International Conference on Computer and Communication Technology 

About: International Conference on Computer and Communication Technology is an academic conference. The conference publishes majorly in the area(s): Cloud computing & Routing protocol. Over the lifetime, 562 publications have been published by the conference receiving 4848 citations.

Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns

Abstract: Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of apple fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.

From Internet of Things towards cloud of things

Abstract: Since the late 1980s the world is working towards connectivity and convergence. In the last three decades, the convergence of information resources has happened. However to achieve a true convergence the information assets have to be shared, used and executed fruitfully by the various gadgets which we use in our daily lives. Internet of Things is a concept which leverages on the power of networks to create ubiquitous sensor-actuator networks. With the advent of the cloud technologies, the concept of IOTs can be integrated with even the basic elements having limited computing power. This paper aims to evaluate the possibilities offered by integrating the two concepts of IOTs and Cloud Computing.

Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects

Abstract: Privacy preserving has originated as an important concern with reference to the success of the data mining. Privacy preserving data mining (PPDM) deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data. People have become well aware of the privacy intrusions on their personal data and are very reluctant to share their sensitive information. This may lead to the inadvertent results of the data mining. Within the constraints of privacy, several methods have been proposed but still this branch of research is in its infancy. The success of privacy preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms etc. However no privacy preserving algorithm exists that outperforms all others on all possible criteria. Rather, an algorithm may perform better than another on one specific criterion. So, the aim of this paper is to present current scenario of privacy preserving data mining tools and techniques and propose some future research directions.

Decision support system for heart disease based on support vector machine and Artificial Neural Network

Abstract: The medical diagnosis process can be interpreted as a decision making process, during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and from his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and fast way. This paper presents a decision support system for heart disease classification based on support vector machine (SVM) and Artificial Neural Network (ANN). A multilayer perceptron neural network (MLPNN) with three layers is employed to develop a decision support system for the diagnosis of heart disease. The multilayer perceptron neural network is trained by back-propagation algorithm which is computationally efficient method. Results obtained show that a MLPNN with back-propagation can be successfully used for diagnosing heart disease than support vector machine.

A survey on scheduling and load balancing techniques in cloud computing environment

Abstract: Now-a-days cloud computing is the most emerging technology due to its elasticity of resource provisioning and the pay-as-you-go pricing model which enables users to pay only according to their need. As cloud can be accessed anytime and anywhere through commodity hardware only its demand is increasing day by day. So it must provide high performance gain to the user and at the same time must be beneficial for the Cloud Service Provider (CSP). To achieve this goal many challenges have to be faced. Load balancing is one of them which helps the CSP to meet the QoS requirements of the users and at the same time maximize his profit by optimum use of the resources. To balance the load in cloud the resources and workloads must be scheduled in an efficient manner. A variety of scheduling algorithms are used by load balancers to determine which backend server to send a request to. The selected server allocates resources and schedules the job dynamically on some virtual machine (VM) located on the same physical machine. It is also the responsibility of the provider to dynamically reallocate or migrate the VM across physical machines for workload consolidation and to avoid over utilization or under utilization of resources. In this paper, we have discussed different algorithms proposed to resolve the issue of load balancing and task scheduling in Cloud Computing. We have mentioned some of their shortcomings for further development. VM migration issues involved in load balancing are also described briefly.