Use of Electronic Resources among Users of Medical College Libraries in Multan Division, Pakistan

The rationale of this report was to evaluate the current use of electronic resources among users of medical college libraries in Multan division. Moreover, mean variation about medical e-resources in respect of awareness, satisfaction, utilization and barriers with respondents’ gender trait were also explored. This study used a questionnaire to conduct a cross-sectional survey research design. The survey questionnaire was send to the target population of medical college libraries users in Multan division of Pakistan and response rate was 88 per cent. The findings of the study shows that users of the medical libraries were slightly aware about the medical e-resources and majority of the participants were used medical e-resources for education, learning and to update knowledge purposes. In addition, Medical e-resources that are MEDLINE, PubMed, Springer Link, Science Direct, Black-Well Synergy and ProQuest Database were utilized rarely by the respondents. Users are partially satisfied with medical e-resources of libraries. Lack of training/ orientation, low speed of internet, energy crisis/load shedding, lack of printing facility, non-availability of full text access to the most of Journals, lack of awareness, information overload and inadequate IT infrastructure were the major problems faced to medical colleges library users while using e-resources. There is no statistically significant difference in the mean scores of awareness, utilization, satisfaction and barriers about the medical e-resources with respect to male and female. The findings allow library professionals to be more effective, proactive, and successful in achieving digitize library services and resources. This study also made a significant contribution to the existing literature on medical e-resources and services. The findings could contribute in the promotion of digital library services and products, as well as virtual culture, in Pakistani medical college libraries, particularly in the Multan Division

Applying deep neural networks for user intention identification

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods

Isoflavones and alzheimer’s disease: the effects of soy in diet

Pathologically, Alzheimer’s disease is a result of aggregation of amyloid peptides and protein tau in the brain forming neurofibrillary tangles which are highly toxic to neuronal circuits in the brain. Recent evidences report that apart from aging, estrogen deficiency is one of the risk factors predisposing to the development of Alzheimer’s disease. Isoflavones, also known as phytoestrogens, are metabolized by the body forming compounds that are known to interfere with neurotoxic pathways and through their anti-fibrillization effects they play a role in reducing apoptosis of neurons and glial cells and promote axonal regeneration. Experimental studies on transgenic models with Alzheimer’s disease as well as various observational and clinical trials suggest that dietary interventions with Isoflavones may have a significant role in improving portions of memory, cognition and decreasing the risk of Alzheimer’s disease

Improving M-Learners\u27 Performance through Deep Learning Techniques by Leveraging Features Weights

© 2013 IEEE. Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners\u27 interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancing their study behavior

Stock market trend prediction using supervised learning

© 2019 Association for Computing Machinery. The stock trend prediction has received considerable attention of researchers in recent times. It is an important application in machine learning domain. In this work, we propose a machine learning based stock trend prediction system with a focus on minimizing data sparseness in the acquired datasets. We perform outlier detection on the acquired dataset for dimensionality reduction and employ K-nearest neighbor classifier for predicting stock trend. Results obtained show the effectiveness of the proposed system, when compared with baseline studies

Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on Twitter dataset and robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively

Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users

© 2020 IEEE. In the recent times, the social networking sites act as a rich source of information, which is shared among online users, who post comments and express their opinions in the form of likes and dislikes. Such content reflects important clues about the personality and behavior of the online community. The dark triad personality traits, such as the psychopathic behavior of individuals, can be detected using computational models. The earlier studies on the dark triad (psychopath) prediction exploit traditional machine learning techniques with limited dataset size. Therefore, it is required to develop an advanced deep neural network-based technique. In this work, we implement a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users. Experimental results depict that the proposed model attained an improved AUC (0.82) when compared to the baseline study

Rumor Detection in Business Reviews Using Supervised Machine Learning

© 2018 IEEE. Currently, a high volume of business data is generating with a high velocity in different forms like unstructured, structured or semi-structured. Due to social media arrival, there is a deluge of business rumors and their manual screening is time-consuming and difficult. In the current social computing era, it is necessary to move towards an automated process for the detection of business rumors. This work aims at developing an automated system for detecting business rumors from online business reviews using supervised machine learning classifiers, namely Logistic Regression, Support Vector Classifier (SVC), Naïve Bayesian (NB), K-Nearest Neighbors (KNN) to classify the business reviews into rumor and nonrumor. Experimental results show that Naïve Bayesian (NB), achieved efficient results with respect to other classifiers with an accuracy of 72.43 %

Emergence of fluoroquinolone resistance among drug resistant tuberculosis patients at a tertiary care facility in Karachi, Pakistan

Background: Pakistan is classified as one of the high multi-drug resistant tuberculosis (MDR-TB) burden countries. A poorly regulated private sector, over-prescription of antibiotics and self-medication has led to augmented rates of drug-resistance in the country. Pakistan\u27s first national anti-tuberculosis drug resistance survey identified high prevalence of fluoroquinolone resistance among MDR-TB patients. Further institutional evidence of fluoroquinolone drug-resistance can support re-evaluation of treatment regimens as well as invigorate efforts to control antibiotic resistance in the country.Findings: In this study, data for drug-susceptibility testing (DST) was retrospectively analyzed for a total of 133 patients receiving MDR-TB treatment at the Chest Department of Jinnah Postgraduate Medical Center, Karachi, Pakistan. Frequency analyses for resistance patterns was carried out and association of fluoroquinolone (ofloxacin) resistance with demographics and past TB treatment category were assessed. Within first-line drugs, resistance to isoniazid was detected in 97.7% of cases, followed by rifampicin (96.9%), pyrazinamide (86.4%), ethambutol (69.2%) and streptomycin (64.6%). Within second-line drugs, ofloxacin resistance was detected in 34.6% of cases. Resistance to ethionamide and amikacin was 2.3% and 1.6%, respectively. Combined resistance of oflaxacin and isoniazid was detected in 33.9% of cases. Age, gender and past TB treatment category were not significantly associated with resistance to ofloxacin.Conclusion: Fluoroquinolone resistance was observed in an alarmingly high proportion of MDR-TB cases. Our results suggest caution in their use for empirical management of MDR-TB cases and recommended treatment regimens for MDR-TB may require re-evaluation. Greater engagement of private providers and stringent pharmacy regulations are urgently required

An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends

Background/introduction: In recent years, stock market forecasting has received a lot of attention from researchers. This attention and the growing stock market investments have highlighted this as an important and emerging application of machine learning.Methods: In this research work, we present a stock trend forecasting system with a focus on reducing the amount of sparseness in the data collected using machine learning. We conduct an outlier detection of the data available for reducing dimensionality and implement a K-nearest neighbor algorithm to classify stock trends.Results and conclusions: The experimental results show the performance and effectiveness of the proposed trend forecasting system compared to the existing systems. The proposed system’s model (i.e., KNN classifier) gives better results of low error (MSE = 0.00005, MAE = 0.005 and Logcosh = 0.004) on KSE dataset as compared to previous works