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Basic data that show effect of irrigation on the cyclic fatigue resistance of the two rotary endodontic instruments

Data nowadays is the language of technologies as every process needs a data to be processed the input is data and the output also is data. Analyzing the data is a significant task especially with the increasing production of the data particularly data as a text, it would be difficult to manually analyze the data, extract information and detect the hidden patterns from unstructured text. Data mining is automated technique for gathering or deriving a new high-quality information and uncover the relations among the data. Text mining is one of main branches of the data mining however data mining is more comprehensive this paper, an overview for mining the publication papers via text mining techniques and their results and evaluation would be presented as following: the first approach is keywords extraction using natural language processing (NLP) approach, the second approach named entity recognition and the last approach is document clustering where machine learning techniques are applied to the both of them

Aim/Purpose: This research investigates the influence of soft skills on graduates’ employability in the technology industry, using the technology industry sector in Malaysia as a case. Background: Organizations are looking for appropriate mechanisms to hire qualified employees with strong soft skills and hard skills. This requires that job candidates possess a set of qualifications and skills which impact their employability. Methodology: Fuzzy Delphi analysis was conducted as preliminary study to identify the critical soft skills required by technology industry sector. The preliminary study produced ten critical soft skills to form a conceptual model of their influence on employability. Then, an online questionnaire survey was distributed in two industry companies in Malaysia to collect research data, and regression analysis was conducted to validate the conceptual model. Contribution: This research focuses on the influence of soft skills on graduate employability in the technology industry sector, since the selection of the best candidate in the industry will improve employee performance and lead to business success. Findings: The results of regression analysis confirmed that Communication skills, Attitude, Integrity, Learnability, Motivation, and Teamwork are significantly correlated with employability, which means that these soft skills are the critical factors for employability in Malaysian technology companies. Recommendations for Practitioners: The model proposed in this article can be used by employers to give better assessment of candidates’ compatibility with the jobs available. Impact on Society: This research highlights the critical soft skills required by technology industry sector, which will reduce the unemployment percentages among graduates. Future Research: More studies are required to examine the soft skills found in the literature and to define the most important skills from a general perspective of the industry. Future research should assess the moderating role of other variables, such as skills gap, employee performance, and employee knowledge. Furthermore, it is recommended to conduct similar studies of soft skills for employability in other countries.

Tourism-related websites have turned into an incredible data source that impacts the tourism industry from many points of view. Tourists express their opinions regarding products and services online daily. The interest in understanding and analyzing customer opinions has increased significantly over the past few years as it is vital for the decision making of both customers and companies. Sentiment analysis is the practice of applying natural language processing, statistics and machine learning methods to extract and identify the common opinion behind the text in a review, blog discussion, news, comments or any other document. Sentiment analysis has great potential to directly understand tourists’ opinions. This paper tackles a comprehensive overview of the latest update in this field. The main target of this survey is to give a nearly full image of sentiment analysis approaches, techniques, and challenges in analyzing the correct meaning of sentiments and detecting the suitable sentiment polarity in the field of tourism.

Each individual has his own distinct character, making his own decisions which is based on his personality. Researchers in computer science field have tried to reach a model for extracting personality traits relying on user’s profiles on social network sites as an input. Content created by users such as text posts, photos and shared activities in social network sites are considered as a huge source of data. Regarding user-created text, it has been proved that text pre-processing has a great impact if was applied to text before using it in research. In this paper, the effect of pre-processing (stemming and stop word removal) and adding numerical features is tested on the performance of Arabic personality prediction using AraPersonality dataset, which yielded 3.0% and 6.7% overall improvement to baseline experiments in binary representation and multiclass representation respectively

As everything is shifting online, the demand for sentiment analysis has expanded tremendously in the recent years. Sentiment analysis is an automated process of analyzing people’s opinions and feelings using natural language processing tools. Organizations in the field of tourism can benefit from sentiment analysis to accurately track their customers’ opinions. A deep learning-based approach is introduced in this paper that automatically performs sentiment analysis for hotel reviews using word embedding and gated recurrent unit. The performance of our deep learning-based model outperformed the performance of the traditional machine learning methods in sentiment classification of hotel reviews with 89% accuracy and 92% F-score.

The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using the ML techniques. Then a case study is built to simulate the system phases, showing what is the input and the output in each phase.

Objectives To measure the expression levels of microRNA-451 (miR-451) in patients with chronic myeloid leukemia (CML) and to correlate miR-451 expression levels with response to imatinib mesylate (IM)-based therapeutic protocol. Background miRNAs are regulatory RNAs that have been classified as crucial players of cancer initiation and progression. Recent studies have illustrated an association between certain hematological malignancies and dysregulated miRNAs expressions; other studies have focused on the utilization of miRNAs as molecular biomarkers of cancer detection, outcome, and response to therapy. Patients and methods Real-time quantitative PCR technique was used to estimate the plasma miR-451 expression levels of 10 newly diagnosed patients and 30 patients with CML who received first-line therapy with imatinib. European Leukemia Net-recommended response criteria were used to classify patients according to their response to imatinib treatment. Results Our results revealed up-regulation of miR-451 expression levels in imatinib treated as compared with newly diagnosed patients with CML (P = 0.003). MiR-451 expression was significantly upregulated in IM-responder when compared with the IM-resistant group (P < 0.001). Moreover, we demonstrated that miR-451 was significantly down-regulated in IM-resistant in comparison with the healthy control group (P < 0.001), whereas no significant difference was detected between IM-responder group and healthy controls (P = 0.620). Conclusion miR-451 can be used as a promising biomarker in patients with CML for prediction of imatinib treatment response and may be employed as a novel therapeutic tool.

In search of plants rich in phenolics in Egypt, Kalanchoe marmorata Baker was subjected to phytochemical study. The preliminary phytochemical screening revealed its richness in phenolics. Fractionation of the lyophilized aqueous extract of the leaves of K. marmorata by different organic solvents successively resulted in the isolation and purification of five compounds from the ethyl acetate soluble fraction. These compounds namely; E1 isorhamnetin-3- O -α- l - 1 C 4 -rhamnopyranoside; E2 quercitin; E3 4′-methoxy-myricetin-3- O -α- l - 1 C 4 -rhamnopyranoside; E4 Quercitin-3- O -β- d - 4 C 1 -glucopyranoside and E5 protocatechuic-4′- O -β- d - 4 C 1 -glucopyranoside, were identified by analysis of their spectral data including 1 H NMR and 13 C NMR.