Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from (...) discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets. (shrink)

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam (...) product reviews is a challenging issue in NLP. Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron, K-Nearest Neighbour, and Random Forest and predicts the outcome of the review as spam or real, based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers and state-of-the-art boosting approaches like Generalized Boost Regression Model, Extreme Gradient Boost, and AdaBoost Regression Model in terms of classification accuracy. (shrink)

Acute Myeloid Leukemia is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia in microscopic blood images and compared its performance with LeNet-5-based model in Precision, (...) Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy. (shrink)

With the development of wireless technology, two basic wireless network models that are commonly used, known as infrastructure and wireless ad hoc networks, have been developed. In the literature, it has been observed that channel contention is one of the main reasons for packet drop in WANETs. To handle this problem, this paper presents a routing protocol named CCBR. CCBR tries to determine a least contended path between the endpoints to increase packet delivery ratio and to reduce packet delay and (...) normalized routing overhead. Moreover, throughout the active data section, each intermediate node computes its channel contention value. If an intermediate node detects an increase in channel contention, it notifies the source node. Then the source node determines another least contended route for transmission. The advantages of CCBR are verified in our NS2-based performance study, and the results show that CCBR outperforms ad hoc on-demand distance vector in terms of packet delivery ratio, end-to-end delay, and routing overhead by 4% to 9%. (shrink)

Unmanned aerial vehicles, particularly quadcopters, have several medical, agriculture, surveillance, and security applications. However, the use of this innovative technology for civilian applications is still very limited in low-income countries due to the high cost, whereas low-cost controllers available in the market are often tuned using the hit and trial approach and are limited for specific applications. This paper addresses this issue and presents a novel proof of concept low-cost quadcopter UAV design approach using a systematic Model-Based Design method for (...) mathematical modeling, simulation, real-time testing, and prototyping. The quadcopter dynamic model is developed, and controllers are designed using Proportional Integral, and Derivative, Pole Placement, and Linear Quadratic Regulator control strategies. The stability of the controllers is also checked using Lyapunov stability analysis. For verification and validation of the design, Software-in-the-Loop, Processor-in-the-Loop, Hardware-in-the-loop testing, and Rapid Control Prototyping have been performed. The V&V methods of the MBD approach showed practically valid results with a stable flight of the quadcopter prototype. The proposed low-cost POC quadcopter design approach can be easily modified to have enhanced features, and quadcopters with different design parameters can be assembled using this approach for a diverse range of applications. (shrink)

The 3D Prandtl fluid flow through a bidirectional extending surface is analytically investigated. Cattaneo–Christov fluid model is employed to govern the heat and mass flux during fluid motion. The Prandtl fluid motion is mathematically modeled using the law of conservations of mass, momentum, and energy. The set of coupled nonlinear PDEs is converted to ODEs by employing appropriate similarity relations. The system of coupled ODEs is analytically solved using the well-established mathematical technique of HAM. The impacts of various physical parameters (...) over the fluid state variables are investigated by displaying their corresponding plots. The augmenting Prandtl parameter enhances the fluid velocity and reduces the temperature and concentration of the fluid. The momentum boundary layer boosts while the thermal boundary layer mitigates with the rising elastic parameter strength. Furthermore, the enhancing thermal relaxation parameter ) reduces the temperature distribution, whereas the augmenting concentration parameter drops the strength of the concentration profile. The increasing Prandtl parameter declines the fluid temperature while the augmenting Schmidt number drops the fluid concentration. The comparison of the HAM technique with the numerical solution shows an excellent agreement and hence ascertains the accuracy of the applied analytical technique. This work finds applications in numerous fields involving the flow of non-Newtonian fluids. (shrink)

Responding to a major pandemic and planning for allocation of scarce resources under crisis standards of care requires coordination and cooperation across federal, state and local governments in tandem with the larger societal infrastructure. Maryland remains one of the few states with no state-endorsed ASR plan, despite having a plan published in 2017 that was informed by public forums across the state. In this article, we review strengths and weaknesses of Maryland’s response to COVID-19 and the role of the Maryland (...) Healthcare Ethics Committee Network in bridging gaps in the state’s response to prepare health care facilities for potential implementation of ASR plans. Identified “lessons learned” include: Deliberative Democracy Provided a Strong Foundation for Maryland’s ASR Framework; Community Consensus is Informative, Not Normative; Hearing Community Voices Has Inherent Value; Lack of Transparency & Political Leadership Gaps Generate a Fragmented Response; Pandemic Politics Requires Diplomacy & Persistence; Strong Leadership is Needed to Avoid Implementing ASR … And to Plan for ASR; An Effective Pandemic Response Requires Coordination and Information-Sharing Beyond the Acute Care Hospital; and The Ability to Correct Course is Crucial: Reconsidering No-visitor Policies. (shrink)

Landslide is a critical natural geological hazard that causes severe damage to property, infrastructure, and humans. In general, some location-specific factors trigger a landslide. Wireless sensor network is an enabling technology to monitor most of the parameters associated with these factors. A challenge in landslide monitoring through WSN is that each sensed data item might be critical whereas the underlying wireless communication is often unreliable. In case of landslides, the terrains have irregular shapes, providing harsh conditions for wireless communication thereby (...) more data loss may be expected. This study focuses on the effect of lossy communication in WSN on the efficiency and accuracy of landslide monitoring systems. To this end, collaborative local data analysis is used to enable each node to decide locally whether its sensed data corresponds to a potential event-of-interest. Through extensive simulations, the performance of various landslide prediction and detection models has been evaluated. By and large, the study lets a significant insight into landslide monitoring before implementing a parameterized application for real-world deployment. (shrink)

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For (...) the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. (shrink)

Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval protocols that use peer nodes to preserve privacy. Previously, (...) we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data. (shrink)

Caricatures can help to understand the perception of a face. The prominent facial feature of a subject can be exaggerated, so the subject can be easily identified by humans. Recently, significant progress has been made to face detection and recognition from images. However, the matching of caricature with photographs is a difficult task. This is due to exaggerated features, representation of modalities, and different styles adopted by artists. This study proposed a cross-domain qualitative feature-based approach to match caricature with a (...) mugshot. The proposed approach uses Haar-like features for the detection of the face and other facial attributes. A point distribution measure is used to locate the exaggerated features. Furthermore, the ratio between different facial features was computed using different vertical and horizontal distances. These ratios were used to calculate the difference vector which is used as input to different machine and deep learning models. In order to attain better performance, stratified k-fold cross-validation with hyperparameter tuning is used. Convolution neural network-based implementation outperformed the machine learning-based models. (shrink)

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work (...) presents that Social Media Analysis can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree, with the help of Term Frequency and Inverse Document Frequency. For experimental analysis, two datasets are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques. (shrink)

The current study investigates whether tournament incentives motivate chief executive officer to be socially responsible. Furthermore, it explores the role of sub-national institutional contingencies [i.e., state-owned enterprises vs. non-SOEs, foreign-owned entities vs. non-FOEs, cross-listed vs. non-cross-listed, developed region] in CEO tournament incentives and the corporate social responsibility performance relationship. Data were collected from all A-shared companies listed in the stock exchanges of China from 2014 to 2019. The study uses the baseline methodology of ordinary least squares and cluster OLS regression. (...) Moreover, firm-fixed effects regression, two-stage least squares regression, and propensity score matching deal with the endogeneity problem and check the robustness of the results. The results provide reliable evidence that tournament incentives motivate CEOs to be more socially responsible. On the other hand, sub-national institutional contingencies positively affect the association between CEO tournament incentives and CSRP. The findings have important implications for companies and regulators who wish to enhance CSP by providing incentives to top managers. (shrink)

The legal framework governing claims of medical negligence is extensively documented within South African (SA) jurisprudence, with a predominant focus on the liability of medical practitioners. In contrast, the liability of pharmacists has received comparatively scant attention. This issue was recently highlighted by a case in which a woman from the Western Cape initiated legal action against Clicks, a leading health, beauty and wellness retailer and SA’s largest retail pharmacy chain, alleging that the provision of incorrect medication nearly cost her (...) life. At the time of writing, the case has yet to advance to trial. This article explores the legal and ethical dimensions of medical negligence attributed to pharmacists within the context of their professional duties. Additionally, it investigates how similar cases are adjudicated in foreign jurisdictions, with a specific focus on the US. (shrink)

Introduction : a divided discipline -- A genealogy of agency -- Reforming a paradigm : constructivism to rational constructivism -- A rational constructivist theory of identity and strategy -- Jerusalem : the unsubstitutable core value -- Jihad for Jerusalem : Israel the tiger 1967-1997 -- Jihad for Jerusalem : Iran the cub 1967-1997 -- Jihad for Jerusalem : Saudi Arabia the paper tiger 1967-1997 -- Jihad for Jerusalem : Jordan the mouse 1967-1997 -- Conclusion : the future of Jerusalem.

This paper examines incorrect use of oral contraceptives (OCs) in rural Bangladesh by using data from an OC compliance survey. Of the 1031 current users of OCs interviewed, about 13% took their pills out of sequence, while 17% left incorrect intervals between pill packs. Forty per cent of the women reported missing one active pill during the 6 months prior to the survey, and 74% of them took correct action with the missed pill. Of the women who missed two active (...) pills (16%), only 9% took correct action. Multivariate analyses revealed that women support helped protect against taking incorrect action with a missed pill. The fieldworker support is essential to improve the pill-taking behaviour of Bangladeshi women. (shrink)

Background. Imposter syndrome, associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such (...) as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction. (shrink)

Volume 20, Issue 7, July 2020, Page 150-152.

This study has examined the problems’ related to communicativeecology of pilgrim sojourners in Saudi Arabia and its impact on the levelsof their satisfaction with the services provided in a probability sample of439 Pakistani pilgrims. The sojourners’ communication ecology in problemsituations comprises eleven communication sources. Of these, contactswith family/friends and co-pilgrims made top of the list followed by suchcommunity organization sources like information counters, tour operators, andthe Pakistani Hajj mission officials. The mediated sources of contacts with theethnic newspaper, and the mainstream (...) Saudimass media ranked the 3rd and the 4th. The Internet and the digital billboardswere each cited in less than 10 percent of the responses. Stepwise multipleregressions revealed that the most important sources of impact on satisfactionwere: contact with community organizations, family/friends and co-pilgrims,the ethnic newspaper, and the digital screens. Implications of the impact onsatisfaction are discussed for communicating with the pilgrims. (shrink)

Drawing on the scholarship of Critical Religion, this article shows how the modern category “religion” operates through a gender code which upholds its discursive power and enables the production of religious—and therefore racial—hierarchies. Specifically, it argues that mentioning religion automatically makes gender present in discourse. Acknowledging religion as an inherently gendered category in this way gives further insight into the discursive power and functioning of the religious label. With the example of the Westphalian production of the “myth of religious violence” (...) and the employment of “religion” in colonial contexts, I demonstrate how a gender code upholds and enables the discursive power of religion. Religion is both gendered and gendering. Acknowledging the multiple ways in which religion is gendered and gendering, then, has important bearings on the analysis of religion’s racializing function which is upheld and aided by the gender code through which religion is spoken. (shrink)

We investigate and analyze the dynamics of hepatitis B with various infection phases and multiple routes of transmission. We formulate the model and then fractionalize it using the concept of fractional calculus. For the purpose of fractionalizing, we use the Caputo–Fabrizio operator. Once we develop the model under consideration, existence and uniqueness analysis will be discussed. We use fixed point theory for the existence and uniqueness analysis. We also prove that the model under consideration possesses a bounded and positive solution. (...) We then find the basic reproductive number to perform the steady-state analysis and to show that the fractional-order epidemiological model is locally and globally asymptotically stable under certain conditions. For the local and global analysis, we use linearization, mean value theorem, and fractional Barbalat’s lemma, respectively. Finally, we perform some numerical findings to support the analytical work with the help of graphical representations. (shrink)

In this paper, DSEK model with fractional derivatives of the Atangana-Baleanu Caputo is proposed. This paper gives a brief overview of the ABC fractional derivative and its attributes. Fixed point theory has been used to establish the uniqueness and existence of solutions for the fractional DSEK model. According to this theory, we will define two operators based on Lipschitzian and prove that they are contraction mapping and relatively compact. Ulam-Hyers stability theorem is implemented to prove the fractional DSEK model’s stability (...) in Banach space. Also, fractional Euler’s numerical method is derived for initial value problems with ABC fractional derivative and implemented on fractional DSEK model. The symmetric properties contribute to determining the appropriate method for finding the correct solution to fractional differential equations. The numerical solutions generated using fractional Euler’s method have been plotted for different values of α where α ∈ 0,1 and different step sizes h. Result discussion will be given, describing the changes that occur due to the step size h. (shrink)

The topic of abortion was in the limelight again in Dobbs v Jackson, where the US Supreme Court overturned the decision of Roe v Wade, ‘which guaranteed women and pregnant people a constitutional right to abortion’. While not bound by the judgment, this gives us an opportunity to reflect on the current law in South Africa which regulates the termination of pregnancy. The primary piece of legislation which governs abortion is the Choice on Termination of Pregnancy Act. Section 2 of (...) the Act lists the grounds under which one may lawfully terminate a pregnancy. One of those grounds relates to the period of the 13th up until the 20th week of the gestation period, and states that if a medical practitioner, after consultation with the pregnant woman, is of the opinion that the continued pregnancy would significantly affect the social or economic circumstances of the woman, then the pregnancy may be lawfully terminated. The question is: What exactly is meant by ‘social grounds’? This article considers this aspect from a legal perspective and attempts to provide clarity on the issue, in the hope that this will be of assistance to medical practitioners who are concerned about the outcome of their actions, when assisting persons in this position. (shrink)

Pediatric dentists rely on access to hospital operating rooms for safe, effective, and humane delivery of dental care. The children who benefit most from dental treatment in a hospital operating room are those who are very young, have dental anxieties or phobias, are precommunicative or noncommunicative, need extensive or invasive dental treatments, or have special healthcare needs. Diminishing access to hospital operating rooms for pediatric dental treatment has become an escalating problem in contemporary times. Financial barriers, hospital costs, reimbursement rates, (...) health insurance policies and deductibles, out-of-network hospitals, socioeconomic factors, and the COVID-19 pandemic are prominent contributing factors. This problem in access to care has resulted in long waiting times for hospital operating rooms, deferral of medically necessary dental care, and pain and infection among this vulnerable patient population. Pediatric dentists have responded to the problem by utilizing alternative methods of care delivery, such as in-office deep sedation or in-office general anesthesia, and by implementing aggressive medical management of dental caries. However, the youngest of pediatric patients and children with special healthcare needs still remain at a disadvantage in receiving definitive dental treatment. This article aims to highlight the ethical challenges faced by pediatric dentists in contemporary practice in the face of limitations in hospital operating room access through four case scenarios. (shrink)

Clinicians have an obligation to ensure that patients with adequate capacity can make autonomous decisions. Thus, patients who choose to forego treatment and leave hospitals “against medical advice” are typically allowed to do so. But what happens when they require clinicians’ assistance to physically leave? Is it incumbent upon clinicians to not only respect and fulfill patients’ requests with which they disagree, but to physically assist in their fulfillment? We attempt to develop an ethical framework wherein clinicians can honor patients’ (...) wishes without necessarily sacrificing their own moral position. (shrink)