Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble

Automatic de-noising of close-range hyperspectral images with a wavelength-specific shearlet-based image noise reduction method

Hyperspectral imaging (HSI) has become an essential tool for exploration of different spatially-resolved properties of materials in analytical chemistry. However, due to various technical factors such as detector sensitivity, choice of light source and experimental conditions, the recorded data contain noise. The presence of noise in the data limits the potential of different data processing tasks such as classification and can even make them ineffective. Therefore, reduction/removal of noise from the data is a useful step to improve the data modelling. In the present work, the potential of a wavelength-specific shearlet-based image noise reduction method was utilised for automatic de-noising of close-range HS images. The shearlet transform is a special type of composite wavelet transform that utilises the shearing properties of the images. The method first utilises the spectral correlation between wavelengths to distinguish between levels of noise present in different image planes of the data cube. Based on the level of noise present, the method adapts the use of the 2-D non-subsampled shearlet transform (NSST) coefficients obtained from each image plane to perform the spatial and spectral de-noising. Furthermore, the method was compared with two commonly used pixel-based spectral de-noising techniques, Savitzky-Golay (SAVGOL) smoothing and median filtering. The methods were compared using simulated data, with Gaussian and Gaussian and spike noise added, and real HSI data. As an application, the methods were tested to determine the efficacy of a visible-near infrared (VNIR) HSI camera to perform non-destructive automatic classification of six commercial tea products. De-noising with the shearlet-based method resulted in a visual improvement in the quality of the noisy image planes and the spectra of simulated and real HSI. The spectral correlation was highest with the shearlet-based method. The peak signal-to-noise ratio (PSNR) obtained using the shearlet-based method was higher than that for SAVGOL smoothing and median filtering. There was a clear improvement in the classification accuracy of the SVM models for both the simulated and real HSI data that had been de-noised using the shearlet-based method. The method presented is a promising technique for automatic de-noising of close-range HS images, especially when the amount of noise present is high and in consecutive wavelengths

An assessment of health research impact in Iran

Background: In recent years, Iran has made significant developments in the field of health sciences. However, the question is whether this considerable increase has affected public health. The research budget has always been negligible and unsustainable in developing countries. Hence, using the Payback Framework, we conducted this study to evaluate the impact of health research in Iran. Methods: By using a cross-sectional method and two-stage stratified cluster sampling, the projects were randomly selected from six medical universities. A questionnaire was designed according to the Payback Framework and completed by the principle investigators of the randomly selected projects. Results: The response rate was 70.4%. Ten point twenty-four percent (10.24%) of the studies had been ordered by a knowledge user organization. The average number of articles published in journals per project was 0.96, and half of the studies had no articles published in Scopus. The results of 12% of the studies had been used in systematic review articles and the same proportion had been utilized in clinical or public health guidelines. The results of 5.3% of the studies had been implemented in the Health Ministry’s policymaking. 62% of the studies were expected to affect health directly, 38% of them had been implemented, and among the latter 60% had achieved the expected results. Concerning the economic impacts, the most common expected impact was the reduction of ‘days of work missed because of illness or disability’ and impact on personal and health system costs. About 36% of these studies had been implemented, and 61% had achieved the expected impact. Conclusion: In most aspects, the status of research impact needs improvement. A comparison of Iran’s ranking of knowledge creation and knowledge impact in the Global Innovation Index confirms these findings. The most important problems identified were, not conducting research based on national needs, and the lack of implementation of research results. Keywords: Research impact assessment, Payback, Health research syste

The Mediating Role of Spiritual Health in Adherence to Treatment in Patients with Cancer

Background: Adherence to the treatment regimen is among the behaviors, which predict the successful control of the disease and decrease its intensity and negative consequences, which is influenced by several factors. The patient’s beliefs and attitudes toward the disease are effective factors in disease management and adherence to treatment, and spiritual health is one of these influential variables. Objectives: The aim of this study was to determine the mediating role of spiritual health in adherence to treatment in patients with cancer. Methods: In this descriptive correlational study, the participants were 234 Iranian patients with cancer, who were selected through convenience sampling, admitted to the oncology wards of 9 selected teaching hospitals in the northern, southern, eastern, and western provinces of the country, as well as the capital in 2021. The research instruments included the Demographic and Clinical Information Questionnaire, Spiritual Well-Being Scale, and Morisky Medication Adherence Scale-8. The path analysis was done to determine the factors related to the degree of adherence to treatment, taking into account the mediating role of spiritual health. Results: The mean age of the participants in the study was 47.27 ± 15.36. The mean scores for spiritual health and adherence to treatment were 76.70 ± 13.75 and 6.47 ± 2.1, respectively. A positive and significant relationship was found between spiritual health and adherence to treatment (P-value < 0.05). The variables of marital status, the time of diagnosis, and being a religious person had a direct effect on spiritual health, and the time of diagnosis indirectly affected treatment adherence. Conclusions: According to the results, the level of spiritual health and adherence to the treatment in patients with cancer was moderate. In addition, the variable of diagnostic time affected adherence to treatment indirectly. Besides, in examining the factors affecting spiritual health, the findings indicated the effect of the variables “being religious”, “marital status”, and “the time of diagnosis”. In addition to strengthening spiritual health, it is necessary to highlight the need to follow therapeutic diets in these patients. Therefore, it is suggested to consider a program to meet the patients with cancer spiritual needs along with the physical care program.info:eu-repo/semantics/publishedVersio

The predictors of spiritual dryness among Iranian cancer patients during the COVID-19 pandemic

BackgroundSpiritual struggles affect the wellbeing of religious people. Among them are strugglers with God which is perceived as non-responsive and distant. These perceptions were so far analyzed predominantly in Western societies with a Christian background, but not in Muslims from Iran. The aim of this study was to determine the predictors of spiritual dryness among cancer patients in Iran during the COVID-19 pandemic.MethodsCross-sectional study with standardized questionnaires (i.e., Spiritual Dryness Scale, WHO-5, BMLSS-10, Awe/Gratitude Scale) among 490 cancer patients (mean age 49.50 ± 14.92 years) referring to the selected educational hospitals in Tehran (the capital of Iran), who were selected through convenience sampling and based on the inclusion criteria, enrolled between December 2020–May 2021. Data analysis was done using SPSS software version 26 and the statistical methods including calculating the mean and the standard deviation, correlation coefficients, as well as regression analysis.ResultsThe overall experience of spiritual dryness was perceived regularly in 10.2% of Iranian cancer patients, sometimes in 22.9%, rarely in 22.9%, and never in 43.3%. The mean ± SD was 25.66 ± 5.04, and the scores ranged from 10 to 55. A higher score means greater spiritual dryness. The strongest predictors of spiritual dryness were praying activities Furthermore, the perception of burden due to the pandemic was positively correlated with spiritual dryness. Moreover, each 1 unit increase in its score changed the spiritual dryness score by 0.2 units. The regression of spirituality-related indicators, demographic-clinical variables, and health-related behaviors accounted for 21, 6, and 4% of the total SDS variance, respectively. These findings show that with an increase in praying, performing daily prayers, and the indicators related to spirituality, spiritual dryness will decrease. Most patients were able to cope with these phases often or even regularly, while 31.1% were never or rarely only able to cope.ConclusionThe results of this study showed that in times of crisis, cancer patients’ faith and confidence in God could be challenged. It is not the disease itself which seems to be associated with this form of crisis, but their religious practices. Therefore, it is necessary to support these patients during their struggle, especially as spirituality is one of the best approaches to cope with the disease

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Study of the effect of Panax ginseng extract on histomorphometric changes on cerebrum and cerebellum in 14 days offsprings rat from diabetic mothers

Introduction: Gestational diabetes induces developmental disorders in different parts of CNS and can change dimensions of the cerebrum, cerebellum and spinal cord. Different species of ginseng have been used for many years to control diabetes. This study investigated effect of extract of Panax ginseng on histomorphometric changes on cerebrum and cerebellum in 14 days offsprings rat from diabetic mothers. Methods: 16 rats were divided into four groups: non-diabetic control, non-diabetic recipient of the extract, diabetic control and diabetic recipient of the extract. Diabetes was induced by streptozotocin in diabetic groups and all 4 groups became pregnant. During pregnancy, recipient of the extract groups received ginseng extract at the dose of 400 mg/kg of body weight every day. 14 days of normal delivery; offsprings were anesthetized. The cerebrum and cerebellum was removed by cutting the skull. After the using the techniques of histology, Thickness of gray matter, thickness of white matter, the number of cells in the gray matter in 1 mm2, the number of cells in white matter in 1 mm2, the ratio of gray matter to white matter (in cerebrum and cerebellum), molecular layer thickness of cerebrum in gray matter, purkinje cell thickness in gray matter in cerebellum were measured. Data were analyzed by using SPSS software and ANOVA and Duncan statistical tests (P&le;0.05). Results: A significant reduction was observed in thickness and cell count of gray matter of cerebrum and cell count of white matter of cerebellum in diabetic control group than non-diabetic groups (control and recipient of the extract groups) (P&le;0.05). Also a significant reduction was observed in cell count of white matter of cerebrum in diabetic control group than other groups (non-diabetic control, non-diabetic recipient of the extract and diabetic recipient of the extract groups) (P&le;0.05). Conclusion:&nbsp; Extract of Panax ginseng can control hyperglycemia and reduce disorders on the cerebrum and cerebellum in offsprings of mothers with gestational diabetes by increasing insulin production, inhibition of insulin resistance, increasing &beta;-cells stimulation and reduction of blood sugar. So this plant can be offered as a suitable option for making a medicine to combat gestational diabetes. &nbsp; &nbsp