Nirav Bhatt | CHAROTAR UNIVERSITY OF SCIENCE AND TECHNOLOGY-CHANGA (original) (raw)
Papers by Nirav Bhatt
Advances in knowledge acquisition, transfer and management book series, 2019
In the era of big data, large amounts of data are generated from different areas like education, ... more In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.
Research Square (Research Square), Mar 28, 2023
Cross-modal retrieval(CMR) refers to the task of retrieving semantically related items across dif... more Cross-modal retrieval(CMR) refers to the task of retrieving semantically related items across different modalities. For example, given an image query, the task is to retrieve relevant text descriptions or audio clips. One of the major challenges in CMR is the modality gap, which refers to the differences between the features and representations used to encode information in different modalities. To address the modality gap, researchers have developed various techniques such as joint embedding, where the features from different modalities are mapped to a common embedding space where they can be compared directly. Binary-valued and real-valued representations are two different ways to represent data. A binary-valued representation is a type of discrete representation where data is represented using either 0 or 1. Realvalued representation, on the other hand, represents each item as a vector of real numbers. Both types of representations have their advantages and disadvantages, and researchers continue to explore new techniques for generating representations that can improve the performance of CMR systems. First time, the work presented here generates both the representations and comparison is made by performing experiments on standard benchmark datasets using mean average precision (MAP). The result suggest that real-valued representation outperforms binary-valued representation in terms of MAP, especially when the data is complex and high-dimensional. On the other hand, binary codes are more memorye cient than real-valued embedding, and they can be computed much faster. Moreover, binary codes can be easily stored and transmitted, making them more suitable for large-scale retrieval tasks.
Lecture notes in networks and systems, 2023
International Journal of Mathematical, Engineering and Management Sciences
The tremendous proliferation of Multi-Modal data and the flexible need of users has drawn attenti... more The tremendous proliferation of Multi-Modal data and the flexible need of users has drawn attention to the field of Cross-Modal Retrieval (CMR), which can perform image-sketch matching, text-image matching, audio-video matching and near infrared-visual image matching. Such retrieval is useful in many applications like criminal investigation, recommendation systems and person reidentification. The real challenge in CMR is to preserve semantic similarities between various modalities of data. To preserve semantic similarities, existing deep learning-based approaches use pairwise labels and generate binary-valued representation. The generated binary-valued representation provides fast retrieval with low storage requirement. However, the relative similarity between heterogeneous data is ignored. So, the objective of this work is to reduce the modality-gap by preserving relative semantic similarities among various modalities. So, a model named "Deep Cross-Modal Retrieval (DCMR)"...
American Journal of Ophthalmology, 1994
We studied the use of human retinal pigment epithelial cells cultured on a collagen support as a ... more We studied the use of human retinal pigment epithelial cells cultured on a collagen support as a potential transplantation therapy to replace diseased or damaged retinal pigment epithelium. Using a transvitreal approach, we transplanted human retinal pigment epithelial cells attached to either a sheet of noncross-linked or cross-linked type I collagen into the subretinal space of New Zealand white rabbits, whose eyes lack pigment. Animals were killed after six weeks, and the eyes were fixed for light microscopy. The results demonstrated that, in eyes receiving the noncross-linked collagen support, a layer of pigmented donor retinal pigment epithelium was visible within the subretinal space, with a normal-appearing retina and no evidence of proliferative vitreoretinopathy or graft rejection. We believe this method may be applicable to replace dysfunctional retinal pigment epithelial cells in humans.
Lecture notes in networks and systems, 2023
International Journal of Innovative Technology and Exploring Engineering, 2019
Processing of unordered and unbounded data is the prime requirement of the current businesses. La... more Processing of unordered and unbounded data is the prime requirement of the current businesses. Large amount of rapidly generated data demands the processing of the same without the storage and as per the timestamp associated with it. It is difficult to process these unbounded data with batch engine as the existing batch systems suffer from the delay intrinsic by accumulating entire incoming records in a group prior to process it. However windowing can be useful when dealing with unbounded data which pieces up a dataset into fixed chunks for processing with repeated runs of batch engine. Contrast to batch processing, stream handling system aims to process information that is gathered in a little timeframe. In this way, stream data processing ought to be coordinated with the flow of data. In the real world the event time is always skewed with the processing time which introduce issues of delay and completeness in incoming stream of data. In this paper, we presented the analysis on the...
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE, 2019
Data Streams are having huge volume and it can-not be stored permanently in the memory for proces... more Data Streams are having huge volume and it can-not be stored permanently in the memory for processing. In this paper we would be mainly focusing on issues in data stream, the major factors which are affecting the accuracy of classifier like imbalance class and Concept Drift. The drift in Data Stream mining refers to the change in data. Such as Class imbalance problem notifies that the samples are in the classes are not equal. In our research work we are trying to identify the change (Drift) in data, we are trying to detect Imbalance class and noise from changed data. And According to the type of drift we are applying the algorithms and trying to make the stream more balance and noise free to improve classifier’s accuracy.
Research Anthology on Privatizing and Securing Data, 2021
In the era of big data, large amounts of data are generated from different areas like education, ... more In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.
Selection of most appropriate classification algorithm according to nature of the dataset is crit... more Selection of most appropriate classification algorithm according to nature of the dataset is critical. Using cross-validation, direct comparisons can be made but computational cost is significant. Meta Learning automates this process. The knowledge in Meta Learning is acquired from a set of meta-examples which stores characteristics of the datasets and performance obtained by executing a set of candidate algorithms on meta features. However, generation of meta-example is a costly process because it depends on number of datasets, candidate algorithms and complexity of algorithms. Active Meta Learning technique can be used to overcome this limitation by reducing generation of meta- example, at the same time maintaining performance of candidate algorithms. In our previous work, we proposed a system that provides ranking of the classifiers based on SRR ranking method. In this paper, evaluation methodology based on ideal ranking is presented which shows that proposed method leads to sign...
European Conference on Information Retrieval, 2017
Microblogging is prominent e-communication medium on which short story are updated by the user ba... more Microblogging is prominent e-communication medium on which short story are updated by the user based on their personal matter and other happening or coming immediate information. The quantity of information is large and also most of the data are redundant or irrelevant because of their popularity. This paper provides effectual techniques for summarization of inside story on microblogs sites such as twitter. The twitter data is the incredibly huge amount of small story circulate by users related to occurring situation or events. This technique focuses on finding factual most similar information respect to the query and used the ranking function for retrieving top-ranked twitter data related to query. Apply similarity measure function on top-ranked Relevant Tweets for detecting novel Tweets and which minimize similarity and maximize dissimilarity of twitter data. And also utilize threshold based decision to find a summary of novel tweets.
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1
As Data Stream Mining is trending topic for Research nowadays and more users increases day by day... more As Data Stream Mining is trending topic for Research nowadays and more users increases day by day with online stuff, the size of big data is also getting larger. In traditional data mining extracting knowledge is done mostly using offline phase. While in data stream, Extracting data is from the continuous arriving data or we can say from the online streams. Due to continuously arriving data, it cannot be stored in the memory for processing permanently. So examining of data as fast as possible is important. In this paper we would be interested to discuss about the data stream mining and the issues of stream classification, like Single scan, Load shedding, Memory Space, Class imbalance problem, Concept drift, and possible ways to solve those issues.
As Data Stream Mining is trending topic for Research nowadays and more users increases day by day... more As Data Stream Mining is trending topic for Research nowadays and more users increases day by day with online stuff, the size of big data is also getting larger. In traditional data mining extracting knowledge is done mostly using offline phase. While in data stream, Extracting data is from the continuous arriving data or we can say from the online streams. Due to continuously arriving data, it cannot be stored in the memory for processing permanently. So examining of data as fast as possible is important. In this paper we would be interested to discuss about the data stream mining and the issues of stream classification, like Single scan, Load shedding, Memory Space, Class imbalance problem, Concept drift, and possible ways to solve those issues.
Sorting is the basic operation in most of the applications of computer science. Sorting means to ... more Sorting is the basic operation in most of the applications of computer science. Sorting means to arrange data in particular order inside computer. In this paper we have discussed performance of different sorting algorithms with their advantages and disadvantages. This paper also represents the application areas for different sorting algorithms. Main goal of this paper is to compare the performance of different sorting algorithms based on different parameters. Keywords— Algorithm, Time Complexity, Space Complexity
Smart Systems and IoT: Innovations in Computing
To find most suitable classifier is possible either through cross-validation, which suffers from ... more To find most suitable classifier is possible either through cross-validation, which suffers from computational cost or through expert advice which is not always feasible to have. Meta-Learning can be a better approach to automate this process, by generating Meta-Examples which is a combination of performance results of classification algorithms on input datasets and Meta-Features. With the increasing number of datasets and underlying complexity of algorithms, makes even the Meta-Learning process expensive. So, Active Meta-Learning can help by optimizing the generation of Meta-Examples along with maintaining the performance of classification algorithms. Proposed work here provides a ranking of classifiers using SRR and ARR ranking method and compares Meta-Learning with Active Meta-Learning. In this work, evaluation methodology based on ideal ranking is presented, which shows that proposed method leads to significantly better ranking with reduced Meta-Examples. The executed experiments discovered a considerable improvement in Meta-Learning performance that supports nonexperts users in the selection of classification algorithms.
Information and Communication Technology for Intelligent Systems
Recommendation system is a sub-ordinate of information filtrate system that provides users with s... more Recommendation system is a sub-ordinate of information filtrate system that provides users with suggestions for items a user may want. It plays a censorious role in wide range of online shopping, e-commercial services, and social networking applications. In recent years recommendations have changed different ways of communication between users and websites. Recommendation system sorts huge amount of data to determine interest of users and makes search easier. For that purpose many methods have been used. This paper covers different approaches which are used in recommendation system which are: collaborative approach, content-based approach, and hybrid recommendation approach. We have also mentioned several issues that come across recommendation systems.
International Journal of Computer Sciences and Engineering
International Journal of Computer Applications, 2017
Twitter is most popular microblogging site. It provide us with real time data. This article Provi... more Twitter is most popular microblogging site. It provide us with real time data. This article Provide survey of techniques for retrieving information from twitter stream. This techniques aim is finding real world and most relevant information with respect to the query. For retrieve most relevant information used query expansion techniques. Twitter data contain large amount of information. Information rank retrieval techniques find important data and gives the final score to that information with respect to user interest profile
PeerJ Computer Science, 2021
Stream data is the data that is generated continuously from the different data sources and ideall... more Stream data is the data that is generated continuously from the different data sources and ideally defined as the data that has no discrete beginning or end. Processing the stream data is a part of big data analytics that aims at querying the continuously arriving data and extracting meaningful information from the stream. Although earlier processing of such stream was using batch analytics, nowadays there are applications like the stock market, patient monitoring, and traffic analysis which can cause a drastic difference in processing, if the output is generated in levels of hours and minutes. The primary goal of any real-time stream processing system is to process the stream data as soon as it arrives. Correspondingly, analytics of the stream data also needs consideration of surrounding dependent data. For example, stock market analytics results are often useless if we do not consider their associated or dependent parameters which affect the result. In a real-world application, th...
Advances in knowledge acquisition, transfer and management book series, 2019
In the era of big data, large amounts of data are generated from different areas like education, ... more In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.
Research Square (Research Square), Mar 28, 2023
Cross-modal retrieval(CMR) refers to the task of retrieving semantically related items across dif... more Cross-modal retrieval(CMR) refers to the task of retrieving semantically related items across different modalities. For example, given an image query, the task is to retrieve relevant text descriptions or audio clips. One of the major challenges in CMR is the modality gap, which refers to the differences between the features and representations used to encode information in different modalities. To address the modality gap, researchers have developed various techniques such as joint embedding, where the features from different modalities are mapped to a common embedding space where they can be compared directly. Binary-valued and real-valued representations are two different ways to represent data. A binary-valued representation is a type of discrete representation where data is represented using either 0 or 1. Realvalued representation, on the other hand, represents each item as a vector of real numbers. Both types of representations have their advantages and disadvantages, and researchers continue to explore new techniques for generating representations that can improve the performance of CMR systems. First time, the work presented here generates both the representations and comparison is made by performing experiments on standard benchmark datasets using mean average precision (MAP). The result suggest that real-valued representation outperforms binary-valued representation in terms of MAP, especially when the data is complex and high-dimensional. On the other hand, binary codes are more memorye cient than real-valued embedding, and they can be computed much faster. Moreover, binary codes can be easily stored and transmitted, making them more suitable for large-scale retrieval tasks.
Lecture notes in networks and systems, 2023
International Journal of Mathematical, Engineering and Management Sciences
The tremendous proliferation of Multi-Modal data and the flexible need of users has drawn attenti... more The tremendous proliferation of Multi-Modal data and the flexible need of users has drawn attention to the field of Cross-Modal Retrieval (CMR), which can perform image-sketch matching, text-image matching, audio-video matching and near infrared-visual image matching. Such retrieval is useful in many applications like criminal investigation, recommendation systems and person reidentification. The real challenge in CMR is to preserve semantic similarities between various modalities of data. To preserve semantic similarities, existing deep learning-based approaches use pairwise labels and generate binary-valued representation. The generated binary-valued representation provides fast retrieval with low storage requirement. However, the relative similarity between heterogeneous data is ignored. So, the objective of this work is to reduce the modality-gap by preserving relative semantic similarities among various modalities. So, a model named "Deep Cross-Modal Retrieval (DCMR)"...
American Journal of Ophthalmology, 1994
We studied the use of human retinal pigment epithelial cells cultured on a collagen support as a ... more We studied the use of human retinal pigment epithelial cells cultured on a collagen support as a potential transplantation therapy to replace diseased or damaged retinal pigment epithelium. Using a transvitreal approach, we transplanted human retinal pigment epithelial cells attached to either a sheet of noncross-linked or cross-linked type I collagen into the subretinal space of New Zealand white rabbits, whose eyes lack pigment. Animals were killed after six weeks, and the eyes were fixed for light microscopy. The results demonstrated that, in eyes receiving the noncross-linked collagen support, a layer of pigmented donor retinal pigment epithelium was visible within the subretinal space, with a normal-appearing retina and no evidence of proliferative vitreoretinopathy or graft rejection. We believe this method may be applicable to replace dysfunctional retinal pigment epithelial cells in humans.
Lecture notes in networks and systems, 2023
International Journal of Innovative Technology and Exploring Engineering, 2019
Processing of unordered and unbounded data is the prime requirement of the current businesses. La... more Processing of unordered and unbounded data is the prime requirement of the current businesses. Large amount of rapidly generated data demands the processing of the same without the storage and as per the timestamp associated with it. It is difficult to process these unbounded data with batch engine as the existing batch systems suffer from the delay intrinsic by accumulating entire incoming records in a group prior to process it. However windowing can be useful when dealing with unbounded data which pieces up a dataset into fixed chunks for processing with repeated runs of batch engine. Contrast to batch processing, stream handling system aims to process information that is gathered in a little timeframe. In this way, stream data processing ought to be coordinated with the flow of data. In the real world the event time is always skewed with the processing time which introduce issues of delay and completeness in incoming stream of data. In this paper, we presented the analysis on the...
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE, 2019
Data Streams are having huge volume and it can-not be stored permanently in the memory for proces... more Data Streams are having huge volume and it can-not be stored permanently in the memory for processing. In this paper we would be mainly focusing on issues in data stream, the major factors which are affecting the accuracy of classifier like imbalance class and Concept Drift. The drift in Data Stream mining refers to the change in data. Such as Class imbalance problem notifies that the samples are in the classes are not equal. In our research work we are trying to identify the change (Drift) in data, we are trying to detect Imbalance class and noise from changed data. And According to the type of drift we are applying the algorithms and trying to make the stream more balance and noise free to improve classifier’s accuracy.
Research Anthology on Privatizing and Securing Data, 2021
In the era of big data, large amounts of data are generated from different areas like education, ... more In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.
Selection of most appropriate classification algorithm according to nature of the dataset is crit... more Selection of most appropriate classification algorithm according to nature of the dataset is critical. Using cross-validation, direct comparisons can be made but computational cost is significant. Meta Learning automates this process. The knowledge in Meta Learning is acquired from a set of meta-examples which stores characteristics of the datasets and performance obtained by executing a set of candidate algorithms on meta features. However, generation of meta-example is a costly process because it depends on number of datasets, candidate algorithms and complexity of algorithms. Active Meta Learning technique can be used to overcome this limitation by reducing generation of meta- example, at the same time maintaining performance of candidate algorithms. In our previous work, we proposed a system that provides ranking of the classifiers based on SRR ranking method. In this paper, evaluation methodology based on ideal ranking is presented which shows that proposed method leads to sign...
European Conference on Information Retrieval, 2017
Microblogging is prominent e-communication medium on which short story are updated by the user ba... more Microblogging is prominent e-communication medium on which short story are updated by the user based on their personal matter and other happening or coming immediate information. The quantity of information is large and also most of the data are redundant or irrelevant because of their popularity. This paper provides effectual techniques for summarization of inside story on microblogs sites such as twitter. The twitter data is the incredibly huge amount of small story circulate by users related to occurring situation or events. This technique focuses on finding factual most similar information respect to the query and used the ranking function for retrieving top-ranked twitter data related to query. Apply similarity measure function on top-ranked Relevant Tweets for detecting novel Tweets and which minimize similarity and maximize dissimilarity of twitter data. And also utilize threshold based decision to find a summary of novel tweets.
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1
As Data Stream Mining is trending topic for Research nowadays and more users increases day by day... more As Data Stream Mining is trending topic for Research nowadays and more users increases day by day with online stuff, the size of big data is also getting larger. In traditional data mining extracting knowledge is done mostly using offline phase. While in data stream, Extracting data is from the continuous arriving data or we can say from the online streams. Due to continuously arriving data, it cannot be stored in the memory for processing permanently. So examining of data as fast as possible is important. In this paper we would be interested to discuss about the data stream mining and the issues of stream classification, like Single scan, Load shedding, Memory Space, Class imbalance problem, Concept drift, and possible ways to solve those issues.
As Data Stream Mining is trending topic for Research nowadays and more users increases day by day... more As Data Stream Mining is trending topic for Research nowadays and more users increases day by day with online stuff, the size of big data is also getting larger. In traditional data mining extracting knowledge is done mostly using offline phase. While in data stream, Extracting data is from the continuous arriving data or we can say from the online streams. Due to continuously arriving data, it cannot be stored in the memory for processing permanently. So examining of data as fast as possible is important. In this paper we would be interested to discuss about the data stream mining and the issues of stream classification, like Single scan, Load shedding, Memory Space, Class imbalance problem, Concept drift, and possible ways to solve those issues.
Sorting is the basic operation in most of the applications of computer science. Sorting means to ... more Sorting is the basic operation in most of the applications of computer science. Sorting means to arrange data in particular order inside computer. In this paper we have discussed performance of different sorting algorithms with their advantages and disadvantages. This paper also represents the application areas for different sorting algorithms. Main goal of this paper is to compare the performance of different sorting algorithms based on different parameters. Keywords— Algorithm, Time Complexity, Space Complexity
Smart Systems and IoT: Innovations in Computing
To find most suitable classifier is possible either through cross-validation, which suffers from ... more To find most suitable classifier is possible either through cross-validation, which suffers from computational cost or through expert advice which is not always feasible to have. Meta-Learning can be a better approach to automate this process, by generating Meta-Examples which is a combination of performance results of classification algorithms on input datasets and Meta-Features. With the increasing number of datasets and underlying complexity of algorithms, makes even the Meta-Learning process expensive. So, Active Meta-Learning can help by optimizing the generation of Meta-Examples along with maintaining the performance of classification algorithms. Proposed work here provides a ranking of classifiers using SRR and ARR ranking method and compares Meta-Learning with Active Meta-Learning. In this work, evaluation methodology based on ideal ranking is presented, which shows that proposed method leads to significantly better ranking with reduced Meta-Examples. The executed experiments discovered a considerable improvement in Meta-Learning performance that supports nonexperts users in the selection of classification algorithms.
Information and Communication Technology for Intelligent Systems
Recommendation system is a sub-ordinate of information filtrate system that provides users with s... more Recommendation system is a sub-ordinate of information filtrate system that provides users with suggestions for items a user may want. It plays a censorious role in wide range of online shopping, e-commercial services, and social networking applications. In recent years recommendations have changed different ways of communication between users and websites. Recommendation system sorts huge amount of data to determine interest of users and makes search easier. For that purpose many methods have been used. This paper covers different approaches which are used in recommendation system which are: collaborative approach, content-based approach, and hybrid recommendation approach. We have also mentioned several issues that come across recommendation systems.
International Journal of Computer Sciences and Engineering
International Journal of Computer Applications, 2017
Twitter is most popular microblogging site. It provide us with real time data. This article Provi... more Twitter is most popular microblogging site. It provide us with real time data. This article Provide survey of techniques for retrieving information from twitter stream. This techniques aim is finding real world and most relevant information with respect to the query. For retrieve most relevant information used query expansion techniques. Twitter data contain large amount of information. Information rank retrieval techniques find important data and gives the final score to that information with respect to user interest profile
PeerJ Computer Science, 2021
Stream data is the data that is generated continuously from the different data sources and ideall... more Stream data is the data that is generated continuously from the different data sources and ideally defined as the data that has no discrete beginning or end. Processing the stream data is a part of big data analytics that aims at querying the continuously arriving data and extracting meaningful information from the stream. Although earlier processing of such stream was using batch analytics, nowadays there are applications like the stock market, patient monitoring, and traffic analysis which can cause a drastic difference in processing, if the output is generated in levels of hours and minutes. The primary goal of any real-time stream processing system is to process the stream data as soon as it arrives. Correspondingly, analytics of the stream data also needs consideration of surrounding dependent data. For example, stock market analytics results are often useless if we do not consider their associated or dependent parameters which affect the result. In a real-world application, th...