bluuebunny/crossref_metadata_2025 huggingface dataset:
Graphical password scheme to diminish shoulder surfing
Authors: Sri Ram Varma D. , Meghana K. , Sai Deepak V. , Murugan R.
Published: March 2018

Many authentication schemes are known to us but none of them are completely secure. Textual password is the most common technique used by majority of the people in the industry. But Textual passwords are vulnerable to dictionary attacks, keyloggers, brute-force attacks, even guessing may work out sometimes. Alternative authentication schemes have been proposed to overcome this problem, some of them are Biometric authentication, retina based authentication, graphical password scheme ETC., Authentication Schemes such as biometric and retina scans are too costly, so they are not always preferred. Not every graphical authentication is secure and efficient. In this paper, an authentication scheme with a combination of text and colour is proposed. This allows the user to log-in to the framework a little more secure.
Applicability of Sudoku game for building the cognitive model of a student for career assessment - an analytical study
Authors: Chandra Prakash V. , Sastry J. K. R. , Anusha K.B. , Spandana A.B. , Dhatrija N. , Nikhil V.
Published: March 2018

Career assessment helps a student understand how a variety of personal attributes (i.e., academic record, aptitudes, skills, preferences, motivations, etc.) impact his/her successful professional career and satisfaction. The professional life of a student is very much dependent on the initial choice of the career that the student would like to pursue. Nowadays, during job recruitment process, a student is assessed based on IQ tests, Verbal tests, Reasoning tests, etc. Every student has some psychological factors (i.e., decision-making, patience & perseverance, logical thinking, learning ability, etc.) that are not considered during these tests. As a result, some students may not lead successful careers in future. There are a very few ways to assess those psychological factors of a student and one such way is through Game playing. Through an analytical study, it was found that "Sudoku" is the best game to assess psychological factors, especially decision-making power, of a student. The authors proposed an Expert system that conducts Sudoku game for a student, assesses various psychological factors, builds a cognitive model of the student and provides career assessment.
Telemedical IoT based technology for emergency health-care
Authors: Prasad M. S. R. , Lingampalli Amrutha , C. Verapalle Kushal Kumar , N. Malraju Eshwar
Published: March 2018

While it is possible to save potential victims during emergencies situations (such as cardiac arrests) using the required healthcare infrastructure around, the greatest challenge lies in setting up of the same. People are not always present at places with readily access to qualified doctors. As a result, there should be a provision to remain in touch with a specialist medical practitioner. This paper presents the methodology of medical content management through internet of things. Transmitting sensory data of the victims to any hospital on time can help save numerous lives across the planet. This mode of human-machine interaction constitutes telemedicine. By embracing information technology and telecommunication, telemedicine provides remote emergency healthcare services outside the regular medical establishments. These systems generate and process an increasing amount of sensory data. It supports real-time processing with the help of a content management system that averts impending life-threatening dangers. By utilizing suitable sensory data inputs, medical practitioners can analyze and convey the appropriate measures to be taken to save the victim. The underlying methodology is extended to various scenarios such as aircrafts, high-buildings and remote villages. Finally, potential for future improvement and the challenges that are currently faced are presented in this paper.
A survey for acquiring frequent and sequential items in E-commerce sites
Authors: P Haritha , Devi M Sree , K Ravali , Pruthvi M Manoj
Published: March 2018

Large amounts of data has made available because of the increase in e-commerce industry. Data has high significance and also important for everyone. Hundreds of websites are being deployed and each site offers millions of products. In addition to this there are several types of input forms. Different sites have different input item collection. This means that there is a substantial amount of information being provided resulting in information overload and in turn results in reduced customer satisfaction and interest. This huge amount of data needs to get processed so that we can able to extract the useful information. From this useful information we can able to increase customer interest, satisfaction along with sales of e-commerce sites. Presenting frequent and sequential patterns in e-commerce sites results in increase of sales of products without delay. Different association rule mining techniques and sequential rule mining techniques can be used for different sets of input forms in order to generate frequent and sequential patterns. This paper discusses various algorithms using techniques such as association rule mining, sequence rule mining proposed for mining frequent and sequential items.
Dos flooding attack control in information-centric networks
Authors: Rao T Srinivasa , G Harsha , V Kiran , Kumar K Hemanth
Published: March 2018

ICN (Information-centric networking) is a modern networking standard that mainly works only on the content extraction from a network without taking into case about the storage location or how the content is represented. In ICN providing security for the content is more important. Here we don’t concentrate on protecting path. In order to fulfil the security goals in the new standard, it is very decisive to have a clear complete comprehension about ICN attacks and their brief allocation and the solutions. In this paper we in brief explain the attacks which effect the ICN network and other related attacks which have an impact on ICN. Attacks in ICN are divided to four categories, routing attacks, Naming attacks, Caching attacks, and other various related attacks. There are lot of solutions which are accessible. The main moto in ICN is to protect data which is very hard to achieve. So we develop a dynamic host based IP address scheme including certain snort rules which detect attacker and distinguish them in the clients and secure server from resource exhaustion. Our main center we deal with is on availability, and privacy.
Bug reduction with reliability factors using hybrid reliable model
Authors: Vijaya Saradhi T , Manisha M , Vijaya Lakshmi L , Gowtham K
Published: March 2018

Software reliability is the probability that software will not cause the failure of a product for a specified time under specified conditions. Software Reliability is also an important factor affecting system reliability. Software reliability is different compared to hardware reliability. Hardware reliability is the one which deals with manufacturing something which is related to external features of the system. Reliability is one of the major problem faced by the systems. Hence, there are some factors which make the system much reliable. As there are different types of software reliable models we analyses each of its performance and also the factors which are affecting them. In this paper we propose a hybrid model which is used for the reduction in the count of bugs and also we implement the reliability factors for that model. Therefore, this model reduces number of bugs. As we know reliability is inversely proportional to the number ofbugs and therefore by using this model we are increasing reliability.
Data mining techniques for herbs
Authors: Satish Babu J , Niveditha M , Bhavya V , Gowthami K
Published: March 2018

The most important source of ingredients in the discovery of new drugs are Natural products. Moreover Nagoya protocol is most commonly used in selection of herbs based on similar efficiency, Later scientists have voiced their concern on protocol also proved it as less effective therefore, this project uses data mining classification approaches, novel targeted Selection which makes use of MED - LINE(Medical Literature Analysis and Retrieval system online) database that consists of biomedical information to identify herbs of same efficacy .Neural network technique among all classification techniques is inspired by biological nervous system. AS neural network is successful on wide array of noisy object selection of herbs is done effectively. SOM (self-organizing map) is most popular Neural Network provides a topology preserving mapping from the high dimensional space to map units. The main objective of this project is to survey on various data mining methods and their techniques and to conclude the suitable algorithm.
An efficient method for early stage detection of diabetic retinopathy
Authors: D. Shirbahadurkar S , M. Mane V , V. Jadhav D
Published: March 2018

Diabetic Retinopathy (DR) is one of the leading causes of blindness. The early detection and treatment of DR is significant to save the human vision. The presence of microaneurysms (MAs) is the first sign of the disease. The correct identification of MAs is an essential for finding of DR at the early stages. In this paper, we propose a three phase system for efficient recognition of MAs. The tentative MA lesions are recovered from the fundus image in the first stage. To accurately classify an extracted candidate region into MA or non-MA, the second stage prepares an attribute vector for each tentative MA lesion based on shape, intensity and statistical properties. The third stage is a classification step to classify as MAs and Non-MAs for early stage detection of DR. We present a holoentropy enabled decision tree classifier which combines entropy and total correlation. The best feature for decision tree is selected based on holoentropy to enhance the correctness of the classification. The implemented system is experimented using fundus image database DIARETDB1. The proposed method achieved an overall accuracy of 97.67%.The proposed system has detected the MAs with higher performance using simple features and holoentropy based decision tree classifier. The proposed system is suitable for early stage detection of DR through the accurate identification of MAs.
Neighbordiscovery-based security enhancement using threshold cryptography for IP address assigning in network
Authors: Santhosh Kumar R , Bharanidharan R
Published: March 2018

The security threats related to current IP configurationand also includes with Threshold Cryptography (TC). Circulated situations are attractive more prevalent as these knowledge such as networks, aim to enable a large scale collaboration for resource sharing outline in the network. Secure verification is the interesting concern for such surroundings. The proposed system to maintain a two stages for improving the network security based on IP addressing time in the network. An first stage is threshold cryptography and another one is Neighbor discovery protocol (NDP). In main objectives of our proposed work is Threshold Cryptography based addressing scheme in network, also the authorization process fully depends on Neighbor discovery protocol based frameworks. The goal of the Neighbor discovery protocol is to support a Mobile Node (MN) roaming across network fields without communication disturbance or recognizable delay. Moreover, this approach also ensures correct synchronization between nodes that send overlapping data on the network. Finally, using this NDP-TC during data transfer time packet loss is very low so the network security is automatically increased.
Index split decision tree and compositional deep neural network for text categorization
Authors: Ravikumar N , P. Tamil Selvan Dr
Published: March 2018

Text categorization with machine learning algorithms generally reckons to possess horizontal set of classes. Several advanced machine learning algorithms have been designed in the past few decades. With the growing research work for text categorization, it has become important to categorize the research outcome and provide the learners with an effective machine learning method, a framework called, Hierarchical Decision Tree and Deep Neural Network (HDT-DNN).It investigates machine learning algorithms to create horizontal set of classes and it is used for classification of text. With this objective, a novel and efficient text categorization framework based on decision tree model is used in order to categorize text according to superior and subordinate level. The text to be categorized is presented in the form of a tree with parent text category being superior to all. The intermediate level represents the text that is both superior and subordinate. Then Deep Neural Network model is presented initiating compositional model, where the text has to be categorized, as a layered integration of primitives from the constructed decision tree model. The extra layers enable composition of features from lower layers, potentially modeling complex text with fewer units than a similarly carried out shallow network producing hierarchical classification. The significance of the impact of HDT-DNN framework is evaluated through empirical study. Extensive experiments are carried out and the performance of HDT-DNN framework is evaluated and compared with existing state-of-art methods using parameters such as precision, classification accuracy, classification time, with respect to varied number of features and document size.
Ingenious vehicular system to evade collisions
Authors: Sridhar R. , Nema Aditya , Nalin Anshul , Rai Ankur
Published: March 2018

Driver’s response is indispensable in the aversion of collisions and in stringent circumstances. WiB-IVS (Wireless Broadcast Intelligent Vehicular System) is a proposed design for Intelligent Vehicular Systems enabling Vehicle to Infrastructure (V2I) communication for effectuating an interconnected network of vehicles for easier and faster information sharing in order to prevent collisions. The system centralizes on driver’s response and thus aims to bring extrinsic information to the driver, stimulating situational awareness. An indication system fulfils this purpose and helps the driver respond to the scenario ahead. Information transmission is facilitated by communication between vehicle and broadcasting station based on User Datagram Protocol (UDP). The design approach caters to the problems of the collision at sharp or blind turns, offsite intersections and those occurring due to low visibility in extreme weather conditions such as fog. A pilot model design to test for the effectiveness of the proposed system architecture is also put forth in this paper. The pilot test results confirm the system’s rapid response and its key potential for the future.
Implementation of a low cost ac-dc converter for high and low power applications
Authors: Ilambirai R. C. , Sekhar Dash Subhransu , Rayaguru N K
Published: March 2018

This paper proposes an ac-dc converter that gives multiple outputs capable of feeding both high and low power applications. The input ac supply is converted to dc by a diode bridge rectifier, filtered and fed to a modified multiport converter (MPC). This paper focuses on the design of the modified multiport converter that has a cascaded combination of zeta and a buck converter. This is designed as a single input and multiple output converter (SIMO) structure which can operate two loads, one with a high power and other with a low power application, depending on the time instant. The need for opting a multiport converter, reduces the number of switches utilized, thereby reducing the switching losses in the circuit. The zeta in the MPC boosts the input voltage and the buck converter reduces the input voltage and is accordingly fed to the need of the load. The design of the components have been analysed through steady state. MATLAB Simulink has been used to simulate the converter circuit and the varied outputs of the zeta and buck modes are compared. A hardware prototype of the ac-dc converter has been implemented and their results have been shown.
Energy efficient scheduling mechanism using wireless sensor networks
Authors: . Vengadeshwari , Ruthra Prabha D. , Sree Sravya T.
Published: March 2018

Wireless sensor networks are an electro mechanical system which is used to transfer information to the base station from the beacon node. The objective of our project is to increase the lifetime of a beacon node, parallelly decreasing the energy using WSN by using appropriate scheduling mechanism. Each sensor performs different types of operation such as collecting the information from the sensors, processing it and communicating the source information when required, so energy consumption of these nodes is high. This can be reduced by using centralized approach and average consensus based Distributed algorithm. The lifetime of sensor is first increased by modifying M2CIC into multimodal set coverage problem and using this to produce NP-completeness. Another problem here is the network utility maximization problem which consists of Mixed Integer programming. This is solved by splitting the multi period problem into a single period problem. It is future reduced to Pure Integer programming. This Pure Integer programming can be solved by a centralized way.
Design and performance evaluation through synchro-phasor signals for transient fault analysis in transmission lines and distributions
Authors: Nithyavelam M. , Henry Joseph
Published: March 2018

Contemporary power supplies be obliged to offer trustworthy, superior-quality power. Hence, it is significant to facilitate the transmission line fault identification more precise, consistent and to be rectified immediately. In this paper, a unique organized interrelated structure for identifying the transient fault that occurs in transmission lines and distribution is proposed, by employing the Fast Fourier Transform (FFT). This technique utilizes synchrophasor measurements during disturbances found in transmission lines positioned in the network and compared the obtained flux phase with the reference signal that is 230V, 50Hz flux phase which is considered as the ideal condition. The obtained difference phase angle will provide the details of transients present in the given transmission lines present between the generator and the load. By employing this method, the Initial state of transient fault detection from phasor measurement as well as a Linear Regression Modelling for Transient Fault Detection also be able to be identified. This paper analysis the transient fault using synchro-phasors by comparing the existing fault detection with real-time implemented system.
Shared intelligent optimum route selection through traffic management system in VANET– SIRS
Authors: B Wagh Mukund , Gomathi N.
Published: March 2018

A Vehicular Ad-hoc Network (VANET) consists of the vehicle to vehicle and vehicle to roadside communication. Growing the density of the vehicles on the road causes flooding while transferring packets from a source to one or multiple destinations over a VANET. However, increasing vehicles on road may causes a problem such as redundancy, congestion, collision and increasing delay though receiving emergency warnings. As long as reliable and proficient multi-hop broadcast in vehicular ad hoc networks is challenging one. In the proposed system commence a new protocol which uses the character of elegant route establishment and selection method for transfer information to the destination (dst) with minimum redundancy, delay and highest bits per second delivery. In some environment vehicles are deployed in a dense manner, and it can move at a high speed. Adjacent Access Points (APs) on the road find out route information from their past report gatherings which use a fuzzy logic to select routing conclusion. This approach consists of isolating the whole network area into clusters. Based on which an optimized route is chosen using the input parameters as vehicle density, distance from the adjacent AP, vehicle mobility, receiving signal strength and delay should be jointly measured in the relay node selection. Though, these computation conflicts with each other depend on vehicle mobility. Hence, we employ fuzzy logic to handle this imprecise information to select forwarding vehicles by mutually taking into account of vehicle distance (d), mobility (v) and Receiving Signal Strength (RSS). The selected vehicle can provide an assure data forwarding with a high competence. The performance of the proposed scheme is evaluated on the metrics such as end-to-end delay, packet delivery ratio by varying vehicle speed, and density of vehicles.
Levenberg-marquardt algorithm to identify the fault analysis for industrial applications
Authors: Bhuvaneswari P. , G.P Ramesh
Published: March 2018

The data are collected and forwarding it to the goal is a significant function of a sensor network. For some applications, it is additionally imperative to admit the fault signal to the collected data. To monitor the industrial environment through a wireless sensor network (WSNs), present a neural network based Levenberg-Marquardt (LM) Algorithm for detecting the fault using the gradient value and mean square error of the signal. The data are collected and presented by the magnetic flux sensor and MEMS acoustic sensor. The simulation model is developed in MATLAB/Simulink.
Contagious disease detection in cereals crops and classification as 'solid' or 'undesirable': an application of pattern recognition, image processing and machine learning algorithms
Authors: Thakur Priyanka , Aggarwal Preeti , Juneja Mamta
Published: March 2018

Illnesses in plants diminish the profitability and economy of a nation. Building up a robotization framework for location and arrangement of illnesses in tainted plants is a thriving exploration territory in the field of exactness farming. Oats crops are generally developed temperate product on the planet. Observing of these yields, particularly amid development, empowers us to lessen the harm at the soonest and exact conclusion of these maladies can diminish the sickness spread which will bring about ecological assurance and better return. By utilizing design acknowledgment and picture preparing calculations, the advancement of choice emotionally supportive network for plant security turns out to be more proficient. This paper shows a way to deal with recognize parasitic maladies in three oats trims in particular Maize, Rice and Wheat, utilizing design acknowledgment, machine-learning and picture handling strategies and arrange them as 'Solid' or 'Unfortunate'. It is finished by separating distinctive highlights like shading, shape and surface from the tainted areas of these plant pictures. 227 parasitic infection pictures of three oat crops i.e. Maize (71), Rice (92) and Wheat (64) were downloaded from different sources and considered in this exploration. Some solid pictures of same harvests were additionally downloaded for characterization reason. According to the calculation took after, after the pre-handling step, K-implies grouping strategy was utilized to section the unhealthy zone from the plant and in view of that three bunches of pictures (K=3) were created. Highlight extraction was performed trailed by include decrease utilizing diverse techniques lastly seven diminished highlights for maize, three highlights for rice and five highlights for wheat were chosen which brought about most extreme grouping precision of 87.60% for maize utilizing Naive Bayes classifier, 92.30% for rice utilizing both Naive Bayes and LibSVM classifiers, and 94.18% for wheat utilizing Multilayer Perceptron. On a huge scale, it can be finished up from the outcomes that Naive Bayes classifier gave best characterization exactness of 90.97% for all the three grain crops consolidated.
Optimizing performance of fuzzy decision support system with multiple parameter dependency for cloud provider evaluation
Authors: Rani Uma , Dalal Surjeet , Kumar Jugnesh
Published: March 2018

In today’s world, technological trend offers computing resources as services through the internet in on-demand or pay-as-you-go approach. These services are provided by different cloud service providers. Due to which trust on the any service provider is a choice of any customer. In order to choose a reputed cloud service provider a new method using the concept of fuzzy has proposed in this paper. This method enhanced the customer’s satisfaction level of using cloud services by avoiding ambiguities in fuzzy interface system (FIS) through optimization.Proposed fuzzy rule-based decision support system is collaborating with advanced fuzzy system optimized using a swarm intelligent firefly algorithm that facilitates the consumers in selecting right CSP based upon their rating value. It conducts three different reviews of three different components, i.e. customer review, service provider review and public review. Results are carried out on the basis of both simple and the optimized fuzzy, and it is found that the optimized fuzzy surpasses the simple fuzzy logic.
Intelligent channel aware malicious free data forwarding scheme over wireless sensor networks
Authors: Sirajuddin Mohammad
Published: March 2018

In the communicative world each and every individual needs to perform global communication with failure-free intelligent model. Wireless Sensor Network, a medium which provides efficient communication modes to clients to satisfy their communication needs. However, this kind of wireless network channels are also facing lots of communication issues by means of several fault strategies, such as: link failures, node failures, bandwidth inefficiency, poor energy level, attacks and many more. So that, a fast growing network scheme is required as well as it provides lots of features to communication strategies and routing protocols, called Intelligent-Channel-Aware-Reputation Scheme [ICARS]. In the proposed system, the main objective is to provide the strong and failure-free wireless communication medium over networking with multiple numbers of nodes. As well as to provide high-level of security while the data is communicating from source to destination. For that powerful cryptographic algorithm is employed, called Modified Rijndael Algorithm (MRA) and to clearly state that the attack-free wireless communication channel with the help of intelligent routing strategies such as Route Request (RREQ) and Route Response (RREP).
A precise view of big data based analysis on various parameters under the field of gaming analytics
Authors: Udaya Nirmala Mary R. , Nandhini R.
Published: March 2018

Big data and analytics has brought revolutions in many of the fields. Gaming is one such field. Analytics is being implemented in games to classify the user and predict their behaviors. These techniques require some basic survey on gaming and analytics. This article is such an approach to game analytics focusing on its origin, causes, effects and machine learning techniques used in game analytics. This survey provides a better understanding about game analytics and the publications related to it.
Prediction of diabetes with hybrid prediction model us-ing big data in health care
Authors: Rama Kalaivani E. , Ramesh Marivendhan E. , Suma N.
Published: March 2018

Technology is advancing in healthcare to comply with unique regulatory guidelines designed to support public safety. The realistic way of Big Data can unify all patient related data to get a 360-degree view of the patient to analyze and predict outcomes. Big Data is a buzzword which is reigning the innovation market from quiet sometime and trends which can give birth to new line of treatment of diseases and provide high quality healthcare at lower cost to all. These issues include benefits of Big Data, its applications and opportunities in medical areas and health care. This paper includes the basics of big data, clinical prediction model for predicting whether the diagnosed patient suffers from diabetes. Hybrid prediction model is chosen and it is used to predict the disease.
Technical challenges and perspectives in batch and stream big data machine learning
Authors: Rama Rao KVSN , S Sivakannan , Prasad M.A. , Agilesh Saravanan R.
Published: March 2018

Machine Learning is playing a predominant role across various domains. However traditional Machine Learning algorithms are becoming unsuitable for majority of applications as the data is acquiring new characteristics. Sensors, devices, servers, Internet, Social Networking, Smart phones and Internet of Things are contributing the major sources of data. Hence there is a paradigm shift in the Machine learning with the advent of Big Data. Research works are in evolution to deal with Big Data Batch and stream real time data. In this paper, we highlighted several research works that contributed towards Big Data Machine Learning.
Predicting B.Tech student admission decisions by data mining algorithms
Authors: Ahuja Ravinder , Garg Archit Garg , Jain Daksh , Sachdeva Deepanshu
Published: March 2018

In learning calculations affiliation govern mining is the most intense capacity in information mining. The age of principles includes two stages in which the primary stage finds the arrangements of continuous components and the second stage creates the run the show. Numerous calculations are determined to discover sets of incessant components from successive examples. In our exploration work an imperative perception is made in the information digging calculations for the informational index of the designing understudies. By discovering relationship between qualities, we can discover the potential outcomes for affirmation and anticipate understudy confirmation choices. To discover solid and substantial affiliation rules, distinctive measures are thought about lift, support, cost, confidence and conviction. The gauge is come to with the utilization of the imperative as needs be amid the age of the affiliation rules. As we move towards the objective, to give an examination the affiliation runs, the understudies who pick the branch have utilized the calculations specified to demonstrate the guidelines and the aftereffects of the affiliation in light of the past database of the records of confirmation.
Big data life cycle: security issues, challenges, threat and security model
Authors: SankaramAlladi Bhima , Srinivas Prasad Dr
Published: March 2018

Today the technologies of big data are completely bringing a vast change in the entire conventional technology discipline and it’s successfully applying the required latest security design methods to state the upcoming security provocations. Big Data Architecture is a “Data” centric architecture in which security can be included in all the levels. Data is collected from different sources and Data generation is done, the next step it undergoes is Data Processing, the next step is Data storage and the last step is Data analysis. At all the levels Data plays a vital role. It aims to give basic investigation regarding most of the security risks and Big Data provocation and bought out new provocations, complication to the conventional protective domains and also for conventional trends. This deals with the definition of big data and the characteristics that effect most of the data preservation, such as 3V’s, dynamicity. It analyses the original changes and new challenges to Data security. It also provides pitch for real time practice of security infrastructure peripherals which allows extend trusted non-local virtualized processing environment. This research focus on all levels of Big Data where and when the security services and techniques can be included to acquire accurate results.
Hybrid optimization for feature selection in opinion mining
Authors: Moorthi N. Prasanna , V Mathivanan
Published: March 2018

A sub-discipline of Information Retrieval (IR) is opinion mining and the lexicon of computers is not concerned of the subject of thedocument, but about the opinion expressed.It has caused a large impact in the arena of academics and industry as it has a wide area of research and the applications are widespread.Feature selection is a vital step in opinion mining, as its individual feature decides the opinions expressed by the customers.Feature selection reduces the dimensionality of data by avoiding non-relevant features; it can be con-sidered as a necessary and excellent process for data mining applications.In this study, feature subset is optimized through Particle Swarm Optimization (PSO) algorithm, Cuckoo Search (CS) algorithm and hybridized PSO-CS algorithm.Classification is done through Naïve bayes and K-Nearest Neighbours (KNN) classifiers.Feature extraction has its basis on Term Frequency-Inverse DocumentFrequency (TF-IDF).The accuracy of classification precision is increased by the reduction in size of feature subset and computational com-plexity.
An enhanced voice based chatbot using artificial intelligence
Authors: Saratha Devi G , G Vinothini , Devi.C Janani , C Jeyachandrakala
Published: March 2018

The chatbot application developed using various programming languages with user interface to send input and receive response contains certain boundaries and limitations. This paper proposes a chatbot application for assisting the information regarding the colleges with the use of artificial intelligence. This system has been embedded artificial intelligence to help the user to resolve the questions by providing a human way interactions to identify the sentences and making a decision itself as response to answer a question overcoming the use of programming languages.
Timber robotic fabrication: testing for an integral manufacturing
Authors: El Khoury Chadi , Halabi Marwan
Published: March 2018

Going beyond sustainability and with the great development of technology, timber has a great potential to be explored as a building material. Its physical-mechanical properties such as lightness and elasticity allow the designing of complex structures and the growing trend in research in robotic fabrication has accelerated the development of dimensional design concepts that demonstrate that wood is absolutely contemporary and at the height of other innovative materials. This paper investigates on computer aided integrated architectural design and production of timber using advanced automated tools, aiming to provide integral solutions for the design and production of geometrically complex free-form architecture. Investigations on computer aided geometric design and integrated manufacturing are carried out with equal importance. This research is considering an integral and interdisciplinary approach, including computer science, robotics and architecture. The studies for translation of the geometrical into constructional elements consider integrated manufacturing. Addressing and numbering of the elements by iterative geometric design are investigated and compared to lexicographically ordered addressing systems, in order to provide an adequate data structure for the design, production and assembly of the constructional elements. The integrated digital design methods studied are tested and verified by the realization of one to one scale prototypes. The main aim of the research is to find ways of shifting the perspective of adventurous high quality architecture robotically produced with correspondingly reduced costs and minimized environmental impact.
The quest for environmental sustainability in educational institutions
Authors: Kumar Nand , Kumar Ashwani , Sharma Sunil
Published: March 2018

Over the last four decades “Sustainable Development” has emerged as a catchphrase while defining development. Many reforms have taken up in the past to solve ever-increasing snags related to untenable development at local as well as global level. However, the review of literature pertaining to sustainable development shows that it lacks in proper interpretation and consistency in the definition across the globe. Further, it’s the environmental sustainability aspect that has been compromised again and again to give way to economic development. In practice, environmental sustainability is not considered as equally significant in comparison to the other two aspects of sustainability. The issues of sustainability in educational institutions have historical pedigree starting from Stockholm Declaration, Tbilisi Declarations and subsequently a lot of evolution has taken place in this field. Still, most of the educational institutions lack their roadmap to attain sustainable development. Some of the universities have tried to implement their own sustainability plan apart from signing any major declaration. This review paper suggests that environmental sustainability is the need of the hour and this concept need more workable, elaborated and practical solution which need to be free from political bias and possess intellectual clarity and objectivity.
Improvement of the efficiency of booth multiplier
Authors: Siva Kumar M , Kumar Tulasi Sanath , Srinivasulu N , Krishnam Naidu Yedla G S , Raghuveer E , Hari Kishore K
Published: March 2018

Objective: To Improve the performance of Booth Multiplier and reduce power consumption.Method: The most essential form of multiplication consists of framing the result of two unsigned (positive) binary numbers.Finding: Booth Multiplier consists of pre-defined table. According to this algorithm, multiplication of two numbers x and y (x*y) is same as multiplication of y and x (y*x). At times this rule fails due to which we modify the logic by converting the decimal number in to 4 bit binary number and appending (n-2) zeros at most significant bit and one zero at least significant bit.Improvement: By using this method we can get accurate results in multiplication by multiplying like (x*y) and    (y*x).
Performance evaluation of heuristic algorithms in floor planning for ASIC design
Authors: Nazeer Hussain S. , Hari Kishore K
Published: March 2018

A study on physical design of VLSI Floor planning is discussed using optimization techniques for betterment in performance of VLSI chip. Floor planning in VLSI is considered to be a Non Polynomial problem. Such problems can be solved using computations. The initial step in floor plan is the representation of floor plan design. The floor plan representations show greater impact on the search space and the complexity of the floor plan design. The objective of this paper is to study different algorithms that addressees the problem of handling alignment constraints such as good placement, optimum area and short run time. Different heuristic and meta-heuristic algorithms are proposed and suggested by many researchers for solving the VLSI Floor plan problem. In this paper Simulated Annealing algorithm, Ant Colony Algorithm, Tabu search and Genetic algorithms are discussed.
LDT-MRF: Log decision tree and map reduce framework to clinical big data classification
Authors: Surekha T. , Siva Rama Prasad R.
Published: March 2018

The growth of the data is enormous in the current scenario of the developing information technology and performing the data classification is complex both in time and information extraction. Moreover, there are uncertainties in performing the big data classification that are associated with the unbalanced datasets. In order to overcome the issues, a novel method of big data classification is introduced in this paper. The novel method, Log Decision Tree and Map Reduce Framework (LDT-MRF) uses the Log Decision Tree (LDT) and the Map Reduce Framework (MRF) for performing the parallel data classification. The novel parameter termed as Log-entropy is used to select the best feature attribute for data classification. The data classification is performed using the LDT that enables the efficient data classification. Experimentation is carried out using three datasets, namely the Cleveland dataset, Switzerland dataset, and the Breast Cancer dataset. The comparative analysis is carried out using the performance metrics, such as sensitivity, specificity, and accuracy to prove the effectiveness of the proposed method. The sensitivity, specificity, and accuracy of the proposed method is 84.7596%, 74.633%, and 80.9088% respectively, which is greater when compared with the existing methods of big data classification.
Distributed fuzzy logic based cluster head election scheme (DFLCHES) for prolonging the lifetime of the wireless sensor network
Authors: Ramakrishnan S. , Prayla Shyry S.
Published: March 2018

Wireless sensor networks (WSNs) is considered as the predominant technology due to their high suitability and adaptability that makes it possible to be deployed in wide range of applications like civil and military domain. But energy-constraint is the significant feature that needs to be addressed for sensor networks since energy drain of sensor nodes affects network lifetime, stability and co-operation of sensor nodes in the event of enforce reliable data dissemination. Cluster head election has to been performed periodically in order to handle energy balance for facilitating reliable packet delivery. Most of the cluster head election schemes of the literature elect a node as cluster head either randomly or by elucidating their stochastic probabilities. Hence a Distributed Fuzzy Logic based Cluster Head Election Scheme (DFLCHES) that discriminates and discards packets from the sensor nodes that has the least probability of being elected as cluster head is proposed. DFLCHES utilizes five significant parameters such as trust, energy, node density, hop count and centrality measure for quantifying the probability of cluster head election. This DFLCHES is run on each neighbor nodes of the cluster members to facilitate the action of discrimination. DFLCHES also balances the energy consumption of the cluster members during transmission as it discards packets from ineligible nodes. Further the action of cluster head election has to be optimized periodically for reducing and balancing energy consumption for prolonging the network lifetime. In DFLCHES, the process of optimizing cluster head depends on the incorporation of the concept of Genetic algorithms for enabling and ensuring reliable routing.
EMD based Hilbert energy spectrum analysis of adventitious lung wave signals
Authors: B. B. Shankar , Jayadevappa D.
Published: March 2018

The respiratory adventitious waves are analyzed effectively by time frequency analysis. In this paper, we present a new approach for rectifying the abnormality in adventitious wave. Basically, there are    two types of respiratory sound waves and these are classified as wheezes and crackles. The proposed method utilizes the time frequency analysis using spectrum analysis method. The modified Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to plot energy spectrum of adventitious wave is used in this work. The proposed method decomposes the respiratory adventitious wave into a different Intrinsic Mode Function (IMF). The long and short duration adventitious waves are present in a wheezing subject and this leads to production of non stationary and nonlinear sound waves. The empirical mode decomposition (EMD) decomposes such characteristic waves. The available spectrogram analyzes techniques related to additive expansions and explore amplitude wise time-frequency distribution. The methodology discussed in this context responding greatly even for correlative noise and explores energy spectra in addition to amplitude spectra.  The various IMFs such produced are exhibits the fine details of adventitious wave and thus pattern can be predicted for final residual. The energy spectrum can be viewed as a diagnostic tool for accurate analysis of wheezing pattern. The decomposed frequency patterns indicate the physiological aspects. The instantaneous frequency and Hilbert energy spectrum based on above mentioned a method are employed by IMF to analyze and present the result in time-frequency distribution to explore the characteristics of inherent properties adventitious signals. The Hilbert marginal spectrum has been used to indicate overall energy distribution from each frequency component. Finally, the resultant EMD analysis along with EEMD energy spectrum is better for asthmatic subject and solves mode mixing problems.
Optimization and analysis of high gain wideband microstrip patch antenna using genetic algorithm
Authors: Gaurav Mishra Raj , Mishra Ranjan , Kuchhal Piyush , Prasanthi Kumari N.
Published: March 2018

Microstrip antennas that can operate in single and multiple frequency bands are required in various wireless communication devices. A single patch, square shaped microstrip patch antenna having high directivity and gain is proposed in this paper. The geometry of proposed antenna is optimized using Genetic Algorithm (GA) to operate in X-Band for wideband applications. The proposed antenna design exhibits a wide operating bandwidth 550 MHz (simulated) and 450 MHz (measured), high gain and directivity of about 8.35 dB (simulated) making it suitable for wideband applications. The proposed antenna design works in X-band which has weatherproof characteristics and supports easy communication of voice, data, images and HD videos. The attractiveness of the GA design over the traditional design methods is its ability to achieve the desired performance by using a simple design of single patch antenna.
Performance metric analysis of modified LEACH routing protocol in wireless sensor network
Authors: Priyadarshi Raghul , Tripathi Hitesh , Bhardwaj Abhyuday , Thakur Ankush
Published: March 2018

The lifetime of a network is reflected as one of the vital concerns in wireless sensor network because of a huge number of nodes and further its density and distribution. When the network size increases then routing protocol becomes one of the crucial issues that which routing protocol one should use so that network lifetime to be enhanced. Furthermore, sensor nodes must be alive to promise that network process must continue without any interruption or any loss of data in the network. In fact, there are a lot of clustering method is there to augment the network lifetime. But in this paper, presented routing protocol that is based on existing LEACH protocol called as K-mean clustering routing (KLEACH). Simulation results show that efficiency of the network has been improved and network lifetime has been also enhanced compared to existing LEACH protocol. Network lifetime of KLEACH protocol is almost increased by 40% and energy consumption is also decrease by almost 38% which reflects the good agreement of KLEACH algorithm compared to existing algorithm.
MATLAB/simulink study of multi-level inverter topologies using minimized quantity of switches
Authors: Krishna B.Vijaya , Venkata Prashanth B. , Sujatha P.
Published: March 2018

Multilevel Inverters (MLI) have very good features when compared to Inverters. But using more switches in the conventional configuration will reduce its application in a wider range. For that reason a modified 7-level MLI Topology is presented. This new topology consists of less number of switches that can be reduced to the maximum extent and a separate gate trigger circuit. This will reduce the switching losses, reduce the size of the multilevel inverter, and cost of installation. This new topology can be used in Electrical drives and renewable energy applications. Performance of the new MLI is tested via. Total harmonic distortion. This construction structure of this multilevel inverter topology can also be increased for 9-level, 11-level and so on and simulated by the use of MATLAB/SIMULINK. A separate Carrier Based PWM Technique is used for the pulse generation in this configuration.
Design of low power 10GS/s 6-Bit DAC using CMOS technology
Authors: Ramakrishna P. , Hari Kishore K
Published: March 2018

A Low power 6-bit R-2R ladder Digital to Analog Converter is presented in this paper. Here the    R-2 R network designed using resistors with only two values-R and 2xRand the switch is designed by using both NMOS and PMOS Transistors. This Digital to Analog Converters operated with low voltage, by applying dynamic threshold MOSFET (DTMOS) logic. This design achieved less INL and DNL which is 0.3 and 0.06 respectively. Power supply required to operate this device is only 1V with10GHzconversion rate. This design is implemented by using 0.18μm CMOS technology.