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Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the “context windows” of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation, and others. It is a task that first identifies appropriate syntactic categories for each word in running text and second assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work. Previously, we presented conditional random field (CRF)-based POS tagger with both language dependent and independent feature set. However, in the current study, we offer: 1) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and 2) to highlight diverse challenges which cause Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN), and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward long short-term memory (LSTM)-RNN and LSTM-RNN with CRF output.

Part of speech (POS) tagging, the assignment of syntactic categories for words in running text, is significant to natural language processing as a preliminary task in applications such as speech processing, information extraction, and others. Urdu language processing presents a challenge due to the dual behaviour of various Urdu POS tags in differing situations (morphosyntactic ambiguity). This paper addresses this challenge by developing a novel tagging approach using linear-chain conditional random fields (CRF). Our work is the first instance of a CRF approach for Urdu POS tagging. The proposed model employs a strong, stable and balanced language-independent as well as language dependent feature set. The language-dependent feature considered includes part-of-speech tag of the previous word and suffix of the current word while the language-independent features includes the ‘context words window’. Our approach was evaluated against support vector machine techniques for Urdu POS—considered as state of the art—on two benchmark datasets. The results show our CRF approach to improve upon the F-measure of prior attempts by 8.3–8.5%.

Background: Breast cancer is one of the most common malignant cancers in women around the world. Until now, despite great improvements in treatment the high incidence rate and mortality worldwide are exponential. Thus identification of novel molecular cancer drivers and evaluation of existing breast cancer biomarkers is a critical step. Objective:This study aims to explore one of the driven genes-ERBB2 receptor- a good predictive cancer biomarker, its specific TK receptor domain mutations, biological pathways, and oncoproteins challenging the apoptotic process, involved in the progression of breast cancerusingInsilicoapproach. Methods: We used the multiple advanced bioinformatics tools performing structure-based Domain architecture analysis with predicted functional protein association of ERBB2 gene and the role of post-translational modification-- in instigating TK receptor domain mutations providing an understanding of the underlying mechanism of tumor invasion and metastasis. Results:Considering all approaches as complementary the TK inhibitors and the role of the ERBB2 gene can improve drug targeting prediction strategy highlighting the importance of driver genes. Our results suggest that functional validation is a positive prediction and we provided answers to some of the imperative questions although important concerns in the field remain unresolved like the implications for therapeutics etc. for future biological and clinical accomplishments. Conclusion:The evaluation of the existing cancer candidate gene and the interacting proteins and pathways particularly ERBB2- downstream signaling pathway has the potential to generate novel hypotheses in oncology. Thusprovide a baseline to identify target protein-based pathways involved through wet experiments.&nbsp

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Background Oestrogen is the primary female sex hormone and has important functions in both female and male physiology. Recently oestrogen abuse among male gym participants had raised as it is considered to enhance gym performance and breast size. Aims This study assesses the prevalence of oestrogen use among male gym patrons in Saudi Arabia, their practice related to oestrogen abuse, and the profiles of users. Methods A cross-sectional study was conducted from February 2017 to May 2017 and included 4,860 male gym patrons. The participants were given a questionnaire with a total of 19 questions regarding socioeconomic information, knowledge and practices related to oestrogen, and lifestyle habits. Results The participants had a mean age of 28.6+6.2 years, 6.1 per cent of them abused oestrogen, and the most common forms used were ethinylestradiol (0.03mg) and drospirenone (3mg). Furthermore, 80.7 per cent of the users used it before exercise only. Breast enlargement was the main reason for oestrogen use, and local drug stores were the main source. Compared to non-users, oestrogen users were older (P=0.322), reported lower incomes (P=0.395), were more likely to be active smokers (P=0.597), and had a longer duration of gym participation (P < 0.001). Conclusion The results indicate that 6.1 per cent of the surveyed male participants abused a combination of oestrogen and progesterone for breast enlargement, which was significantly more likely among males who had longer durations of gym participation.

In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon’s point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

Decarbonisation, energy security and expanding energy access are the main driving forces behind the worldwide increasing attention in renewable energy. This paper focuses on the solar photovoltaic (PV) technology because, currently, it has the most attention in the energy sector due to the sharp drop in the solar PV system cost, which was one of the main barriers of PV large-scale deployment. Firstly, this paper extensively reviews the technical challenges, potential technical solutions and the research carried out in integrating high shares of small-scale PV systems into the distribution network of the grid in order to give a clearer picture of the impact since most of the PV systems installations were at small scales and connected into the distribution network. The paper reviews the localised technical challenges, grid stability challenges and technical solutions on integrating large-scale PV systems into the transmission network of the grid. In addition, the current practices for managing the variability of large-scale PV systems by the grid operators are discussed. Finally, this paper concludes by summarising the critical technical aspects facing the integration of the PV system depending on their size into the grid, in which it provides a strong point of reference and a useful framework for the researchers planning to exploit this field further on.

Objectives: This study was conducted to assess the impact of oral health education program on the level of oral health knowledge among female public intermediate school students in Riyadh. As well as to evaluate the correlation between their oral health knowledge and selected socio-demographic variables. Methods: A pre-posttest quantitative study was conducted at public intermediate girls' schools in Riyadh. A sample of schools was selected using stratified random sampling technique to reflect the spectrum of intermediate schools under the Riyadh's educational regions (north, south, middle, east, and west). Five schools were randomly chosen from the department of education listings in each educational region. A total sample of 315 school students between the ages of 12 and 16 years completed the study. Permission to perform this study was received from the Institutional Review Board of King Saud University and Ministry of Education. A 15-item self-administered questionnaire was designed in Arabic language and used to assess the student's oral health knowledge. Followed by the intervention which consisted of 40 minutes interactive lecture using power point presentation presented by the investigator. The impact of the oral health education program was evaluated by measuring the change in the level of oral health knowledge one month after the program implementation. The data obtained from the questionnaire were entered into a Statistical Package for Social Sciences database (IBM, SPSS version 23, IL, USA). Descriptive statistics were used in calculating the frequency and percentage for categorical sociodemographic characteristics. Mean and standard deviation (SD) were calculated for the continuous variables e.g. age, total score of knowledge. The impact of the program was estimated by calculating the percentage of change in the oral health knowledge which is calculated by 100 (post test score – pretest score)/post test score. McNemar's Chi-square test was used to compare correct / incorrect responses to oral health based questions before and one month after the program implementation. Student t-test and one way analysis of variance (ANOVA) were applied to compare responses to oral health questions in relation to selected socio-demographic variables. A p-value of

The auditing services of the outsourced data, especially big data, have been an active research area recently. Many schemes of remotely data auditing (RDA) have been proposed. Both categories of RDA, which are Provable Data Possession (PDP) and Proof of Retrievability (PoR), mostly represent the core schemes for most researchers to derive new schemes that support additional capabilities such as batch and dynamic auditing. In this paper, we choose the most popular PDP schemes to be investigated due to the existence of many PDP techniques which are further improved to achieve efficient integrity verification. We firstly review the work of literature to form the required knowledge about the auditing services and related schemes. Secondly, we specify a methodology to be adhered to attain the research goals. Then, we define each selected PDP scheme and the auditing properties to be used to compare between the chosen schemes. Therefore, we decide, if possible, which scheme is optimal in handling big data auditing.

Background Anabolic steroids (AS) are synthetic testosterone derivatives that last longer than physiological androgens in the body. Anabolic-androgenic steroid (AAS) abuse is spreading among athletes. The aim of this study is to assess the knowledge, attitudes, and practices of gym participants in Saudi Arabia. Methods A cross-sectional survey was carried out among gym users from February 2017 to May 2017. The questionnaire included information on demographics related to the use of AAS and lifestyle habits. Any willing male gym participant could be included. Results A total of 4860 male gym participants with a mean age of 28.6 ± 6.2 years were included. A majority were single, with a bachelor's degree or higher. Moreover, 9.8% of the participants used AAS, of which 76.7% reported improved fitness. Friends were the main source of AAS-related information, but only 38.0% of AAS users sought medical consults. The oral route was most common, and testosterone enanthate was the AAS most used. Conclusion Also, 9.8% of gym participants used AAS and were more likely to be involved in risky habits, such as smoking and growth hormone abuse. They were less aware of potential complications of AAS, with gym trainers being the predominant source of AAS substances.