To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document provides information on cocktails and bartending. It defines a cocktail as a mixed drink made with a base liquor, modifier, and mixer. It describes the three elements of cocktails and provides examples. It also classifies cocktails into categories such as international, tropical, classic, shooters, and mocktails. The document outlines different types of cocktails like pre-dinner drinks, after dinner drinks, long drinks, and fancy drinks. It discusses alcoholic and non-alcoholic ingredients and substitutes. Finally, it provides procedures for making common garnishes like oranges, pineapples, apples, limes, and cherries.
The document provides information on various cocktails categorized by their base spirit. It defines cocktails and describes common methods of preparation like building, stirring, shaking and blending. It then lists the main classifications of cocktails such as whiskey, gin, rum, vodka, brandy and tequila based cocktails. For each category, it provides the recipes for 6-7 classic cocktails including ingredients, measurements, glassware and garnish. The recipes include well known cocktails like Old Fashioned, Mojito, Martini, Daiquiri, Moscow Mule and Margarita among others.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document provides information on cocktails and bartending. It defines a cocktail as a mixed drink made with a base liquor, modifier, and mixer. It describes the three elements of cocktails and provides examples. It also classifies cocktails into categories such as international, tropical, classic, shooters, and mocktails. The document outlines different types of cocktails like pre-dinner drinks, after dinner drinks, long drinks, and fancy drinks. It discusses alcoholic and non-alcoholic ingredients and substitutes. Finally, it provides procedures for making common garnishes like oranges, pineapples, apples, limes, and cherries.
The document provides information on various cocktails categorized by their base spirit. It defines cocktails and describes common methods of preparation like building, stirring, shaking and blending. It then lists the main classifications of cocktails such as whiskey, gin, rum, vodka, brandy and tequila based cocktails. For each category, it provides the recipes for 6-7 classic cocktails including ingredients, measurements, glassware and garnish. The recipes include well known cocktails like Old Fashioned, Mojito, Martini, Daiquiri, Moscow Mule and Margarita among others.
JPD1412 A Cocktail Approach for Travel Package Recommendationchennaijp
This document proposes a new approach called the TAST model for personalized travel package recommendation. The TAST model represents travel packages and tourists using topic distributions that are conditioned on location, season, and other intrinsic features. It also develops a "cocktail approach" which combines the TAST model with additional factors like price and seasonality to generate personalized travel package recommendations. An evaluation on real-world travel data showed the TAST model could effectively capture characteristics of travel data and the cocktail approach performed better than traditional recommendation techniques.
JPJ1414 A Cocktail Approach for Travel Package Recommendationchennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
This document presents a travel package recommendation system called TRAVELMATE that uses data mining techniques. It develops a Topic-Area-Season (TAST) model to understand travel package characteristics and tourist interests. A cocktail recommendation approach is introduced that uses the TAST model output and collaborative filtering to generate customized travel package recommendations. It also extends the TAST model to the TRAST model to capture relationships between tourists in tour groups. The models and recommendation approaches are evaluated on real-world travel package data.
A survey on a cocktail approach for travel package recommendationeSAT Journals
The document discusses a cocktail approach for personalized travel package recommendation. It proposes a Tourist-Area-Season Topic (TAST) model to represent travel packages using topics related to tourists, areas, and seasons. A cocktail recommendation approach is then presented that uses the TAST model output and collaborative filtering to generate personalized package recommendations. The approach is evaluated on travel package data. The TAST model is also extended to the Tourist-Relation-Area-Season Topic (TRAST) model to incorporate relationships between tourists.
This document presents a research project that aims to analyze tourist behavior based on geo-tagged location data from community websites. The researchers plan to analyze sequences of places visited by tourists each day to identify popular tourist locations and understand tourism demographics. They will use data from the Tourpedia website about tourism in Paris. The analysis will help tourism industry stakeholders better plan services and manage destinations. It will involve clustering location data, constructing time series to show tourist numbers over time, and structuring demographic data for locations.
Travel route scheduling based on user’s preferences using simulated annealingIJECEIAES
Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several user’s preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method.
This document presents a proposed end-to-end tour system that uses machine learning and recommendation algorithms. The system would allow users to take a quiz to receive personalized tour destination recommendations. It would also provide pre-packaged tour options and allow users to customize their own tours by selecting different components. The system is divided into three modules: a prediction quiz, a recommendation system using singular value decomposition, and a customization module. The goal is to make the tour planning process easy and hassle-free for users.
This document discusses a proposed smart tourism recommender system that aims to overcome issues with existing tourism systems and websites. It begins with an introduction that outlines the growth of tourism information online and challenges users face in filtering through vast options. It then reviews literature on previous recommender systems and their limitations. The proposed system aims to formalize user preferences and recommendations while accounting for changing conditions that could impact travel plans. Key challenges for recommendation systems include scaling algorithms for large real-world databases, protecting user privacy, and providing diverse recommendations. The conclusion states that recommendation systems help with information overload and future work will focus on enhancing prediction quality.
This document reviews 13 papers related to using data analytics and machine learning techniques to analyze tourist behavior from large datasets. The papers explore using geotagged photos, reviews, and location data to cluster and classify tourist activities and interests, identify representative landmarks and destinations, predict future locations, and uncover patterns in tourist flows and behaviors. Machine learning methods discussed include density-based clustering, convolutional neural networks, random forest classification, and deep learning models. The goal of this research is to better understand tourist preferences and decision-making to help tourism organizations improve destination management and personalize services.
IRJET- Intelligent Globetrotting Information System using Association Rule Mi...IRJET Journal
This document presents an intelligent tourism recommendation system that uses association rule mining algorithms like Apriori. The system aims to overcome drawbacks in existing systems like a lack of personalized recommendations and difficulty choosing locations from massive data. It collects location data and asks users simple questions to filter recommendations based on distance, ratings, and other preferences. The Apriori algorithm is used to generate association rules from user data and location tags to recommend complementary locations with high support and confidence. The system aims to provide a better user experience through highly personalized recommendations made efficiently from limited user input.
The document describes a proposed system to provide travel route recommendations. It uses collaborative filtering to recommend sequences of points of interest (POIs) along trajectories, rather than just individual POIs. It generates candidate routes by combining subsequences from existing trajectories. The routes are visualized on a map with POIs marked along them. Users can search for and view recommended POIs, provide reviews to help future recommendations, and book transportation. The system aims to improve on existing anonymous review-based systems by providing personalized recommendations and marking exact POI locations on routes.
Personalized Route Recommendation for self-drive tourists based on V2V commun...IRJET Journal
The document discusses personalized route recommendation for self-driving tourists based on vehicle-to-vehicle communication. It proposes using graph neural networks and attention mechanisms to improve the A* search algorithm for route recommendation. Specifically, it uses recurrent neural networks to learn the cost function for estimating the cost from the source to candidate locations. It also uses an assessment network incorporating position-aware graph attention to predict the cost from candidates to the destination. The goal is to learn accurate cost estimates for candidate locations to determine better routes in a principled manner. Evaluation on three real-world datasets shows the effectiveness of the proposed model.
The document describes the design of a prototype called SmartTravel that aims to support cooperative travel advisory between agents and customers. It discusses how traditional travel agencies are challenged by abundant online information and transactions. The prototype uses a large shared display to allow agents and customers to jointly explore travel options and information. This is intended to reduce information asymmetry and increase trust and customer involvement in the advisory process. The system integrates professional and user-generated travel content to provide richer information. Initial evaluations found that customers valued the system for providing more comprehensive and trustworthy information in an enjoyable environment.
This document discusses a smart tourism recommender system that uses machine learning algorithms to provide personalized tourism recommendations to users. It first introduces the problem of information overload that tourists face when searching for travel information online. It then discusses how the proposed system aims to overcome this issue by using a naive Bayes classifier to analyze a user's input and preferences and provide the most relevant tourism location recommendations. The system architecture and challenges of building recommender systems at scale are also outlined. Finally, it summarizes the implemented website and discusses using machine learning algorithms like naive Bayes to classify users' requirements and provide better assistance in finding desired travel results.
IRJET- Location-Based Route Recommendation System with Effective Query KeywordsIRJET Journal
The document proposes a location-based route recommendation system that uses keyword queries to find optimal routes for travelers. It extracts keywords from check-in data on location-based social networks to understand user preferences. An efficient framework is developed to retrieve representative travel routes that match a user's keyword requirements. It uses knowledge extraction from historical mobility records and social interactions to classify POI tags and effectively match keywords. The goal is to recommend diverse and personalized routes to help users plan trips based on their specific interests.
Hybrid recommendation technique for automated personalized poi selectionIAEME Publication
This document summarizes a research paper that proposes a hybrid recommendation technique to select personalized points of interest (POIs) for tourists. The technique combines content-based, collaborative filtering, and demographic recommendation approaches. It first uses the hybrid approach to assign personal interest scores to POIs based on a user's profile and preferences. It then clusters the POIs following the user's criteria to select the most appropriate trip area. The clustering aims to reduce the computational time needed for tourists to select POIs and plan trips. The paper argues this approach differs from previous work by incorporating clustering after the initial POI selection.
The document presents a proposal for an Instant Auto Booking System called TripInn. It aims to help travelers find places and trips that match their preferences by providing narrowed-down suggestions without having to compromise on factors. The problem statement discusses how current research is stressful and time-consuming. It then outlines the objectives of TripInn to provide personalised suggestions and create custom itineraries. The literature survey evaluates papers that address problems in the travel industry. Finally, it discusses the proposed software requirements and implementation of TripInn.
D:\Settings\U115501\Desktop\Designing The Tourist Agency Of The FutureThomas Cook Belgium
This document proposes a new business model for travel agencies in the digital age that focuses on advisory services rather than information provision or transactions. It presents the SmartTravel system, which was designed to support travel advisors in having rich, interactive discussions with customers to uncover hidden travel needs. The system uses a large display and integrates professional and user-generated content. An evaluation in a real travel agency found that customers valued the system for providing more trustworthy and enjoyable information. The document argues this approach can help advisors better understand customers' objective needs and create a better experience than searching online alone.
D:\Settings\U115501\Desktop\Designing The Tourist Agency Of The FutureThomas Cook Belgium
Customer:
- Enters selection - Explores offers
criteria - Selects favorites
- Refines search - Provides feedback
queries - Shares experiences
- Provides - Makes final choice
contextual
information
Shared large display
Figure 6: Architecture of the SmartTravel prototype
The system integrates information from different sources:
- Catalogue information from the agency’s booking system (e.g. hotel descriptions, photos, prices)
- User-generated content from travel communities (e.g. photos, videos, reviews, tips)
- Contextual information from maps and virtual globes (e.g. Google Maps,
Travelogue: A Travel Package Recommendation using PythonIRJET Journal
This document summarizes a research paper on a travel package recommendation system called Travelogue developed using Python. Travelogue helps travelers plan trips by generating optimized itineraries for destinations based on user preferences like number of days and interests. It uses the FourSquare API to retrieve popular places and the Travelling Salesman Problem algorithm to create efficient routes. The system also allows customizing itineraries by removing or adding places. Maps plot the itinerary locations and show routes between places. Future work aims to incorporate a budget module and machine learning for improved destination and place recommendations. The paper concludes that Travelogue successfully implements key trip planning features to assist independent travel.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
More Related Content
Similar to 2014 IEEE JAVA DATA MINING PROJECT A cocktail approach for travel package recommendation
JPD1412 A Cocktail Approach for Travel Package Recommendationchennaijp
This document proposes a new approach called the TAST model for personalized travel package recommendation. The TAST model represents travel packages and tourists using topic distributions that are conditioned on location, season, and other intrinsic features. It also develops a "cocktail approach" which combines the TAST model with additional factors like price and seasonality to generate personalized travel package recommendations. An evaluation on real-world travel data showed the TAST model could effectively capture characteristics of travel data and the cocktail approach performed better than traditional recommendation techniques.
JPJ1414 A Cocktail Approach for Travel Package Recommendationchennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
This document presents a travel package recommendation system called TRAVELMATE that uses data mining techniques. It develops a Topic-Area-Season (TAST) model to understand travel package characteristics and tourist interests. A cocktail recommendation approach is introduced that uses the TAST model output and collaborative filtering to generate customized travel package recommendations. It also extends the TAST model to the TRAST model to capture relationships between tourists in tour groups. The models and recommendation approaches are evaluated on real-world travel package data.
A survey on a cocktail approach for travel package recommendationeSAT Journals
The document discusses a cocktail approach for personalized travel package recommendation. It proposes a Tourist-Area-Season Topic (TAST) model to represent travel packages using topics related to tourists, areas, and seasons. A cocktail recommendation approach is then presented that uses the TAST model output and collaborative filtering to generate personalized package recommendations. The approach is evaluated on travel package data. The TAST model is also extended to the Tourist-Relation-Area-Season Topic (TRAST) model to incorporate relationships between tourists.
This document presents a research project that aims to analyze tourist behavior based on geo-tagged location data from community websites. The researchers plan to analyze sequences of places visited by tourists each day to identify popular tourist locations and understand tourism demographics. They will use data from the Tourpedia website about tourism in Paris. The analysis will help tourism industry stakeholders better plan services and manage destinations. It will involve clustering location data, constructing time series to show tourist numbers over time, and structuring demographic data for locations.
Travel route scheduling based on user’s preferences using simulated annealingIJECEIAES
Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several user’s preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method.
This document presents a proposed end-to-end tour system that uses machine learning and recommendation algorithms. The system would allow users to take a quiz to receive personalized tour destination recommendations. It would also provide pre-packaged tour options and allow users to customize their own tours by selecting different components. The system is divided into three modules: a prediction quiz, a recommendation system using singular value decomposition, and a customization module. The goal is to make the tour planning process easy and hassle-free for users.
This document discusses a proposed smart tourism recommender system that aims to overcome issues with existing tourism systems and websites. It begins with an introduction that outlines the growth of tourism information online and challenges users face in filtering through vast options. It then reviews literature on previous recommender systems and their limitations. The proposed system aims to formalize user preferences and recommendations while accounting for changing conditions that could impact travel plans. Key challenges for recommendation systems include scaling algorithms for large real-world databases, protecting user privacy, and providing diverse recommendations. The conclusion states that recommendation systems help with information overload and future work will focus on enhancing prediction quality.
This document reviews 13 papers related to using data analytics and machine learning techniques to analyze tourist behavior from large datasets. The papers explore using geotagged photos, reviews, and location data to cluster and classify tourist activities and interests, identify representative landmarks and destinations, predict future locations, and uncover patterns in tourist flows and behaviors. Machine learning methods discussed include density-based clustering, convolutional neural networks, random forest classification, and deep learning models. The goal of this research is to better understand tourist preferences and decision-making to help tourism organizations improve destination management and personalize services.
IRJET- Intelligent Globetrotting Information System using Association Rule Mi...IRJET Journal
This document presents an intelligent tourism recommendation system that uses association rule mining algorithms like Apriori. The system aims to overcome drawbacks in existing systems like a lack of personalized recommendations and difficulty choosing locations from massive data. It collects location data and asks users simple questions to filter recommendations based on distance, ratings, and other preferences. The Apriori algorithm is used to generate association rules from user data and location tags to recommend complementary locations with high support and confidence. The system aims to provide a better user experience through highly personalized recommendations made efficiently from limited user input.
The document describes a proposed system to provide travel route recommendations. It uses collaborative filtering to recommend sequences of points of interest (POIs) along trajectories, rather than just individual POIs. It generates candidate routes by combining subsequences from existing trajectories. The routes are visualized on a map with POIs marked along them. Users can search for and view recommended POIs, provide reviews to help future recommendations, and book transportation. The system aims to improve on existing anonymous review-based systems by providing personalized recommendations and marking exact POI locations on routes.
Personalized Route Recommendation for self-drive tourists based on V2V commun...IRJET Journal
The document discusses personalized route recommendation for self-driving tourists based on vehicle-to-vehicle communication. It proposes using graph neural networks and attention mechanisms to improve the A* search algorithm for route recommendation. Specifically, it uses recurrent neural networks to learn the cost function for estimating the cost from the source to candidate locations. It also uses an assessment network incorporating position-aware graph attention to predict the cost from candidates to the destination. The goal is to learn accurate cost estimates for candidate locations to determine better routes in a principled manner. Evaluation on three real-world datasets shows the effectiveness of the proposed model.
The document describes the design of a prototype called SmartTravel that aims to support cooperative travel advisory between agents and customers. It discusses how traditional travel agencies are challenged by abundant online information and transactions. The prototype uses a large shared display to allow agents and customers to jointly explore travel options and information. This is intended to reduce information asymmetry and increase trust and customer involvement in the advisory process. The system integrates professional and user-generated travel content to provide richer information. Initial evaluations found that customers valued the system for providing more comprehensive and trustworthy information in an enjoyable environment.
This document discusses a smart tourism recommender system that uses machine learning algorithms to provide personalized tourism recommendations to users. It first introduces the problem of information overload that tourists face when searching for travel information online. It then discusses how the proposed system aims to overcome this issue by using a naive Bayes classifier to analyze a user's input and preferences and provide the most relevant tourism location recommendations. The system architecture and challenges of building recommender systems at scale are also outlined. Finally, it summarizes the implemented website and discusses using machine learning algorithms like naive Bayes to classify users' requirements and provide better assistance in finding desired travel results.
IRJET- Location-Based Route Recommendation System with Effective Query KeywordsIRJET Journal
The document proposes a location-based route recommendation system that uses keyword queries to find optimal routes for travelers. It extracts keywords from check-in data on location-based social networks to understand user preferences. An efficient framework is developed to retrieve representative travel routes that match a user's keyword requirements. It uses knowledge extraction from historical mobility records and social interactions to classify POI tags and effectively match keywords. The goal is to recommend diverse and personalized routes to help users plan trips based on their specific interests.
Hybrid recommendation technique for automated personalized poi selectionIAEME Publication
This document summarizes a research paper that proposes a hybrid recommendation technique to select personalized points of interest (POIs) for tourists. The technique combines content-based, collaborative filtering, and demographic recommendation approaches. It first uses the hybrid approach to assign personal interest scores to POIs based on a user's profile and preferences. It then clusters the POIs following the user's criteria to select the most appropriate trip area. The clustering aims to reduce the computational time needed for tourists to select POIs and plan trips. The paper argues this approach differs from previous work by incorporating clustering after the initial POI selection.
The document presents a proposal for an Instant Auto Booking System called TripInn. It aims to help travelers find places and trips that match their preferences by providing narrowed-down suggestions without having to compromise on factors. The problem statement discusses how current research is stressful and time-consuming. It then outlines the objectives of TripInn to provide personalised suggestions and create custom itineraries. The literature survey evaluates papers that address problems in the travel industry. Finally, it discusses the proposed software requirements and implementation of TripInn.
D:\Settings\U115501\Desktop\Designing The Tourist Agency Of The FutureThomas Cook Belgium
This document proposes a new business model for travel agencies in the digital age that focuses on advisory services rather than information provision or transactions. It presents the SmartTravel system, which was designed to support travel advisors in having rich, interactive discussions with customers to uncover hidden travel needs. The system uses a large display and integrates professional and user-generated content. An evaluation in a real travel agency found that customers valued the system for providing more trustworthy and enjoyable information. The document argues this approach can help advisors better understand customers' objective needs and create a better experience than searching online alone.
D:\Settings\U115501\Desktop\Designing The Tourist Agency Of The FutureThomas Cook Belgium
Customer:
- Enters selection - Explores offers
criteria - Selects favorites
- Refines search - Provides feedback
queries - Shares experiences
- Provides - Makes final choice
contextual
information
Shared large display
Figure 6: Architecture of the SmartTravel prototype
The system integrates information from different sources:
- Catalogue information from the agency’s booking system (e.g. hotel descriptions, photos, prices)
- User-generated content from travel communities (e.g. photos, videos, reviews, tips)
- Contextual information from maps and virtual globes (e.g. Google Maps,
Travelogue: A Travel Package Recommendation using PythonIRJET Journal
This document summarizes a research paper on a travel package recommendation system called Travelogue developed using Python. Travelogue helps travelers plan trips by generating optimized itineraries for destinations based on user preferences like number of days and interests. It uses the FourSquare API to retrieve popular places and the Travelling Salesman Problem algorithm to create efficient routes. The system also allows customizing itineraries by removing or adding places. Maps plot the itinerary locations and show routes between places. Future work aims to incorporate a budget module and machine learning for improved destination and place recommendations. The paper concludes that Travelogue successfully implements key trip planning features to assist independent travel.
Similar to 2014 IEEE JAVA DATA MINING PROJECT A cocktail approach for travel package recommendation (20)
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
The document discusses supporting privacy protection in personalized web search. It proposes a framework called UPS that can generalize user profiles for each query based on user-specified privacy requirements to balance personalization utility and privacy risk. Two algorithms, GreedyDP and GreedyIL, are developed for runtime profile generalization, with GreedyIL significantly outperforming GreedyDP in efficiency. Extensive experiments demonstrate the effectiveness of the UPS framework in improving search quality while preserving user privacy.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
The document proposes a converged architecture for broadcast and multicast services in a heterogeneous network combining LTE and DVB-H. It suggests several logical entities for content management, electronic service guide management and resource management including an integrated contents server, integrated ESG server and integrated management server. Several scenarios for implementing the converged architecture are presented and their advantages compared.
We propose a framework for anonymous query processing in road networks. The framework designs location obfuscation techniques that provide anonymous access to location-based services for users while also allowing efficient query processing by the services. The techniques exploit existing network database infrastructure and do not require specialized storage or functionality. Experimental comparisons of alternative designs in real road networks demonstrate the effectiveness of the techniques.
This document discusses opinion mining and sentiment analysis. It begins by explaining that the rise of social media has created opportunities to understand public opinions on various topics by analyzing user comments. It then defines opinion mining as using computational techniques to extract, classify, understand and assess opinions expressed online, with sentiment analysis identifying sentiments in text. The document goes on to provide hardware and software requirements for a proposed system related to these techniques.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses searching incomplete databases. Existing work addresses when data values on certain dimensions are unknown, but real-life data like from sensors may have missing dimension information as well. The proposed system develops a probabilistic framework to model similarity search on dimension incomplete data, allowing users to find similar objects with probability guarantees. It derives lower and upper bounds on the probability of similarity to efficiently filter irrelevant objects without examining all missing dimension combinations. Experimental results on real data demonstrate the approach's effectiveness and efficiency.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
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2014 IEEE JAVA DATA MINING PROJECT A cocktail approach for travel package recommendation
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A Cocktail Approach for Travel Package Recommendation
ABSTRACT:
Recent years have witnessed an increased interest in recommender systems.
Despite significant progress in this field, there still remain numerous avenues to
explore. Indeed, this paper provides a study of exploiting online travel information
for personalized travel package recommendation. A critical challenge along this
line is to address the unique characteristics of travel data, which distinguish travel
packages from traditional items for recommendation. To that end, in this paper, we
first analyze the characteristics of the existing travel packages and develop a
tourist-area-season topic (TAST) model. This TAST model can represent travel
packages and tourists by different topic distributions, where the topic extraction is
conditioned on both the tourists and the intrinsic features (i.e., locations, travel
seasons) of the landscapes. Then, based on this topic model representation, we
propose a cocktail approach to generate the lists for personalized travel package
recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-
season topic (TRAST) model for capturing the latent relationships among the
tourists in each travel group. Finally, we evaluate the TAST model, the TRAST
model, and the cocktail recommendation approach on the real-world travel package
2. data. Experimental results show that the TAST model can effectively capture the
unique characteristics of the travel data and the cocktail approach is, thus, much
more effective than traditional recommendation techniques for travel package
recommendation. Also, by considering tourist relationships, the TRAST model can
be used as an effective assessment for travel group formation.
EXISTING SYSTEM:
There are many technical and domain challenges inherent in designing and
implementing an effective recommender system for personalized travel package
recommendation.
1. Travel data are much fewer and sparser than traditional items, such as
movies for recommendation, because the costs for a travel are much more
expensive than for watching a movie.
2. Every travel package consists of many landscapes (places of interest and
attractions), and, thus, has intrinsic complex spatio-temporal relationships.
For example, a travel package only includes the landscapes which are
geographically colocated together. Also, different travel packages are
usually developed for different travel seasons. Therefore, the landscapes in a
travel package usually have spatial temporal autocorrelations.
3. Traditional recommender systems usually rely on user explicit ratings.
However, for travel data, the user ratings are usually not conveniently
available.
3. DISADVANTAGES OF EXISTING SYSTEM:
Recommendation has a long period of stable value.
To replace the old ones based on the interests of the tourists.
A values of travel packages can easily depreciate over time and a
package usually only lasts for a certain period of time
PROPOSED SYSTEM:
In this paper, we aim to make personalized travel package recommendations for
the tourists. Thus, the users are the tourists and the items are the existing packages,
and we exploit a real-world travel data set provided by a travels for building
recommender systems. we develop a tourist-area-season topic (TAST) model,
which can represent travel packages and tourists by different topic distributions. In
the TAST model, the extraction of topics is conditioned on both the tourists and the
intrinsic features (i.e., locations, travel seasons) of the landscapes. Based on this
TAST model, a cocktail approach is developed for personalized travel package
recommendation by considering some additional factors including the seasonal
behaviors of tourists, the prices of travel packages, and the cold start problem of
new packages.
ADVANTAGES OF PROPOSED SYSTEM:
Represent the content of the travel packages and the interests of the tourists.
TAST model can effectively capture the unique characteristics of travel data.
4. The cocktail recommendation approach performs much better than
traditional techniques.
SYSTEM ARCHITECTURE:
5. SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL
REFERENCE:
Qi Liu, Enhong Chen, Hui Xiong, Yong Ge, Zhongmou Li, and Xiang Wu ,“A
Cocktail Approach for Travel Package Recommendation”, IEEE
TRANSACTIONS, VOL. 26, NO. 2, FEBRUARY 2014.