The technological advances in mobile phone and their widespread use has resulted in the big volum... more The technological advances in mobile phone and their widespread use has resulted in the big volume and varied types of mobile data we have today. Researchers have begun to mine mobile data in order to predict a variety of social, economic, personal, location and health related events. Mobile data directly reflects individual's life without disclosing personal information, and therefore it is an important source to analyze and understand the underlying dynamics of human behaviors or activities. In this paper, we describe an innovative and challenging process to predict user's activity using mobile based data. We propose a graph-based framework that uses the user's activities, social network, and product-keywords in order to provide recommendations which are also delivered through mobile phones. This paper summarizes the different types of prediction logic algorithms by constructing graphs from different data sources. Our graph-based approach is highly scalable and can be ...
Individuals and organizations rely heavily on social media these days for consumer reviews in the... more Individuals and organizations rely heavily on social media these days for consumer reviews in their decision-making on purchases. However, for personal gains such as profit or fame, people post fake reviews to promote or demote certain target products as well as to deceive the reader. To get genuine user experiences and opinions, there is a need to detect such spam or fake reviews. This paper presents a study that aims to detect truthful, useful reviews and ranks them. An effective supervised learning technique is proposed to detect truthful and useful reviews and rank them, using a ‘deceptive’ classifier, ‘useful’ classifier, and a ‘ranking’ model respectively. Deceptive and nonuseful consumer reviews from online review communities such as amazon.com and Epinions.com are used. The proposed method first uses the ‘deceptive’ classifier to find truthful reviews followed by the ‘useful’ classifier to find whether a review is useful or not. Manually labeling individual reviews is very d...
How to get target advertising in digital signage is an innovative and challenging question. This ... more How to get target advertising in digital signage is an innovative and challenging question. This paper summarizes the result of a field trial related to targeted and non-targeted digital signage (DS) with Anonymous Video Analytics (AVA) conducted over 9 month period of time and across 10 different retailers. The goal was to understand the impact of targeted and non-targeted digital signage on sales and offers in a different environment like super market store, electronic store, and general retails. By correlating AVA viewership information with proof-of-sale (POS) data relation can be established between the response time to an ad seen by a certain demographic group and the effect on the sale of the advertised product, so as to provide retailers/advertisers with intelligence to show the right ads to right audience at right time and in the right location. The result of experiment revealed that targeted digital signage has a prominent sale impact on instant food packets, beverages, an...
105 Background: Physical activity recommendations are often made through survivorship care plans ... more 105 Background: Physical activity recommendations are often made through survivorship care plans and survivorship guidelines. However, recommendations may not translate into actions and hence it is important to understand the ground reality of cancer survivors and determine how much physical activity they are able to do in their daily life. Methods: Data were obtained from a self-reported survey of patients attending survivorship clinic at a comprehensive cancer center. Participants were asked what kind of physical activity they were engaging in, how many times a week and how many minutes per session. A composite variable was created by multiplying the number of times by number of visits and adding all the activity types patients reported engagement in. This number was compared against the current recommendations of 150minutes of physical activity weekly as recommended National Comprehensive Cancer Network (NCCN) guidelines. Data were stratified by age, gender and cancer type. Resul...
The technological advances in mobile phone and their widespread use has resulted in the big volum... more The technological advances in mobile phone and their widespread use has resulted in the big volume and varied types of mobile data we have today. Researchers have begun to mine mobile data in order to predict a variety of social, economic, personal, location and health related events. Mobile data directly reflects individual's life without disclosing personal information, and therefore it is an important source to analyze and understand the underlying dynamics of human behaviors or activities. In this paper, we describe an innovative and challenging process to predict user's activity using mobile based data. We propose a graph-based framework that uses the user's activities, social network, and product-keywords in order to provide recommendations which are also delivered through mobile phones. This paper summarizes the different types of prediction logic algorithms by constructing graphs from different data sources. Our graph-based approach is highly scalable and can be ...
Individuals and organizations rely heavily on social media these days for consumer reviews in the... more Individuals and organizations rely heavily on social media these days for consumer reviews in their decision-making on purchases. However, for personal gains such as profit or fame, people post fake reviews to promote or demote certain target products as well as to deceive the reader. To get genuine user experiences and opinions, there is a need to detect such spam or fake reviews. This paper presents a study that aims to detect truthful, useful reviews and ranks them. An effective supervised learning technique is proposed to detect truthful and useful reviews and rank them, using a ‘deceptive’ classifier, ‘useful’ classifier, and a ‘ranking’ model respectively. Deceptive and nonuseful consumer reviews from online review communities such as amazon.com and Epinions.com are used. The proposed method first uses the ‘deceptive’ classifier to find truthful reviews followed by the ‘useful’ classifier to find whether a review is useful or not. Manually labeling individual reviews is very d...
How to get target advertising in digital signage is an innovative and challenging question. This ... more How to get target advertising in digital signage is an innovative and challenging question. This paper summarizes the result of a field trial related to targeted and non-targeted digital signage (DS) with Anonymous Video Analytics (AVA) conducted over 9 month period of time and across 10 different retailers. The goal was to understand the impact of targeted and non-targeted digital signage on sales and offers in a different environment like super market store, electronic store, and general retails. By correlating AVA viewership information with proof-of-sale (POS) data relation can be established between the response time to an ad seen by a certain demographic group and the effect on the sale of the advertised product, so as to provide retailers/advertisers with intelligence to show the right ads to right audience at right time and in the right location. The result of experiment revealed that targeted digital signage has a prominent sale impact on instant food packets, beverages, an...
105 Background: Physical activity recommendations are often made through survivorship care plans ... more 105 Background: Physical activity recommendations are often made through survivorship care plans and survivorship guidelines. However, recommendations may not translate into actions and hence it is important to understand the ground reality of cancer survivors and determine how much physical activity they are able to do in their daily life. Methods: Data were obtained from a self-reported survey of patients attending survivorship clinic at a comprehensive cancer center. Participants were asked what kind of physical activity they were engaging in, how many times a week and how many minutes per session. A composite variable was created by multiplying the number of times by number of visits and adding all the activity types patients reported engagement in. This number was compared against the current recommendations of 150minutes of physical activity weekly as recommended National Comprehensive Cancer Network (NCCN) guidelines. Data were stratified by age, gender and cancer type. Resul...
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Papers by Kalpana Algotar