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

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

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

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 %

Engagement of private healthcare providers for case finding of tuberculosis and diabetes mellitus in Pakistan

Background: The rising co-epidemic of tuberculosis (TB) and diabetes mellitus (DM) is a challenge for constrained health systems in low and middle-income countries. Diabetes is a known risk factor for tuberculosis and associated with poor tuberculosis treatment outcomes, while tuberculosis is associated with worsening glycemic control. We investigated the performance of bi-directional TB and DM case finding approaches through a private-sector engagement model in Karachi, Pakistan.Methods: Between July 2016 and July 2018, private health care providers were engaged to generate referrals for bi-directional TB and DM screening at private diagnostic and treatment centers in Karachi, Pakistan. Individuals diagnosed with TB underwent glycated hemoglobin (HbA1c) testing at the time of anti-tuberculous treatment initiation and at three -month follow up stage. All individuals with a history of diabetes or random blood sugar of greater than 200 mg/dl were screened for TB using a chest X-ray and Xpert MTB/RIF.Results: A total of 6312 persons with tuberculosis were tested on HbA1c at treatment initiation, of whom 1516 (24%) were newly diagnosed with DM. About one third of those with HbA1c in the diabetic range (≥ 6.5%) at baseline were found to have a normal HbA1c (\u3c 5.7%) result at 3-month follow-up. A total of 3824 individuals with DM, of whom 2396 (63%) were known cases and 1428 (37%) were newly identified with random blood sugar \u3e 200 mg/dl, underwent chest x-ray and Xpert MTB/RIF testing, with 321 (13.4%) known and 54 (3.8%) new diabetics respectively identified with tuberculosis.Conclusion: This study demonstrates a high yield of TB and DM through bidirectional screening and the feasibility of engagement of private sector in finding missing cases of tuberculosis and diabetes. Given the high prevalence of undiagnosed DM in individuals with TB tuberculosis patients, there is a need to scale-up DM screening within TB programmes. Increased awareness of the high risk of TB among individuals with DM is needed among private health providers and screening for TB among diabetics should be strongly considered

Exploiting Ontology Recommendation Using Text Categorization Approach

Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies

Barriers to access of healthcare services for rural women – Applying gender lens on TB in a rural district of Sindh, Pakistan

Background: Women in rural districts of Pakistan face numerous barriers to healthcare, rendering gender-responsive health programming important, including for Tuberculosis (TB). This study was conducted to assess the general understanding of TB and of access to healthcare for women, as a first step towards implementation of a gender responsive TB programme in TandoAllayar, a rural district of Pakistan.Methods: A total of 36 participants were interviewed. The focus group discussion guide comprised of questions on: (1) family/household dynamics (2) community norms (3) healthcare system (4) women’s access to healthcare (5) TB Awareness;, and (6) women’s access to TB Care.Results: Limited autonomy in household financial decision-making, disapproval of unassisted travel, long travel time, lack of prioritization of spending on women’s health and inadequate presence of female health providers, were identified as barriers to access of healthcare for women, higher in younger women. Facilitators to access of TB care included a reported lack of TB-related stigma, moderate knowledge about TB disease, and broad understanding of tuberculosis as a curable disease. Other suggested facilitators include health facilities closer to the villages and the availability of higher quality services.Conclusion: Significant barriers are faced by women in access to TB care in rural districts of Pakistan. Programme implementers in high burden countries, should shift towards improved gender-responsive TB programming

The Outcome of Bicolumnar Acetabular Fracture Treated by Single Anterior Ilioinguinal Approach

Background: Acetabular fracture therapy, being complicated, is generally treated by non-operative methods due to a lack of surgeons’ expertise in pelvis surgery. The surgical exposure and reduction of acetabular fractures may become more direct and practical with the altered technique since it is closer to the acetabular quadrilateral plate. This study aimed to determine the efficacy of a single anterior illio-inguinal approach for the management of a Bi-columnar acetabulum fracture. Methods: Sixty patients, fulfilling the selection criteria were selected for descriptive case series from Orthopedic Surgery Department at Lahore General Hospital, from 02-12-2020 to 02-06-2021. After informed consent, surgery was performed on all patients under general anesthesia. Patients were followed-up and evaluated for efficacy in OPD after 12 weeks of surgery with a Harris hip score. All demographic and other information was recorded on a Proforma. SPSS 22 was used to assess data. Post-stratification, efficacy was compared by using chi-square, p-value ≤0.05 was considered significant. Results: Out of 60 patients, 45(75 %) were male, whereas 15(25%) were female (Mean age 52.71±10.50yrs). The Mean of duration fracture (in days) and Harris score are 11.03±5.29 and 2.83±0.45 respectively. The efficacy concerning lateral side was 60% for left side and 26.7% for right side. Efficacy was higher in less than 10 days old fractures at 55.0% while after 10 days it was 31.7%. We found that the percentage of efficacy was 86.7%. Conclusion: The current study concluded that the anterior illio-inguinal approach is highly effective (p=0.001) in the management of acetabular fractures. Keywords: Acetabulum; Pelvic; Fracture; General Surgery