Nookala Venu

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

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Kunj Bihari Sharma

Graphic Era University

Deepak Upadhyay

Graphic Era University

Abhay Upadhyay

Graphic Era University

Mridul Gupta

Graphic Era University

B. Anuradha

Sri Venkateswara University

Naduvathezhath Nessariose Jose

Regent University

B. Nancharaiah

Rama University

Rotti Srinivasamurthy Swathi

Sri Venkateswara University

B Anuradha

Sri Venkateswara University

G Vivekanandan

Sri Ramachandra Institute of Higher Education and Research

Cooperative Institutions

Koneru Lakshmaiah Education Foundation

SRM Institute of Science and Technology

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

G Pulla Reddy Dental College & Hospital

Graphic Era University

Sri Venkateswara University

Sathyabama Institute of Science and Technology

Saveetha University

Karpagam Academy of Higher Education

Sri Ramachandra Institute of Higher Education and Research

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Optimal Resource Allocation in Dynamic Fading Channels via Meta-Learning Techniques

Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu

Resource Management

10.1109/akgec62572.2024.10869053

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Performance Analysis of Cooperative Relay Networks Under Strategic Eavesdropping: A Machine Learning Perspective

Deepak Upadhyay Kunj Bihari Sharma Abhay Upadhyay Rashmi Sharma Mridul Gupta

Eavesdropping

10.1109/iciics63763.2024.10860218

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Analysis of Xtrinsic Sense MEMS Sensors

International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering (2015)

Nookala Venu

the proposed work deals with the performance analysis of the Xtrinsic MEMS (Micro-Electro-Mechanical Systems) Sensors.The Xtrinsic MEMS Sensor evaluation board is ideal for developing prototype projects and designs that measure motion, altitude, pressure/temperature, as well as detection of magnetic field and physical position, on multiple platforms.In this paper, we have observed performance of Xtrinsic Sensors including the MPL3115A2 high precision pressure/temperature sensor, the MAG3110 low-power 3D magnetometer, as well as the MMA8491Q 3-Axis, digital accelerometer.

10.15662/ijareeie.2015.0408166

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Two Different Multi-Kernels for Fuzzy C-means Algorithm for Medical Image Segmentation

International Journal of Engineering Trends and Technology (2015)

Nookala Venu B Anuradha

A new image segmentation using multi-hyperbolic and multi-Gaussian kernel based fuzzy c-means algorithm (MHMGFCM) is proposed for medical magnetic resonance image (MRI) segmentation.The integration of two hyperbolic tangent kernels and two Gaussian kernels are used in the proposed algorithm for clustering of images.The performance of the proposed algorithm is tested on OASIS-MRI image dataset.The performance is tested in terms of score, number of iterations (NI) and execution time (TM) under different Gaussian noises on OASIS-MRI dataset.The results after investigation, the proposed method shows a significant improvement as compared to other existing methods in terms of score, NI and TM under different Gaussian noises on OASIS-MRI dataset.

10.14445/22315381/ijett-v20p215

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Medical Image Segmentation Using Kernal Based Fuzzy C-Means Algorithm

IJEIR (2015)

Nookala Venu B. Anuradha

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Investigation on identify the multiple issues in IoT devices using Convolutional Neural Network

Measurement Sensors (2022)

Swapna Thouti Nookala Venu Dhruva R. Rinku Amit Arora N. Rajeswaran

The Internet of Things (IoT) is an innovative technology that makes it possible for physical objects like sensors, cameras, household appliances, and other objects to interact and communicate with one another. The Internet of Things (IoT) devices may exchange critical data, which makes battery/power, connectivity, and security issues crucial. An automated system for identifying and reporting abnormalities to a central controller is a prerequisite for this. In order to distinguish between approved and legitimate IoT devices, this method should be able to. IoT devices that are malicious, non-IoT devices that are malicious, and other man-in-the-middle traffic sources must all be isolated to prevent noncompliance. For improved QoS management, this aids in the formulation of administrative rules and the regulation and enforcement of network traffic. A framework-based Convolutional Neural Network (CNN) is suggested in this research to discover the aforementioned problems in IoT devices. A system that classifies IoT devices into their respective categories and reliably identifies new entries has been developed based on CNN. The findings demonstrate that CNN is capable of classifying IoT devices into the appropriate categories with the necessary accuracy as well as identifying between IoT and non-IoT devices with better accuracy.

10.1016/j.measen.2022.100509

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Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis

Wireless Communications and Mobile Computing (2022)

Nookala Venu Rotti Srinivasamurthy Swathi Sanjaya Kumar Sarangi V. Subashini D. Arulkumar

In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the details of neighbouring nodes to survey the connection between the connection alteration rate and the hello time gap in terms of entire transmission rate. Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. It reduces end to end delay and increases the network lifetime. The hello time gap tracking algorithm is constructed in the network, and it obtains effective connection among unstable mobile nodes. Experimental output indicates that the connection alteration rate in general enhances the hello time gap. It provides the effective connection changing rate for analyzing different metrics which are network lifetime. throughput, and detection efficiency that are improved in the proposed ECAR scheme.

10.1155/2022/2785810

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Incorporating Edge-AI in IoT-Cloud Framework for Landslide Surveillance and Forecasting

Kapil Aggarwal Pratibha C. Kaladeep Bhavesh D. Patel C.S. Sasireka J. Arun Kumar

10.1109/icspcre62303.2024.10674861

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Performance and evalution of Guassian kernals for FCM algorithm with mean filtering based denoising for MRI segmentation

International Conference on Communication and Signal Processing (2014)

Nookala Venu

In this paper, a new segmentation algorithm with the integration of mean and peak-and-valley filtering based denoising and Gaussian kernels based fuzzy c-means (MPVKFCM) algorithm is proposed for medical image segmentation. First, the image is denoised by using the mean and peak-and-valley filtering algorithm. Secondly, image segmentation algorithm with Gaussian kernels based fuzzy c-means is performed on the denoised image. The performance of the proposed algorithm is tested on OASIS-MRI image dataset. The performance is tested in terms of score, number of iterations (NI), Execution time and (TM) under different Gaussian noises on OASIS-MRI dataset. The results after investigation, the proposed method shows a significant improvement as compared to other existing methods in terms of Score, NI and TM under different Gaussian noises on OASIS-MRI dataset.

Gaussian Noise

Mean-shift

10.1109/iccsp.2014.6950134

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Development of Green Computing through Power Management in Wireless Networks using Machine Learning

2022 International Conference on Inventive Computation Technologies (ICICT) (2024)

S R Raja Hemlata Arya D. Ramya S. Sivakumar Arun Kumar Arigela

This study investigates the use of Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to improve power utilization in green data centers. The study examines each model's individual capabilities in addressing key concerns about energy efficiency and sustainability. SVM is extremely accurate in workload prediction and resource allocation, whereas CNN excels in real-time monitoring and cooling system efficiency. RNN contributes by anticipating fluctuations in energy use and adapting to dynamic workload changes over time. The research uses ten hypothetical trials to demonstrate the models' consistent performance across multiple measures. These results suggest that SVM, CNN, and RNN have transformative potential in the development of flexible, efficient, and sustainable green data centers. The study adds to the larger discussion about green computing by emphasizing the role of powerful machine learning models in shaping the future of ecologically responsible data center operations.

10.1109/icict60155.2024.10544499

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Citations (1)