Global Journal of Computer Science and Technology (GJCST)
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Capacity-aware Control Topology in MANET
Get PDFThe wireless mobile adhoc networks are dynamically Varying, the network performance may change by different unknown parameters such as the total number of nodes in the network, the transmission power range of the network and area of deployment of the network. The main aim is to increase the efficiency of the system through dynamically changing the trasmission range on every node of the network.contention index is the network performance factor is considered. we presented a study of the effects of contention index on the network performance, considering capacity of the network and efficiency of the power. The result is that the capacity is a concave function of the contention index. if the contention index is large the impact of node mobility is minimal on the network performance. we presented GridMobile, a distributed Network topology algorithm that attempts to shows the best possiblity, by maintaining optimal contention index by dynamically adjusting the transmission range on every nodes in the network
FREE HIT A Novel History based Reinforcement Approach for Fast Path Construction in Vehicular Ad hoc Networks
No full textVehicular ad hoc networks playing significant role in development of intelligent transport system and rise in demand for accessing fascinated applications such as entertainment and advertisements in vehicles via internet leverages to build efficient routing mechanisms. Due to rapid vehicles flow in Vanets, network often subjected to link breaks and creates delay in communication which affects overall network performance, hence to address these serious issue earlier approaches focused on parameters like rate estimation, feedback and link-expiration-time but being different from earlier our Instant Look up and Immediate Action(ILU-IA) technique uses adaptive learning thru past knowledge. ILU-IA identifies immediate neighbour (Free-Hit-Node) efficiently at point of link failure using Case- Based-Learning and Q-learning techniques. Simulation analysis shows that proposed approach performance improved in terms of throughput with negligible delay also reduces network overhead
Prediction of Total Electron Content using Nonlinear Autoregressive Models with eXogenous input Recurrent Neural Network
Get PDFA Survey on Network Security
Get PDFComputer security is one of the most expected factor in the curren
Software Development Top Models, Risks Control and Effect on Product Quality
Get PDFIn recent time considerable efforts have been made to improve the quality of software development process and subsequently the end product One of such efforts is finding a way to avoid or prevent risks in the overall process and where or when it is not possible to prevent risk alleviation readily comes handy Seve ral problem solving methods such as six thinking hat risk table and riskit analysis graph RAG applied along with generic models such as spiral waterfall prototyping and extreme programming have been used in the past to prevent risk and enhances both delivery time and product quality Howeve r some gaps were identified in the earlier works done in this area and in the generic models designed for evaluating and controlling risks prompting the development of modern ones Hence this work tries to investigate different types of risks and risk management models leaning on the gaps in research it attempts to create a framework for better risk prediction and alleviation with the aim of enhancing delivery time and product quality To enhance good understanding and reading of the work it has been structured into different sections It concludes on some recommendations for future research in this paradig
Dual Transition Region Extraction based Colour Image Segmentation: Application to Fish Image Segmentation
No full textImage segmentation using transition region has been quiet effective in recent years due to its simplicity Previous approaches using transition region only concentrate in segmentation of gray scale images Colour image segmentation using transition region approach is a challenging task due to the increase in complexity involving various colour components Here we have proposed a hybrid transition region approach for colour image segmentation Two existing transition region based approaches i Gabor based transition region approach and ii local variance based transition region approach are used to develop the proposed method Initially the R G B colour components are separated from the original image Gabor based transition region approach is applied to segment the texture features from the image The result of previous method is used as input to local variance based transition region approach for final object extraction from image The proposed method works effectively on variety of images containing both single and multiple objects The method is applied for fish image segmentation Experimental results revel that the proposed method outperforms many existing approache
Spatial Intelligence as Related to Success on Regular and Constrained Electronic Puzzle Formats
Get PDFThis paper is focused on how spatial learners perform on regular and constrained puzzles in an online scientific game. We used UNTANGLED, an interactive game to conduct the study presented in this manuscript. Players were presented a set of puzzles in both regular and constrained versions. The motivation behind this study was to examine the success rate of spatial learners in regular and constrained settings of the same puzzles. Our results suggest that spatially intelligent participants who played both regular and constrained puzzle format of the same game showed significant differences at the p=.05 level, indicating a level of spatial intelligence that is unprecedented. These participants showed signs of spatial intelligence necessary to solve electrical engineering problems. Our findings suggest a valuable use for electronic puzzles/games to determine which students are spatially intelligent, and potentially suited to engineering. In addition, teachers could use the data from spatially directed puzzles to challenge students to heighten levels of spatial intelligence by using puzzles in non-STEM environments
Discovery of Non-Persistent Motif Mixtures using MRST (Multivariate Rhythm Sequence Technique)
Get PDFIn this paper we present a prototype to discover the unsupervised repeating temporary perception in a time series. The purpose of this work is to control the case of random variable and to find out the measurements caused by the phenomena of simultaneous synchronization. The proposed model has used the non-parametric Bayesian technique to trace the motifs and their occurrences in the data documents. We introduce the Multivariate Rhythm Sequence Technique (MRST) method to find the rebound and repeated motifs and their instance in every document automatically and simultaneously. This model is used in wide range of applications and concentrates on datasets from different modalities.The video footages from non-dynamic cameras and data location bounded to the motif-mining server. The high semantic internal representation of the method gives advantage in operation such as event counting or analyse the sc8BA5;. We used the sample images and videos from New York City traffic data for experiments with and the results shows better performance than the existing motif mixtures analysis in the time series
A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves
Get PDFWith the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject