A Noval Approach for Web Page Ranking Based on Weights of Links

As the web is the large collection of the information and also due to the changing content/nature of the web (plenty of pages or documents and pages are newly added and deleted on the time basis).The information present on the web is of great need, the world is full of questions and the web is serving as the major source of gaining information about specific query made by the user. As per the search engine for the query made a number of pages are retrieved among which the quality of the page that are retrieved is questioned. On the pages retrieved the search engine apply the certain algorithms to bring a order to the pages retrieved so that the most relevant document or pages are displayed at the top of list. Page ranking is done on the basis of the different approaches as the content based approaches, link based approaches. This paper will provide a review to few of the linked based page ranking algorithms. DOI: 10.17762/ijritcc2321-8169.15084

Cost engineering for manufacturing: current and future research

The article aims to identify the scientific challenges and point out future research directions on Cost Engineering. The research areas covered in this article include Design Cost; Manufacturing Cost; Operating Cost; Life Cycle Cost; Risk and Uncertainty management and Affordability Engineering. Collected information at the Academic Forum on Cost Engineering held at Cranfield University in 2008 and further literature review findings are presented. The forum set the scope of the Cost Engineering research, a brainstorming was held on the forum and literatures were further reviewed to understand the current and future practices in cost engineering. The main benefits of the article include coverage of the current research on cost engineering from different perspectives and the future research areas on Cost Engineering

Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index

Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset. Feature selection removes the irrelevant features, which improves both the accuracy and prediction performance. The significant advantages of reducing the number of features from the dataset are reducing the training time, reducing overfitting, decreasing the curse of dimensionality, and simplifying the prediction model. The filter feature selection techniques can handle the issues with the high number of features, and this paper uses the symmetric uncertainty coefficient to verify the relevance of the independent features. In this paper, a new feature selection method named as kurtosis-based feature selection has been proposed to select the relevant features which affect the air pollution. Kurtosis-based feature selection is compared with seven filter feature selection techniques on air pollution dataset and validated the performance of the proposed algorithm. It has been observed that the kurtosis-based feature selection extracts only PM2.5 as the key feature and has been compared to the accuracy of the five existing methods. The experimental results illustrate that the kurtosis-based feature selection algorithm reduces the original feature set up to 91.66\%, but the existing filter feature selection techniques reduce the feature set to only 50\%

Influence of information product quality on informing users: A web portal context

Web portals have been used as information products to deliver personalized, feature-rich, and flexible information needs to Internet users. However, all portals are not equal. Most of them have relatively a small number of visitors, while a few capture the majority of surfers. This study seeks to uncover the factors that contribute the perceived quality of a general portal. Based on 21 factors derived from an extensive literature review on Information Product Quality (IPQ), web usage, and media use, an experimental study was conducted to identify the factors that are perceived by web portal users as most relevant. The literature categorizes quality factors of an information product in three dimensions: information, physical, and service. This experiment suggests a different clustering of factors: Content relevancy, Communication interactiveness, Information currency, and Instant gratification. The findings in this study will help developers find a more customer-oriented approach to developing high-traffic portals.ope

The contributions of image content and behavioral relevancy to overt attention

During free-viewing of natural scenes, eye movements are guided by bottom-up factors inherent to the stimulus, as well as top-down factors inherent to the observer. The question of how these two different sources of information interact and contribute to fixation behavior has recently received a lot of attention. Here, a battery of 15 visual stimulus features was used to quantify the contribution of stimulus properties during free-viewing of 4 different categories of images (Natural, Urban, Fractal and Pink Noise). Behaviorally relevant information was estimated in the form of topographical interestingness maps by asking an independent set of subjects to click at image regions that they subjectively found most interesting. Using a Bayesian scheme, we computed saliency functions that described the probability of a given feature to be fixated. In the case of stimulus features, the precise shape of the saliency functions was strongly dependent upon image category and overall the saliency associated with these features was generally weak. When testing multiple features jointly, a linear additive integration model of individual saliencies performed satisfactorily. We found that the saliency associated with interesting locations was much higher than any low-level image feature and any pair-wise combination thereof. Furthermore, the low-level image features were found to be maximally salient at those locations that had already high interestingness ratings. Temporal analysis showed that regions with high interestingness ratings were fixated as early as the third fixation following stimulus onset. Paralleling these findings, fixation durations were found to be dependent mainly on interestingness ratings and to a lesser extent on the low-level image features. Our results suggest that both low- and high-level sources of information play a significant role during exploration of complex scenes with behaviorally relevant information being more effective compared to stimulus features.publisher versio

Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network

The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms

Feature selection in credit risk modeling: an international evidence

This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers. As such, to examine the impact of the feature selection method on classifier performance, we use two Chinese and three other real-world credit scoring datasets. The utilized feature selection methods are the least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression trees (CART), logistic regression (LR), artificial neural network (ANN), and support vector machines (SVM). Empirical findings confirm that LASSO’s feature selection method, followed by robust classifier SVM, demonstrates remarkable improvement and outperforms other competitive classifiers. Moreover, ANN also offers improved accuracy with feature selection methods; LR only can improve classification efficiency through performing feature selection via LASSO. Nonetheless, CART does not provide any indication of improvement in any combination. The proposed credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding of this study has practical value, as to date, there is no consensus about the combination of feature selection method and prediction classifiers

Unsupervised graph-based feature selection via subspace and pagerank centrality

Feature selection has become an indispensable part of intelligent systems, especially with the proliferation of high dimensional data. It identifies the subset of discriminative features leading to better learning performances, i.e., higher learning accuracy, lower computational cost and significant model interpretability. This paper proposes a new efficient unsupervised feature selection method based on graph centrality and subspace learning called UGFS for ‘Unsupervised Graph-based Feature Selection’. The method maps features on an affinity graph where the relationships (edges) between feature nodes are defined by means of data points subspace preference. Feature importance score is then computed on the entire graph using a centrality measure. For this purpose, we investigated the Google’s PageRank method originally introduced to rank web-pages. The proposed feature selection method has been evaluated using classification and redundancy rates measured on the selected feature subsets. Comparisons with the well-known unsupervised feature selection methods, on gene/expression benchmark datasets, demonstrate the validity and the efficiency of the proposed method