Point-of-interest Recommendation for Location Promotion in Location-based Social Networks Data Mining IEEE-2017 Java Project Abstract From Cloud Technologies Hyderabad
DESIGNING A RECOMMENDER SYSTEM BASED ON SOCIAL NETWORKS AND LOCATION BASED ...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable
frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and
preferences in a suggestion provided using just place-based services. Current generation of place-based
services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of
using social recommender systems was discussed which contains capability of identifying user’s interests
and preferences and based on them and user’s current place, it offers some suggestions. Social
recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible
to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can
acquire their favorite information and services.
Designing a recommender system based on social networks and location based se...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and preferences in a suggestion provided using just place-based services. Current generation of place-based services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of using social recommender systems was discussed which contains capability of identifying user’s interests and preferences and based on them and user’s current place, it offers some suggestions. Social recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can acquire their favorite information and services.
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORKcsandit
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
A location based movie recommender systemijfcstjournal
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient. This could be very obvious in the systems that locations would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed through experiments in real datasets.
Finding nearest Neighbor in Geo-Social Query Processingrahulmonikasharma
Recording the region of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they habitually visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, improves the social network, providing an integrated geo-social graph. Queries over such graph excerpt information on users, with respect to their location history, and excerpt information on geographical entities in correspondence with users who normally visit these entities. A repeated type of query in spatial networks (e.g., road networks) is to find the k- nearest neighbors (k-NN) of a given query objects. With these networks, the distances between objects depend on their network connectivity and it is expensive to compute the distances (e.g., shortest paths) between objects. We present the concept of a geo-social graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges more specifically to allow finding a group of users in a Geo-Social network whose members are close to each other both socially and geographically. We proposed a new approach to find the group of k users who are geo-socially attached to each other and satisfy the all the query points. We used the Bottom up pruning technique for effective pruning of geo-social group queries. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated geo-social graphs.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
DESIGNING A RECOMMENDER SYSTEM BASED ON SOCIAL NETWORKS AND LOCATION BASED ...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable
frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and
preferences in a suggestion provided using just place-based services. Current generation of place-based
services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of
using social recommender systems was discussed which contains capability of identifying user’s interests
and preferences and based on them and user’s current place, it offers some suggestions. Social
recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible
to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can
acquire their favorite information and services.
Designing a recommender system based on social networks and location based se...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and preferences in a suggestion provided using just place-based services. Current generation of place-based services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of using social recommender systems was discussed which contains capability of identifying user’s interests and preferences and based on them and user’s current place, it offers some suggestions. Social recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can acquire their favorite information and services.
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORKcsandit
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
A location based movie recommender systemijfcstjournal
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient. This could be very obvious in the systems that locations would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed through experiments in real datasets.
Finding nearest Neighbor in Geo-Social Query Processingrahulmonikasharma
Recording the region of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they habitually visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, improves the social network, providing an integrated geo-social graph. Queries over such graph excerpt information on users, with respect to their location history, and excerpt information on geographical entities in correspondence with users who normally visit these entities. A repeated type of query in spatial networks (e.g., road networks) is to find the k- nearest neighbors (k-NN) of a given query objects. With these networks, the distances between objects depend on their network connectivity and it is expensive to compute the distances (e.g., shortest paths) between objects. We present the concept of a geo-social graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges more specifically to allow finding a group of users in a Geo-Social network whose members are close to each other both socially and geographically. We proposed a new approach to find the group of k users who are geo-socially attached to each other and satisfy the all the query points. We used the Bottom up pruning technique for effective pruning of geo-social group queries. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated geo-social graphs.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), “Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel”, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.org
User Studies for APG: How to support system development with user feedback?Joni Salminen
Presentation at QCRI's Science Monday of the Social Computing group. January 14, 2019. Doha, Qatar. Access the Automatic Persona Generation system: https://persona.qcri.org
Research Roadmap for Automatic Persona Generation (2018)Joni Salminen
Automatic Persona Generation (APG) is a system and methodology developed at Qatar Computing Research Institute, Hamad Bin Khalifa University. Read more: https://persona.qcri.org
The goal of Automatic Persona Generation is to give faces to social and online analytics data. Personas can be generated from YouTube, Facebook, and Google Analytics data.
If you are interested in research collaboration, please contact Professor Jim Jansen at bjansen@hbku.edu.qa
In the online world, user engagement refers to the quality of the user experience that emphasizes the phenomena associated with wanting to use an application longer and frequently. This talk looks at the role of Big Data in measuring user engagement. It does so through two case studies on using absence time, within sessions and across sessions.
Presentation at "Data-Driven Business Day" at Strata + HW Barcelona 2014.
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
Predicting Venues in Location Based Social Network cscpconf
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
A User Experience Audit (UX Audit) is a method for identifying problematic areas of a digital product, exposing which aspects of a website or mobile application are causing user frustration and inhibiting conversions.
A lecture in digital analytics at Aalto University. The lecture is a part of a module in Information Technology Program (ITP).
Summer 2015, Helsinki
--
Dr. Joni Salminen is a lecturer in digital marketing. Besides online marketing, his interests include startups and web platforms. Contact: joolsa@utu.fi
Tutorial on metrics of user engagement -- Applications to Search & E- commerceMounia Lalmas-Roelleke
User engagement plays a central role in companies operating online services, such as search engines, news portals, e-commerce sites, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engage them short-term and more importantly long-term. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial reviews these metrics and proposes a taxonomy of metrics. As case studies, it focuses on two types of services, search and e-commerce. The tutorial also discusses how to develop better machine learning models to optimize online metrics, and design experiments to test these models.
This tutorial was given by Mounia Lalmas from Spotify and Liangjie Long from Etsy Inc.
This tutorial was presented at WSDM 2018 (11th ACM International Conference on Web Search and Data Mining). It is the first delivery of this tutorial, so feedbacks and comments are welcome. We intend to continue working on this material.
Want Your Customers to Come Back? Make Sure UX is of Top PriorityRick Hevier
Rick Hevier explains the profitable power behind creating a top-of-the-line user experience for consumers both on and offline. Explore the intricacies of how to create this type of user experience here.
These slides use concepts from my (Jeff Funk) course on Business Models at National University of Singapore to analyze the business model of Augmented Reality for travel. Augmented reality superimposes information on top of our sensory data. One way to do AR is to use a smart phone’s camera to view a world with information superimposed on the smart phone’s display. For travel, nearby places of interest can be provides along with ratings, reviews directions, public transport, and other information on them. This information can be obtained from Google Earth and other sources. The slides describe the value proposition, method of value capture, customers, scope of activities, and method of strategic control for two startups involved with AR and travel.
Traditionally development of digital tools was solely an IT initiative, but today it is a customer-needs driven initiative. Digital experiences are often times the first impression that potential customers have of you, and the first tools they turn to when they have questions or needs throughout their journey.
Best practices for developing digital tools exist, and it is common to partner with an advertising or web development agency for this purpose. However, each market and healthcare brand are unique, necessitating the inclusion of voice of the customer insight to ensure that digital tools are being built around the real (not just assumed) needs and priorities of users. So how can marketing and digital teams engage users in order to develop the digital strategy and deliver the ideal digital experience? In this paper, we present a proven process and research-based tools for obtaining direct user feedback about digital needs, preferences, and priorities.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), “Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel”, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.org
User Studies for APG: How to support system development with user feedback?Joni Salminen
Presentation at QCRI's Science Monday of the Social Computing group. January 14, 2019. Doha, Qatar. Access the Automatic Persona Generation system: https://persona.qcri.org
Research Roadmap for Automatic Persona Generation (2018)Joni Salminen
Automatic Persona Generation (APG) is a system and methodology developed at Qatar Computing Research Institute, Hamad Bin Khalifa University. Read more: https://persona.qcri.org
The goal of Automatic Persona Generation is to give faces to social and online analytics data. Personas can be generated from YouTube, Facebook, and Google Analytics data.
If you are interested in research collaboration, please contact Professor Jim Jansen at bjansen@hbku.edu.qa
In the online world, user engagement refers to the quality of the user experience that emphasizes the phenomena associated with wanting to use an application longer and frequently. This talk looks at the role of Big Data in measuring user engagement. It does so through two case studies on using absence time, within sessions and across sessions.
Presentation at "Data-Driven Business Day" at Strata + HW Barcelona 2014.
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
Predicting Venues in Location Based Social Network cscpconf
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
A User Experience Audit (UX Audit) is a method for identifying problematic areas of a digital product, exposing which aspects of a website or mobile application are causing user frustration and inhibiting conversions.
A lecture in digital analytics at Aalto University. The lecture is a part of a module in Information Technology Program (ITP).
Summer 2015, Helsinki
--
Dr. Joni Salminen is a lecturer in digital marketing. Besides online marketing, his interests include startups and web platforms. Contact: joolsa@utu.fi
Tutorial on metrics of user engagement -- Applications to Search & E- commerceMounia Lalmas-Roelleke
User engagement plays a central role in companies operating online services, such as search engines, news portals, e-commerce sites, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engage them short-term and more importantly long-term. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial reviews these metrics and proposes a taxonomy of metrics. As case studies, it focuses on two types of services, search and e-commerce. The tutorial also discusses how to develop better machine learning models to optimize online metrics, and design experiments to test these models.
This tutorial was given by Mounia Lalmas from Spotify and Liangjie Long from Etsy Inc.
This tutorial was presented at WSDM 2018 (11th ACM International Conference on Web Search and Data Mining). It is the first delivery of this tutorial, so feedbacks and comments are welcome. We intend to continue working on this material.
Want Your Customers to Come Back? Make Sure UX is of Top PriorityRick Hevier
Rick Hevier explains the profitable power behind creating a top-of-the-line user experience for consumers both on and offline. Explore the intricacies of how to create this type of user experience here.
These slides use concepts from my (Jeff Funk) course on Business Models at National University of Singapore to analyze the business model of Augmented Reality for travel. Augmented reality superimposes information on top of our sensory data. One way to do AR is to use a smart phone’s camera to view a world with information superimposed on the smart phone’s display. For travel, nearby places of interest can be provides along with ratings, reviews directions, public transport, and other information on them. This information can be obtained from Google Earth and other sources. The slides describe the value proposition, method of value capture, customers, scope of activities, and method of strategic control for two startups involved with AR and travel.
Traditionally development of digital tools was solely an IT initiative, but today it is a customer-needs driven initiative. Digital experiences are often times the first impression that potential customers have of you, and the first tools they turn to when they have questions or needs throughout their journey.
Best practices for developing digital tools exist, and it is common to partner with an advertising or web development agency for this purpose. However, each market and healthcare brand are unique, necessitating the inclusion of voice of the customer insight to ensure that digital tools are being built around the real (not just assumed) needs and priorities of users. So how can marketing and digital teams engage users in order to develop the digital strategy and deliver the ideal digital experience? In this paper, we present a proven process and research-based tools for obtaining direct user feedback about digital needs, preferences, and priorities.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This is an outline of how a PR planning and management process is developing. It is media agnostic and uses the Relationship Values Theory and semantics to form the basis for research, landscaping, planning, execution, evaluation and insights.
Steer intent, drive behavior, improve web traffic with web content management...Bridgeline Digital
Understanding your website users and how to drive their behavior is a fundamental imperative to the success of your online marketing initiatives. See the details behind understanding user intent and behavior — using real-life examples.
Machine Learning Classification to predict water purity based on Viruses and ...CloudTechnologies
Machine Learning Classification to predict water purity based on Viruses and Bacteria levels
Water is a major resource in every day’s life for humans, animals and plants. The quality of water polluting due to the industrialization, mining, and some other factors. Drinking water and irrigation water are two different types, and the quality levels to be measured based on the usage. The world health organization released some threshold values based on some water parameters. The metrics are named as The Water Purity by Assessing and Eliminating Viruses and Bacteria (WPAEVB) and Irrigation WQI (IWQI) which can measure the water quality. This paper proposed a network architecture to analyze all the parameters by using machine learning tools (ML) tools which will determine the drinking water an irrigation water based on virus and bacteria values. The model is developed based on LoRa and land topology. Here we used three models SVM, logistic regression (LR), and random forest (RF) to know whether irrigation water is being used for drinking water by detecting the percentage and levels of bacteria and virus. The dataset was developed based on the ML models due to the lower availability of datasets related to irrigation and drinking water and bacteria and virus percentage is also calculated by using three models. After applying all the models, LR given the best performance for drinking water and SVM given the best results for irrigation water. The recursive feature elimination was done by applying all three mL models.
iot based safety and health monitoring for construction workersCloudTechnologies
iot based safety and health monitoring for construction workers
Cloud Technologies providing Complete Solution for all
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Intelligent neonatal monitoring system based on android application using mul...CloudTechnologies
Intelligent neonatal monitoring system based on android application using multi sensors
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An iot based smart garden with weather station systemCloudTechnologies
An iot based smart garden with weather station system
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A deep learning facial expression recognition based scoring system for restau...CloudTechnologies
A deep learning facial expression recognition based scoring system for restaurants
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Image based estimation of real food size for accurate food calorie estimationCloudTechnologies
Image based estimation of real food size for accurate food calorie estimation
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IEEE 2019 Data Mini Projects for Btech & Mtech Students
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Raspberry Pi based voice-operated personal assistant (Neobot)CloudTechnologies
Raspberry Pi based voice-operated personal assistant (Neobot)
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Automation in Agriculture and IoT
Cloud Technologies providing Complete Solution for all
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Gas Leakage Detection Based on IOT
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Build a Module in Odoo 17 Using the Scaffold Method
Point of-interest recommendation for location promotion in location-based social networks
1. Point-of-interestRecommendation for Location Promotion in Location-based
Social Networks
Abstract:
With the wide application of location-based social networks (LBSNs), point-of-interest (POI)
recommendation has become one of the major services in LBSNs. The behaviors of users in
LBSNs are mainly checking in POIs, and these checking in behaviors are influenced by user’s
behavior habits and his/her friends. In social networks, social influence is often used to help
businesses to attract more users. Each target user has a different influence on different POI in
social networks. This paper selects the list of POIs with the greatest influence for recommending
users. Our goals are to satisfy the target user’s service need, and simultaneously to promote
businesses’ locations (POIs). This paper defines a POI recommendation problem for location
promotion. Additionally, we use sub modular properties to solve the optimization problem. At
last, this paper conducted a comprehensive performance evaluation for our method using two
real LBSN datasets. Experimental results show that our proposed method achieves significantly
superior POI recommendations comparing with other state-of-the-art recommendation
approaches in terms of location promotion.
Existing System:
Recently, many researchers have been engaged in location-aware services. In LBSNs, users can
post comments on locations or activities, upload photos, and share check-in locations in which
users are interested with friends. These locations are called points-of-interest (POIs). Currently,
POI recommendation has become one of main location-aware services in LBSNs. POI
recommendation approaches mostly involve recommending users with some locations in which
users may be interested based on users’ characters, preferences, and behavioral habits. Through
the detailed analysis above, we observe traditional POI recommendations rarely focus on the
effect of social relationships for businesses location promotion through the POI recommendation
process.
Disadvantages:
No Concept for on the location promotion in LBSNs.
Helps only for business people not for users.
Proposed System:
2. In view of POIs, POIs (e.g.restaurants, hotel, markets) have to explore checking-in records to
attract more users to visit; more users (e.g., friends of users that checked in these POIs) will be
influenced to check in these locations. In this paper, we regard the influence on the business as a
maximization location promotion problem. The essential goals of recommendation system are to
satisfy users’ service demands and merchants’ advertising needs. this paper proposes POI
recommendation method for promoting POIs. Our proposed method is not only a tool for
businesses to use to promote their products and attract more customers to visit their stores, but
also recommends users with some POI’s satisfying users preferences.
Advantages:
We propose a novel point-of-interest recommendation problem, and its goal is to promote
the businesses’ locations ( POIs ).
We define the user’s IS under special POI categories in an entire social network, and
model user mobility to describe the geographical influence between user.
Few Points:
In this paper, focus on POI recommendation to social user based on his friends and friends of
friends instead of unknown recommendation.
Main Application collects check in data with geo properties. Like user from his location move to
POI , PG
u,v(l) tradeoff between geographical influence. And Target user to another user relation,
PT
u,v(l) semantic influence b/w u and v.
POI recommendation approaches mostly involve recommending users with some locations in
which users may be interested based on users’ characters, preferences. Like Facebook no suggest
you some business locations according to your interests.
3. Algorithm for POILP
Input: POI data P
Output: POIre (POI Recommendation)
Initialization:
i. Recommended POI categories RCre
ii. u is target user, v is a influences user .
let RCre ø
let POIuT = {a(1), a(2), · · · , a(K)}; where uT influence scope of social network
Compute POILP (POI recommendation problem for location promotion)
for each POIuT (1 to k)
Pu→v(l) = β × PG
u,v(l) + (1 − β) × PT
u,v(l),
Where -Pu→v The user u influences user v (u ≠ v)
-β(∈ [0, 1]) avg 0.5
- PG
u,v(l) tradeoff between geographical influence
- PT
u,v(l) semantic influence b/w u and v.
RCre RCre ∪ Pu→v(l);
Sort RCre ;
Return RCre;
4. SYSTEM REQUIREMENTS
HARDWARE REQUIREMENTS:
Hardware : Pentium
Speed : 1.1 GHz
RAM : 1GB
Hard Disk : 20 GB
SOFTWARE REQUIREMENTS:
Operating System : Windows Family
Technology : Java and J2EE
Web Technologies : Html, JavaScript, CSS
Web Server : Apache Tomcat 7.0/8.0
Database : My SQL 5.5 or Higher
UML's : StarUml
Java Version : JDK 1.7 or 1.8
Implemented by
Development team : Cloud Technologies
Website : http://www.cloudstechnologies.in/
Contact : 8121953811, 040-65511811