In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many hum... more In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with pri...
Continuous growth in software, hardware and internet technology has enabled the growth of interne... more Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of in...
Bio-data analysis deals with the most vital discovering problem of similarity search and finding ... more Bio-data analysis deals with the most vital discovering problem of similarity search and finding relationship among bio sequences and structures. In this paper, we are trading the problem of discovering the most recurrently occurring patterns in a given DNA or protein sequence. Several on hand tools need the user to spell out gap constraints in advance in turn to find specific patterns. Practically it is not possible for the user to provide the gap constraints. So the need arises of budding an algorithm to obtain the patterns easily on its own without the need of user intervention in the form of mentioning of gap constraints. We have got two analytical methods to find out the recurrent subsequences and guesstimate the maximum support for data with complexity O(|T|.Sup) where |T| stands for text sequence length and Sup represents the number of occurrences of the pattern. We are proposing an altered version of the previously proposed algorithm with complexity O(|T|).
In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many hum... more In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with pri...
Continuous growth in software, hardware and internet technology has enabled the growth of interne... more Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of in...
Bio-data analysis deals with the most vital discovering problem of similarity search and finding ... more Bio-data analysis deals with the most vital discovering problem of similarity search and finding relationship among bio sequences and structures. In this paper, we are trading the problem of discovering the most recurrently occurring patterns in a given DNA or protein sequence. Several on hand tools need the user to spell out gap constraints in advance in turn to find specific patterns. Practically it is not possible for the user to provide the gap constraints. So the need arises of budding an algorithm to obtain the patterns easily on its own without the need of user intervention in the form of mentioning of gap constraints. We have got two analytical methods to find out the recurrent subsequences and guesstimate the maximum support for data with complexity O(|T|.Sup) where |T| stands for text sequence length and Sup represents the number of occurrences of the pattern. We are proposing an altered version of the previously proposed algorithm with complexity O(|T|).
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Papers by Nagaraja Gundluru