Currency Exchange Forecasting Using Sample Mean Estimator and Multiple Linear Regression Machine Learning Models

In recent time, there is an increasing growth in the amount of trading taking place in the currency exchange market. However, effective analysis and simulation tools for performing accurate prediction of these exchange rates are lacking. To alleviate this challenge, this work presents a hybrid machine learning and prediction model by suitably combining the Sample Mean Estimator (SME) simulation architecture with the multiple linear regression technique-based training of feed-forward parameters. The developed model has the capability to overcome prediction inaccuracy, inconsistent forecasting, slow response due to computational complexity and scalability problems. The SME method is used to overcome the problems of uncertainty and non-linearity nature of the predictive variable as it’s always affected by economic and political factors. The implementation of the proposed currency exchange rate forecasting system is achieved through the use of a developed in-house Java program with Net Beans as the editor and compiler. Performance comparison between the present system and two baseline methods which are the Autoregressive Moving Average and the Deep Belief network techniques demonstrates that the present forecasting model out-performed the baseline methods studied. The experimental result shows that the precision rate of the present system is equal to or greater than 70%. Therefore, the present foreign exchange predictive system is capable of providing usable, consistent, efficient, faster and accurate prediction to the users consistently at any-time

Intelligent Citizenship Identity through Family Pedigree Using Graph-Signature Based Random-Forest Model

There has been a global upsurge of interest in the topic of citizenship identity over the past decades, specifically in the world dominated by profound insecurity, inequalities, proliferation of identities, and rise of identity politics,engendered by capitalism. However finding effective solution to these problems has been rendered difficult. To alleviate these problems, this paper presents an analytical Machine learning model that suitably combined the graph signature with random forest techniques. This study presents the design and realization of a novel Intelligent Citizenship Identity through family pedigree using Graph Signature based random forest (GSB-RF) model. The study also showcases the development of a novel graph signature technique referred to as Canonical Code Signature(CCS) method. The CCS method is used at the pre-processing stage of the identification process to build signature for any given tuple. Performance comparisim between the present system and the baseline techniques which includes: the K-Nearest Neighbour and the traditional Random Forest shows that the present system outperformed the baseline method studied. The proposed system shows capability to perform continuous re-identification of Citizens based on their family pedigree with ability to select best sample with low computational complexity, high identification accuracy and speed. Our experimental result shows that the precision rate and identification quality of our system in most cases are equal to or greater than 70%. Therefore, the proposed Citizenship Identification machine is capable of providing usable, consistent, efficient, faster and accurate identification, to the users, security agents, government agents and institutions on-line, real-time and at any-time

Histological study of smoke extract of Tobacco nicotiana on the heart, liver, lungs, kidney, and testes of male Sprague-Dawley rats

Background: Some of the effects of tobacco on man′s health are well documented in many scientific reports. Whenever tobacco is used either in smoked or chewed form, nicotine is absorbed by the lungs and oral cavity and is spontaneously moved into the bloodstream where it is circulated throughout the body system. Materials and Methods: Ten male Sprague-Dawley rats were used for this investigation. The animals were randomly assigned into two groups, A and B, of five animals each. The animals in group B (treatment group) were exposed to smoke from a completely burnt 0.74 g leaf extract of Tobacco nicotiana, wrapped in 0.5 g of sterilized cotton wool for 5 minutes three times daily (7 am, 10 am, and 1 pm). The animals in group A (control group) were exposed to smoke from completely burnt 1.24 g of sterilized cotton wool with the same parameters as observed with the treatment groups. The duration of exposure was 5 days. Three hours after the last exposure, all the animals were killed by cervical dislocation. The heart, liver, lungs, kidney, and testes were carefully excised, blotted dry, and fixed in formol saline for histological analysis using Hematoxylin and Eosin stain. Results: Using the light microscope, it was observed that the histoarchitectural profiles of the studied organs in the sections obtained from the control animals were well preserved. Histopathological observations of the heart, liver, lungs, kidney, and testes in the treated animals showed a varying pattern of histological alterations, and distortions such as mild edema and occasional destruction of myocardial fibers, degeneration of the hepatocytes, reduction in the population of the germ cells, enlargement of the alveoli, alveolar hemorrhage, shrinkage of the glomerulus and glomerular hemorrhage were observed in the sections of the organs of the study of the animals in the treatment group when compared with the control group, hence showing that the smoke extract of Tobacco nicotiana has adverse and compromising effects on the heart, liver, lungs, kidney, and testes of male Sprague-Dawley rats. Conclusion: From these observations, it can be inferred that the exposure of male Sprague-Dawley rats to the smoke extract of Tobacco nicotiana may be associated with structural damage of some vital organs

Adaptation of the Wound Healing Questionnaire universal-reporter outcome measure for use in global surgery trials (TALON-1 study): mixed-methods study and Rasch analysis

BackgroundThe Bluebelle Wound Healing Questionnaire (WHQ) is a universal-reporter outcome measure developed in the UK for remote detection of surgical-site infection after abdominal surgery. This study aimed to explore cross-cultural equivalence, acceptability, and content validity of the WHQ for use across low- and middle-income countries, and to make recommendations for its adaptation.MethodsThis was a mixed-methods study within a trial (SWAT) embedded in an international randomized trial, conducted according to best practice guidelines, and co-produced with community and patient partners (TALON-1). Structured interviews and focus groups were used to gather data regarding cross-cultural, cross-contextual equivalence of the individual items and scale, and conduct a translatability assessment. Translation was completed into five languages in accordance with Mapi recommendations. Next, data from a prospective cohort (SWAT) were interpreted using Rasch analysis to explore scaling and measurement properties of the WHQ. Finally, qualitative and quantitative data were triangulated using a modified, exploratory, instrumental design model.ResultsIn the qualitative phase, 10 structured interviews and six focus groups took place with a total of 47 investigators across six countries. Themes related to comprehension, response mapping, retrieval, and judgement were identified with rich cross-cultural insights. In the quantitative phase, an exploratory Rasch model was fitted to data from 537 patients (369 excluding extremes). Owing to the number of extreme (floor) values, the overall level of power was low. The single WHQ scale satisfied tests of unidimensionality, indicating validity of the ordinal total WHQ score. There was significant overall model misfit of five items (5, 9, 14, 15, 16) and local dependency in 11 item pairs. The person separation index was estimated as 0.48 suggesting weak discrimination between classes, whereas Cronbach's α was high at 0.86. Triangulation of qualitative data with the Rasch analysis supported recommendations for cross-cultural adaptation of the WHQ items 1 (redness), 3 (clear fluid), 7 (deep wound opening), 10 (pain), 11 (fever), 15 (antibiotics), 16 (debridement), 18 (drainage), and 19 (reoperation). Changes to three item response categories (1, not at all; 2, a little; 3, a lot) were adopted for symptom items 1 to 10, and two categories (0, no; 1, yes) for item 11 (fever).ConclusionThis study made recommendations for cross-cultural adaptation of the WHQ for use in global surgical research and practice, using co-produced mixed-methods data from three continents. Translations are now available for implementation into remote wound assessment pathways