IPv6 has features, like a) "no header checksum calculation" and b) "no IP packet fragmentation at intermediate routers", which makes it better than IPv4 from router/routing point of view. Existing Internet technology supports both IPv6 and IPv4 protocols for transport of packets and hence dual addressed machines are widely present. Maximizing QoS in IPv6 networks, as compared to IPv4 networks, for sites having dual addresses is an active area of research. Results of our study on QoS gains in networks connected to IPv6 Internet as compared to IPv4 Internet for a network of about 2500 nodes are presented here. The technique used to estimate QoS gains in the migration from IPv4 to IPv6 is also presented. The test-bed data of one month with 25000 most visited websites was analyzed. The results show that an alternate IPv6 channel exists for a large number of major global websites and substantial QoS gains in terms of reduced access times – averaging up to 35% for some websites - can be expected by intelligent per site IP address selection for dual stack machines.

Multi-party contract signing (MPCS) is a way for signers to agree on a predetermined contract by exchanging their signature. This matter has become crucial with the growing number of communications. In this paper, we focus mainly on studying the state of the art protocols and more specifically the cryptography involved. We identify the major advances in MPCS, highlight a few gaps with the current protocols and propose an algorithm for contract signing to be abuse-free, optimistic for many signers in industrial standards.

Text classification is a method which involves managing and processing important information that can be categorized into predefined classes within a collection of text data. This method plays a vital role in the field of information processing and information retrieval. Different approaches to text classification specifically based on machine learning algorithms have been discussed and proposed in various research works. This paper discusses a classification approach based on the frequencies of some important text parameters and classifies a given text accordingly into one among multiple categories. Using a newly defined parameter called wf-icf, classification accuracy obtained in a previous work was significantly improved upon.

In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

Nowadays, various Wireless Health Monitoring Systems use Internet of Things to transmit patient's data over Wireless Sensor Network and then the data is stored and processed via Cloud Computing, however, the use of different kind of Wireless Sensor on each system leads to power efficiency problem. This paper analyses and compares the consumption of power on six Wireless Health Monitoring Systems, which are invented to monitor the patient's condition and transfer the data using Wireless Sensor Network. Three different techniques are analyzed, namely GPRS/UMTS (used in one WHMS), Wi-Fi (used in one WHMS), and Bluetooth (used in four WHMS). This paper concludes that the systems that use Bluetooth as their transmission medium are more effective in reducing power consumption than the other systems that use GPRS/UMTS or Wi-Fi.

Feature extraction task are primarily about making sense of the discriminative features/patterns of facial information and extracting them. However, most real world face images are almost always intertwined with imaging modality problems of which illumination is a strong factor. The compensation of the illumination factor using various illumination compensation techniques has been of interest in literatures with few emphasis on the adverse effect of the techniques to the task of extracting the actual discriminative features of a sample image for recognition. In this paper, comparative analyses of illumination compensation techniques for extraction of meaningful features for recognition using a single feature extraction method is presented. More also, enhancing red, green, blue gamma encoding (rgbGE) in the log domain so as to address the separability problem within a person class that most techniques incur is proposed. From experiments using plastic surgery sample faces, it is evident that the effect illumination compensation techniques have on face images after pre-processing is highly significant to recognition accuracy.

Software effort estimation is the process of predicting the number of persons required to build a software system. Effort estimation is calculated in terms of person per month for the completion of a project. If any new project is launched into a market or in industry, then cost and effort of a new project will be estimated. In this context, a number of models have been proposed to construct the effort and cost estimation. Accurate software effort estimation is a challenge within the software industry. In this paper we propose a novel method, Grey Relational Analysis (GRA) to estimate the effort of a particular project. To estimate the effort of a project, traditional methods have been used as algorithmic models to evaluate the parameters of the basic model i.e. basic COCOMO model. In this paper, to show the minimum error rate we have used Grey Relational Analysis (GRA) to predict the effort estimation on Kemerer dataset. When compared to the traditional techniques for estimation, the proposed method proved better results. The efficiency of the proposed system is illustrated through experimental results.

Target searching is one of the challenging research areas in defense. Different types of sensor networks are deployed for searching targets in critical zones. The selection of optimal strategies for the sensor nodes under certain constraints is the key issue in target searching problem. This paper addresses a number of target searching problems related to various defense scenarios and introduces new strategic approaches to facilitate the search operation for the mobile sensors in a two-dimensional bounded space. The paper classifies the target searching problems into two categories: preference-based and traversal distance based. In the preference based problems, the strategies for the mobile sensors are determined by Stable Marriage Problem, College Admission Problem, and voting system; they are analyzed with suitable examples. Alternatively, traversal distance based problems are solved by our proposed graph searching approaches and analyzed with randomly chosen examples. Results obtained from the examples signify that our proposed models can be applied in defense-related target searching problems.