bluuebunny/crossref_metadata_2025 huggingface dataset:
Rank Based Virtual Community Detection in Mobile User Network
Authors: Sunanna S. S. , Aji S.
Published: August 2021

A virtual community network is a network of individuals who are sharing something in common such as behavior, thoughts, and ideas. The detection of virtual communities in social networks encourages the researchers in the computational and social science to invest more effort and time to explore the different properties of virtual networks. This paper introduces, mobile user network, another application area of virtual community networks with some proven techniques. The work explains the link prediction methods in which the relation between the different parameters in the dataset is explored. The virtual network hence formed could give more insights to the investors in the mobile communication domain to fulfill the customers’ needs and attract more users to their tent.
Recommender System for Student Performance Using EDM
Authors: JothiLakshmi S. , Thangaraj M.
Published: August 2021

Student’s performance plays an important role in an educational institutions and economic growth of society by producing graduates. Educational Data mining algorithms are used to extract the hidden knowledge from the Educational institutions. The recommender system is a special type of information filtering system. This paper provides a recommender system for evaluate student performance that helps the students who need the special attentions.
Designing Factors Affecting the Performance and Lifetime of Wireless Sensor Network
Authors: Saha Sunita
Published: August 2021

Performance and lifetime of a Wireless Sensor Network (WSN) are the evaluation parameters of a network that how well and how long a network is doing well. A network can start giving wrong information at any time during the whole lifespan of the network and it might stop working as well. The performance of a network can be evaluated and improved up to a level of satisfaction with the improvement of network parameters and the components used in designing the network. There are a handful of works that address the major tradeoffs, coverage, and connectivity in designing of WSN. Also, an efficient decision of locating the sensors in WSN can lead to obtaining the required degree of network performance. There are hundreds of research, demanding various deployment methods and optimization goal in various environments. This article aims at the investigation of those designing factors which can affect the performance and lifetime of a WSN at the same time improving those designing issues can produce a guaranteed degree of throughput.
Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India)
Authors: Mohd Razeef , Ahmed Butt Muheet , Baba Majid Zaman
Published: August 2021

Prediction of rainfall is one of the most essential and demanding tasks for the weather forecasters since ages. Rainfall prediction plays an important role in the field of farming and industries. Precise rainfall prediction is vital for detecting the heavy rainfall and to provide the information of warnings regarding the natural calamities. Rainfall prediction involves recording the various parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. From last few decades, it has been seen that data mining techniques have achieved good performance and accuracy in weather prediction than traditional statistical methods. This research work aims to compare the performance of few data mining algorithms for predicting rainfall using historical weather data of Srinagar, India, which is collected from http://www.wundergrounds.com website. From the collected weather data which comprises of 9 attributes, only 5 attributes which are most relevant to rainfall prediction are considered. Data mining process model is followed to obtain accurate and correct prediction results. In this paper, various data mining algorithms were explored which include decision tree based J48, Random forest, Naive Bayes, Bayes Net, Logistic Regression, IBk, PART and bagging. The experimental results show that J48 algorithm has good level of accuracy than other algorithms.
Survey on White-Box Attacks and Solutions
Authors: Porkodi V. , Sivaram M. , Mohammed Amin Salih , Manikandan V.
Published: August 2021

This Research paper provides special white-box attacks and outputs from it. Wearable IoT units perform keep doubtlessly captured, then accessed within an unauthorized behavior due to the fact concerning their bodily nature. That case, they are between white-box attack systems, toughness the opponent may additionally have quantity,visibility about the implementation of the inbuilt crypto system including complete control over its solution platform. The white-box attacks on wearable devices is an assignment without a doubt. To serve as a countermeasure in opposition to these problems in such contexts, here analyzing encryption schemes in imitation of protecting the confidentiality concerning records against white-box attacks. The lightweight encryption, intention in accordance with protecting the confidentiality over data towards white-box attacks is the recently stability good algorithm. In that paper, we read the extraordinary algorithms too.
Implementation of Energy HEED (ER-HEED) Protocol Using Super Cluster Head for WSN
Authors: Pattabirani G. , Selvakumar K.
Published: August 2021

Wireless Sensor Network (WSN) is used in almost all applications in developing environment. This is due to their ability and easy implementation through several applications. The most important criteria in WSN are to minimize the energy consumption and improve the network lifetime. Clustering algorithms are considered as one of the effective way to improve the network lifetime in WSN. Hybrid, Energy-Efficient and Distributed (HEED) clustering approach uses energy-efficient clustering algorithm. This paper proposes an Enhanced Rotational HEED (ER-HEED) protocol using super cluster head for minimizing energy consumption and to improve the network lifetime. The proposed work is carried out in two stages, first stage, super cluster head is introduced. In second stage, the node with maximum threshold is chosen as a cluster head on rotation within in the cluster. The results show that the ER-HEED performs well when compared with HEED and LEACH.
Selection of Features on Mining Techniques for Classification
Authors: Kumar Gurrampally , Mohan S. , Prabakaran G.
Published: August 2021

Feature selection has been developed by several mining techniques for classification. Some existing approaches couldn’t remove the irrelevant data from dataset for class. Thus it needs the selection of appropriate features that emphasize its role in classification. For this it consider the statistical method like correlation coefficient to identify the features from feature set whose data are very important for existing classes. The several methods such as Gaussian process, linear regression and Euclidean distance have taken into consideration for clarity of classification. The experimental results reveal that the proposed method identifies the exact relevant features for several classes.
Freshwater Microalage Image Identification and Classification Based on Machine Learning Technique
Authors: Victoria Anand Mary A. , Prabakaran G. , Mohan S.
Published: August 2021

Algae is an aquatic organism of an enormous and diverse group, which has the ability to conduct photosynthesis. The various sorts of microalgae play trivial roles in marine and fresh water environment. Microalgae are of various sizes and shapes, ranging from unicellular to multicellular forms. These algae were from the division of Anabaena, Oscillatoria, Microcystis Scenedesmus, Pediastrum and Cosmarium found in fresh water lake. In very high density these microalgae may discolor the water, outcompete, and become poisonous to other life forms. This is technically termed as harmful algal blooms. It is one of the most serious water pollution problems. Today, humans in many ways to use microalgae’s for example, as fertilizers, soil conditioners, and livestock feed. A hybrid method is apply to automatic detection and recognition of some selected freshwater algae genera by combining the image processing technique with ANN approaches. Thus, analysis and prediction of algae is significant, which can be achieved using machine learning processing.
IoT Enabled Soil Testing
Authors: Sindhu P. , Indirani G.
Published: August 2021

Agriculture is the main occupation of our country and it plays a vital role in our country. Using too much of fertilizers may lead to the inferior quality of the crop production. So the measurement of soil nutrients is greatly required for better plant growth. Determining the amount of nutrients in the soil is the key function. pH value is also one of the most important and informative soil parameter to detect the soil fertility and it is measured to identify the soil fertility. In the proposed system, it determines the crops which are suitable for the particular soil type. It will analyze moisture content, temperature and humidity in soil at real time and it will also suggest the crops based on determined PH of soil. This system is proposed to help the farmers to increase the production and the suggestions are made through the mobile application.
A Study on Vehicular Content Delivery
Authors: Kunisetti Subramanyam , Ravichandran Suban
Published: August 2021

The presence of the net of Vehicles licenses comforts driving encounters and substance rich sight and sound framework administrations for in-vehicle clients. The movement arranges gives particular situation centrically content conveyance administrations including data of auto standing, client practices, and ecological choices. In this paper, we tend to target transport content conveyance from a gigantic in view of a data point. At the point when an exhaustive survey of dynamic works, we tend to expand the potential cost of huge data in vehicular information and substance benefits by presenting numerous regular application circumstances. Per the data qualities, we tend to group the transport data into 3 classes, that is, an area is driven, client-driven, and vehicle-driven, and afterward represent relate execution of huge data arrangement and investigation. A true enormous data application in social-based transport systems is given, and reenactment results demonstrate that the tremendous data empowered substance conveyance technique will get an execution gain of client fulfillment with the conveyed substance compared to the case rudely of social huge information. At last, we tend to close the article with disjoining al future examination subjects.
A Review on Wireless Sensor Network Simulation Tools
Authors: Sudha C. , Suresh D. , Nagesh A.
Published: August 2021

Wireless Sensor Networks (WSNs) were increasing developing enthusiasm for as a long way returned as years; WSN assignments of a Every sensor network incorporates gazing and estimating sure marvels, as well as turning in collected data. Sensor network require applying distinctive strategies utilized in stressed out and wireless network. Simulation tool for wireless sensor system are steadily being utilized to contemplate sensor networks and to check new programs and Protocols in this developing research field. It is valuable to researcher to verify new though and dissect with proposed algorithm in virtual environment and assist to keep away from expensive hardware usage and time utilization too. This paper gives a whole overview and comparisons of various wireless sensor network simulators with the intention to help researchers with choosing the best simulator on hand for a selected application environment.
A Proposed Method for Mining Breast Cancer Pattern Using Particle Swarm Optimization
Authors: Dewangan Pranjali , . Neelamsahu
Published: August 2021

Breast cancer is one of the leading causes of death among women in many parts of the world. In this paper, we have developed an efficient hybrid data mining approach to separate from a population of patients who have and who do not have breast cancer. The proposed data mining approach has consisted of two phases. In first phase, the statistical method will be used to pre-process the data, which can eliminate the insignificant features. It can reduce the computational complexity and speed up the data mining process. In the second phase, we proposed a new data mining methodology, which based on the fundamental concept of the standard particle swarm optimization (PSO), namely discrete PSO. This phase aimed at creating a novel PSO in which each particle was coded in positive integer numbers and had a feasible system structure. Based on the obtained results, our proposed DPSO can improve the accuracy to 98.71%, sensitivity to 100%, and specificity to 98.21%. When compared with the previous research, the proposed hybrid approach shows the improvement in both accuracy and robustness. According to the high quality of our research results, the proposed DPSO data mining algorithm can be used as the reference for deciding on hospital and provide the reference for the researchers.
An Improvement in Digital Image Watermarking Scheme Based on Singular Value Decomposition and Wavelet Transform
Authors: Saqib Manasha , Naaz Sameena
Published: August 2021

An enormous growth of multimedia information in the internet has given rise to varied unauthorized use and modification. This authenticity issue is equally a limitation both in the defense data transmission and secured transmission. Digital watermarking is one of the legitimate solutions to the above problem since it makes possible validation and secure transmission of secret data. This paper presents a robust and secure digital image watermarking scheme that can be used for copyright protection. The scheme involves Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). The latest approach to wavelet transform is the Lifting Wavelet Transform and the significant transform technique for robust digital image watermarking is Singular Value Decomposition. The results are cross-validated by using inverse LWT and SVD. The digital signature mechanism is used to generate and embed a digital signature after the watermark is embedded, then the ownership is authenticated before extracting watermarks. In order to check the robustness of the method against various common image processing attacks like Mean, Median, Gaussian, Shear, Rotation and Crop, certain performance metrics such as peak to signal noise ratio (PSNR)and mean square error (MSE) is computed. To check similarity, normal cross-correlation (NCC) is used.
Determinants of Information Security Awareness among Employees of Capital Market Registrars in Lagos, Nigeria: An Empirical Study
Authors: Oladipupo Samuel Oluranti
Published: August 2021

The purpose of the study is to examine the determinants of information security awareness (ISA) among employees of Capital Market Registrars (CMRs) in Lagos, Nigeria based on established factors from the existing literatures on ISA. The main objectives of the study are; to determine the level of information security awareness among CMRs’ employees and to identify the components that influence information security awareness. This study utilised a survey design. Stratified random sampling technique was used to select the respondents for the study. A total of 326 copies of questionnaires were distributed among the employees in CMRs, of which 267 properly completed questionnaires were returned. Descriptive statistics and simple regression were used for data analysis. Finding revealed that information security policy, information security education, knowledge of technology, and employee’s behaviour significantly influenced information security awareness. The results of the study further revealed the strong correlation between employee’s behaviour and information security awareness. Overall, the study showed that the level of information security awareness is high, which implies that employees of CMRs in Lagos are aware of the potential threats and risk associated with information security. Based on these research findings, recommendations were therefore made.
Different Techniques for Person Re-Identification: A Review
Authors: Reshma O. S. , Sheik Reshma
Published: August 2021

In investigation, person re-identification is also a tough task of matching persons determined from utterly completely different camera views. It is necessary applications in AI, threat detection, human trailing and activity analysis. Person re-identification is also a tough analysis topic as a result of partial occlusions, low resolution images and massive illumination changes. Also, person determined from utterly completely different camera views has very important variations on poses and viewpoints. This paper summarises the challenges related to the person re-identification jointly discuss varied techniques utilized person re-identification.
A Descriptive Study on Applicability and Adoptability of Cloud Computing Based Technology in the Global Education Scenario
Authors: Shiva Shankar K. C. , Jagadeesha G. T.
Published: August 2021

Technology Transformation has given an added advantage among the service providers of the educational institutions to provide unique services to their stakeholders in terms of quality delivery. At the same time, these technological introductions certainly have helped individual learners towards learning and at the same time, institututional stake holders got benefited in multiple ways by certainly enhancing their day to day operations in a most effective and efficient way by the adoption of advent technological sources. The Introduction and adaptation of advanced technology has abruptly raised the quality level of delivering educational services. Due to technological advancement, stakeholders could be benefited through early adoption and also they could be able to bring out the best of it. Technological advancement and usage certainly brings more benefits to the educational institutions by building up such a brand value. Here, in this research paper, the researcher tries postulates the major importance of technology and its need into field of education. Cloud Computing Technology Based Model, Artificial Intelligence, Machine Learning, Big Data are those advanced technology had been widely used in different dimensions of the education and in return it provides higher satisfaction and greater flexibility to all of its stakeholders.
Clustering Based Approach for Novelty Detection in Text Documents
Authors: Kumar Sushil , Bhatia Komal Kumar
Published: August 2021

As the information is overloaded over the internet accessing of information from the internet according to a given query provides redundant and irrelevant information. It is necessary to retrieve relevant and novel information from a given query by the user. With the result of this the user will require minimum effort to access the information need. In this work we proposed a clustering based approach for novelty detection which will provide the relevant and novel documents for the information need. Based on the user query the incoming stream of documents will be clustered using k-means algorithm. Then the cluster heads are selected from the various clusters with the minimum distance. These cluster heads are the novel documents from a collection of documents from different clusters having the large distance. The proposed technique can be further used in the field of information retrieval.
Wavelength Converter Based Optimized RWA for All Optical Networks
Authors: Patil Shilpa S. , Chaudhari Bharat S.
Published: August 2021

Wavelength converters are used in WDM networks to avoid call blocking and minimizing the blocking probability. Optimal placement of wavelength converters restricts the call blocking probability, the complexity and improves the overall network performance of the network. In this paper, we propose a new weight dependent routing and wavelength assignment algorithm for the optimal placement of the wavelength converters. The wavelength converter placement was considered separately at all the nodes and the partial nodes. Our algorithm outperforms the previously reported studies and requires a lesser number of wavelength converters to achieve the required performance. It reduces the blocking probabilities up to 5.4% and shows that the first four nodes primarily control the blocking performance of the network. The study also reveals that instead of merely increasing the number of converters, their placement at the right location plays a crucial role in improving the performance. Initially, although an increase in the number of the wavelengths also improves the network performance, the further increase does not contribute much to the reduction of the blocking probability.
An Enhanced Technique for Fractal Image Compression Using Block Based Features (BBF)
Authors: Velumani T. , Karthekeyan A. R. , Saveetha R.
Published: August 2021

Image Compression is very active research area specializing on how to compression and decompression of images. The various methods have been proposed for fractal image compression techniques for storage and drawbacks. The complexity in process will affect performance of the existing system to make insufficient. In this paper, the proposed research work presented a block based feature compression of image in the generation of feature sets. The feature sets are generated based such as vertical, horizontal and are extracted from the image based on range and domain blocks. The extracted features are carried out with encoding and decoding the images. The experimental result shows that block based feature method provides better compression ratio and other parameters when compared with the existing methods such as BTC, Wavelet and DCT.
Improved Weighted Page Ranking Algorithm Based on Principal Component Analysis and Map Reduce Frame work for Web Access
Authors: Mylsami T. , Shivakumar B. L.
Published: August 2021

In general the World Wide Web become the most useful information resource used for information retrievals and knowledge discoveries. But the Information on Web to be expand in size and density. The retrieval of the required information on the web is efficiently and effectively to be challenge one. For the tremendous growth of the web has created challenges for the search engine technology. Web mining is an area in which applies data mining techniques to deal the requirements. The following are the popular Web Mining algorithms, such as PageRanking (PR), Weighted PageRanking (WPR) and Hyperlink-Induced Topic Search (HITS), are quite commonly used algorithm to sort out and rank the search results. In among the page ranking algorithm uses web structure mining and web content mining to estimate the relevancy of a web site and not to deal the scalability problem and also visits of inlinks and outlinks of the pages. In recent days to access fast and efficient page ranking algorithm for webpage retrieval remains as a challenging. This paper proposed a new improved WPR algorithm which uses a Principal Component Analysis technique called (PWPR) based on mean value of page ranks. The proposed PWPR algorithm takes into account the importance of both the number of visits of inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages. The weight values of the pages is computed from the inlinks and outlinks with their mean values. But in PWPR method new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. To solve this problem is a MapReduce (MR) framework is promising approach to refreshing mining results for mining big data .The proposed MR algorithm reduces the time complexity of the PWPR algorithm by reducing the number of iterations to reach a convergence point.
A Novel Digital Voting System with Int egrated Technologies (DVSIT)
Authors: Vamsi T. M. N. , Sri Charan Dutta S. , Harika K. , Kiran V. , Abhiram V.
Published: August 2021

Over the years, technology has been growing fast. With growing technology there are many modern problems being created. With innovation many novel approaches are being proposed to find a better solution. Coming to the present days, the “Electronic Voting Machines (“EVM”)” based voting system is prone to much vulnerability. There were several issues regarding tampering and security of EVMs which have not been proved. With the upcoming elections the biggest challenge is to conduct a fraud-free polling. Due to vulnerabilities in current voting system the security of a vote is being compromised by many malpractices such as duplicate voters, dummy candidates, booth capturing, EVM rigging, etc. Introducing Blockchain Technology into digital voting process can minimize most of the frauds as it’s almost impossible to breach the security level of Blockchain. The proposed system assures authenticity of a voter by providing Dual Authentication process.
Providing Security to Ensure Biometric Identification System in Cloud
Authors: Kotte Bhuvaneswari , Sirisha Madhuri T.
Published: August 2021

Biometric identification has rapidly growing in recent years. With the development of cloud computing, database owners are incentivized to outsource the bulk size of biometric data and identification tasks to the cloud to liberate the costly storage and computation costs, which however brings potential attacks to users’ privacy. In this paper, we propose an adequate and security to keep biometric identification outsourcing scheme. Categorically, the biometric data is encrypted and outsourced to the cloud server. To get a biometric identification, the database owner encrypts the query data and submits it to the cloud. The cloud implements identification operations over the encrypted database and returns the result to the database owner. An exhaustive security analysis indicated the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with antecedent protocols, experimental results show the proposed scheme achieves a better performance in both preparation and identification procedures.
A Novel Remote Sensing Technology on Land Analysis Use Change Detection
Authors: Suresh Babu J. , Sudha T.
Published: August 2021

Change location is helpful in numerous applications identified with land use and land cover changes, for example, moving development and scene changes, arrive corruption and desertification. Remotes sensing technology has been utilized for the location of the adjustment in land use arrive cover in upper Rib watershed. The fundamental target of this examination was to identify the land use change utilizing Remotes sensing for manageable land use arranging in Upper Rib watershed. The two satellite pictures for the year 2007 and 2018 were downloaded and utilized for recognizing the land cover changes. Most extreme probability arrangement was utilized in ERDAS Imagine device for characterizing the pictures. Ground truth focuses were gathered and utilized for check of picture arrangement. This was identified with the proceeded with extension of developed and settlement over years in River watershed. The information about the adjustment in land use is so fundamental for the organization and land use arranging exercises in upper Rib watershed. This is so for, the expansion and profitability of Rib repository by decreasing the upland disintegration through powerful land use arranging and soil preservation rehearses. Consequently, this examination uncovered that there is an expansion of horticultural land which needs due consideration towards soil protection for the improvement of the helpful existence of the supply.
Cognitive Computing For Sustainable Agriculture
Authors: Manjula T. , Sudha T.
Published: August 2021

Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase the per meter yield, So many techniques have been for seen in agriculture in terms of usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. Now the use of modern technologies such as artificial intelligence and cognitive computation is going to bring a new big revolution for sustainable agriculture. The present paper focuses on the problems faced by the modern society in agriculture and how the cognitive computation provides an ultimate solution to the problems. We also discuss some illustrations for the usage of cognitive technologies and machine learning in the field of agriculture.
A Research Travelogue on Feature Sub Set Selection Algorithms
Authors: Ravikumar R. , Babu Reddy M.
Published: August 2021

In machine learning as the dimensionality of the data rises, the amount of data required to provide a reliable analysis grows exponentially. To perform dimensionality reduction on high-dimensional micro array data, many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. Analyzing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. Feature selection has been an active and fruitful field of research area in pattern recognition, machine learning, statistics and data mining communities. The main objective of this paper is feature selection is to choose a subset of input variables by eliminating features.
A Data Mining Approach for Intrusion Detection in a Computer Network
Authors: Priya M. , Rajeshwari M.
Published: August 2021

As activities being done on the internet keep expanding every day due to the fact that we are in the era of the information age, securing sensitive and crucial data on computer networks against malicious attacks tends to be a challenging issue. Designing effective Intrusion Detection Systems (IDSs) with maximized accuracy and low rate of false alarms is an imperative need in the world of cyber-attacks. This work was designed to employ an ensemble data mining technique for improving IDSs by carrying out some experiments using the KDD 99 intrusion dataset. Dataset was fragmented into five, representing the major categories of attacks: Normal, DOS (Denial of Service), Probing (Information gathering), R2L (Remote to Local) and U2R (User to Root). An ensemble classifier using the Stacking method with the Naïve Bayes and Multilayer perceptron algorithms as the base classifiers and J48 as the meta learner was developed. The base classifiers were also employed on the dataset individually, and performance comparison was done between individual classifiers and the ensemble classifier. A 10-fold cross validation for training and testing of data and Gain ratio technique for filtering of the dataset was adopted. Ensemble classifier maximized accuracy the most and helped in reduction of false positives of the U2R attack type.
Identifying User Personality in Facebook Using their Activity Data
Authors: Dhinesh Kumar P. , Anusuya S.
Published: August 2021

The facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modelling user’s behaviour. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modelling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented. Gaining insight in a web user’s personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user’s personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.
Implementation of Effective Data Emplacement Algorithm in Heterogeneous Cloud Environment
Authors: Annapoorani S. , Srinivasan B.
Published: August 2021

This paper is concerned with the study and implementation of effective Data Emplacement Algorithm in large set of databases called Big Data and proposes a model for improving the efficiency of data processing and storage utilization for dynamic load imbalance among nodes in a heterogeneous cloud environment. With the era of explosive information and data receiving, more and more fields need to deal with massive, large scale of data. A method has been proposed with an improved Data Placement algorithm called Effective Data Emplacement Algorithm with computing capacity of each node as a predominant factor that promotes and improves the efficiency in data processing in a short duration time from large set of data. The adaptability of the proposed model can be obtained by minimizing the time with processing efficiency through the computing capacity of each node in the cluster. The proposed solution improves the performance of the heterogeneous cluster environment by effectively distributing data based on the performance oriented sampling as the experimental results made with word count applications.
Implementation of Chunks of Image Password in Cloud Computing System
Authors: Sulochana V.
Published: August 2021

This article proposed a graphical password authentication which uses chunks of images to overcome the loopholes present in the traditional authentication methods. The graphical password contains a selection of chunks of images which creates password for authenticating cloud users for accessing cloud services. The article includes the details of chunks of images authentication, algorithm and implementation of chunk of images.
Boosted Edge Detection Algorithm for Unstructured Environment in Document Using Optimized Text Region Detection
Authors: Revathy N. P. , Janarthanam S. , Sukumaran S.
Published: August 2021

Document images are more popular in today’s world and being made available over the internet for Information retrieval. The document images becomes a difficult task compared with digital texts and edge detection is an important task in the document image retrieval, edge detection indicates to the process of finding sharp discontinuation of characters in the document images. The single edge detection methods causing the weak gradient and edge missing problems adopts the method of combining global with local edge detection to extract edge. The global edge detection obtains the whole edges and uses to improve adaptive smooth filter algorithm based on canny operator. These combinations increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time document retrieval system to detect the edges in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the objects. This proposed work enhancing the searching ability of the document to move towards the optimal solution and to verify the capability in terms of detection efficiency.
Quality Monitoring of School Education and Evaluation Platform Based on Big Data
Authors: Vanisri C.
Published: August 2021

The Collection and analysis of quality monitoring data of Schools. I consider this data along with combination of effective and efficiency processing of big data and data interpretation, evaluation, and monitor the status of school education and construct a school education quality monitoring and evaluation platform. The platform is teaching centred with schools including system of collecting data, analysis of data, systematic data storage and other areas [1]. With the application of the school education quality monitoring platform, it is possible to understand the current scenario of the development of school education scientifically, and provide the good decision making to the schools administration department.
Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique
Authors: Jayasudha J. , Christina Esther A.
Published: August 2021

Text document were transmitted over the internet for the text communication. So they were occurred many problems like repeated text occurred because of same data were provided in the internet. To characterize and extracting that is a most critical task for the researchers. Many researchers were characterized and applied in many fields like real-life scenarios, such as real-time monitoring on abnormal user behaviors, etc. In this case to detect and characterize the personalized behavior of the user were provide some drawbacks. To solve this problem, this paper analyzing the sequential data and characterize the user behavior with the help of the data mining sequential pattern matching algorithm.
LI-FI: An Emerging Technology for Health Care
Authors: Pandiyan M. , Revathi C. S.
Published: August 2021

In today’s world, everything around us are about to expose the harmful radiations. In India, there is no proper automatic monitoring system for the patients in the hospital. Even in the hospitals, the surgeries are done using Internet connectivity where WI-FI leads the way. The Wi-Fi technology uses RF spectrum which exhibits the harmful radiations. The LI-FI Technology will overcome this effect as it uses visible light as a source which is non-hazardous. Some of the death in the medical field happens due to the lack of proper electric facilities and monitoring system. By means of using LI-FI we can overcome the problems of power insufficiency and also the data transmission. Our Li-Fi kit will act as a mediator that converts any kind of visible light source into data for transmission. So, the one who has our kit can send and receive data in high speed even at a rate of several Gbps.
Survey of Security and Privacy Issues in Big Data Analytics
Authors: Mylavathi G. A. , Mallika N. M. , Mohanraj K.
Published: August 2021

Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost each trade is making an attempt to address this large information. Big data phenomenon has begun to gain importance. However, it’s not solely terribly tough to store massive information and analyses them with ancient applications, however conjointly it’s difficult privacy and security issues. For this reason, this paper discusses the massive information, its scheme, considerations on massive information and presents comparative read of massive information privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications associate overall perspective of security and privacy problems in massive information is usually recommended
Detecting and Preventing a Black Hole Attack in VANET
Authors: Sasirekha V. , Nithyadevi S.
Published: August 2021

In recent years, Vehicular Adhoc Network is one of fastest ongoing emerging field in the networking industry. But it faces lot of challenges today. Security is the major concern in the VANET. Now a day’s hacking is the hobby of the programmers. Lots of applications were developed for security attack. VANET is a dynamic network, it require secure communication. The VANET is vulnerable to various types of attacks. In this paper we have focused on black hole attack. The Black hole attack may interrupt the packets and insert the false information in the packets and sends to the other vehicle. The receiver of the vehicles is directly affected. Here we developed a mechanism with AODV protocol called blackhole attack detection and prevention which is mainly focused on vehicles sequence number. The source vehicles sequence number compared with the destination vehicles sequence number. If it is larger than the destination vehicle then the vehicle is marked as malicious vehicle. And this vehicles information is send to the Road Side Unit(RSU). The Road Side Unit analyzes the malicious vehicle and blocks the vehicle.
A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment
Authors: Deepika N. , Abdul Qadir O. S.
Published: August 2021

Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.
A Study on Image Segmentation Techniques
Authors: Abdul Saleem S. , Vinitha G.
Published: August 2021

Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.
Study and Survey on Image Compression Techniques
Authors: Ramya G. , Abdul Kader Nihal K. N.
Published: August 2021

In the Modern World, image is the important source for new technologies. The image compression is one of the important techniques which play a vital role in many applications. This paper presents the study of various image compression techniques which are used in the different Image Processing Applications. The advancement in the computer vision increases the demand of high data transmission speed also there is a need of large storage space. So, the image compression field is a necessary one for such applications where high volume of data is required to store and transmit. This paper focuses on the area of image compression, advantages of image compression, types of image compression and its different types of techniques. In addition to that, one of the new lossless image compression techniques ABO is explained. In this paper, it gives the overview of the ABO Framework and the ABO mechanism.
Network Traffic Analysis in Cloud: A Survey
Authors: Ruckshana K. , Ravi G.
Published: August 2021

A data center (DC) denotes to any huge faithful group of computers that is retained and operated by an organization. Data centers of numerous sizes are being made and hired for a dissimilar set of resolves today. On the one hand, big universities and isolated enterprises gradually consolidating their IT services within on-site data centers comprising hundreds to thousands of servers. On the other hand, huge online service providers such as Google, Microsoft, and Amazon quickly constructing geographically varied cloud data centers often have more than 10K servers; to offer a variation of cloud-based services such as Email, Web servers, Gaming, Storage, and Instant Messaging. Though there is great interest in planning developed networks for data centers, very little is identified about the network-level traffic characteristics of present data centers. In this paper, we focused on a study of the network traffic in data centers and defining the anomaly detection system in secure cloud computing environment.
Mining of High Average-Utility Pattern Using Multiple Minimum Thresholds in Big Data
Authors: Vasumathi R. , Murugan S.
Published: August 2021

In the past years most of the research have been conducted on high average-utility itemset mining (HAUIM) with wide applications. However, most of the methods are used for centralized databases with a single machine performing the mining job. Existing algorithms cannot be applied for big data. We try to solve this issue, by developing a new method for mining high average-utility itemset mining in big data. Map Reduce also used in this paper. Many algorithms were proposed only mine HAUIs using a single minimum high average-utility threshold. In this paper we also try solve this by mining HAUIs multiple minimum high average-utility thresholds. We have developed two pruning methods namely Reduction of utility co-occurrence pruning Method (RUCPM) and Pruning without Scanning Database (PWSD).
A Study: Breast Cancer Prediction Using Data Mining Techniques
Authors: Gousbi B. , Mohamed Shanavas A. R.
Published: August 2021

Data mining is the extraction of unseen predictive info from huge databases, is the process of arranging through enormous data sets to recognize patterns and create relationships to resolve the problems through data analysis. Cancer is one of the primary reasons of death wide-reaching. Timely detection and prevention of cancer plays a very vital role in decreasing deaths affected by cancer. Identification of genetic and environmental factors is very significant in emerging novel methods to identify and avert cancer. Many researchers’ use data mining techniques like clustering, classification and prediction find potential cancer patients. This paper focuses on a breast cancer prediction system built on data mining techniques. With the help of this system, people can guess the possibility of the breast cancer in the former stage itself.
A Study of Privacy Preserving Using Anonymization Techniques
Authors: Renuka S.
Published: August 2021

Now a day’s there is an extensive use of technology that has led to a massive increase in the amount of data that is generated. The analysis of such information will help the business and organization in various ways and also contributing beneficially to society in many different fields. As this data also contains the considerable amount of user-sensitive and private information, it will lead to the potential threats to the user’s privacy if the data is published without applying any privacy preserving techniques to the data. This paper discusses the various anonymization techniques such as generalization and suppression which are used to preserve privacy during data publishing.