Digital libraries suffer from the problem of information overload due to immense proliferation of research papers in journals and conference papers. This makes it challenging for researchers to access the relevant research papers. Fortunately, research paper recommendation systems offer a solution to this dilemma by filtering all the available information and delivering what is most relevant to the user. Researchers have proposed numerous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, including co-citation and bibliographic coupling, have proven to be significant. Researchers have extended the co-citation approach to include content analysis and citation proximity analysis and this has led to improvement in the accuracy of recommendations. However, in co-citation analysis, similarity between papers is discovered based on the frequency of co-cited papers in different research papers that can belong to different areas. Bibliographic coupling, on the other hand, determines the relevance between two papers based on their common references. Therefore, bibliographic coupling has inherited the benefits of recommending relevant papers; however, traditional bibliographic coupling does not consider the citing patterns of common references in different logical sections of the citing papers. Since the use of citation proximity analysis in co-citation has improved the accuracy of paper recommendation, this paper proposes a paper recommendation approach that extends the traditional bibliographic coupling by exploiting the distribution of citations in logical sections in bibliographically coupled papers. Comprehensive automated evaluation utilizing Jensen Shannon Divergence was conducted to evaluate the proposed approach. The results showed significant improvement over traditional bibliographic coupling and content-based research paper recommendation.

Abstract:Research paper recommendation has been a hot research area for the last few decades. Thus far, numerous different paper recommendation approaches have been proposed. Some of these include methods based on metadata, content similarity, collaborative filtering, and citation analysis, among others. Citation analysis methods include bibliographic coupling and co-citation analysis. Much research has been done in the area of co-citation analysis.Researchers have also performed experiments using the proximity of in-text citations in co-citation analysis and have found that it improves the accuracy of paper recommendation. In co-citation analysis, the similarity is discovered based on the frequency of co-cited papers in different research papers and those citing papers may belong to different areas. However, when proximity is used to calculate co-citation, the accuracy of recommendations improves significantly.Bibliographic coupling finds bibliographic coupling strength based on the common references between two papers. In bibliographic coupling, a large number of common references of two papers means that they belong to the same area, unlike co-citation analysis, in which there is a possibility that the citing papers may belong to different areas. Based on the observation that with the use of proximity analysis the accuracy in cases of co-citation analysis has improved, this paper investigates if the accuracy of paper recommendation can be further improved by using proximity analysis in bibliographic coupling. This paper proposes an approach that extends the traditional bibliographic coupling by exploiting the proximity of in-text citations of bibliographically coupled articles. The proposed approach takes into account the proximity of in-text citations by clustering the in-text citations using a density-based algorithm called DBSCAN. Experiments on a data set of research papers are presented to show that there is a substantial increase in accuracy of the recommendations produced by DBSCAN based on proximity analysis of in-text citations compared to traditional bibliographic coupling and content-based approaches.

The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medical IoT, the fusion of these two disciplines can be extremely beneficial as it allows the creation of a receptive and interconnected environment and offers a variety of services to medical professionals and patients. Doctors can make early decisions to save a patient's life when disease forecasts are made early. IoT sensor captures the data from the patients, and machine learning techniques are used to analyze the data and predict the presence of the fatal disease i.e., diabetes. The goal of this research is to make a smart patient's health monitoring system based on machine learning that helps to detect the presence of a chronic disease in patient early and accurately. For the implementation, the diabetic dataset has been used. In order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The performance of the proposed model is evaluated by using four evaluation metrics i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed remaining algorithms in terms of accuracy (81%), precision (75%), and F1-Score (65%). However, the recall (56%) for ANN was higher as compared to SVM and logistic regression, CNN, RNN, and LSTM. With the help of this proposed patient's health monitoring system, doctors will be able to diagnose the presence of the disease earlier.

the proposed approach gives more than 90% accuracy on benchmark dataset that is better than the results of existing approach.

The pharmacological activities of the leaf gel of Aloe vera have been extensively evaluated. Gel and latex are two basic products of aloe leaves. Latex and gel contain biologically active components. Polysaccharides contained in the leaf gel attribute most of the health benefits like anti-inflammatory, pain and fever, associated with aloe vera. In the present study, chloroform extract of A. barbadensis at various concentrations was investigated for its anti-inflammatory, antipyretic and analgesic activities in albino rats. Twenty-four albino rats were randomly divided into three groups (control, standard and experimental group). Division of groups was the same for all activities. Control and standard groups contain 4 rats in each group whereas experimental group contains 16. All the rats in three groups were treated with carrageenan to induce oedema, Brewer’s yeast to induce pyrexia and acetic acid to induce pain. The control group was treated with normal saline for all the activities. Standard group rats were treated with the reference drug diclofenac for anti-inflammatory and analgesic activities and paracetamol for antipyretic activity. Experimental group rats were given chloroform extract of A. barbadensis with 50, 100, 200 and 400mg/kg concentration. The result showed a significant inhibition (98%) in oedema at 3rd hour at the dose of 400mg/kg as compared to control group. For antipyretic activity, there was a significant reduction (66%) in pyrexia at 4th hour at the dose of 50mg/kg as compared to control group. And in analgesic model a significant reduction (64%) in the writhing at the dose of 400mg/kg as compared to control group. These results demonstrated that the chloroform extract of Aloe barbadensis miller have anti-inflammatory, antipyretic and analgesic activity and suggested its inhibitory actions on inflammation, fever and pain.

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

This study will help to encourage a better understanding of eating disorders; we can help people feel safe to tell someone about what they're experiencing and ensure the people around those suffering are able to see that there's something wrong earlier. The aim of a study is to check the association through their common underlying psychological factors, as well as their effect on internet usage among the young generation. To provide a basic understanding of self-control issue's association with both the eating disorder symptoms and excessive internet use, while emotional issue’s association with the eating disorder symptoms.

Although there has been a vast increase in nutritional knowledge, a healthy dietary pattern is still established due to a lack of knowledge, availability, and accessibility. Even if vegetables are consumed higher percentage of starchy vegetables is consumed hence a balanced and adequate diet is still not followed. This research collected data from different cities of Pakistan via a questionnaire which was filled both face to face as well as online. The sample size was 60 with 10 people from each group. Data was collected majorly in form of a food frequency questionnaire and then percentages and frequencies were calculated for all food groups. In age groups 3-12 years there was a higher consumption of beverages both hot and sweet and nuts and seeds were consumed in higher amounts. On the other hand, the age group 13-18 consumed more fruits and vegetables. Similarly, when moving to age 19-25 and 26-40 there is a higher consumption of milk and milk products. Cereals are consumed most by adults of age 41-60 years and 60+ leaned more towards sweets and snacks. Women tend to consume a more calorie-dense diet than men. Also, children and older adults rely more on sweets and snacks rather than fruits, vegetables, and other nutrient-dense foods.