Genetic algorithm (GA) based feature selection method is an evolving search heuristic, used to provide solutions to optimization problems. Feature selection is an important aspect that improves classification accuracy. The main objective of this work is to utilize GA for feature selection by integrating it with a bank of multi-class Support Vector Machine (SVM) for identification of the effective feature set. The proposed GA based approach finds its application in epileptic seizure detection. EEG dataset containing artefacts and noise were removed by employing constrained Independent Component Analysis (cICA) and Stationary Wavelet Transform (SWT). The features of the input data are constructed in the form of feature vector by FastICA technique. The fitness calculation for the selection of individuals in the GA is calculated by a Linear Discriminant Analysis (LDA) classifier. The multi-class Support Vector Machine (SVM) (one-against-all) classifier is used for the validation of the selected features. The samples are taken from 948 patients and the classes are divided as normal, seizure, and seizure-free using artificial neural networks. Experimental results demonstrate that the GA - multi-SVM feature selection technique can achieve higher accuracies as compared to the case without feature selection.

Wireless Sensor Network (WSN) consists of spatially distributed and dedicated sovereign sensor nodes with confined resources to politely monitor physical and environmental conditions. In recent years, there has been a rising interest in WSN. One of the major confrontations in WSN is developing an energy-efficient routing protocol to enhance the network longevity. With that concern, this work contributes in providing a novel approach called DAO-LEACH (Data Aggregation based Optimal- LEACH) by which the energy efficient routing in WSN is attained based on effective data ensemble and optimal clustering. Aggregating the data sent by cluster members comprehend in draining network load and amending the bandwidth. In order to minimize the energy dissipation of sensor nodes and optimize the resource utilization, cluster head is elected for each cluster. Moreover, the energy efficient route in WSN is obtained by combining the nodes having maximum residual energy. Experimental results have shown that the proposed approach furnishes efficient route for data transmission among the sensor nodes in an adept manner, thereby prolonging the network lifetime.

The prodigious growth of internet as an environment for learning has led to the development of enormous sites to offer knowledge to the novices in an efficient manner. However, evaluating the quality of those sites is a substantial task. With that concern, this paper attempts to evaluate the quality measures for enhancing the site design and contents of an e-learning framework, as it relates to information retrieval over the internet. Moreover, the proposal explores two main processes. Firstly, evaluating a website quality with the defined high-level quality metrics such as accuracy, feasibility, utility and propriety using Website Quality Assessment Model (WQAM) and secondly, developing an e-learning framework with improved quality. Specifically, the quality metrics are analyzed with the feedback compliance obtained through a Questionnaire Sample (QS). By which, the area of the website that requires improvement can be identified and then, a new e-learning framework has been developed with the incorporation of those enhancements.

In recent years, lung tumor diagnosis and the projection of tumor segmentation in 3D has gained significant momentous in the therapeutic field. Establishing the dissimilarity exists in the three dimensional volume representation of tumor cells affords more information, which can sharpen the treatment of a multiplicity of tumors. The volume reconstruction information is indispensable in the case of surgical operations. This process introduces a contour based segmentation algorithm to acquire the appropriate differentiation of pixel boundary that scrutinizes the exact difference between tumor and non tumor cells along the tumor boundary. With the aid of aforementioned formulation, extracted tumor part pixels are reconstructed for the entire 2D slices of the patient data set. Proposal work on 3D voxel reconstruction relies on encountering the isosurfaces. Originally, volume data are subjected to the smoothening process which computes the isosurface data from the smoothened volume data. The generated outcome of this process comprises the vertices and faces of the isosurfaces and directly flows to patch the data. Exploit the 3D reconstructed model to enumerate the voxel damaged by tumor. Proposal work associated with the percentage of damaged voxel along with accurate and reliable perception, simplifies the physician task in lung tumor diagnosis and assist the surgical procedure. Experimental evaluations across the wide range of images show the superiority of the proposed work with the classification accuracy rate of 99.33%.

This paper aims to reveal a comparative analysis of classifier performance of MR brain images, particularly for the brain tumor detection and classification. The detection of brain tumor stands in need of Magnetic Resonance Imaging (MRI). The moment invariant feature extraction has been evaluated to categorize the MRI Slices as Normal, Benign and Malignant by Neural Network Classifier. In our comparative study, we examine the precision rate of aforementioned classification with extracted features and the classification of brain images with selected features by association rule based neural network classifier. The results are then analyzed with Receiver Operating Characteristics (ROC) curve and compared to illustrate the method producing higher accuracy rate in tumor recognition. Factually, our analysis proves that the classifier works below feature extraction followed by rule pruning method affords better accuracy rate.

Utilization of hydrogen energy has many striking attributes, including energy renewability, flexibility, and nil greenhouse emissions Hydrogen is considered to be the most viable energy carrier for the future. In order to enhance the production of hydrogen from plasma electrolysis of water, the capability of diode pumped solid state laser (DPSS) with second harmonic of wavelength 532 nm has been investigated. Different acids and bases have been used as catalyst. A comparative study of acids and bases has been ascertained as a function of different laser constraints. The efficiency of bases was found to be greater than the acids. Different factors such as laser power, temperature variations and laser irradiation time have been drawn attention during experiment. The simulation results were found to be in good agreement with experimental data.

Sorption behavior of anthracene onto Nigerian montmorillonite clay mineral was investigated by concentration variations under constant pH, time and temperature. The amount of anthracene sorbed was determined spectrophotometrically. The metal oxide compositions, specific surface area, pH, point of zero charge (PZC), specific gravity, percentage moisture, loss in ignition and cation exchange capacity (CEC) of the natural clay were determined. The results of the physico – chemical analysis showed an improvement on the adsorption capacity of the montmorillonite clay. The sorption isotherm fitted well with Langmuir isotherm model equation with correlation coefficient (R2) of 0.987. The monolayer sorption capacity revealed that the sorbent has affinity for anthracene and the negative value of the Langmuir parameter; KL related to energy confirmed the physical nature of the sorption process. The results obtained showed that natural clay mineral was reasonably effective sorbent for the removal of anthracene (organic contaminant), which is an important source of environmental pollution.