Exploring New Vista of Intelligent Recommendation Framework for Tourism Industries: An Itinerary through Big Data Paradigm

Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Each of these organizations must figure out how to improve the general client experience and meet every client’s novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients’ preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows’ assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms

Ventilator-associated pneumonia: Its incidence, the risk factor and drug resistance pattern in a tertiary care hospital

Background: Ventilator-associated pneumonia (VAP) is an infection of the lung that develops 48 h or longer after mechanical ventilation. Objectives: The present study was aimed to find out the bacteriological profile of VAP along with the resistance pattern of bacteriological isolates. Materials and Methods: A prospective observational study was conducted from January 2013 to May 2014 among 791 patients admitted in critical care units of our tertiary care hospital. After selection by applying inclusion and exclusion criteria endotracheal aspirates were collected from ventilated patients. Samples were subjected to further processing by Gram-staining, culture, biochemical testing and antibiogram. Results : Out of 791 patients admitted in intensive care unit in this tertiary care hospital with VAP 540 (68.2%) patients were culture positive. Pseudomonas aeruginosa was most commonly isolated pathogen of both early onset and late onset VAP. In early VAP Acinetobacter baumannii showed 62.5% metallo-beta-lactamase (MBL) positivity. P. aeruginosa showed 27.5% MBL positivity, whereas in late onset VAP, 71.4% A. baumannii isolates and 75.8% P. aeruginosa isolates showed MBL positivity, respectively. Conclusion : Simple prevention of aspiration, sterilization of equipments, hand washing of personnel can reduce VAP in hospital care setting

Exploring New Vista of Intelligent Recommendation Framework for Tourism Industries: An Itinerary through Big Data Paradigm

Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Each of these organizations must figure out how to improve the general client experience and meet every client’s novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients’ preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows’ assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms

Configuring a Trusted Cloud Service Model for Smart City Exploration Using Hybrid Intelligence

International audienceEmerging research concerns about the authenticated cloud service with high performance of security and assuring trust for distributed clients in a smart city. Cloud services are deployed by the third-party or web-based service providers. Thus, security and trust would be considered for every layer of cloud architecture. The principle objective of cloud service providers is to deliver better services with assurance of trust about clients' information. Cloud's users recurrently face different security challenges about the use of sharable resources. It is really difficult for Cloud Service Provider for adapting varieties of security policies to sustain their enterprises' goodwill. To make an optimistic decision that would be better suitable to provide a trusted cloud service for users' in smart city. Statistical method known as Multivariate Normal Distribution is used to select different attributes of different security entities for developing the proposed model. Finally, fuzzy multi objective decision making and Bio-Inspired Bat algorithm are applied to achieve the objective

Configuring a Trusted Cloud Service Model for Smart City Exploration Using Hybrid Intelligence

International audienceEmerging research concerns about the authenticated cloud service with high performance of security and assuring trust for distributed clients in a smart city. Cloud services are deployed by the third-party or web-based service providers. Thus, security and trust would be considered for every layer of cloud architecture. The principle objective of cloud service providers is to deliver better services with assurance of trust about clients' information. Cloud's users recurrently face different security challenges about the use of sharable resources. It is really difficult for Cloud Service Provider for adapting varieties of security policies to sustain their enterprises' goodwill. To make an optimistic decision that would be better suitable to provide a trusted cloud service for users' in smart city. Statistical method known as Multivariate Normal Distribution is used to select different attributes of different security entities for developing the proposed model. Finally, fuzzy multi objective decision making and Bio-Inspired Bat algorithm are applied to achieve the objective

TILLING by Sequencing (TbyS) for targeted genome mutagenesis in crops

TILLING (Targeting Induced Local Lesions in Genomes) by Sequencing (TbyS) refers to the application of high-throughput sequencing technologies to mutagenised TILLING populations as a tool for functional genomics. TbyS can be used to identify and characterise induced variation in genes (controlling traits of interest) within large mutant populations, and is a powerful approach for the study and harnessing of genetic variation in crop breeding programmes. The extension of existing TILLING platforms by TbyS will accelerate crop functional genomics studies, in concert with the rapid increase in genome editing capabilities and the number and quality of sequenced crop plant genomes. In this mini-review, we provide an overview of the growth of TbyS and its potential applications to crop molecular breeding