Importance analysis of a blog quality model for criteria and families in different blog categories

A blog quality model has been proposed for bloggers to promote readers satisfaction. However, the model does not determine whether readers consider some quality criteria important, particularly with respect to different blog categories. In this paper, we employed a Rasch analysis to rank the importance of blog quality criteria for the Personal Diary, Socio-political Commentary, Humor/Entertainment, Lifestyle, and Technology blog categories. The authors identified the most important quality criteria and families for each category. The authors discovered that (1) the importance of quality criteria and/or families depends on the particular category with which a reader engages; (2) some quality criteria and/or families are more important for some categories but less important for others; and (3) certain quality criteria and/or families are equally important for some categories but not for others. The authors provide empirical evidence of the most important criteria bloggers and evaluators can focus on when they examine different blog categories. A blog quality model has been proposed for bloggers to promote readers satisfaction. However, the model does not determine whether readers consider some quality criteria important, particularly with respect to different blog categories. In this paper, a Rasch analysis to rank the importance of blog quality criteria for the Personal Diary, Socio-political Commentary, Humor/Entertainment, Lifestyle, and Technology blog categories. The authors identified the most important quality criteria and families for each category. The authors discovered that (1) the importance of quality criteria and/or families depends on the particular category with which a reader engages; (2) some quality criteria and/or families are more important for some categories but less important for others; and (3) certain quality criteria and/or families are equally important for some categories but not for others. The authors provide empirical evidence of the most important criteria bloggers and evaluators can focus on when they examine different blog categories

Application of Rasch model in validating the content of measurement instrument for blog quality

Research in blog quality is very crucial nowadays in order to have a good quality blog in the blogosphere. The blog quality criteria have been derived from a rigorous metadata analysis. Yet, these criteria have not been reviewed and their significance has not been proven systematically. In this paper, Rasch Model is applied to produce an empirical evidence of content validity of the blog quality criteria. This study confirms that the definitions of 11 families and the 49 criteria assigned have content validity by mean of online survey. These criteria will then be used as a basis of constructing the instrument to measure the acceptability of the criteria for blog quality

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely

Construction of Pilgrim Framework-Information Seeking Based on New Norm Selection Criteria of Hajj

The pilgrimage quota for each country is 0.1% of the total population. The demand to perform Hajj increases yearly and demands more quotas, but it is limited due to providing exemplary services and maintaining comfort for pilgrims. The selection process is challenging and informative. Upon registration, the Malaysian waiting period was between 89 and 116 years. With COVID-19 and the new norm, the waiting period will be much longer, making things worse and more ridiculous. This paper explains and proposes constructing a proper framework for fulfilling the need and selecting future candidates for Hajj in Malaysia

Blog quality model

Breakthroughs in technology are making the internet an ever-growing phenomenon, and we have witnessed an enormous growth of blogs in the blogosphere. However, the blogosphere has been crippled by disorganised and uncontrolled growth, and many blogs are of poor quality. Development domains, such as software engineering, website engineering, and information systems, have provided accepted models for the assessment of the quality of their products. However, to the best of our knowledge, there appears to be no standard model for the assessment of blog quality. In this paper, we propose a blog quality model as a guide for bloggers at large, with a set of 49 criteria grouped into 11 families of features that are relevant to blog quality. This model has been constructed by determining a set of criteria from a review of the relevant literature and blogs and then measuring the acceptability of these criteria by means of questionnaire surveys sent to sample populations of blog readers

A model for assessing personal blog quality

Technological breakthroughs have contributed to the Internet’s continuing growth, as witnessed in the significant growth of the blogosphere. Blogs may serve to provide information to accomplish important tasks or to keep readers informed on latest developments. However, the blogosphere has been crippled by its disorganized and uncontrolled growth, which affects the accuracy, context, representation, and accessibility of the medium. This will contribute to the problem of having poor quality blogs in the blogosphere given blog-readers’ easy accessibility. Consequently, blog has become a medium to distribute rumours and if the accusations involve the integrity of systems,institutions, and personalities, thus jeopardizing national security, it could lead to countrywide chaos. Various developmental domains, such as software engineering, website engineering, and information systems, have provided acceptable models to assess their product quality. However, some criteria of these models are irrelevant and inappropriate for assessing blog quality. In the blogosphere, researchers and bloggers have proposed guidelines, checklists, rules, and tips to create good quality blogs. Nevertheless, these criteria are only pertinent from the perspective of the blogger, not the readers. Thus, there is no evidence to show that the criteria are acceptable by the blog-readers. Many studies have been conducted to determine blog popularity, and credibility, but none of these focuses on blog quality. The aim of this research is to develop such a model to assess blog quality. First, the model was constructed by determining a set of criteria based on review of relevant literature and blogs. The acceptability of these criteria was subsequently measured through survey questionnaires sent to a sample of blog readers. A case study was conducted among the Personal Diary blog readers, the most popular blog category in Personal blog type, to validate the proposed model. The results show that the proposed model, comprising 49 criteria grouped into 11 families of features relevant to blog quality, was accepted and validated as a tool to assess blog quality. Second, case studies were conducted across five different Personal blog categories to analyse the importance of quality criteria. Our findings suggest that: (i) the importance of quality criteria or families of quality criteria depends on the respective blog category; (ii) certain quality criteria or families of quality criteria are more important for one blog category but less important for others; and (iii) some quality criteria or families of quality criteria are equally important for some blog categories but not for others. Third, a prototype of the Blog Quality Assessment Tool (BQAT) was successfully developed. Subsequently, a technology acceptance test using the Technology Acceptance Model (TAM) was conducted to investigate whether the prototype had been accepted by the blog readers. This study explored the impact of blog readers’ perceptions pertaining to ease of use, usefulness, attitude towards and intention to use the system to their liking. The findings indicate that the BQAT is an easy, effective, and useful method to help blog readers make a high quality assessment. This puts them in a positive frame of mind towards using the tool. Most importantly, blog readers suggest that bloggers should have the intention to participate in the blog quality assessment project. Hence, this research shows that the model can be used as a guide for blog readers to determine the quality of blogs visited. The model can also be used by bloggers to promote their readers’ satisfaction. This research not only implies that different blog categories should be designed using distinct quality criteria, in line with their relative importance to the respective category, but also recommends that blog categories should be assessed using different sets of quality criteria in accordance with their relative importance. Additionally, this research also provides a valid prototype of the BQAT that assists readers to assess blog quality. The prototype can also be used to manage and control a blog’s expansion, such that only high quality blogs continue to exist in the blogosphere

Blog Quality Assessment Tool (BQAT)

A blog quality model and guidelines to determine important features of different blog categories have been proposed to determine blog quality and to promote readers’ satisfaction. However, no tools have been developed to assist blog readers in the evaluation of their favorite blogs based on their blog satisfaction. This paper discusses each process in the development of the Blog Quality Assessment Tool (BQAT) in detail. The main functions of the BQAT are to calculate the probability of a blog to be of good quality based on blog-reader satisfaction, and to accumulate the results for the assessed blog. Thus, blog-readers can easily assess their favourite blogs and obtain information on the quality of the blogs visited. This study also shows that the more satisfied the blog is, the higher its quality

Software defect prediction harnessing on multi 1-dimensional convolutional neural network structure

Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer frominadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1- Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying andmodelling the knowledge in the data sequence, reduce overfitting, and finally, predict whether the units of code are defects prone. We design large-scale empirical studies to reveal the proposed model\u27s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets. The experimental results demonstrate that in terms of f-measure, an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79% and 23.88%, respectively, in ways that minimize overfitting and improving prediction performance for software defects. According to the results, 1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering. This, in turn, could lead to saving software development resources and producing more reliable software

Robust Malware Family Classification Using Effective Features and Classifiers

Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent

Blog quality measurement: Analysis of criteria using The Rasch Model.

Research in blog quality has become increasingly important, as preferences evolve in the way people gain information. For this study, blog quality categories and criteria were derived from metadata analysis and recent literature and then tested in two surveys. Rasch model analysis of responses provides systematic evidence of construct validity for the 11 quality categories and 49 criteria. The first survey, addressed to expert reviewers, supports the content aspect of construct validity, with one modification to a quality category. The second survey, given to blog readers, finds strong agreement with the quality items after the removal of three criteria because of redundancy. The second survey supports the substantive, structural, generalizability, external and consequential aspects of construct validity. These results constitute an important step toward development of a valid and widely applicable blog quality model