Global Journal of Computer Science and Technology (GJCST)
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Energy Efficient Cluster based Multipath Routing in Wireless Sensor Networks
Get PDFWireless sensor network can be defined as a network of densely deployed sensor nodes. These sensor nodes have limited energy and have low processing and storage capabilities. Due to this, we require energy efficient routing protocols so that much of the energy of the nodes is not wasted in routing of data packets. In this paper, we present and energy efficient routing scheme. This routing protocol is a combination of cluster-based routing and multipath routing. We arrange all the sensor nodes in the network in the form of small clusters. Each of these clusters has a cluster head. Nodes which lie within a cluster send its data to its respective cluster head. The transfer of data from nodes to cluster head is through direct communication. All the cluster heads transfer their data to the sink or base station. This transfer of data is through multipath routing
An Effective Authentication Scheme for Distributed Mobile Cloud Computing Services using a Single Private Key
Get PDFMobile cloud computing comprises of cloud computing, mobile computing and wireless network. Providing secure and convenience for the mobile users to access multiple cloud computing services is essential. This paper furnish an effective way of providing the authentication for the mobile users to access multiple cloud computing services. The proposed scheme outfit a secure and expediency for mobile users to access several cloud computing services from multiple service providers using a single private key. Our proposed scheme is based on bilinear pairing cryptosystem. In addition, the scheme also supports mutual authentication, key exchange, user anonymity. To overcome the vulnerabilities of traditional methods, from system implementation point of view, the proposed scheme eliminates the usage of verification tables that are required to store the user credentials(user ID and password) which are the part of smart card generator service and cloud computing service provider
A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining
Get PDFThe data generated contemporarily from different communication environments is dynamic in content different from the earlier static data environments. The high speed streams have huge digital data transmitted with rapid context changes unlike static environments where the data is mostly stationery. The process of extracting, classifying, and exploring relevant information from enormous flowing and high speed varying streaming data has several inapplicable issues when static data based strategies are applied. The learning strategies of static data are based on observable and established notion changes for exploring the data whereas in high speed data streams there are no fixed rules or drift strategies existing beforehand and the classification mechanisms have to develop their own learning schemes in terms of the notion changes and Notion Change Acceptance by changing the existing notion, or substituting the existing notion, or creating new notions with evaluation in the classification process in terms of the previous, existing, and the newer incoming notions. The research in this field has devised numerous data stream mining strategies for determining, predicting, and establishing the notion changes in the process of exploring and accurately predicting the next notion change occurrences in Notion Change. In this context of feasible relevant better knowledge discovery in this paper we have given an illustration with nomenclature of various contemporarily affirmed models of benchmark in data stream mining for adapting the Notion Change
Chaotic Sequence based Steganography for Pair-Wise Communication
Get PDFSteganography is the art and science of hiding sensitive data inside an image. There are so many cryptosystems that use Steganography as a major tool. Also in recent years there is a rising trend towards chaotic sequence based cryptosystems. This paper attempts to combine the two with a new algorithm for data hiding. Here key images required for Steganography are generated using chaotic sequence. Also an attempt is made to overcome the limitations of Steganography on the file size ratio and the security offered by Steganography
Comparative Analysis: Heart Diagnosis Classification using BP-LVQ Neural Network Models For Analog and Digital Data
Get PDFDecades onwards companies are creating massive data warehouses to store the collected resources. Even though the stored resources are available, only few companies have been able to know that the actual value stored in the database. Procedure used to extract those values is known as data mining. We use so-many technologies to apply this data-mining technique, artificial neural network(ANN) also includes in this data-mining techniques ,ANN is the information processing units which are similar to biological nervous systems. Backpropagation is one of the techniques that used for classification and LVQ (learning Vector Quantization) can be plotted under the competitive learning scheme which is also used for classification. This paper elaborates artificial neural networks, its characteristics and working of backpropagation and LVQ algorithms. In this paper we show the intriguing comparisons between backpropagation and LVQ (Learning Vector Quantization) for both analog and digital data. It also attempts to explain the results between back-propagation and LV
Review of Contemporary Literature on Machine Learning based Malware Analysis and Detection Strategies
Get PDFAbstract: malicious software also known as malware are the critical security threat experienced by the current ear of internet and computer system users. The malwares can morph to access or control the system level operations in multiple dimensions. The traditional malware detection strategies detects by signatures, which are not capable to notify the unknown malwares. The machine learning models learns from the behavioral patterns of the existing malwares and attempts to notify the malwares with similar behavioral patterns, hence these strategies often succeeds to notify even about unknown malwares. This manuscript explored the detailed review of machine learning based malware detection strategies found in contemporary literature
Low Power High Gain Op-Amp using Square Root based Current Generator
Get PDFA very high gain two stage CMOS operational amplifier has been presented The proposed circuit is implemented in 180nm CMOS technology with a supply voltage of 0 65V The current source in the OPAMP is replaced by a square root based current generator which helps to reduce the impact of process variations on the circuit and low power consumption due to the operation of MOS in subthreshold region So with the help of square root based current generator the better controllability over gain can be obtained The proposed opamp shows a high gain of 121 9dB and low power consumption of 11 89uW is achieve
Fast Search Approaches for Fractal Image Coding: Review of Contemporary Literature
Get PDFFractal Image Compression FIC as a model was conceptualized in the 1989 In furtherance there are numerous models that has been developed in the process Existence of fractals were initially observed and depicted in the Iterated Function System IFS and the IFS solutions were used for encoding images The process of IFS pertaining to any image constitutes much lesser space for recording than the actual image which has led to the development of representation the image using IFS form and how the image compression systems has taken shape It is very important that the time consumed for encoding has to be addressed for achieving optimal compression conditions and predominantly the inputs that are shared in the solutions proposed in the study depict the fact that despite of certain developments that has taken place still there are potential chances of scope for improvement From the review of exhaustive range of models that are depicted in the model it is evident that over period of time numerous advancements have taken place in the FCI model and is adapted at image compression in varied levels This study focus on the existing range of literature on FCI and the insights of various models has been depicted in this stud
Performance Assessment of WhatsApp and IMO on Android Operating System (Lollipop and KitKat) during VoIP Calls using 3G or WiFi
Get PDFThis paper assesses the performance of mobile messaging and VoIP connections. We compared the CPU requirements of WhatsApp and IMO under different scenarios. This analysis also enabled a comparison of the performance of these applications on two Android operating system (OS) versions: KitKat or Lollipop. Two models of smartphones were considered, viz. Galaxy Note 4 and Galaxy S4. The applications behavior was statistically investigated for both sending and receiving VoIP calls. Connections have been examined over 3G and WiFi. The handset model plays a decisive role in CPU requirements of the application. t-tests shown that IMO has a statistical better performance that WhatsApp whatever be the Android at a significance level 1%, on Galaxy Note 4. In contrast, WhatsApp requires less CPU than IMO on Galaxy S4 whatever be the OS and access (3G/WiFi). Galaxy Note 4 using WiFi has always better performance than S4 in terms of processing
Human Face Detection and Segmentation of Facial Feature Region
Get PDFHuman face, facial feature detection and Segmentation have attracted a lot of attention because of their wide applications. In computer-human interaction, face recognition, video surveillance, security system and so many application use automatic face detection. This paper is about a study of detecting human faces within images and segmenting the face into numbered regions which are the face-, mouth-, eyes- and nose regions respectively. For face detection we have used the Viola2013;Jones object detection framework. Sometime the VJOD make a false frame of object detection. Here trying to detect the problem of identification and improve the detection quality by changing the threshold value. It detect the frontal face of human which is 2D. From detected face image we separate the extracted part of face in a single image and Segment nose, eyes, lip and hole face portion by Discontinuous based Image Segmentation. The development and experiments demonstration of this research is done on MATLAB 2013. The learning behavior of the algorithm was tested on different face of human