Showing papers in "Indian journal of science and technology in 2015"

Studying the Effect of Indentation on Flow Parameters and Slow Heat Transfer of Water-Silver Nano-Fluid with Varying Volume Fraction in a Rectangular Two-Dimensional Micro Channel

Abstract: The purpose of this study is to investigate the influence of indentations on the parameters of fluid flow and heat of water-silver nanofluid in a rectangular two-dimensional micro channel. It includes heat transfer water silver nano fluid in an indented micro channel under the constant temperature. The system is numerically modeled, by Finite Volume Method. After solving the governing equations for U, V and θ, other useful quantities such as Nusselt number and friction factor can be determined. The hot fluid inlet exits after cooling by the cold walls of the micro channel. Calculations are done for the two ranges of Reynolds number (Re). It was observed that at times the fluid has more indentations; it has a greater temperature drop that is at the output cross section of the micro channel. With increasing Reynolds number (Re), number of the indentations and the increasing volume fraction of the nanoparticles, greater temperature drop occurs. The presence of indentation in the micro channel increases the speed and the dimensionless temperature at the center line. Finally, the results are provided in the form of the contour of flow and isothermal lines, the coefficient of friction, Nusselt number, temperature and velocity profiles in different micro channel sections. The results of the numerical simulation indicate that the heat transfer rate is significantly affected by the solid volume fraction and Reynolds number.

Impact of Over the Top (OTT) Services on Telecom Service Providers

Abstract: The telecom industry has been one that has had to deal with a continuously changing business and technology environment more than most other industries over the past half century. Traditionally the principal revenue streams for telecom operators have been voice and messaging (SMS) with data coming in at a far third till recently. But while telcos had been quick to react to previous game changing developments such as the internet explosion and the emergence of cellular mobile communications in the 1990s, they seem to have been caught napping in the face of the newest challenge to their revenues, Over The Top (OTT) service providers. The growing impact of OTT services on telcos' voice and messaging revenue is a widely accepted phenomenon. Their impact on mobile data traffic and telco data revenue is also areas that have been acknowledged as critical points for consideration. This study attempts to link these issues to shift in consumer preferences, identify the underlying factors there of and to understand the factors driving users towards OTT services.

A Survey of Data Mining Techniques on Risk Prediction: Heart Disease

Abstract: Comparison of classification techniques in Data mining to find the best technique for creating risk prediction model of heart disease at minimum effort. In Data mining, different methods used to find risk prediction of heart disease. There are two types of model used in analysis of data. First one is applying single model to various heart data and another one is applying combined model to the data. The combined model also known as hybrid model. This paper provides a quick and easy understanding of various prediction models in data mining and helps to find best model for further work. This is unique approach because various techniques listed and expressed in bar chart to understand accuracy level of each. These techniques are chosen based on their efficiency in the literature. In previous studies of different researcher expressed their effort on finding best approach for risk prediction model and here we found best model by comparing those researcher’s findings as survey. This survey helps to understand the recent techniques involved in risk prediction of heart disease at classification in data mining. Survey of relevant data mining techniques which are involved in risk prediction of heart disease provides best prediction model as hybrid approach comparing with single model approach.

A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance

Abstract: Objectives: Data mining techniques are implemented in many organizations as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Data mining can be applied to wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational data mining is rapidly developing as a key technique in the analysis of data generated in the educational domain. Methods: The aim of this study presents an analysis of final year results of UG degree students using data mining technique, which carried out in three of the private colleges in Tamil Nadu state of India. The primary objective of this research work is to apply the classification techniques to the prediction of the performance of students in end semester university examinations. Particularly, the decision tree algorithm C4.5 (J48), Bayesian classifiers, k Nearest Neighbor algorithm and two rule learner’s algorithms namely OneR and JRip are used for classifying the performance of students as well as to develop a model of student performance predictors. Results: The result of this study reveals that overall accuracy of the tested classifiers is above 60%. In addition classification accuracy for the different classes reveals that the predictions are worst for distinction class and fairly good for the first class. The JRip produces highest classification accuracy for the Distinction. Classification of the students based on the attributes reveals that prediction rates are not uniform among the classification algorithms. Also shows that selected data attributes have found to be influenced the classification process. The results showed to be satisfactory. Improvements: The study can be extended to draw the performance of other classification techniques on an expanded data set with more distinct attributes to get more accurate results.

A Study of Data Storage Security Issues in Cloud Computing

Abstract: Cloud Computing is defined as an environment in which users can share their resources with others in pay per use model. The resources are stored centrally and can access from anywhere. Despite these advantages, there still exist significant issues that need to be considered before shifting into cloud. Security stands as major obstacle in cloud computing. This paper gives an overview of the security issues on data storage along with its possible solutions. It also gives a brief description about the encryption techniques and auditing mechanisms.

Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification

Abstract: Backgrounds/Objectives: Data Mining (DM) techniques are extremely utilized for the extraction of useful information which is available in data warehouses and other database repositories. In medical diagnose, the role of DM approach rises quick recognition of disease over symptoms. To classify the medical data, a number of DM techniques are used by researchers. One of such techniques is classification. The classification algorithms predict the hidden information in the medical domain. The breast cancer is the very dangerous disease for women in developed countries like India. Most of the women death happens in the world, they are affected by the breast cancer. Methods/Statistical Analysis: The role of classification is importantin the real world applications in every field. Classification is used to classify the elements permitting to the features of the elements through the predefined set of classes. This research work analyses the breast cancer data using classification algorithms namely j48, Classification and Regression Trees (CART), Alternating Decision Tree (AD Tree) and Best First Tree (BF Tree). Findings: To find the performance of classification algorithms, this work uses cancer data as input. Particularly, this work is carried out to compare the four decision tree algorithms in the prediction of the performance accuracy in breast cancer data. All the algorithms are applied for breast cancer data to classify the data set for classification and prediction. Among these four methods, this work concludes the best algorithm for the chosen input data on decision tree supervised learning algorithms to predict the best classifier. Applications/Improvements: The breast cancer data is analyzed by taking the images using the same algorithms in future. Also, the microcalcifications of the breast cancer imagery are to be investigated in the same work.

Learning Cyber Security Through Gamification

Abstract: Objectives: Introducing gaming approach in the jeopardy round of InCTF (Indian Capture the Flag). Methods: Present Jeopardy round of InCTF can be compared as take-away assignments, where participants are given set of questions to solve, this is aimed at testing their knowledge in various computer security concepts. To make jeopardy round more attractive and motivating to the students we introduce a gaming approach to it. A game is developed which is divided into various levels and at each level the knowledge of students in Cyber security concepts is tested. Our design will help others to host jeopardy round CTF and get maximum learning outcome from the students. Findings: Security competition like CTF (Capture the Flag) are effective tool in providing computer security training. Conclusion: Gaming approach in Cyber security education will be a big step forward in training more students in computer security and create a secure online world.

Multiclass classification for meteorological data using modified cbs algorithm with multiple minimum support

Abstract: Weather forecast is one of focuses in data mining which uses meteorological data for its process. As the common technique used in forecasting weather is sequential pattern, several algorithms have been developed by scholars. The common algorithms used in forecasting weather are: CBS algorithm, CBS algorithm using FEAT and CBS algorithm using FSGP. Previous studies remark the weaknesses of these three algorithms especially related to classifying weather with more than one class. In this paper, we use multiple minimum supports to modify CBS algorithm in order to improve the performance of weather forecasting. The result shows that making use multiple minimum supports to the three algorithms, the three modified algorithms are able to classify the weather with six categories from a given minimum support. In addition, the simulation result shows that the covacc parameter of the modified CBS algorithm is better than the three common algorithms.

The Relationship between Airbnb and the Hotel Revenue: In the Case of Korea

Abstract: This study investigates the impact of Airbnb’s listing on the hotel revenue in Korea. We use the panel regression model for this purpose. First, it finds that Airbnb’s listing is not related to the hotel revenue. Even though the number of tourists is continuously increasing, most of them use hotels rather than Airbnb in Korea. Because the website of Airbnb has started from 2010 in Korea and has a low awareness, it has a less effect in Korea. The listing of Airbnb is rapidly growing in 2014. Given that the data of 2014 adds to the study, the result can be changed. Second, the estimate of the unemployment rate is strongly significant. If unemployment rate increases, the demand for hotel decreases. Because unemployment rate explains the present economic situation, the increase of the unemployment rate can imply a recession and the decrease of the trip can be followed. Third, the exchange rate has a positive effect on the hotel revenue, especially in Jeju. When the exchange rate is higher, the tourists from the abroad increase, and thus hotel revenue can increase. It implies that the tourists sensitively respond to the exchange rate. Forth, the estimates of the vacation dummy variable are strongly significant in Busan and Jeju. There is a ton of tourists in the holiday (especially summer) season, and thus it is common in the increase in the hotel revenue during this season.

Utilisation of Waste Plastics as a Partial Replacement of Coarse Aggregate in Concrete Blocks

Abstract: The rapid industrialization and urbanization in the country leads lot of infrastructure development. This process leads to several problems like shortage of construction materials, increased productivity of wastes and other products. This paper deals with the reuse of waste plastics as partial replacement of coarse aggregate in M20 concrete. Usually M20 concrete is used for most constructional works. Waste Plastics were incrementally added in 0%, 2%, 4%, 6%, 8% and 10% to replace the same amount of Aggregate. Tests were conducted on coarse aggregates, fine aggregates, cement and waste plastics to determine their physical properties. Paver Blocks and Solid Blocks of size 200 mm X 150 mm X 60 mm and 200 mm X 100 mm X 65 mm were casted and tested for 7, 14 and 28 days strength. The result shows that the compressive strength of M20 concrete with waste plastics is 4% for Paver Blocks and 2% for Solid Blocks.

Heart Disease Prediction Using Hybrid Genetic Fuzzy Model

Abstract: The objective of the work is to diagnose heart disease using computing techniques like genetic algorithm and fuzzy logic. The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper a hybrid genetic-fuzzy heart disease diagnosis system is designed. The genetic algorithm is used for a stochastic search that provides the optimal solution to the feature selection problem. The relevant features selected from the dataset help the diagnosing system to develop a classification model using fuzzy inference system. The rules for the fuzzy system are generated from the sample data. Among the entire rule set the important and relevant subset of rules are selected using genetic algorithm. The proposed work uses the benefits of genetic algorithms and fuzzy inference system for effective prediction of heart disease in patients. The selected features are sex, serum cholesterol (chol), maximum heart rate achieved (thalach), Exercise induced angina (exang), ST depression induced by exercise relative to rest (oldpeak), number of major vessels coloured (ca) and thal value. Fuzzification using Fuzzy Gaussian membership function and defuzzification using centroid method improves the performance of the system. The work has been evaluated using the performance metrics like accuracy, specificity, sensitivity, confusion matrix that help in proving the efficiency of the work. The obtained classification accuracy is 86% using the stratified k fold technique with the values for specificity and sensitivity as .90 and .80 respectively. The number of attributes has been reduced from 13 to 7 from heart disease dataset available in the UCI Machine learning repository. When compared with the existing system the accuracy of the proposed work has been increased by 1.54%. The proposed model is named as GAFL model called Genetic Algorithm Fuzzy Logic model for effective heart disease prediction. It is easy to build the model thereby providing an easy option to be used in hospitals and medical centers for the aid of the physicians.

Ontology Development: A Comparing Study on Tools, Languages and Formalisms

Abstract: This paper reviews and compares some Ontology Development Tools, Formalisms and Languages from those reported in the Literature, with a special attention accorded to the interoperability between them. Additionally, this paper presents the Structure and Basic Features of Tools, Formalisms and languages. The main criterion for comparison of these tools and languages was the user interest and their application in different kind of real world tasks. The primary goal of this study is to introduce several tools and languages to ensure more understanding from their use. Consequently,we can solve the problems of current tools and languages and ensure the easy development of a new generation of tools and languages.

A Survey on Cryptography using Optimization algorithms in WSNs

Abstract: Objective: The main intent of this research is to provide the secure communication in the wireless sensor networks. For that, several cryptography using optimization algorithms is investigated. Methods: In this manuscript, a survey has been made on the cryptography using optimization methods for secure communication. Several optimization algorithms are presented for cryptography to create the keys for the encryption. One of the suggested techniques is ant Colony Optimization Key Generation based image encryption method that is used to create the keys for encryption of text. The ant colony optimization method is used to generate the keys for encryption. Results: This survey comprehensively studies the issues in the cryptographic optimization methods for providing security in the wireless sensor networks. The performance of the different methods is compared with various parameters such as maximum number of keys stored, battery capacity, and runtime. The maximum number of keys store in the Ant Colony Optimization based key generation is 52, for Novel stream cipher cryptosystem 256, for fast and secure stream cipher 256, and also for RC4 256 keys. Conclusion: This survey investigates the several cryptographic optimization methods and provides the idea for efficient methods for future work.

A New Approach in Bloggers Classification with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms

Abstract: Blogs are one of the effective tools of web2 which are considered as one of the major module and of social and interactive capabilities in making IT world wonderful for the cyber and virtual living. Two methods were used in this paper: K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). These methods are classified based on Kohkiloye and Boyer Ahmad province bloggers dataset considering input features of each blogger to the other methods and previously provided algorithms as more optimal. Our simulation and experiments not only provide hopeful results but also higher anticipation and classification rate.

Image Enhancement Techniques: A Study

Abstract: Image enhancement is considered as one of the most important techniques in image research. The main aim of image enhancement is to enhance the quality and visual appearance of an image, or to provide a better transform representation for future automated image processing. Many images like medical images, satellite, aerial images and also real life photographs suffer from poor and bad contrast and noise. It is necessary to enhance the contrast and remove the noise to increase image quality. One of the most important stages in medical images detection and analysis is Image Enhancement Techniques. It improves the clarity of images for human viewing, removing blurring and noise, increasing contrast, and revealing details. These are examples of enhancement operations. The enhancement technique differs from one field to another depending on its objective. The existing techniques of image enhancement can be classified into two categories: Spatial Domain and Frequency Domain Enhancement. In this paper, we present an overview of Image Enhancement Processing Techniques in Spatial Domain. More specifically, we categorise processing methods based representative techniques of Image enhancement. Thus the contribution of this paper is to classify and review Image Enhancement Processing Techniques as well as various noises has been applied to the image. Also we applied various filters to identify which filter is efficient in removing particular noises. This is identified by comparing the values obtained in PSNR and MSE values. From this we can get an idea about which filters is best for removing which types of noises. It will be useful and easier to detect the filters for future research.

Smartphone Addiction: (Focused Depression, Aggression and Impulsion) among College Students

Abstract: In this study, a survey was conducted to examine the relationship among smartphone addiction tendency, depression, aggression and impulsion in college students. The data were collected via structural questionnaires completed by 353 college students located in Cheonan who agreed to participate in this study. There was a statistically significant positive correlation between smartphone addiction and depression, and there were positive correlations among smartphone addiction, aggression and impulsion. Hierarchial regression analysis was used to determine the influence of smartphone addiction tendency and to identify its correlation with depression, aggression and impulsions. First step hierarchy that controlled general characteristics shows that gender (p<.001) influenced smartphone addiction. Explanatory power for explaining the smartphone addiction of the control variables is found to be 5.6%. Including independent variables Model 2 shows a significant increase and the explanatory power for explaining the smartphone addiction is 31.2% (p<.001).

An Analysis on Efficient Resource Allocation Mechanisms in Cloud Computing

Abstract: Cloud computing is considered as a striking computing model which allows for the provisioning of resources on-demand. Cloud computing environment enables multiple users to place request for various cloud services simultaneously. Effective and efficient resource allocation is the challenging task in cloud computing. The efficiency of allocation is measured by optimizing appropriate parameters such as execution time, demand, network delay time, capacity of resources and cost. This paper reveals how effectively resource allocation problem can be addressed in the perspective of cloud service provider and also provides a comparative analysis which helps in selecting parameters to meet the objective function for optimizing the demand to maximize the profit.

Detection of Brain Tumor by Particle Swarm Optimization using Image Segmentation

Abstract: Background/Objectives: Image segmentation is one of the fundamental techniques in image processing. During past few years, the image processing mechanisms are extensively used in various medical fields for early stage detection, separation and identification of diseases; in this, the time consumption is an important criteria to discover the diseases for the patient. Methods/Statistical Analysis: This research work analyses about the detection and separation of brain tumor through Magnetic Resonance Imaging (MRI) medical images using Particle Swarm Optimization (PSO), a heuristic global optimization method based on swarm intelligence. The algorithm is widely used and rapidly developed for its ease implementation. This work has four stages that includes conversion, implementation, selection and extraction. Findings: The research work starts with converting the Digital Imaging and Communications in Medicine (DICOM) into image file format which is the first stage. Applying the PSO algorithm with the change in the values of n (segmentation level) is the second stage. Based on the time, selecting the best resultant images is the third stage. The final stage is extraction of tumor affected region with the suitable filtering techniques. The research work takes the axial and coronal plane of the Magnetic Resonance (MRI) images. Finally, this work concludes with the extraction of the resultant image, which is taken as input, and using the best filtering technique the affected region is easily separated and identified efficiently. The work also identifies the best suitable plane for the PSO algorithm. Applications/Improvements: The same PSO algorithm is applied to find the size and the type of calcifications in MRI brain images and are extracted in future. The possibilities of using other algorithms are also considered for further implementation.

Social Information Retrieval Based on Semantic Annotation and Hashing upon the Multiple Ontologies

Abstract: Ontology is the best way for representing the useful information. In this paper, we have planned to develop a model which utilizes multiple ontologies. From those ontologies, based on the mutual information among the concepts the taxonomy is constructed, then the relationship among the concepts is calculated. Thereby the useful information is extracted. There is multiple numbers of ontologies available through the web. But there are various issues to be faced while sharing and reusing the existing ontologies. To resolve the ambiguity which exists, when comparing two concepts are semantically similar, but physically different, an approach is proposed here to index and retrieve the documents from two different ontologies. The ontologies used are WordNet and SWETO ontology. The results are compared based on semantic annotation based on RMS and hashing between the cross ontologies using Rabin Karp fingerprinting algorithm. Also the datasets are trained to yield better results.

Extreme Pedagogy: An Agile Teaching-Learning Methodology for Engineering Education

Abstract: Traditional instructor-centered, lecture-based teaching methods in engineering education have been criticized for being too linear, dogmatic, systematic and constraining. This paper proposes 'Extreme Pedagogy', a student-centered teaching-learning conceptual framework to improve quality of engineering education which is built on four core values: students and teachers and their interactions, working knowledge, collaboration with students and responding to change. Extreme Pedagogy derives its philosophy from Extreme Programming, an agile software methodology. Extreme Pedagogy aims at continuous improvement of student learning, keeping students' needs and satisfaction as its focus.

Production and Identification of Vanadium Oxide Nanotubes

Abstract: Lately there has been much interest in synthesizing characterizing new vanadium oxide host/ guest compounds These compounds have open structures and the ability to intercalate atoms or molecules They may be used as catalysts, molecular sieves, absorbents and energy storage devices We are studying the synthesis and characteristics of vanadium oxides nanotubes This experiment has been synthesized by sol-gel methods and based on hydrothermal treatment at temperature 150°C-180°C For characterization of samples, SEM, XRD, TEM, and FT-IR were used Also, we consider the effect of ultrasonic on the formation of vanadium oxides nanotubes Results show that diameter of the vanadium oxides nanotubes are varied from 15 to 25 nm (inner diameter) and 50 to 90 nm (outer diameter) In this research, vanadium oxide nanotubes had been synthesized via gelation of V2O5 and ethanol as solvent followed by hydrothermal treatment for one (1) to seven (7) days at 180oC The main objective of the study is to produce good quality VOX-NTS at a fastest synthesis time

Issues and Challenges of using E-Learning in a Yemeni Public University

Abstract: Yemen is confronted with several challenges in implementing e-learning in public universities. Thus, this research paper aims to identify and addressed the major issues and challenges faced by a public university in Yemen in establishing e-learning as a successful medium of imparting learning process. These challenges and barriers are discussed in detail. Many issues of e-learning relevant to the context of Yemen are also identified based on literature review. This paper also discusses the enabling factors and benefits for using e-learning. Two separate research methods are implemented: a survey and an interview to study the respondent’s individual learning experience and what are the challenges and problems of using e-learning from their point of view. This study was conducted at Hodeidah University. Two major groups of informants are used and they are academic staff and administrators in the university. Findings show that barriers such as lack of quality e-content, lack of awareness, lack of skills, lack of foreign language skill, attitudinal hampering, infrastructural obstacles, cultural barriers and high rate of computer illiteracy in addition to barriers related to integrating e- learning into traditional education methods are the major challenges of implementing e-learning. The results show that all the obstacles can be categorized into five dimensions which are a human constraints, administrative constraints, technical constraints, financial and physical constraints. The results also revealed that factors such as technical suppor, social and cultural support of e-learning, financial support for instructors and faculty members, improving working conditions, improving technological background knowledge, improving foreign language skill, providing royalties on copyrighted materials and academic staff attention in professional development are the important issues in implementing e-learning in any public university in Yemen.

A Study on Security Issues in Cloud based E-Learning

Abstract: Cloud based E-Learning is the method to reduce cost and complexity of data accessing, which are controlled by third party services. Traditional E-Learning methods are incorporated with cloud computing technology to provide massive advantages to the academic users but it compromises in security aspects. Proposed methodology ensures data availability and provides solution to protect indispensable data from the attackers. This study identifies different security issues in cloud service delivery model with an aim to suggest a solution in the form of security measures related to the cloud based e-learning. Different types of attacks in service delivery models of e-learning proposed by different researchers are discussed. Threats, security requirements, and challenges involved are also taken into consideration. This study of e-Learning models advocates users to access their data in the cloud through a secured layer using the internet.

Performance and Security Analysis for Image Encryption using Key Image

Abstract: Objective: This work is a review of image encryption algorithm using a key image, namely a secure image encryption algorithm based on bitplane principle. Method/Analysis: The analysis of the algorithm is done in terms of the parameters like histogram analysis, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Mean value analysis and Correlation coefficient. Findings: The results show that the algorithm reviewed offers commendable security against common types of attacks. Conclusion/Application: Most of the image encryption techniques have some security and performance issues. So there is a need to evaluate and analyze the efficiency of the algorithms used for encryption. These parameters are useful in judging the quality of encryption algorithms and can also be used for checking the level of security, the method actually provides to the actual image.

Smart Power Monitoring and Control System through Internet of things using Cloud Data Storage

Abstract: Background/Objectives: Lack of resources established in the present world is initiating everyone towards energy efficient technologies.Among all these resources, power is one which needs to be monitored and controlled as per the need since electricity consumption is increasing day-by day. Methods/Statistical Analysis: Internet of things reduces the effort of human by introducing machine to machine interaction. This work has been designed to implement smart power monitoring and control system through IoT using cloud data storage. Findings: Power consumed by various appliances is monitored through an ARM based controller interfaced to Hall Effect current sensors and stored in a cloud data base known as Xively. Power control of home appliances is achieved through actuators such as relays which can be controlled by client with the help of a web server. The web server is designed using Hyper Text Transfer Protocol for communication between client and server by establishing Remote Procedure Calls between client and server. Conclusion/Improvements: The designed system enables client to monitor and control the appliances at home from anywhere availing the IoT features of the designed system thereby reducing the wastage of energy.

A Hybrid of Ant Colony Optimization and Chaos Optimization Algorithms Approach for Software Cost Estimation

Abstract: The main challenge in the production and development of large and complex software projects is the cost estimation with high precision. Thus it can be said that estimating the cost of software projects play an important role in the organization productivity. With the increasing size and complexity of software projects the demand to offer new techniques to accomplish this important task increases day by day. Therefore, researchers have long attempted to provide models to fulfill this important task. The most documented algorithmic model is the Constructive Cost Model (COCOMO), which was introduced in 1981 by Barry W. Boehm. But due to the lack of values for the constant parameters in this model, it cannot meet the high precision for all software projects. Nowadays, regarding the increasing researches on machine learning algorithms and the success of these studies, in this paper, we have tried to estimate the cost of software projects according to meta-heuristic algorithms. In this paper, Ant Colony Optimization (ACO) and Lorentz transformation have been used as Chaos Optimization Algorithm (COA) and NASA datasets as training and testing sets. To compare and evaluate the results of the proposed method with COCOMO model, MARE is used, and the results show a decline in MARE to 0.078%.

Analysis of Microstructure, Hardness and Wear of Al-SiC-TiB 2 Hybrid Metal Matrix Composite

Abstract: Good mechanical and thermal properties of hybrid metal matrix composites make them more demanding in various fields such as automotive, aerospace and structural applications.In this paper an effort has been made to fabricate a hybrid metal matrix composite, silicon carbide and titanium diboride reinforced in Al 6061 matrix using stir casting method. Microstructure and mechanical properties such as micro hardness and wear are studied for various compositions of reinforcements, 10% SiC and 2.5%, 5% and 10% TiB 2 . The results indicate that the hardness value increases with the addition of the SiC and TiB 2 reinforcements to matrix Al6061, while the wear resistance increases up to certain amount and reduces drastically when crossed the transition load.

Comparison of Fuzzy AHP and Fuzzy TOPSIS Methods for Math Teachers Selection

Abstract: Math teachers’ selection is a multi-criteria evaluation decision and has a strategic importance for many institutions. The conventional methods for Math teachers’ selection are inadequate for dealing with the imprecise or vague nature of linguistic assessment. To overcome this difficulty, fuzzy multi criteria decision-making methods are proposed. The aim of this study is to use Fuzzy Analytic Hierarchy Process (F.AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F.TOPSIS) methods for the selection of Math teachers’ in education and institutions. The proposed methods have been applied to a Math teachers’ selection problem of education in Iran. After determining the criteria that affect the Math teachers’ decisions, fuzzy AHP and fuzzy TOPSIS methods are applied to the problem and results are presented. The similarities and differences of two methods are also discussed.

Prediction of Heart Diseases and Cancer in Diabetic Patients Using Data Mining Techniques

Abstract: Background: The heterogeneous, chronic diseases like heart diseases and cancer are commonly occur and increased nowadays in diabetic patients. Most of the people do not know the symptoms of these diseases and its chronic complications. Objective: The aim of this paper is to predict the diseases such as heart diseases and cancer in diabetic patients. The association between these diseases can be analyzed based on the factors that cause these diseases which include obesity, age, associated diabetic duration, and some other life style factors. Methods: This work consists of two stages. In the first stage, the attributes are identified and extracted using Particle Swarm Optimization (PSO) algorithm. In the second stage, ANFIS (Adaptive Neuro Fuzzy Inference System) with Adaptive Group based K-Nearest Neighbor (AGKNN) algorithm has been used to classify the data. Findings: The experimental results show a very good accuracy and signify the ANFIS with AGKNN along with feature subset selection using PSO. The performance is evaluated using performance metrics and proved this classifiers efficiency for the prediction of heart disease and cancer in diabetic patients. Application/ Improvement: This work demonstrates the diagnosis of diseases and its importance to predict it earlier. In future it can be implemented for other related diseases in medical data mining and healthcare.

Smart Funding Options for Developing Smart Cities: A Proposal for India

Abstract: With the advent of proposed Smart Cities for the issues like limited resource, population growth and climatic changes which will help India achieving the holistic development of the economy by achieving economic feasibility and sustainable growth through integration of design and technology. The proposed structure will help the Indian Economy to provide long term funding and employment opportunities in future. The rapid urbanization of Indian Economy will put immense pressure on the different facets of life such as infrastructure, managing finances, and quality of life which will lead to evolution of smart concepts and models. The proposed structure for smart cities will address four fundamental areas as organizational, infrastructure, social and economic aspects. The Government of India has allocated 70.6 billion (USD 1.2 billion) for Smart Cities in Budget 2014-15 for developing 100 smart cities over a period of 5 years. The Smart City Mission will be operated as a Centrally Sponsored Scheme (CSS) and the Central Government proposes to give financial support to the Mission to the extent of Rs. 48,000 crores over five years i.e. on an average Rs. 100 crores per city per year. An equal amount, on a matching basis, will have to be contributed by the State/ULB; therefore, nearly Rupees one lakh crores of Government/ULB funds will be available for Smart Cities development. The paper proposes the various sources of funding available in the world economy and their feasibility considering the Indian Economy which can be adopted for generation of required funds. The paper focuses on evaluating the various funding options and their feasibility with reference to Indian perspective.