Multi-label Convolution Neural Network for Personalized News Recommendation based on Social Media Mining

Prediction of user’s multi label interests and recommending the users interest based popular news articles through mining the social media are difficult task in Hybrid News Recommendation System (HYPNRS). To overcome this issue, this study proposes a deep learning approach - Multi-label Convolution Neural Network for predicting users' diversified interest in 15 labels using the binary relevance method. Based on labels of user’s interest, the most popular news articles are determined and their labels were clustered by mining social media feeds Facebook and Twitter along with current trends. The reliability of retrieved popular news articles also verified for recommendation. Eventually, the latest news articles catered from news feeds integrated along popular news articles and current trends together provide a recommendation list with respect to user interest. Experimental results show the proposed method diversified users interest labels prediction performance improved 5.87%, 12.09%, and 18.49% with the following state of art Support Vector Machine (SVM), Decision Tree and Naïve Bayes. The recommendation performance concerning users’ interest achieved 90%, 93.3%, 90% with social media feeds Facebook, Twitter and News Feeds accordingly

A community-based study to evaluate the prevalence and risk factors for osteoporosis among menopausal and pre-menopausal women

Background: To determine the prevalence of osteoporosis among pre and post menopausal women using quantitative ultrasound of calcaneal bone and to identify the risk factors associated with osteoporosis.Methods: This prospective community based epidemiological study was conducted during 2019 in a suburban area attached to Sri Muthu Kumaran Medical College Hospital and Research Institute, Chennai. 305 subjects met the inclusion and exclusion criteria. Using a structured questionnaire, demographic details, obstetric, gynaecological and medical history were collected. Quantitative ultrasound of the calcaneal bone was used to calculate the bone mineral density. Using statistical methods, risk factors for osteoporosis were analysed.Results: The mean age of the participants was 52.67±9.41 years, 62.2% were post menopausal and 37.38% were premenopausal. The BMD ‘T’ score was normal in 29.8%, osteopenia was diagnosed in 38.4% and osteoporosis in 31.8% of participants. 14% of premenopausal women and 42.4% of postmenopausal women were osteoporotic. Age, menopausal status, duration of menopause, and previous history of fractures emerged as significant risk factors for osteoporosis.Conclusions: The prevalence of osteoporosis is high among both pre-menopausal and menopausal women, but the awareness is limited. This study highlights the need for screening all women after the age of 40 years which is feasible using portable and easily available technology such as quantitative ultrasound of peripheral bones

Knowledge and awareness of cervical cancer prevention and HPV vaccination among medical and nursing students in a tertiary care hospital

Background: This study was done to assess the level of knowledge and awareness about cervical cancer and its prevention among medical and nursing students. Methods: This descriptive cross-sectional study was conducted during 2022 in a tertiary care hospital in Chennai. The study included 407 subjects; 224 medical students, and 183 nursing students and consent was obtained from each participant prior to the study. A questionnaire survey was used to assess their knowledge on cervical cancer and its prevention. Results: Out of the 407 participants, 95.5% of the medical students and 30.6% of the nursing students knew that HPV virus causes cervical cancer. Medical students had more knowledge on risk factors. 84.38% of medical students and 43.72% of nursing students were aware that vaccine is available for the prevention of cervical cancer. Overall nursing students had limited knowledge on cervical cancer and its prevention. 51.79% of the medical students and 27.87% of the nursing students acquired their knowledge about HPV vaccine through social media. The uptake of HPV vaccine was very low among both medical and nursing students. Conclusions: The results of our study demonstrate that there is a need for creating more awareness about cervical cancer and its prevention among medical and nursing students

An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users’ diversified interest and its categories by analysing their implicit user profile. Depending on users’ interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users’ diversified interest. The performance of the proposed approach for predicting overall users’ interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users’ interest categories

QRFXFreeze: Queryable Compressor for RFX

The verbose nature of XML has been mulled over again and again and many compression techniques for XML data have been excogitated over the years. Some of the techniques incorporate support for querying the XML database in its compressed format while others have to be decompressed before they can be queried. XML compression in which querying is directly supported instantaneously with no compromise over time is forced to compromise over space. In this paper, we propose the compressor, QRFXFreeze, which not only reduces the space of storage but also supports efficient querying. The compressor does this without decompressing the compressed XML file. The compressor supports all kinds of XML documents along with insert, update, and delete operations. The forte of QRFXFreeze is that the textual data are semantically compressed and are indexed to reduce the querying time. Experimental results show that the proposed compressor performs much better than other well-known compressors

Multi-label Convolution Neural Network for Personalized News Recommendation based on Social Media Mining

785-797Prediction of user’s multi label interests and recommending the users interest based popular news articles through mining the social media are difficult task in Hybrid News Recommendation System (HYPNRS). To overcome this issue, this study proposes a deep learning approach - Multi-label Convolution Neural Network for predicting users' diversified interest in 15 labels using the binary relevance method. Based on labels of user’s interest, the most popular news articles are determined and their labels were clustered by mining social media feeds Facebook and Twitter along with current trends. The reliability of retrieved popular news articles also verified for recommendation. Eventually, the latest news articles catered from news feeds integrated along popular news articles and current trends together provide a recommendation list with respect to user interest. Experimental results show the proposed method diversified users interest labels prediction performance improved 5.87%, 12.09%, and 18.49% with the following state of art Support Vector Machine (SVM), Decision Tree and Naïve Bayes. The recommendation performance concerning users’ interest achieved 90%, 93.3%, 90% with social media feeds Facebook, Twitter and News Feeds accordingly

Performance Analysis of Surface Roughness modeling using Soft Computing Approaches

In this paper, classification algorithms are used to classify the test data samples for determining the error rate by comparing its classification response with actual response. In this paper, Random Forest (RF) and Adaptive Neuro Fuzzy Inference System (ANFIS) classification algorithms are used as soft computing techniques to determine the error rate for the prediction of surface roughness of the materials. The parameters feed, depth of cut, speed and mean are extracted from the test sample materials and they are given to classification mode of the ANFIS classifier which produces vision measurement value. The error rate is determined by subtracting the vision measurement values from the stylus instrument values. The performance is compared with other conventional methods