bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of cost effective hierarchical Hadoop cluster–a case study for education
Authors: S. Kalyan Chakravarthy N , Sudhakar N , Srinivasa Reddy E
Published: May 2018

To equip the younger generation of the province with computing skills and provide them access to a wide variety of modern educational resources such as multimedia based on educational content in government schools and colleges to set a strong foundation at an early stage of their education. Educational Media help to empower educational institutions by altering the way of using Information Communication Technology (ICT). To put this into practice, several challenges need to be addressed. It requires a scalable technological architecture and algorithms to form a cluster in which effective resource sharing like CPU, Memory among multiple nodes, tools to monitor, assess and evaluate data under hierarchical Hadoop cluster is needed. By analyzing text, audios, videos information, Periodic reports will be generated to assist the students, teachers and Government. In this case, a software framework is required to process big data stored in hierarchical nodes. As the architecture of Hadoop, an open source software framework doesn't support processing the data stored in hierarchical nodes. This case study proposes the Hierarchical Hadoop cluster to alter the way of using ICT. The proposed work helps in monitoring, reporting the usage of ICT and also acts a help desk to address the issues of the educational institutions. This establishes a novel communication media by generating reports on text, audio, video information based on analysis.
Origami Foldaway Support for Beginners using Image Processing
Authors: Gayathri L , Kumar Praveen
Published: May 2018

Various origami works are distributed as origami books in which a succession of collapsing operations with basic outlines is portrayed. In any case, the origami book reader often will give up the instruction of the book middle, because it is too difficult to understand a way to fold in accordance with the diagrams. This paper proposes an approach to find the next step how to do the folding operation, especially for origami beginners. First, a method of detecting the folding operation based on camera images is been detected by canny edge detection. Then, in order to get the next operation camera image is been compare with the database images with the help of Bag of a visual word and Speed up robust features(SURF) detector to detect the key points by finding out the nearest neighboring distance ration (NNDR) measures to find out the similarities.
Optimal design selection and analysis of single sided linear induction motor
Authors: Issac Sijitha , Poorani S
Published: May 2018

This paper investigates the design and implementation of single-sided linear induction motor towards determining the end effect intensity, reduces the primary weight of the system and improves the efficiency by multi-objective optimisation approach. This optimisation technique is a combination of genetic with cuckoo search (GCS) algorithm is introduced to improvise the efficiency and power factor, as well as to minimize the end effect intensity, simultaneously. The performance of the designed novel approach will be validated based on the measure of different parameters such as Efficiency, Power factor, Maximum thrust slip, Aluminum thickness, Primary width/pole pitch, Primary current density and the braking force due to the end effect. Finally, to validate the optimisation results, 3D finite element method is employed and compared the results with the existing technique like GA, PSO, and Cuckoo search.
Investigations on wavelet and Fourier transform based channel estimation in MIMO-OFDM system
Authors: Somasekhar B , Mohana Krishna Ch , Murty Y
Published: May 2018

In this paper channel estimation methods for MIMO-OFDM system are investigated based on Fourier Transform and Wavelet Transform. The channel estimation algorithm based on Discrete Fourier Transform (DFT) cause energy leakage in multipath channel with non-sample-spaced time delays. Discrete Cosine Transform (DCT) based channel estimator can mitigate the drawback of Discrete Fourier Transform based channel estimator, when the non-sample spaced path delays are available in multipath fading channels. Wavelet based systems provide better spectral efficiency because of no cyclic prefix requirement, with narrow side lobes and also exhibit improved BER performance. Simulation results reveal that the DWT based transform outperforms the conventional DFT and DCT based channel estimator in terms of bit error rate and mean square error.
Development of gas sensor array based on phthalocyanine functionalized bilayer TiO2:ZnO thin film towards l iver disease detection
Authors: Jareena Begam J , Hariharan M , Karthik Raj V , Gnanavel S , Sarathbavan M , Yuvaraj S
Published: May 2018

This paper has been withdrawn
Improvement of power quality of power system using contingency analysis
Authors: C. Ramachandran M , Elango K
Published: May 2018

Contingency analysis is a widely known feature in present day Energy Management System (EMS).The purpose of this strength gadget evaluation feature is to offer the operator records approximately the static security. Contingency analysis of electricity gadget is a major pastime in power device planning and operation. In fashionable an outage of any person of transmission line or transformer might also result in over hundreds in other branches and/or unexpected machine voltage upward push or drop. Contingency evaluation is used to calculate violation. This paper shows the instance on IEEE-14 bus system which offers statistics of violation & remedial action to do away with violations. Exact studies had been completed to work out the contingency plans.
Gene selection and dynamic neutrosophic cognitive map with bat algorithm (DNCM-BA) for diagnose of rheumatoid arthritis (RAs)
Authors: Chithra B , Nedunchezhian R
Published: May 2018

Rheumatoid Arthritis (RA) is an autoimmune inflammatory rheumatic disease that has emotional impact on various body parts and tissue, principally the synovial joints. RA is a complex disease similar to many other autoimmune diseases, in which environmental aspects, genetic variants, as well as arbitrary events cooperate to activate pathological pathways. Choosing the appropriate gene for sample classification is very hard in numerous gene expression analyses in RA, in which authors attempt to find out the least probable set of genes, even now which could attain better predictive performance. On the other hand, the accuracy of classification is not up to the mark. As a result, for identifying RA disease, this research presents a gene selection as well as classification technique. Initially, with the aim of decreasing the time complexity, this disease dataset is preprocessed. Next, so as to decrease the amount of gene, the gene data is chosen from the preprocessed data by means of filter based gene selection techniques: Chi-square (CHI), Information Gain (IG), Consistency Based Subset Evaluation (CS) and Correlation Based Gene Selection (CGS). Thirdly, for the purpose of the classification of RA disease, a Dynamic Neutrosophic Cognitive Map with Bat Algorithm (DNCM-BA) is presented, that is well-suited with the medical routine and it is proposed for supporting gene expression beforehand and accurate diagnosis of RA patients.  As a result, RA disease is not permitted from moving to progressive phases and the difficulty of emerging insistent as well as erosive arthritis for RA patients will get reduced. Finally, the outcome confirms that the DNCM-BA technique provides better performance while matched up with FCM–Particle Swarm Optimization (FCM-PSO), Fuzzy C Means (FCMs), Dynamic Firefly Algorithm Fuzzy C Means (DFAFCM) and Dynamic Fuzzy C Mean (DFCM) clustering algorithms in regard to precision, accurateness, recall and F-measure.
Studies on mechanical properties of luffa acutangula/lignite fly ash reinforced composites
Authors: Jino R , Sriraman M , Arthika B , G. Ashok K
Published: May 2018

The demand  of  using natural fibers in various  applications were  increasing day by day due to its eco friendly nature over the artificial fiber based polymer matrix composites.  This paper describes the fabrication and evaluation of mechanical and thermal properties of Luffa Acutangula fiber with and without Lignite Fly Ash. The lignite fly ash was added as a filler material in the ratio of 0, 0.5, 1, 1.5, percent of weight along with the matrix and fiber. The laminates were fabricated using hand layup technique and the samples were prepared based on ASTM standards for testings.The tensile test was performed using universal testing machine. The addition of filler lignite fly ash has shown the improvement in mechanical and thermal properties of laminated composites. The Tensile, flexural, and compression test was conducted for the fabricated samples and it was observed that the influence of lignite fly ash on natural fiber leads to the increase in tensile strength and compressive strength but the flexural strength was reduced for the fabricated laminates with the filler material lignite fly ash. The morphological analysis was carried out for finding the fractured surface and material failures using scanning electron microscope (SEM).
Behaviour of concrete by using artificial aggregates a review
Authors: Manju K , Dhanush Kumar B , Suresh Kumar S , Nirmal Kumar S
Published: May 2018

Natural aggregate is not an environmental friendly material due to its destructive resource consuming nature. GGBS is a byproduct of steel industries, whereas GGBFS mixed with cement of 1:2 ratio making an artificial aggregate, whereas hardening powder used to spray made upon artificial aggregate to develop the strength of concrete. The basic properties of natural and artificial aggregates were determined. The mix design to be determined obtained for conventional concrete of control mix of grade M 25. The different mix proportions were prepared by replacement of 20, 40, 60, 80, 100 of natural aggregate instead of artificial aggregate. To check the compressive strength, split tensile strength determine. Based on the result we choose optimum percentage of aggregate and make it and concrete to determine the durability properties of concrete compared with natural aggregate.
Design of buried flexible pipelines during liquefaction
Authors: Prasad Valleti Durga , Sathyam Neelima , Sivaranjani S. , Shahin C. , Mondal Sneha
Published: May 2018

Pipelines are important facilities over the huge area to encounter a seismic hazards and conditions of soil. In India pipe lines run through high seismic areas and exposed to considerable risk. The pipelines have advanced in India compare through the world scenario there is no uniform guideline available for seismic design. Therefore we need to establish at least degree of safety for standard seismic design of pipelines. As a part of this, a number of flexible pipelines of different diameter, length, and thickness have been taken into consideration. The density, internal pressure and density of surrounding soil are taken into account and is checked against permanent ground deformation (PGD) due to liquefaction. Using ABAQUS SOFTWARE we will analyse the soil pipeline interaction and based on the results obtained some consideration are given for the design of pipeline in the Liquefied zone, which improve the capability of the pipeline to withstand buoyancy force due to soil liquefaction. The safety of buried pipelines is analyzed as per IITK-GSDMA (IIT-Kanpur-Gujarat state Disaster Management Authority) guidelines on seismic design.
GLCM and GLRLM based Feature Extraction Technique in Mammogram Images
Authors: Preetha K , S.K. Jayanthi Dr
Published: May 2018

A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammogram helps for early diagnosis before showing symptoms of cancer. The aim of this paper is to extract the various features of pre-processed mammogram images to improve the performance of the diagnosis, which helps the radiologists in reducing the false positive predictions. Mammogram images are pre-processed using hybrid filter MAX_AVM. Shape, Intensity, Gray Level Co-occurrence Matrix and Gray Level Run-Length Matrix features that help to represent the various classes of objects are extracted and used as inputs to the classifier. The classifier helps to classify the mammogram images into a normal or abnormal pattern. Experiments were conducted on MIAS database. The result shows that the combination of GLCM and GLRLM features are efficient and achieved the maximum classification accuracy rate when compared to other features.
Incognito-authentication for preserving information on cloud with distributed approach control
Authors: Gopala Varma P , Balakrishna R , Sajeev Ram A , Manikandan A
Published: May 2018

A new distributed approach control methods which are considered on preserve information repository in clouds in order to back incognito authentication. In the current method, the cloud checks the credibility of the series by not getting the user’s integrity formerly saving the information. In this work we developed a method which include major aspects like approach control for all authentic users who can able to decode the preserved knowledge. This method blocks epitomize barrage along with backing formation, alteration as well as educating information reposted in the cloud. We moreover deal customer repudiation, verification and approach control methods are distributed and vigorous, unlike alternative approach control methods created for clouds which are unify. The conversation, calculation along with repository aloft which is as good as to unify access.
Performance of solar PV-fuel cell (FC) hybrid system for stand-alone applications
Authors: Ramesh T , Dharunprasath L , Kaliappan S , Emayavaramban G
Published: May 2018

This paper presents the performance of a hybrid solar PV/ Fuel Cell/ FC system with a PMDC motor load. The hybrid system supplies power to the DC motor during day and night. In order to obtain continuous supply to the load, a control strategy is implemented in the system. By using this control strategy, the solar PV and the fuel cell supply the voltage to the load by getting constant supply from the DC link during day and night. The DC-DC interleaved soft switching boost converter (ISSBC) with Incremental conductance maximum power point tracking technique (INC MPPT) is used to maintain constant DC link voltage. When the power deficit happens in the solar PV, the hydrogen stored in the tank is used by the fuel cell stack and supplies power to the load. The entire system is modeled and the performance of the hybrid system is analyzed in MATLAB/Simulink environment.
Internet of things for industrial monitoring and control applications
Authors: A. Karthika R , Rahamtula Shaik , Anusha Yalavarthi
Published: May 2018

Smart enterprise is an observing, controlling and investigating carrier which incorporates wireless transmission generation and electronic sensor innovation. It permits the client to get the overall scope of services, the opportunity for continuous monitoring and automated controlling of industrial environment.  This paper was advanced to provide internet based totally smoke and temperature and security tracking. This device is allowed to track the facts every time & everywhere from the source of the internet whenever we login into internet. This paper also concludes that person can set restriction for above parameters & if these parameters cross beyond that cost, it's going to activate the devices. As a part of its alarm gadget, it'll play the recorded sounds: “intruder” or “smoke detected” when there may be a detection. The credit score card size Raspberry Pi (RPI) with Open source pc vision (OpenCV) software program handles the photo processing, control algorithms for the alarms and sends captured snap shots to consumer’s e mail through wireless. In this project Raspberry Pi3B+ is used.
Traffic accident monitoring system using deep learning
Authors: Manikandan A , Anandan R
Published: May 2018

A short time period in development of rural places and public vehicle transportation system globally increased. The road accident are increased by the traffic problems last five years. It is a big problem of human society. These traffic accident are how can we happen and how to solve traffic management. Here we collect the traffic accident data and GPS record data using these data to build a deep learning model of stochastic gradient descent learning algorithm method used to solve critical problem of a traffic accident risk.
Digital marketing in hotel industry
Authors: Javed Parvez Shaik , M. Moyeenudin H , Arun S , Anandan R , Kumar Janahan Senthil
Published: May 2018

Digital marketing is the promotion of products or brands through online. In recent days the demand of digital marketing in hotels has become increasingly high with Social Media Marketing (SMM) which works with social networking sites and Search Engine Optimization (SEO) making the website to appear in search results with advertisement on Google and online magazines by search engine marketing (SEM), video marketing through you tube and websites are types of digital marketing. Searching hotels through online by computers and mobile with the hotel websites is the new trend of digital marketing and it makes the possibilities to enhance the local search on hotels by giving required content, User profiles, managing citations and in search results. The aim is to study the role of digital marketing in hotel industry. This article is also focused to identify which type of digital marketing will work best with hotel industry.
Mining of high dimensional data using enhanced clustering approach
Authors: Sivakumar S , Narayanan Kumar , Paul Chinnaraju Swaraj , Kumar Janahan Senthil
Published: May 2018

Extraction of useful data from a set is known as Data mining. Clustering has top information mining process it supposed to help an individual, divide and recognize numerous data from records inside group consistent with positive similarity measure. Clustering excessive dimensional data has been a chief undertaking. Maximum present clustering algorithms have been inefficient if desired similarity is computed among statistics factors inside the complete dimensional space. Varieties of projected clustering algorithms were counseled for addressing those problems. However many of them face problems whilst clusters conceal in some space with low dimensionality. These worrying situations inspire our system to endorse a look at partitional distance primarily based projected clustering set of rules. The aimed paintings is successfully deliberate for projects clusters in excessive huge dimension space via adapting the stepped forward method in k Mediods set of pointers. The main goal for second one gadget is to take away outliers, at the same time as the 1/3 method will find clusters in numerous spaces. The (clustering) technique is based on the adequate Mediods set of guidelines, an excess distance managed to set of attributes everywhere values are dense.
A novel technique for detecting the misbehavior nodes in wireless adhoc networks
Authors: Narayanan Kumar , Anandan R , Paul Chinnaraju Swaraj , Manikandan A
Published: May 2018

Wireless adhoc network acts as the most emerging platform in the current trend that plays a vital role in the field of sensing, analyzing the network route, rectifying the problem within the network. This also pays route for many advanced studies. Observing aid depletion attacks on the routing layers is the main objective of this paper. It also attempts to entirely disable community nodes by draining their battery strength immediately. Such an attempt to drain their battery strength is said to be as depletion attack, and these attacks are not specific to any particular protocol. Under the worst case, even a single depletion can empower a community-wide usage by O(N), where N is the total number of network nodes used.
CAD system: a content based image retrieval approach for pulmonary nodule detection in CT images
Authors: Ram Sajeev , . Shylaja , Sahayadhas Arun
Published: May 2018

This paper proposes Computerized Aided Detection System (CAD) which uses Content Based Image Retrieval (CBIR) to detect cancer nodules present in an image. The CAD system is concerned for the radiologists to identify lung cancer at premature stages, which are very tiny nodules that are not able to seen by naked eye. In modern years, Image processing techniques play a key role in predicting diseases at early stages in particular in various cancer types such as liver cancer, breast cancer etc. This paper comprises of four steps: i) preprocessing an image in order to lessen the noise level and the accuracy of the image is to be improved, so that the accuracy in detection will be higher. ii) The image is segmented based on Marker-Controlled Watershed Segmentation. iii) The features of the nodules present in the image are extracted using GLCM. iv) The nodules are classified based on the extracted features using KNN classifier. The Content Based Image Retrieval Technique is used which is used to redeem query based images in the database by combining feature extraction and similarity matching methods.  For experimentation of proposed technique, CT images are used which are extracted from Lung Image Database Consortium database (LIDC).
Transformation from legacy storage to software defined storage–a review
Authors: Paul Chinnaraju Swaraj , Gunasekaran G , Kumar N , Anandan R
Published: May 2018

In IT Industry the new trend is everything to be in Software defined. And so the storage industry has started its transformation from Legacy storage like storage area network and network attached storage. This is enable through the software defined networks. Software defined networks helps organizations to accelerate its application deployment, thus reducing IT costs by applying some policy enabled workflows. In this paper, we will have a review over the need for it from the storage vendor perspective.
IoT based smart traffic signal monitoring system using vehicles counts
Authors: Kumar Janahan Senthil , R.M. Veeramanickam M , Arun S , Narayanan Kumar , Anandan R , Javed Parvez Shaik
Published: May 2018

Traffic signal management is one of the major problematic issues in the current situation. Such scenarios, every signal are getting 60 seconds of timing on the road at a regular interval, even when traffic on that particular road is dense. As per this proposed model in this article, which will be optimized the timing interval of the traffic signal purely depends on the number of vehicles on that particular roadside. The major advantage of this system is that it can able to decrease the more waiting time for the drivers to cross road signal.  In this model, we are using the clustering algorithms model which is based on KNN algorithm. Using this algorithm new model will be liable to determine expected required timing as per provided inputs to the signal which is vehicles count. The input of these systems is vehicles counts on each side of the road from crossing signal.  And this input will be determined on much time is to be provided. “Case studies on this system are traffic network and real-time traffic sub-networks are organized to get the effectiveness of the proposed model.”
Enhancement of the data fusion and sensor selection in cloud computing
Authors: V. Saikumar V , S.R. Rohith Reddy M , Narayanan Kumar , Swaraj Paul C , Anandan R
Published: May 2018

The IoT is helping individuals to get connected using sensible devices on the large function which is a big thing in past. The most difficult challenge for IoT is large quantity forgetting information generated from the induced devices that are less in number with resources and with missing information which results in the basic failures. By using IoT in collaboration with cloud, we have a function to present a cloud-based answer that takes into process that link quality and function to reduce energy usage by choosing sensors for sampling and dependent data. We have proposed a multi-phase adaptive sensing algorithm which shows belief propagation protocol, which may give high information quality and cut back energy usage by turning on mode with a little variety of nodes within the network. We have proposed a system which retrieves the data when the connection between device and cloud is lost. We will try then to use our message transferring rule for the proposed system. System is calculated support with the information collected from original elements. The basic function is whether maintaining is at the desired level of information quality and future accuracy will give large amount equalization in various sensing elements with success that stores about80% information within the compared object to other cases with all other area unit actively concerned.
Development of ANN models for optimization of methane yield from floating dome digester
Authors: Sathish S , Parthiban A , Balakrishna R , Anandan R
Published: May 2018

The development of methane generation is mainly based on a desirable combination of operating parameters. The essential objective of this analysis systematically analyzes the prediction of methane yield with different operating parameters. Current work is to analyse the reaction of Temperature (T), Agitation time (AT), pH, value and Substrate Loading Rate (SLR) which are all considered to be the different factors. Artificial Neural Network (ANN) is the modern method aid to solve complex issues that could not be addressed by conventional methods. In this work examine the study employ the ANN as a tool for prediction of methane from floating dome anaerobic digester with press mud. The result showed that ANN model is found the value of methane yields much closed to theoretical methane yield. It is obtained the percentage of predicted value of methane is 58% and theoretical value of methane is 62 % with the temperature of 45ºC and agitation time of 20 min, pH value of 7.2 and substrate loading rate of 120 kg.
A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets
Authors: Jacob Soman Saini , Swaminathan P , Anandan R , Kalaivani K
Published: May 2018

With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples’ emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.
Prediction of gestational diabetes diagnosis using SVM and J48 classifier model
Authors: Saradha S , Sujatha P
Published: May 2018

Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may  occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common   predictive data mining tasks is classification. It classifies the data and builds a model based on the test data values and attributes to produce the new classified data. For detecting GDM and also its risk factors, two classifier models namely modified SVM and modified J48 classifier models are proposed. The data set were collected from various hospitals and clinical labs and preprocessed with discretize filter using weka tool. Missing values are replaced by the suitable values. The final preprocessed data applied in the proposed classifier Model.  The output of the proposed model is compared with all the other existing methodologies. Since the proposed model modified J48 classifier model produces more accuracy and low error rate against other existing classifier models.
Data management with PMS in hotel industry
Authors: Mohideen Moyeenudin Hussam , Javed Parvez Shaik , Anandan R , Narayanan Kumar
Published: May 2018

The rapid growth of hotel industry has increased the usage of data with the help of property management system (PMS). It deals with sharing information in a systematic procedure within the organization. The major benefit of data management with PMS is the information can be easily stored and shared between staff members; it is useful during recruitment and when an employee goes on vacation, gets sick, or leaves the organization, It comprise with procedure of work, duties and responsibilities, initiatives, performance, documentation, forming business strategies and systems which enhance the organization. Data management helps in storing the data for handling plans and on methods of executing and developing policies for various situations in hotel. Data Management is a way of managing knowledge base and making it accessible for specific purposes in any industry, also to improve the quality of performance and standards, by various methods of data application in Management practices which involves in understanding the industry and effectively utilizing data in learning and implementing policies of an organisation. This article describes about how Data Management in hotels works with property management system (PMS) and the advantages of having PMS in hotel Industry. This paper also focuses on the advancement in PMS with cloud technology for future demands.
A survey on big data analytics for enhanced security on cloud
Authors: Anandan R , Phani Kumar S , Kalaivani K , Swaminathan P
Published: May 2018

Cloud based data storage has become a common activity these days. Because cloud storage offers more advantages than normal storage methods those are dynamic access and unlimited storage capabilities for pay and use. But the security of the data outsourced to the cloud is still challenging. The data owner should be capable of performing integrity verification as well as to perform data dynamics of his data stored in the cloud server. Various approaches like cryptographic techniques, proxy based solutions, code based analysis, homomorphic approaches and challenge response algorithms have been proposed. This survey depicts the limitations of the existing approaches and the requirements for a novel and enhanced approach that ensures integrity of the data stored in cloud enabling better performance with reduced complexity.
A survey on big data analytics with deep learning in text using machine learning mechanisms
Authors: Anandan R , Bhyrapuneni Srikanth , Kalaivani K , Swaminathan P
Published: May 2018

Big Data Analytics and Deep Learning are two immense purpose of meeting of data science. Big Data has ended up being major a tantamount number of affiliations both open and private have been gathering huge measures of room specific information, which can contain enduring information about issues, for instance, national cognizance, motorized security, coercion presentation, advancing, and healing informatics. Relationship, for instance, Microsoft and Google are researching wide volumes of data for business examination and decisions, influencing existing and future progression. Critical Learning figuring's isolate odd state, complex reflections as data outlines through another levelled learning practice. Complex reflections are learnt at a given level in setting of all around less asking for thoughts figured in the past level in the dynamic framework. An indispensable favoured perspective of Profound Learning is the examination and culture of beast measures of unconfirmed data, making it a fundamental contraption for Great Statistics Analytics where offensive data is, everything seen as, unlabelled and un-arranged. In the present examination, we investigate how Deep Learning can be used for keeping an eye out for some essential issues in Big Data Analytics, including removing complex cases from Big volumes of information, semantic asking for, information naming, smart data recovery, and streamlining discriminative errands .Deep learning using Machine Learning(ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the front line as of late mostly attributable to the advert of huge information. ML counts have never been remarkable ensured while tried by gigantic data. Gigantic data engages ML counts to uncover more fine-grained cases and make more advantageous and correct gauges than whenever in late memory with deep learning; on the other hand, it exhibits genuine challenges to deep learning in ML, for instance, show adaptability and appropriated enlisting. In this paper, we introduce a framework of Deep learning in ML on big data (DLiMLBiD) to guide the discussion of its opportunities and challenges. In this paper, different machine learning algorithms have been talked about. These calculations are utilized for different purposes like information mining, picture handling, prescient examination, and so forth to give some examples. The fundamental favourable position of utilizing machine learning is that, once a calculation realizes what to do with information, it can do its work consequently. In this paper we are providing the review of different Deep learning in text using Machine Learning and Big data methods.
Exploratory analysis on prediction of loan privilege for customers using random forest
Authors: Ulaga Priya K , Pushpa S , Kalaivani K , Sartiha A
Published: May 2018

In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.
Interference-based topology control algorithm for noise removal and delay-constrained mobile ad hoc networks using clustering technology
Authors: Kiruthika S , Iswarya N , Sartiha A , Kalaivani K , Ulaga Priya K
Published: May 2018

MANET is ad hoc network which is in the top layer of the network. This ad hoc network are connected by mobile devices with flexible topology network to control the delay constrain in the mobile ad hoc network for noise removal in network. The transmission power is managed to minimize the interference taking into account the topology control algorithm also accounts the delay constraint and finds the duplicate data.
A study on competence and enrichment of virtualization in cloud computing
Authors: V. Anbazhagu U , Maheshwaran P , S. Archana K , Gowr Sheela
Published: May 2018

The aim of this research is to study and enhance the applications of most profitable technology in cloud computing ever "Virtualization". Back then, a 15 years before, nobody knows about the technology named cloud computing exists. But now, every technology such as personal computer, mobile phone, tablet PC, even televisions rely on cloud computing. Cloud computing helps greatly in reducing costs, scalability and flexibility in computer services. Virtualization is the technology that helps cloud computing to emerge in a large profitable level. Using virtualization (such as server, network and storage virtualization technologies), the resource cost is reduced in great level.
Fire fighting robot with vision camera and gas sensors
Authors: Boopalan S , Narendra Prasad G , Swaraj Paul C , Narayanan Kumar , Anandan R
Published: May 2018

Yearly there are 150+ deaths are occurring due to fire fighting operations. Most of the deaths are due to presence of poisonous gases being present in the fire fighting areas. If we able to inform fire fighter worker about the presence of gases and hazardous situation, we can able to save their lives.  In now-a-days  robots play a vital part in industry and normal life of human beings , robots  are  the assemble   of  different parts or things which are made by human in order to complete any work with more accuracy.  “Fire Fighting Robot with gas sensors”. The design is costless and will do lot of work. It can be used in industry and for domestic purpose. Important parts involved in the construction of robot are gas sensors, water tank, wireless remote and wireless android device Wi-Fi enabled Camera, A wireless robot will do effective work, thereby making it possible to control the robot from remote location. Keeping all above factors in mind the Robot is capable of being remotely controlled and live video buffering i.e. possessing a multimedia interface was convinced and developed.
Comparison of preprocessing techniques for coin recognition using image processing methods
Authors: Hemapriya T , S. Archana K , Anupriya T
Published: May 2018

Coin is very important role in human’s day life. For daily routine like shop, super market, banks etc the coins to be used. The coin is important part of economies and currency and it is used to pay for goods and also for our needs. Here the Indian coin has many number of count five rupee, ten rupee, two rupee, from this any one of the coin we are going to extract the texture feature for our Indian coin, first step is to preprocess the image is that method to enhance the image and remove the noise from enhanced image. For extracting clear information the image has to be preprocessed through some of the filtering techniques such as image size has to be resized, changing the contrast of the image, changing RGB to grayscale conversion for further operation such as segmentation and classification. At last the values to be compared by using PSNR, SNR, MSE of Filter noise removal with respective coin images.
Cloud computing security: a survey
Authors: Sheela Gowr P , Kumar N
Published: May 2018

Cloud computing was a hasting expertise which has innovated to a collection of new explores. A sub-ordinate device for Information services, it has an ability towards encourage development by feeding convenient environments for a choice of forms of development is different sequence. Clouds usually consider being eco-friendly, however keep it has open to the diversity of some security issues to can change together the feeder as well as users of these cloud services. In this issue are principally associated to the protection of the information flow throughout also being store in the cloud, with simple problems along with data ease of use, data right to use and data confidentiality. Data encryption and service authentication scheme has been initiated by the industries to deal with them. In this paper analyse and examine different issues on security beside with the different procedure worn by the industries to solve these effects.
Synergetic research response classifiers for multiple domains
Authors: . Ranjith , Sheela Gowr P , S. Archana K , Mouli Sai
Published: May 2018

A Collaborative Multi-domain sentiment type of communicate to teach view point classifier for more than one company at a time. For this method, the view point facts in one-of-a-type domain names is given to teach more precise and strong view point classifier for both area while labelled records is short supply. Particularly, we putrefy the view point classifier of area into activities, an international one and website precise one. The global model can seize the general sentiment information and is given by using the usage of numerous companies. The vicinity unique model can seize the appropriate view point voicing in every area. Further, we extract region specific view point records for every labelled and unlabelled representative in every area and use it to intensify the mastering area-precise sentiment classifiers. Except, we comprise the opposition among companies to communicate standardise over an area precise view point classifiers to inspire the sharing of view point data among the same domain names. sorts of area standardise compute are explored, one based mostly on text and the alternative based one totally in view point voicing. Here after, we initiate green algorithms to remedy the version of same method. Probing consequences on Benchmark datasets display this method can efficiently make better the overall showing of multi area view point class and substantially overstep baseline strategies.
Performance comparison of various denoising filters for brain MRI images
Authors: Latha M , Arun S
Published: May 2018

Communication in the modern age has been done via Visual information which is being transmitted in the form of digital images. The transmitted image often contains noise and need to be preprocessed before applied in algorithms. Image provides some useful structural and functional information about the brain after involving into a simple and non-invasive procedure. Various functional modalities like CT, SPECT and MRI detects some changes in normal metabolism and in flow of blood. If the original image is noisy or has any structural changes, it becomes difficult to identify the required features from the original image and hence preprocessing becomes an essential step. An experimental methodology has been done which compares and classify the various denoising filters.
An overview of various ad hoc routing protocols in MANET
Authors: Arumugam Saritha , Kumar N
Published: May 2018

Mobile ad hoc network is a wireless network which has peer to peer process .it is established to achieve a group of network to be connected in wireless network. It is a large network that transfers the data from source to destination .Here we are computing the routing protocols like (DSDV, AODV, OLSR, DSR, TORA, and ZRP) which is analyzed in the form of packet delivery ratio, throughput, end to end delay, Routing overhead. There are three types of ADHOC network routing protocol 1) table driven proactive protocol acts as background traffic generally, in ADHOC  network the location of one node is send to other node through location this nodes transfer the data from one to other its done by DSDV and WRP. 2) on demand driven reactive it establish route between nodes when they are required to route packet data it is done by DSR and AODV. 3)  hybrid protocol each node has its own zone radius.
Effects of zero defects approach-a case study at an Indian industry
Authors: Karunakaran K , Saravanan R , Venkumar P , Sridha R
Published: May 2018

Quality is free when reaches zero defects, quality is measured various aspects. The rework is carried out when the product finish not up to the standard, which added additional expenditure. On other side if there is no chance for rework the cost of raw material and value added cost up to that non conformity is lost. This work shares a success story and reveals a systematic scientific approach to reach zero defects. The case study conducted in the leading supplier who focuses on climate and energy efficient solutions. The traditional powerful quality tools employed to focus, understanding and analyse the problem. The Decision support system preferred and adopted to solve the problem with affordable cost. The outcomes of this work measured in terms of modern Total Productive Maintenance style that is in terms of Productivity, Quality, Cost, Delivery, Safety, Motivation and Morale (PQCDSM). The results in all the way found more significant.
Comparison of various filters for noise removal in paddy leaf images
Authors: S. Archana K , Sahayadhas Arun
Published: May 2018

Automatic detection of plant diseases is one the important challenging problems in agriculture field. So the basic analyzing method for automatic identification is filtering technique of preprocessing method. Hence, this Image filtering plays a important role to remove noise from image. Consequently this preprocessing method is the initial stage to make better image quality. The purpose of this paper is comparing four types of filtering techniques to differentiate the image quality in Gaussian filter, median filter, mean filter and weiner respectively filter using common data set. The image quality of overall results shows that the comparison of various filtering technique performed to enhancement quality using hybrid technique. So this paper gives best starting for researchers to automatic detection of rice plant disease detection.
Resolution enrichment of side scan sonar image using wavelet based interpolation methods
Authors: Kumudham R , Tarun S , Avinash R , Rajendran V
Published: May 2018

The resolution of the side scan sonar image which is used to detect on seabed such as mines, ship wrecks, etc is low. This paper helps to utilizes image processing techniques to enhance the resolution and thereby it makes detection and classification of underwater objects accurately. The proposed methods discussed in this paper are Discrete wavelet transform and stationary wavelet transform for enhancing the resolution.
Sequence of residential building construction in 99 days
Authors: Durgalakshmi S , Mahendran K
Published: May 2018

Time management is one of the vital key factor in the project management of any construction project.  Effective planning, scheduling & control of construction project resulted in the reduction of construction time, cost overrun and disputes.  Not only effective planning, but also proper organization & sufficient flow of resources to a project can automatically achieve a good result when preliminary works has been done for each & every activity. The study area mainly focuses on the execution of work as it is scheduled before the commencement of construction.  This paper describes the sequence of residential building construction (G+2) in 99 days has been executed as a real time project and achieved the completion in 99 days.  For normal construction of (G+2) may be completed in 180days, but this project completed in 99 days by using the technique called Task Based Activity Execution (TBAE) to the labours.  During execution, delays are observed and overcome by using the preliminary works for the next activity.  The sequence of construction discussed here will definitely enhance the project planning skills for the forthcoming project.