Papers by harshith singathala
Network and Communication industry has seen a huge advancement in the technology aspect and also ... more Network and Communication industry has seen a huge advancement in the technology aspect and also the way they function. It was due to the rapid increase in the use and development of the Internet and the essential role it is playing in our everyday life. The change in these technologies has led to provide many different type of services such as gaming, online orders, e-shopping, etc.. and mainly e-healthcare. E-healthcare is to provide the services related to the health with the usage of modern technologies like the Internet, advancement in mobile devices, personal computers and all our surrounding technology. It mainly works with the patients medical records and details with which we can know the whole insights of the patient problems. In traditional method all the information is written manually and kept safely. But may be due to the lack of good documentation, non-track ability from remote locations, communicating his health records to other location hospitals, losing of records ...
2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)
IRJET, 2023
The proliferation of Internet of Things (IoT)
devices and applications is on the rise, resulting ... more The proliferation of Internet of Things (IoT)
devices and applications is on the rise, resulting in an increase
in both the quantity and complexity of malicious attacks. It is
imperative to establish robust security measures for IoT
networks to counteract malicious attacks, particularly with
the aim of preventing unauthorized control over these devices.
While numerous security solutions for IoT have been proposed
in recent years, a significant portion of them lacks
standardization and interoperability. As the IoT landscape
continues to expand, the diversity and intricacy of IoT
applications also grow, rendering such networks susceptible to
attacks aimed at data theft, device takeover, and service
disruption.
A multitude of protocols and networking frameworks have
been developed for IoT, with some achieving standardization
and facilitating interoperability among devices and internet
connectivity. These protocols have received endorsement from
prominent standardization bodies such as IETF, IEEE, and
industry consortia like the Rawan coalition and thread group.
This paper's objective is to present a model that establishes
secure communication channels among IoT devices as well as
between these devices and a server or router, accomplished
through the implementation of encryption algorithms. The
popularity of smart home IoT systems is increasing due to
their efficiency in simplifying various tasks. However, this
trend also introduces vulnerabilities in terms of user privacy.
Safeguarding the privacy of personal data remains a
paramount concern for electronic services. To address this
challenge, we employ well-established encryption algorithms
such as RSA to create and utilize secure communication
channels through Socket Programming for IoT devices.
IRJET, 2023
Rice stands as a favored and extensively
consumed cereal grain in Asian countries, while also enj... more Rice stands as a favored and extensively
consumed cereal grain in Asian countries, while also enjoying
global accessibility. Within the rice market, the overarching
determinant of milled rice lies in its quality, an attribute that
assumes heightened significance in the context of import and
export trade. Rice samples often harbor assorted extraneous
elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is
to introduce an alternative avenue for quality control and
analysis, characterized by reduced expenditure in terms of
effort, cost, and time. Image processing emerges as a pivotal
and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute
targeted operations, thereby refining and enhancing the
desired outcome. Moreover, this technique enables the
extraction of valuable insights from input images. This study
strives to develop image processing algorithms with a specific
focus on segmenting and identifying rice grains. By harnessing
image processing algorithms, it becomes possible to efficiently
analyze the quality of grains based on their size. This paper
furnishes a solution for the classification and assessment of
rice grains, predicated on their dimensions and morphology,
through the application of image processing techniques. While
prior research has focused on the morphological attributes of
grains, encompassing parameters such as area and shape,
these endeavors often struggle to yield a generalized formula
capable of classifying diverse rice varieties due to the
considerable variance in shapes and sizes. In a distinctive
departure, this paper augments the analysis by incorporating
Fourier features extracted from grain images, thus
augmenting the accuracy of classification outcomes
IRJET , 2023
For adding the safety of the drivers, pedestrians and vehicles as well, to the driver easement sy... more For adding the safety of the drivers, pedestrians and vehicles as well, to the driver easement systems, traffic sign recognition feature is required. For developing TSR systems, we need the use of CV (Computer Vision) techniques, which could be viewed as principal in the field of pattern recognition all in all. We are going to use two latest architectures called Lenet-5 model and VGGNet model architectures in two different approaches. In this project, we are going to present the study of two major approaches which are required for developing traffic sign detection and recognition systems. We propose a methodology for traffic sign identification dependent on Convolutional Neural Networks (CNN). First, we are going to transform the original image into greyscale image with the help of SVM(support vector machine) and then use CNN(convolutional neural network) for detecting and recognizing things with fixed and learnable layers we use CNN(convolutional neural network). With fixed layers, we can lessen the measure of interest zones to identify, and trim the limits near the boundaries of traffic signs. The accuracy of detection can be increased with the help of learnable layers. By researching and study of many research papers, we want to give a real-time solution for this challenging problem called TSR (Traffic Sign detection and Recognition).
International Research Journal of Engineering and Technology, 2023
Rice stands as a favored and extensively
consumed cereal grain in Asian countries, while also enj... more Rice stands as a favored and extensively
consumed cereal grain in Asian countries, while also enjoying
global accessibility. Within the rice market, the overarching
determinant of milled rice lies in its quality, an attribute that
assumes heightened significance in the context of import and
export trade. Rice samples often harbor assorted extraneous
elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is
to introduce an alternative avenue for quality control and
analysis, characterized by reduced expenditure in terms of
effort, cost, and time. Image processing emerges as a pivotal
and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute
targeted operations, thereby refining and enhancing the
desired outcome. Moreover, this technique enables the
extraction of valuable insights from input images. This study
strives to develop image processing algorithms with a specific
focus on segmenting and identifying rice grains. By harnessing
image processing algorithms, it becomes possible to efficiently
analyze the quality of grains based on their size. This paper
furnishes a solution for the classification and assessment of
rice grains, predicated on their dimensions and morphology,
through the application of image processing techniques. While
prior research has focused on the morphological attributes of
grains, encompassing parameters such as area and shape,
these endeavors often struggle to yield a generalized formula
capable of classifying diverse rice varieties due to the
considerable variance in shapes and sizes. In a distinctive
departure, this paper augments the analysis by incorporating
Fourier features extracted from grain images, thus
augmenting the accuracy of classification outcomes.
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Papers by harshith singathala
devices and applications is on the rise, resulting in an increase
in both the quantity and complexity of malicious attacks. It is
imperative to establish robust security measures for IoT
networks to counteract malicious attacks, particularly with
the aim of preventing unauthorized control over these devices.
While numerous security solutions for IoT have been proposed
in recent years, a significant portion of them lacks
standardization and interoperability. As the IoT landscape
continues to expand, the diversity and intricacy of IoT
applications also grow, rendering such networks susceptible to
attacks aimed at data theft, device takeover, and service
disruption.
A multitude of protocols and networking frameworks have
been developed for IoT, with some achieving standardization
and facilitating interoperability among devices and internet
connectivity. These protocols have received endorsement from
prominent standardization bodies such as IETF, IEEE, and
industry consortia like the Rawan coalition and thread group.
This paper's objective is to present a model that establishes
secure communication channels among IoT devices as well as
between these devices and a server or router, accomplished
through the implementation of encryption algorithms. The
popularity of smart home IoT systems is increasing due to
their efficiency in simplifying various tasks. However, this
trend also introduces vulnerabilities in terms of user privacy.
Safeguarding the privacy of personal data remains a
paramount concern for electronic services. To address this
challenge, we employ well-established encryption algorithms
such as RSA to create and utilize secure communication
channels through Socket Programming for IoT devices.
consumed cereal grain in Asian countries, while also enjoying
global accessibility. Within the rice market, the overarching
determinant of milled rice lies in its quality, an attribute that
assumes heightened significance in the context of import and
export trade. Rice samples often harbor assorted extraneous
elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is
to introduce an alternative avenue for quality control and
analysis, characterized by reduced expenditure in terms of
effort, cost, and time. Image processing emerges as a pivotal
and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute
targeted operations, thereby refining and enhancing the
desired outcome. Moreover, this technique enables the
extraction of valuable insights from input images. This study
strives to develop image processing algorithms with a specific
focus on segmenting and identifying rice grains. By harnessing
image processing algorithms, it becomes possible to efficiently
analyze the quality of grains based on their size. This paper
furnishes a solution for the classification and assessment of
rice grains, predicated on their dimensions and morphology,
through the application of image processing techniques. While
prior research has focused on the morphological attributes of
grains, encompassing parameters such as area and shape,
these endeavors often struggle to yield a generalized formula
capable of classifying diverse rice varieties due to the
considerable variance in shapes and sizes. In a distinctive
departure, this paper augments the analysis by incorporating
Fourier features extracted from grain images, thus
augmenting the accuracy of classification outcomes
consumed cereal grain in Asian countries, while also enjoying
global accessibility. Within the rice market, the overarching
determinant of milled rice lies in its quality, an attribute that
assumes heightened significance in the context of import and
export trade. Rice samples often harbor assorted extraneous
elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is
to introduce an alternative avenue for quality control and
analysis, characterized by reduced expenditure in terms of
effort, cost, and time. Image processing emerges as a pivotal
and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute
targeted operations, thereby refining and enhancing the
desired outcome. Moreover, this technique enables the
extraction of valuable insights from input images. This study
strives to develop image processing algorithms with a specific
focus on segmenting and identifying rice grains. By harnessing
image processing algorithms, it becomes possible to efficiently
analyze the quality of grains based on their size. This paper
furnishes a solution for the classification and assessment of
rice grains, predicated on their dimensions and morphology,
through the application of image processing techniques. While
prior research has focused on the morphological attributes of
grains, encompassing parameters such as area and shape,
these endeavors often struggle to yield a generalized formula
capable of classifying diverse rice varieties due to the
considerable variance in shapes and sizes. In a distinctive
departure, this paper augments the analysis by incorporating
Fourier features extracted from grain images, thus
augmenting the accuracy of classification outcomes.
devices and applications is on the rise, resulting in an increase
in both the quantity and complexity of malicious attacks. It is
imperative to establish robust security measures for IoT
networks to counteract malicious attacks, particularly with
the aim of preventing unauthorized control over these devices.
While numerous security solutions for IoT have been proposed
in recent years, a significant portion of them lacks
standardization and interoperability. As the IoT landscape
continues to expand, the diversity and intricacy of IoT
applications also grow, rendering such networks susceptible to
attacks aimed at data theft, device takeover, and service
disruption.
A multitude of protocols and networking frameworks have
been developed for IoT, with some achieving standardization
and facilitating interoperability among devices and internet
connectivity. These protocols have received endorsement from
prominent standardization bodies such as IETF, IEEE, and
industry consortia like the Rawan coalition and thread group.
This paper's objective is to present a model that establishes
secure communication channels among IoT devices as well as
between these devices and a server or router, accomplished
through the implementation of encryption algorithms. The
popularity of smart home IoT systems is increasing due to
their efficiency in simplifying various tasks. However, this
trend also introduces vulnerabilities in terms of user privacy.
Safeguarding the privacy of personal data remains a
paramount concern for electronic services. To address this
challenge, we employ well-established encryption algorithms
such as RSA to create and utilize secure communication
channels through Socket Programming for IoT devices.
consumed cereal grain in Asian countries, while also enjoying
global accessibility. Within the rice market, the overarching
determinant of milled rice lies in its quality, an attribute that
assumes heightened significance in the context of import and
export trade. Rice samples often harbor assorted extraneous
elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is
to introduce an alternative avenue for quality control and
analysis, characterized by reduced expenditure in terms of
effort, cost, and time. Image processing emerges as a pivotal
and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute
targeted operations, thereby refining and enhancing the
desired outcome. Moreover, this technique enables the
extraction of valuable insights from input images. This study
strives to develop image processing algorithms with a specific
focus on segmenting and identifying rice grains. By harnessing
image processing algorithms, it becomes possible to efficiently
analyze the quality of grains based on their size. This paper
furnishes a solution for the classification and assessment of
rice grains, predicated on their dimensions and morphology,
through the application of image processing techniques. While
prior research has focused on the morphological attributes of
grains, encompassing parameters such as area and shape,
these endeavors often struggle to yield a generalized formula
capable of classifying diverse rice varieties due to the
considerable variance in shapes and sizes. In a distinctive
departure, this paper augments the analysis by incorporating
Fourier features extracted from grain images, thus
augmenting the accuracy of classification outcomes
consumed cereal grain in Asian countries, while also enjoying
global accessibility. Within the rice market, the overarching
determinant of milled rice lies in its quality, an attribute that
assumes heightened significance in the context of import and
export trade. Rice samples often harbor assorted extraneous
elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is
to introduce an alternative avenue for quality control and
analysis, characterized by reduced expenditure in terms of
effort, cost, and time. Image processing emerges as a pivotal
and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute
targeted operations, thereby refining and enhancing the
desired outcome. Moreover, this technique enables the
extraction of valuable insights from input images. This study
strives to develop image processing algorithms with a specific
focus on segmenting and identifying rice grains. By harnessing
image processing algorithms, it becomes possible to efficiently
analyze the quality of grains based on their size. This paper
furnishes a solution for the classification and assessment of
rice grains, predicated on their dimensions and morphology,
through the application of image processing techniques. While
prior research has focused on the morphological attributes of
grains, encompassing parameters such as area and shape,
these endeavors often struggle to yield a generalized formula
capable of classifying diverse rice varieties due to the
considerable variance in shapes and sizes. In a distinctive
departure, this paper augments the analysis by incorporating
Fourier features extracted from grain images, thus
augmenting the accuracy of classification outcomes.