(CDRGI)-Cancer detection through relevant genes identification

Cancer is a genetic disease that is categorized among the most lethal and belligerent diseases. An early staging of the disease can reduce the high mortality rate associated with cancer. The advancement in high throughput sequencing technology and the implementation of several Machine Learning algorithms have led to significant progress in Oncogenomics over the past few decades. Oncogenomics uses RNA sequencing and gene expression profiling for the identification of cancer-related genes. The high dimensionality of RNA sequencing data makes it a complex and large-scale optimization problem. CDRGI presents a Discrete Filtering technique based on a Binary Artificial Bee Colony coupling Support Vector Machine and a two-stage cascading classifier to identify relevant genes and detect cancer using RNA seq data. The proposed approach has been tested for seven different cancers, including Breast Cancer, Stomach Cancer (STAD), Colon Cancer (COAD), Liver Cancer, Lung Cancer (LUSC), Kidney Cancer (KIRC), and Skin Cancer. The results revealed that the CDRGI performs better for feature reduction while achieving better classification accuracy for STAD, COAD, LUSC and KIRC cancer types

Parallel tensor factorization for relational learning

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL

Novel Model Design of Compact Dual Notch-Bands of Attenuation for Bandpass Filters

In this paper, a new microstrip model design of bandpass filter is proposed. Open stubs are applied to the band pass filter to refine the skirt selectivity on both sides of the upper and lower frequency band. Two attenuation poles are produced by this new structure. The upper band pole is introduced by the hairpin-comb structure. The lower band pole is introduced by the tapped open stubs. We fabricated a new structure using milling machine at the Lab in University of North Texas (UNT). Two attenuation poles are realized at the upper band and the lower band respectively, one at 1.71GHz and one at 1.93GHz. Sharp roll-off on both sides of the pass band is obtained. Measured results show good selectivity at both upper and lower band

The Impacts of Mind Maps Strategy on the Acquisition and Retention of Geographical Concepts for the First Intermediate Stage Students

هدفت الدراسة الى التعرف على اثر استراتيجية خرائط العقل في اكتساب المفاهيم الجغرافية والاحتفاظ بها لدى طلاب الصف الاول المتوسط ولتحقيق هدف البحث استخدم الباحث التصميم التجريبي المعروف بتصميم المجموعة الضابطة والتجريبية على (63) طالباً من طلاب الصف الاول المتوسط في محافظة كربلاء المقدسة. تم توزيعهم بمجموعتين بواقع (32) طالباً في المجموعة التجريبية و(31) طالباً في المجموعة الضابطة، المجموعة التجريبية دورست باستخدام استراتيجية خرائط العقل، والمجموعة الضابطة درست باستخدام الطريقة الاعتيادية. ولجمع البيانات تم اعتماد اختبار الاكتساب للمفاهيم الجغرافية، واخر احتفاظ المفاهيم الجغرافية, ودلت النتائج على وجود فرق ذي دلالة احصائية عند مستوى الدلالة (0,05) بين متوسط درجات طلاب المجموعة التجريبية والضابطة في اختبار اكتساب المفاهيم الجغرافية لصالح المجموعة التجريبية. وكذلك وجود فرق ذي دلالة احصائية عند مستوى الدلالة (0,05) بين متوسط درجات طلاب المجموعة التجريبية والضابطة في اختبار الاحتفاظ بالمفاهيم الجغرافية لصالح المجموعة التجريبية. ومن اهم توصيات الدراسة. ضرورة تضمين برنامج اعداد المدرسين في كليات التربية بأساليب تنمية قدرة الطلبة على التفكير بشكل عام. تصميم المناهج الحديثة بنحو يسهل ويساعد على تطبيق استراتيجية خرائط العقل في المرحلة المتوسطة.The present study aims at finding out the impact of strategy maps of the mind in the acquisition of geographical concepts and retention among students in the first grade intermediate. To achieve the goal of the research, the researcher chose an experimental design with partial adjustment. The random selection of the sample was the selection of Al-Majd secondary school for boys belonging to the Directorate of Education of Karbala. The number of students in the first grade was 63 students and 32 students in the experimental group And 31 students in the control group.The researcher conducted an equivalence between the students of the two research groups in several variables (age calculated by months, IQ test and grades of geography in the first semester test for the academic year 2016-2017, the academic achievement of the parents and the academic achievement of the mothers.There is a statistically significant difference at the level of significance (0.05) between the average score of the students of the experimental group who study the principle of general geography according to the strategy of mind maps andthe average score of the control group students who study the traditional method in the results of the test of acquisition of geographical concepts and for the benefit of the experimental group. There is a statistically significant difference at the level of significance (0.05) between the average score of the students of the experimental group who study the general principles of geography according to the strategy of the maps of the mind and the average score of the control group students who study the traditional method in the test of retention of geographical concepts and for the benefit of the experimental group. The researcher recommends the following: Teaching students, especially students of colleges of education on how to apply modern methods in teaching, including maps of the mind. Including the curricula of the geography of the prescribed for students in colleges of education to be steps to build maps of the mind and training on how to prepare them

COVID-19 Patient Count Prediction Using LSTM

IEEE In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients\u27 estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model\u27s prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients\u27 count of the proposed model is much closer to the actual patient count

The Effectiveness of the ‘Extended Sick Neonatal Score in Predicting Mortality in a ResourceConstrained Neonatal Care Uni

Objectives: To determine the diagnostic accuracy of the extended sick neonatal score (ESNS) in neonates admitted in a resource-limited neonatal intensive care unit (NICU) at Peshawar. Study Design: Cross-sectional analytical study Place and Duration of Study: Neonatal Intensive Care Unit, CMH Peshawar Pakistan, from Mar to May 2020. Methodology: Primary data was collected from 60 neonates admitted to NICU after taking consent from the parents. The receiver operating characteristic curve (ROC) was plotted to determine the clinical score (ESNS) cut-off value in predicting mortality. Result: The sensitivity and specificity of the Extended sick neonatal score to predict mortality among neonates was 93.3% and 97%, respectively, for a cut-off of 12.5. The area under the ROC curve was 0.990 (95% CI: 0.971–1.000). This was statistically significant with a p-value of <0.001 Conclusion: Extended Sick Neonatal score is an important tool that helps predict the risk of mortality of a neonate without the help of any invasive diagnostic procedure, thus enhancing the prioritization of health care to the most deserving neonates

Structured knowledge creation for Urdu language: A DBpedia approach

Wikipedia information is extracted by DBpedia and linked to other web resources as Linked Open Data, which is an important contribution to the field of semantics. As part of its internationalisation endeavour, DBpedia now has 20 language chapters that have been mapped to it; nonetheless, there have been very few attempts from Urdu. This article outlines the procedures and highlights the efforts put forward as the first contribution to the manual creation of Urdu mappings with DBpedia Ontology classes. Our approach led to an increase in the number of mapped infoboxes, thus enhancing the DBpedia. The mapping procedure is broken down into two parts. The infobox template is first mapped to the DBpedia ontology's relevant class, and then the attributes of the infobox are mapped to the properties of that class. In addition, alongside other mapped languages, Urdu labels are included to the description of Ontology classes. We have covered around a thousand properties and attributes of Urdu with English DBpedia Ontology on DBpedia mapping server

Hypolipidemic effect of Silymarin in Dyslipidaemia of Different Etiologies

Background: Many drug and non drug approaches are utilized for the treatment of dyslipidemia; flavonoids, the major constituents of silymarin, have been proved to positively modify lipoproteins in experimentally – induced dyslipidemia.  Objective: This study was designed to evaluate the effect of silymarin, when used alone or in combination with other hypolipidemic agents, on the lipid profile in dyslipidemic patients.  Patients and Methods: Fifty seven patients with dyslipidaemia of various etiologies are involved in this clinical trial. They are randomized into three groups treated with either 400mg / day silymarin (gr. A) or 20 mg / day lovastatin (gr. B) or a combination of 200 mg/day silymarin and 10 mg/day lovastatin (gr. C) for 2 months, only 45 patients complete the study . Serum lipid profile (total cholesterol, triglycerides, LDLC, VLDL-C and HDL-C) and liver functions indices (SGOT, SGPT, total bilirubin) were evaluated each month during the follow up period. Results: Treatment with silymarin results in a significant decrease in TC, TG, LDL-C and VLDL-C levels, with a significant elevation in HDL-C levels, without any significant changes in liver function. Meanwhile, adjunct use of silymarin with lovastatin widens the scope of lovastatin-hypolipidemic effect, without increasing in the score of adverse effects, and ameliorating the hepatic damage emerged due to its use. Conclusions: The results presented in this study indicated that silymarin can be used alone in clinical practice for the treatment of dyslipidemia, and when combined with other hypolipidemic agents like lovastatin, improves therapeutic profile and ameliorate some of its adverse effects