HMBO-LDC: A Hybrid Model Employing Reinforcement Learning with Bayesian Optimization for Long Document Classification

With the emergence of distributed computing platforms and cloud-big data eco-system, there has been increased growth of textual documents stored in cloud infrastructure. It is observed that most of the documents happened to be lengthy. Automatic classification of such documents is made possible with deep learning models. However, it is observed that deep learning models like CNN and its variants do have many hyper parameters that are to be optimized in order to leverage classification performance. The existing optimization methods based on random search are found to have suboptimal performance when compared with Bayesian Optimization (BO). However, BO has issues pertaining to choice of covariance function, time consumption and support for multi-core parallelism. To address these limitations, we proposed an algorithm named Enhanced Bayesian Optimization (EBO) designed to optimize hyper parameter tuning. We also proposed another algorithm known as Hybrid Model with Bayesian Optimization for Long Document Classification (HMBO-LDC). The latter invokes the former appropriately in order to improve parameter optimization of the proposed hybrid model prior to performing long document classification. HMBO-LDC is evaluated and compared against existing models such as CNN feature aggregation method, CNN with LSTM and CNN with recurrent attention model. Experimental results revealed that HMBO-LDC outperforms other methods with highest classification accuracy 98.76%

On Optimality of Long Document Classification using Deep Learning

Document classification is effective with elegant models of word numerical distributions. The word embeddings are one of the categories of numerical distributions of words from the WordNet. The modern machine learning algorithms yearn on classifying documents based on the categorical data. The context of interest on the categorical data is posed with weights and the sense and quality of the sentences is estimated for sensible classification of documents. The focus of the current work is on legal and criminal documents extracted from the popular news channels, particularly on classification of long length legal and criminal documents. Optimization is the essential instrument to bring the quality inputs to the document classification model. The existing models are studied and a feasible model for the efficient document classification is proposed. The experiments are carried out with meticulous filtering and extraction of legal and criminal records from the popular news web sites and preprocessed with WordNet and Text Processing contingencies for efficient inward for the learning framework

Al Ain Oases Mapping Project: Qattārah Oasis, past and present (poster)

The Al Ain Oases Mapping Project is a collaboration between the Abu Dhabi Tourism and Culture Authority and Zayed University. It aims to document the present oasis landscape and identify surviving historic components, while at the same time engaging Emirati students with their heritage and building capacity for archaeology in the UAE. The project utilizes a non-intrusive field-walking methodology suitable for a class of undergraduate students. It further draws on the students\u27 community links and bilingualism to contact former residents of the oasis villages and undertake oral history interviews. The first season\u27s work focused on Qattārah Oasis and contributes to the established programme of works there; future seasons will expand the survey to neighbouring Jīmī Oasis and the other oases of al-\u27Ayn. The results will inform continued archaeological exploration of the oases by TCA

Self-reported health and smoking status, and body mass index: a case-control comparison based on GEN SCRIP (GENetics of SChizophRenia In Pakistan) data

Introduction Individuals with schizophrenia are at a high risk of physical health comorbidities and premature mortality. Cardiovascular and metabolic causes are an important contributor. There are gaps in monitoring, documenting and managing these physical health comorbidities. Because of their condition, patients themselves may not be aware of these comorbidities and may not be able to follow a lifestyle that prevents and manages the complications. In many low-income and middle-income countries including Pakistan, the bulk of the burden of care for those struggling with schizophrenia falls on the families.Objectives To determine the rate of self-reported physical health disorders and risk factors, like body mass index (BMI) and smoking, associated with cardiovascular and metabolic disorders in cases of schizophrenia compared with a group of mentally healthy controls.Design A case-controlled, cross-sectional multicentre study of patients with schizophrenia in Pakistan.Settings Multiple data collection sites across the country for patients, that is, public and private psychiatric OPDs (out patient departments), specialised psychiatric care facilities, and psychiatric wards of teaching and district level hospitals. Healthy controls were enrolled from the community.Participants We report a total of 6838 participants’ data with (N 3411 (49.9%)) cases of schizophrenia compared with a group of healthy controls (N 3427 (50.1%)).Results BMI (OR 0.98 (CI 0.97 to 0.99), p=0.0025), and the rate of smoking is higher in patients with schizophrenia than in controls. Problems with vision (OR 0.13 (0.08 to 0.2), joint pain (OR 0.18 (0.07 to 0.44)) and high cholesterol (OR 0.13 (0.05 to 0.35)) have higher reported prevalence in controls. The cases describe more physical health disorders in the category ‘other’ (OR 4.65 (3.01 to 7.18)). This captures residual disorders not listed in the questionnaire.Conclusions Participants with schizophrenia in comparison with controls report more disorders. The access in the ‘other’ category may be a reflection of undiagnosed disorders

Obesity, inflammatory and thrombotic markers, and major clinical outcomes in critically ill patients with COVID‐19 in the US

Objective This study aimed to determine whether obesity is independently associated with major adverse clinical outcomes and inflammatory and thrombotic markers in critically ill patients with COVID‐19. Methods The primary outcome was in‐hospital mortality in adults with COVID‐19 admitted to intensive care units across the US. Secondary outcomes were acute respiratory distress syndrome (ARDS), acute kidney injury requiring renal replacement therapy (AKI‐RRT), thrombotic events, and seven blood markers of inflammation and thrombosis. Unadjusted and multivariable‐adjusted models were used. Results Among the 4,908 study patients, mean (SD) age was 60.9 (14.7) years, 3,095 (62.8%) were male, and 2,552 (52.0%) had obesity. In multivariable models, BMI was not associated with mortality. Higher BMI beginning at 25 kg/m2 was associated with a greater risk of ARDS and AKI‐RRT but not thrombosis. There was no clinically significant association between BMI and inflammatory or thrombotic markers. Conclusions In critically ill patients with COVID‐19, higher BMI was not associated with death or thrombotic events but was associated with a greater risk of ARDS and AKI‐RRT. The lack of an association between BMI and circulating biomarkers calls into question the paradigm that obesity contributes to poor outcomes in critically ill patients with COVID‐19 by upregulating systemic inflammatory and prothrombotic pathways