Dr.P.Shanmuga Sundari Asst.Prof - CSE Dept (original) (raw)
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Papers by Dr.P.Shanmuga Sundari Asst.Prof - CSE Dept
Pervasive Computing and Social Networking, 2022
The importance of technology to network teachers and learners in all levels of education across t... more The importance of technology to network teachers and learners in all levels of education across the globe is being realized due to the impact of COVID-19. Technologies that were used for business conferences earlier reformed themselves and emerged as the solution to bridge institutions with its students. Enterprises like Zoom, Google, Microsoft, and Cisco Webex unlocked their basic features for free to the education sector during this pandemic which is a good sign of humanity. Teachers are being exposed to LMS and other integrating technologies for the first time to stabilize the situation, thereby ensuring continuous learning. An empirical study was conducted among teachers in higher education institutions to understand the effectiveness of Microsoft Teams in helping them to transfer the knowledge to their learners without any halt. The questionnaire was designed to understand how the tool serves the purpose of teaching under various factors. The data was collected from teachers currently employed in higher education institutions across India through Google Forms. The data that was analysed using Excel revealed that MS Teams is found very convenient among teachers to build a strong network with their students in delivering lectures, discussions, assessments, off-class assistance, and integrating other features. It was also found user-friendly and competent when compared to other similar networking tools. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
In the era of advanced technology in healthcare system, data are produced in huge amount from var... more In the era of advanced technology in healthcare system, data are produced in huge amount from varied sources like clinical, e-health record, prescription, tests report and test image records. The greatest challenging is to extract knowledge from raw data and provide useful information to the medical researchers and practitioners, who in turn put it to work in real life scenario which will benefit a common man. Big Data analytics in healthcare is used to predict disease from historical data such that it predict epidemics disease, adverse drug reaction from social media improves quality of life and avoid preventable decease in earlier. In traditional system handling varied health care data for collection, storage and processing is complex, and this complication leads more opportunities for big data analytics. The main objective of this paper is to focus on various types of analysis and techniques to solve the big data problem in healthcare system. Keyword: Big data, Healthcare, Big da...
Journal of King Saud University - Computer and Information Sciences, 2020
The proposed work aims to solve data sparsity problem in the recommendation system. It handles tw... more The proposed work aims to solve data sparsity problem in the recommendation system. It handles twolevel pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item's genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache's spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods.
International Journal of Information Technology & Decision Making, 2020
Most of the traditional recommendation systems are based on user ratings. Here, users provide the... more Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation syste...
Research Journal of Pharmacy and Technology, 2017
In traditional DBMS system handled well structured and no two elements occur twice. But more than... more In traditional DBMS system handled well structured and no two elements occur twice. But more than one occurrence is quite natural in big data processing. Moreover last decades many characteristics (like volume, variety, value) coupled with the data, makes the searching complex for the traditional database system. Effective way of storing the data makes it easier way to processes the data. The main objective of this paper is to find similarity over large data that needs effective and efficient processing of raw data within a satisfactory response time.
Intelligent Decision Technologies, 2021
The recommendation system is affected with attacks when the users are given liberty to rate the i... more The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed usi...
Pervasive Computing and Social Networking, 2022
The importance of technology to network teachers and learners in all levels of education across t... more The importance of technology to network teachers and learners in all levels of education across the globe is being realized due to the impact of COVID-19. Technologies that were used for business conferences earlier reformed themselves and emerged as the solution to bridge institutions with its students. Enterprises like Zoom, Google, Microsoft, and Cisco Webex unlocked their basic features for free to the education sector during this pandemic which is a good sign of humanity. Teachers are being exposed to LMS and other integrating technologies for the first time to stabilize the situation, thereby ensuring continuous learning. An empirical study was conducted among teachers in higher education institutions to understand the effectiveness of Microsoft Teams in helping them to transfer the knowledge to their learners without any halt. The questionnaire was designed to understand how the tool serves the purpose of teaching under various factors. The data was collected from teachers currently employed in higher education institutions across India through Google Forms. The data that was analysed using Excel revealed that MS Teams is found very convenient among teachers to build a strong network with their students in delivering lectures, discussions, assessments, off-class assistance, and integrating other features. It was also found user-friendly and competent when compared to other similar networking tools. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
In the era of advanced technology in healthcare system, data are produced in huge amount from var... more In the era of advanced technology in healthcare system, data are produced in huge amount from varied sources like clinical, e-health record, prescription, tests report and test image records. The greatest challenging is to extract knowledge from raw data and provide useful information to the medical researchers and practitioners, who in turn put it to work in real life scenario which will benefit a common man. Big Data analytics in healthcare is used to predict disease from historical data such that it predict epidemics disease, adverse drug reaction from social media improves quality of life and avoid preventable decease in earlier. In traditional system handling varied health care data for collection, storage and processing is complex, and this complication leads more opportunities for big data analytics. The main objective of this paper is to focus on various types of analysis and techniques to solve the big data problem in healthcare system. Keyword: Big data, Healthcare, Big da...
Journal of King Saud University - Computer and Information Sciences, 2020
The proposed work aims to solve data sparsity problem in the recommendation system. It handles tw... more The proposed work aims to solve data sparsity problem in the recommendation system. It handles twolevel pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item's genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache's spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods.
International Journal of Information Technology & Decision Making, 2020
Most of the traditional recommendation systems are based on user ratings. Here, users provide the... more Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation syste...
Research Journal of Pharmacy and Technology, 2017
In traditional DBMS system handled well structured and no two elements occur twice. But more than... more In traditional DBMS system handled well structured and no two elements occur twice. But more than one occurrence is quite natural in big data processing. Moreover last decades many characteristics (like volume, variety, value) coupled with the data, makes the searching complex for the traditional database system. Effective way of storing the data makes it easier way to processes the data. The main objective of this paper is to find similarity over large data that needs effective and efficient processing of raw data within a satisfactory response time.
Intelligent Decision Technologies, 2021
The recommendation system is affected with attacks when the users are given liberty to rate the i... more The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed usi...