In this article, we will introduce a new assessment method of the quality performance of state-of-the-art Oracle ERP HCM implementations using the ISO 9126 standard. The goal is to build a systematic framework to evaluate the functionality, dependability, attractiveness, effectiveness, maintainability, and portability of Oracle ERP HCM systems. By utilizing the literature review and case study, this study investigates the applicability of the ISO/IEC 9126 within the Oracle ERP HR presentations, where the positive and negative features of the ISO/IEC 9126 are explored, and further improvements are proposed. The clear results of the evaluation are part of the evaluation process that underlines the applicability of the proposed methodology to locate spots that need improvements and drive a better ERP HCM implementation. The results emphasize the need for organizations to use ISO 9126 standards for assessing ERP system quality and thereby produce helpful clues to enrich the scope of ERP. Stepping into the direction of a consistent and systematic quality assessment process and using the ISO 9126 criteria, companies can achieve enhanced stability, efficacy, and reliability in their Oracle ERP HCM implementations and, as a result, successful and competitive business outlay.

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.

Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS) for efficient summarization of document, which achieves the aforementioned requirement. Here, the documents are preprocessed initially to remove the information that is less important. Summary of each preprocessed document is obtained through the sentence extraction process. Single document summarization is carried out based on graph model. A ranking method named Ingenious Ranking (IR) is proposed to rank and order the extracted single document summaries. It ranks the sentences in the generated summaries of each document and incorporates the individual summaries to generate a concise summary. Empirical results presented in this paper demonstrate the efficiency of the proposed AMDS framework.

Regression testing is a re-testing technique to test the changes, which is taken in the modified or enhanced application to ensure that the changes do not impairment the accessible behavior of the application. Modifications in the applications mainly focus on three types namely binding, process and interfaces. In order to accomplish the regression testing for a modified portion of an application, test cases are selected from a test suite. Selection, generation and prioritization of the test cases are more important and also it is a tough process in regression testing. In this article, we proposed a technique to automatically generate the test cases for testing the changes of various versions of BPEL (Business Process Execution Language) dataset. We construct a hierarchical test tree (HTT) for both the new and old versions composite services that are modified for an application and also for the unmodified. The changes are tracked by analyzing the control flow of both trees constructed above using the BPEL dataset. Test Case Prioritization Algorithm (TCPA), which uses multiple criteria are used to prioritize the tests cases. We analyzed the performance of the proposed technique and the experimental results showed that our method performs well than the earlier techniques.

Neural networks are a computational paradigm model of the human brain that has become popular in recent years. We have tried to address the problem of Glioma by creating a more accurate classifier which can act as an expert assistant to medical practitioners. Brain stem gliomas are now recognized as a heterogenous group of tumors. In this paper, proposed a prediction of Glioma in MR images using weight optimized neural network. Magnetic Resonance (MR) images are affected by rician noise which limits the accuracy of any quantitative measurements from the data. A recently proposed filter for rician noise removal is analyzed and adapted to reduce this noise in MR images. This parametric filter, named Non-Local Means (NLM), is highly dependent on setting its parameters. Experimental results reveal the efficacy of the adduced methodology as compared to the related work.

Effective and efficient image segmentation acts as a preliminary stage for the computer-aided diagnosis of medical images. For image segmentation, many FCM-based clustering techniques have been proposed. Regrettably, the existing FCM technique does not generate accurate and standardized segmentation results. This is due to the noise present in the image as well as the random initialization of membership values for pixels. To address this issue, this paper has enhanced the existing FCM technique and proposed a technique named Ameliorate FCM (AFCM). Initially, the given image is preprocessed to remove the noise using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. The preprocessed image is given as input to a Bayesian classifier to classify the images into two set namely normal and abnormal using a Hybrid feature selection method. The classified images are given as input to the proposed segmentation technique, which overcomes the drawbacks of existing FCM technique. Here, the membership value of the pixels of an image is standardized and clustered to segment the regions. Experiments are carried using lung images to determine the efficiency of the proposed technique. Results of the experiment show that the proposed technique outperforms the existing FCM technique.