Estimating cost is a very wearisome activity in all aspect. A person with broad scope and good thinking for the future makes more precise decisions. It helps in governing and planning the software risks which are admirably correct and precise. In 1960 regression analysis and mathematical formulae were practiced to determine cost. We need to think more than simply putting numbers into a formula and accept the results to attaining the accuracy of software cost estimation. The changing methods of estimating software cost have made the researchers to think diversely. Barry Bohem birthed COCOMO model for software cost estimation in 1981 which is considered to be more efficient as compared to previous models. Thereafter number of researchers has been trying to improve the efficiency by keeping the base of COCOMO model. The paper drafts a novel variable reduction technique called feed-forward neural network with PCA to measure the estimation model accuracy. This is based on a COCOMO sample data set which collects and maintains a large software project data repository. PCA is a kind of classification method which can reduces number of factors into a few absolute factors.
Internet of Things (IoT) is a ubiquitous network that assists the system to monitor and organize the world by processing, collection, and analysis of produced data by IoT nodes. The IoT is susceptible to numerous attacks, wherein the sinkhole attack is the most destructive one, which affects the communication amongst network devices. The security of the IoT is affected due to the lack of dynamic network topology, centralized control, and network communications, so that the trust-based routing is important. The conventional trust-based routing strategies are ineffective to offer protection against attack and thus, trust management remains a major challenge in routing. This paper proposes a novel technique namely Atom Search Sunflower Optimization (ASSFO) for providing trust-based routing in IoT network. The proposed ASSFO is designed by incorporating Sunflower Optimization (SFO) in Atom Search Optimization (ASO). The method utilizes several trust factors for determining and isolating attacks while optimizing the performance of the network. The trust factors considered in the method include indirect trust, recent trust, integrity factor, direct trust, and availability factor. Moreover, the trusted nodes are selected using the trust factor and further used for secure routing using the proposed ASSFO algorithm. The proposed ASSFO outperformed other methods with maximal energy of 0.908, maximal trust of 0.845, maximal PDR of 0.907, and maximal throughput of 0.909.
There are few studies on type 2 diabetes complications from Indian metropolitan and non-metropolitan cities. Macrovascular and microvascular complications during the second year of LANDMARC, a 3-year nationwide prospective observational study (CTRI/2017/05/008452) , were evaluated in participants from metropolitan versus non-metropolitan cities. LANDMARC included participants with T2D who were on ≥2 antihyperglycemic medications. Of the total 6234 participants, 2376 and 3858 were from metropolitan and non-metropolitan cities, respectively. Age, T2D duration, and baseline A1C were similar across groups. Microvascular complications were significantly higher in participants from non-metropolitan than metropolitan cities (12.93% vs. 4.65%; p<0.0001) during the 2 years. Neuropathy was the most common microvascular complication in both metropolitan and non-metropolitan cities. Among macrovascular complications, a greater number of participants reported heart failure (p=0.0160) and CV deaths in non-metropolitan cities than metropolitan cities (Table) . The present data from India demonstrates that participants from non-metropolitan cities may have higher complications, particularly microvascular, and may need to be better understood through future studies. The LANDMARC results elicit a pattern of disease progression among participants with T2D. Disclosure S. Kalra: Speaker's Bureau; Boehringer Ingelheim International GmbH, Novo Nordisk A/S, Sanofi. A.K. Das: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Roche Diagnostics, Sanofi. Speaker's Bureau; Dr. Reddy’s Laboratories Ltd., Lupin Pharmaceuticals, Inc., USV Private Limited. S. Joshi: Advisory Panel; Abbott, Boehringer Ingelheim International GmbH, Dr. Reddy's Laboratories Ltd., Eli Lilly and Company, Novo Nordisk, Roche Diabetes Care. Consultant; Biocon, Glenmark Pharmaceuticals, Sanofi, USV Private Limited. A. Mithal: Advisory Panel; Eris Lifesciences Ltd. Consultant; Glenmark Pharmaceuticals, Lupin Pharmaceuticals, Inc., USV Private Limited. Speaker's Bureau; Abbott Diabetes, AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Dr. Reddy’s Laboratories Ltd., Novartis AG, Novo Nordisk, Sanofi. K. Kumar: None. A. Unnikrishnan: Advisory Panel; Intas Pharmaceuticals Ltd. Speaker's Bureau; Abbott, AstraZeneca, Boehringer-Ingelheim, Sanofi. Other Relationship; Novo Nordisk, Serdia Pharmaceuticals (India) Pvt. Ltd., Torrent Pharmaceuticals Ltd. H. Thacker: None. B. Sethi: None. S. Chowdhury: None. A. Nair: None. S. Mohanasundaram: Employee; Sanofi. V. Salvi: Employee; Sanofi. D. Chodankar: Employee; Sanofi. C. Trivedi: None. S. Wangnoo: None. A.H. Zargar: Advisory Panel; Sanofi. Speaker's Bureau; AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Intas Pharmaceuticals Ltd., Janssen Pharmaceuticals, Inc., Lupin Pharmaceuticals, Inc., Novo Nordisk, USV Private Limited. N. Rais: None. Funding Sanofi, India
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