Heart Disease Prediction Using Binary Classification

In this project, I built a neural network model to predict heard disease with binary classification technique using patient information dataset from UCI Machine Learning repository. This dataset was preprocessed to remove missing elements and performed feature extraction. Our result shows that the model that I built has the best performance accuracy in heart disease classification if compared to other models and algorithms. The model achieved 94.98% accuracy after hyperparameter tuning and 0.947 area under the curve in ROC curve analysis. In addition, to identify the most important factors in heart disease prediction, I also performed feature importance analysis. Our analysis showed that factors such as type of chest pain, peak heart rate, and exercise-induced ST-segment depression were among the strongest predictors of heart disease. Overall, the project demonstrated the effectiveness of neural network models in medical diagnosis and provided insights into heart disease classification. The model developed can be used as a decision support tool for healthcare professionals in planning the diagnosis and treatment of heart disease. However, further research is needed to confirm the model\u27s performance in larger and more diverse patient populations

Security Challenges and Policies in Cloud Computing for Services

Cloud computing is becoming most emerging trend in IT industry. With its potential growth and lucrative services cloud computing has acquired mass market in the industry large enterprises running their business on the cloud. A greater acceptance of public cloud by various businesses has given it a wide popularity, strengthening of public cloud security is big milestone. The article talks about cloud computing and its service models and deployment models. Different threats have been discovered in recent years. Database security in public cloud raised some critical issues for cloud service provider.Further, various security issues and policies related to cloud computing also discussed. Findings emphasis that rapid adaptation to the clouds have increased concerns on a critical issue for successive growth of communication technology and information security. From a cloud security perspective, a number of unexplored risks and challenges are faced by cloud because of migration, causing degradation of the effectiveness of traditional protection mechanisms

AgroFIMS v.1.0 - User manual

The Agronomy Field Information Management System (AgroFIMS) has been developed on CGIAR’s HIDAP (Highly Interactive Data Analysis Platform) created by CGIAR’s International Potato Center, CIP. AgroFIMS draws fully on ontologies, particularly the Agronomy Ontology (AgrO)1. It consists of modules that represent the typical cycle of operations in agronomic trial management (seeding, weeding, fertilization, harvest, and more) and enables the creation of data collection sheets using the same ontology-based set of variables, terminology, units and protocols. AgroFIMS therefore enables a priori harmonization with metadata and data interoperability standards and adherence to the FAIR Data Principles essential for data reuse and increasingly, for compliance with funder mandates - without any extra work for researchers. AgroFIMS is therefore of value to anyone (scientist, researcher, agronomist, etc.) who wishes to easily design a standards-compliant agronomic research fieldbook following the FAIR Data Principles. AgroFIMS also allows users to collect data electronically in the field, thereby reducing errors. Currently this is restricted to the KDSmart Android platform, but we expect to enable this capability with other platforms such as the Open Data Kit (ODK) and Field Book in v.2.0. Once data is collected using KDSmart, the data can be uploaded back to AgroFIMS for data validation, statistical analysis, and the generation of statistical analysis reports. V.2.0 will allow easy upload of the data from AgroFIMS to an institutional or compliant repository of the user’s choice

SCREENING OF ANTIMICROBIAL ACTIVITY OF HERABAL EXTRACT OF MORINDA PUBESCENCEâ€, CHLORHEXIDINE & AMOXICILLIN AGAINST SALIVARY MICROFLORA OF MIXED DENTITION AGE GROUP.

Objectives : In  this  study  the  Antimicrobial  activity  of  active Morinda Pubescenceâ€in  acetone  extracts  were  compared with  Chlorhexidine  and  Amoxicillin  125mg  and  Amoxicillin  250mg  against  human  salivary  microflora  at  different  concentrations. Method : The  antimicrobial  activity  was  assisted  by  measuring  the  inhibition  zones  by well  diffusion  method.  Saliva  was  collected  from  children  of  age  group  6-12  years  having DMFT  value  four  or  above  four.  Ten  salivary  samples  were  tested  for  antimicrobial  property  to  determine  the  Minimum  Inhibition  Concentration  in  order  to  increase  the  reliability  and  precision  of  the  study. Result: This study compares antimicrobial activity of Morinda Pubescenceâ€with  0.2percent  chlorhexidine and Amoxicillin 125mg and Amoxicillin 250mg. The zone of inhibition are measured by excluding the diameter of well. These zones of inhibition are directly proportional to the concentration. Conclusion : The  results  confirmed  the  antimicrobial  potential  of  Morinda Pubescenceâ€plant  at  different  concentrations  in  acetone  extracts  are  comparable  with  chlorhexidine  and Amoxicillin  and  can  be  used  as  preventive  and  therapeutic  measure  in  dentistry

The mealybug chromosome system I: unusual methylated bases and dinucleotides in DNA of a Planococcus species

The methylation status of the nuclear DNA from a mealybug, a Planococcus species, has been studied. Analysis of this DNA by High Performance Liquid Chromatography and Thin Layer Chromatography revealed the presence of significant amounts of 5--methylcytosine. Since analysis of DNA methylation using the Msp I/Hpa II system showed only minor differences in susceptibility of the DNA to the two enzymes, it seemed possible that 5-methylcytosine (5mC) occurred adjacent to other nucleotides in addition to its usual position, next to guanosine. This was verified by dinucleotide analysis of DNA labelledin vitro by nick translation. These data show that the total amount of 5-methylcytosine in this DNA is slightly over 2.3 mol %, of which 0.61% occurs as the dinucleotide 5mCpG, 0.68% as 5mCpA, 0.59% as 5mCpT and 0.45% as 5mCpC. 5mCpG represents approximately 3.3% of all CpG dinucleotides. The experimental procedure would not have permitted the detection of 5mCp5mC, if it occurs in this system. Unusually high amounts of 6-methyladenine (approximately 4 mol %) and 7-methylguanine (approximately 2 mol %) were also detected, 6-methyladenine and 7-methylguanine occurred adjacent to all four nucleotides. The total G+C content was 33.7% as calculated from dinucleotide data and 32.9% as determined from melting profiles

Governing agricultural data: Challenges and recommendations

The biomedical domain has shown that in silico analyses over vast data pools enhances the speed and scale of scientific innovation. This can hold true in agricultural research and guide similar multi-stakeholder action in service of global food security as well (Streich et al. Curr Opin Biotechnol 61:217–225. Retrieved from https://doi.org/10.1016/j.copbio.2020.01.010, 2020). However, entrenched research culture and data and standards governance issues to enable data interoperability and ease of reuse continue to be roadblocks in the agricultural research for development sector. Effective operationalization of the FAIR Data Principles towards Findable, Accessible, Interoperable, and Reusable data requires that agricultural researchers accept that their responsibilities in a digital age include the stewardship of data assets to assure long-term preservation, access and reuse. The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. Community-based hurdles around the development and governance of standards and fostering their adoption also abound. This chapter discusses challenges and possible solutions to making FAIR agricultural data assets the norm rather than the exception to catalyze a much-needed revolution towards “translational agriculture”