users that makes the encrypted search over mobile cloud terribly difficult. During this paper, we tend to propose traffic and energy saving encrypted search (TEES), a bandwidth and energy efficient encrypted search design over mobile cloud. The planned design offloads the computation from mobile devices to the cloud, and that we any optimize the communication between the mobile clients and also the cloud. It’s demonstrated that the information privacy doesn’t degrade once the performance sweetening ways square measure applied. Our experiments show that TEES reduces the computation time by twenty three to forty six p.c and save the energy consumption by 35 to 55 percent per file retrieval; meanwhile the network traffics throughout the file retrievals also are considerably reduced.
Web Mining System in a Natural Language Processing Based for Social Media Analysis
Authors: Vasavi Gadamsetty , Sudha T.
Published: August 2021

Social Media Monitoring and Analysis are the new trends in technology business. The challenge is to extract correct information from free-form texts of social media communication. Natural Language Processing methods are sometimes used in social media monitoring to improve accuracy in extracting information. This paper discusses a web mining system that is based on Natural Language Processing to analyze social media information. In that process, this research examines Natural Language methods that are important for such analysis. Then the traditional web mining steps are discussed along with proposed use of Natural Language Processing methods.
Big Data Analytics: A New Flavour for Educational Industry
Authors: SathyaHari Prasad S. K. , Bhavya P. , Kalpana Devi M.
Published: August 2021

Mushrooming of educational institutions in recent times has led to cut throat competition and the success of an institution has become necessary. The technological development has increased the availability of information. Big data analytics has made a rapid advancement and gained a huge momentum in recent years, which feeds into the field of academic institutions to better understand the learner’s needs and address them appropriately. Hence, it is important to have an understanding of Big Data and its applications in the educational industry. The purpose of this descriptive paper is to provide an overview of Big Data, some of the benefits and challenges of big data in the field of education. Though Big Data can provide big benefits, it is the institutions to understand their own needs and act according to their infrastructure and resources.
A Survey on Big Data Analytics Using HADOOP
Authors: Mamatha S. , Sudha T.
Published: August 2021

In this digital world, as organizations are evolving rapidly with data centric asset the explosion of data and size of the databases have been growing exponentially. Data is generated from different sources like business processes, transactions, social networking sites, web servers, etc. and remains in structured as well as unstructured form. The term ― Big data is used for large data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data varies in size ranging from a few dozen terabytes to many petabytes of data in a single data set. Difficulties include capture, storage, search, sharing, analytics and visualizing. Big data is available in structured, unstructured and semi-structured data format. Relational database fails to store this multi-structured data. Apache Hadoop is efficient, robust, reliable and scalable framework to store, process, transforms and extracts big data. Hadoop framework is open source and fee software which is available at Apache Software Foundation. In this paper we will present Hadoop, HDFS, Map Reduce and c-means big data algorithm to minimize efforts of big data analysis using Map Reduce code. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools and related fields.
Data Mining Methods for Communication Technology
Authors: Saraswathi Bai V.
Published: August 2021

The aim of the project was to analyze the behavior of military communication networks based on work with real data collected continuously since 2005. With regard to the nature and amount of the data, data mining methods were selected for the purpose of analyses and experiments. The quality of real data is often insufficient for an immediate analysis. The article presents the data cleaning operations which have been carried out with the aim to improve the input data sample to obtain reliable models. Gradually, by means of properly chosen SW, network models were developed to verify generally valid patterns of network behavior as a bulk service. Furthermore, unlike the commercially available communication networks simulators, the models designed allowed us to capture non-standard models of network behavior under an increased load, verify the correct sizing of the network to the increased load, and thus test its reliability. Finally, based on previous experience, the models enabled us to predict emergency situations with a reasonable accuracy.
Extraction of Knowledge from Web Server Logs Using Web Usage Mining
Authors: Harika B. , Sudha T.
Published: August 2021

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.
Providing a Secure Cloud Storage by Using Attribute Based Temporary Key Word Search Scheme
Authors: Meka Likhita , Pagadala Srivyshnavi
Published: August 2021

The cloud providers are not fully trusted in the accept of temporary keyword search on confidential data. Hence this is the main focus of this research, it is necessary to outsource data in the encrypted format. In the attribute-based keyword search scheme the authorized users generate some tokens which were in encrypted format and send them to cloud for the search operation. These tokens can be used to extract all the cipher texts which are generated at any time and contain the search token which were generated by authorized users. Since this may lead to some information leakage, a new cryptographic primitive is introduced which is more secure to propose a scheme in which the search tokens can only extract the cipher texts generated in a specified time interval and that cryptographic primitive is called key-policy attribute-based temporary keyword search (KPABTKS) which provide this property. To evaluate the security, we have to prove that the proposed scheme achieves the keyword secrecy property and is secure against selectively chosen keyword attack (SCKA) both in the random oracle model and Decisional Bilinear Diffie-Hellman (DBDH) assumption. And at last the research will show the complexity of the encryption algorithm is linear with respect to the number of the involved attributes.
An Innovative Method of Estimation Hewing for Invention Report Mining and Estimation Summarization
Authors: Ravichandran S. , Sathiamoorthy J.
Published: August 2021

With the assistance of Web 2.0, the bases on client interest, posting on the web surveys has become an undeniably mainstream path for individuals to impart their perspectives to different client’s suppositions and conclusions toward items and administrations. It turns into a typical practice for online business sites to give the offices to individuals to convey and distribute their audits between them. These online audits present an abundance of data on the Services and Products, which will encourage the improvement of their business. Consequently a developing number of late examinations have been centred on the Opinion Mining. For example the Opinion Mining alludes to computational method for assessing the sentiments that are mined from different Web Sources.
A couple of Opinion Mining based techniques have been considered and broke down. From our investigation, it is seen that a couple of feeling mining based directed and unaided techniques had not delivered great outcomes because of alluding less number of sentiments inside a similar URL’S and treating the highlights with comparable significance as various. To beat this issue, Topic Anatomy Model TSCAN was proposed, where the Task is called as Topic Anatomy and which sums up and relates the primary pieces of a point with the goal that the per users could comprehend the substance without any problem.
By utilizing this model, the more data can be removed and related through their transient closeness, which will give conceivable substance. This model is including imperative part in the Opinion Mining since clients can impart their insights about the items. From our usage, it is seen that this plan gives the best reasonable answer for the client’s advantages and requests. Notwithstanding, it burns-through more opportunity to anticipate the best performing items because of huge informational collections respectively.
Consequently our exploration work is proposed and actualized a productive strategy for Opinion Mining called an Efficient Parallel Opinion Mining (EPOM) constructed TSCAN Algorithm separately. It is centring more sites and it is removing more data in equal way, so we can get advanced productive outcome with least execution time. From our outcomes, it is noticed that it gives the best reasonable answer for the client’s advantages and requests and it I s improving the presentation of existing method regarding Quality of Information, Prediction and Execution Time.