Dr. N. Anusha

@http:

Associate Professor and Computer Science and Engineering
Vidhya Jyothi Institute of Technology



              

https://researchid.co/anusha123

RESEARCH INTERESTS

Image Processing
Remote Sensing & GIS
Machine learning and Artificial Intelligence
Cloud Computing

38

Scopus Publications

Scopus Publications

  • A swarm intelligence optimization for lung cancer detection from RNA-seq gene expression data using convolutional neural networks



  • Agriculture land surveying rover using internet of things
    N. Anusha, K. Sanjeev Kumar, M. Harshavardhan, Mohammed Huzaifa Moinuddin, V. Durga Prasad, M. Pavan, and A. Srujana
    AIP Publishing

  • Enhancing Multiple Object Detection in UAV Images: A Comparative Study of YOLO, RCNN and SSD Algorithms
    N. Anusha, A.Vijji Amutha Mary, K.V. SubbaReddy, G.Srinivasa Rao, Y.Praveen Kumar, and Mawahed Ali
    IEEE
    Unmanned Aerial Vehicles (UAVs) have emerged as invaluable tools for various applications, ranging from surveillance and reconnaissance to agriculture and disaster management. The utilization of UAVs for data collection, particularly in the form of high-resolution images, has led to significant advancements in computer vision and machine learning. One of the critical challenges in leveraging UAV imagery lies in the efficient detection of multiple objects within these images, a task that is pivotal for applications such as environmental monitoring, infrastructure inspection, and security surveillance. This research paper aims to contribute to the field of multiple object detection in UAV images by conducting a comprehensive comparative analysis of state-of-the-art machine learning algorithms. The focus is on popular object localization models, including You Only Look Once (YOLO), Region-based Convolutional Neural Network (RCNN), and Single Shot Multi box Detector (SSD). The study extends beyond comparing algorithms and explores different underlying networks connected to these algorithms, aiming to understand how network architecture affects object detection performance.

  • Enhanced Web based Multi-Platform E-voting Solution
    Anusha Nallapareddy, T Swapna, Sundaramurthy Shanmugam, Jany Shabu, and J Refonaa
    IEEE
    In democratic nations such as India, the act of voting is a basic right that grants individuals the power to choose their leaders. Traditionally, voting has typically taken place at specific locations called polling booths, either in centralized or distributed. Every Indian citizen above 18 years of age has the right to vote and participate in choosing their representatives. Voters go to the polling booths where they can cast their votes under the observation of authorized individuals. However, this traditional voting system has various challenges such as low voter turnout and instances of fraudulent voting. To address these issues, a secure multi-platform E-Voting system is proposed in this research, developed using the Flutter framework and Firebase backend services. The system aims to verify each voter before they can cast their vote, employing multiple levels of verification. The proposed application consists of an administrator (admin) interface and a voter interface. The admin interface allows for the addition of elections, candidates, and constituencies, while the voter interface requires voters to register, $\\log \\mathrm{in}$, and cast their vote. The verification process includes One Time Password (OTP) authentication, face recognition, fingerprint verification, and validation through government-issued identification cards. Two commonly used identification cards in India are the Aadhaar card and the Permanent Account Number (PAN) card, which are unique to each citizen. By utilizing this proposed online voting application, eligible voters can conveniently cast their votes from anywhere in the world, using internet-connected devices like mobile phones or tablets. The primary objectives of the proposed system are to increase voter turnout and reduce instances of fraudulent voting. Furthermore, conducting elections through this application also helps reduce the required workforce.


  • Crypto Tracking Web Application
    N. Anusha, Akepogu Vivek, Ananya Gullapally, Ponugoti Ram Teja, and Regadamilli S R S Rahul
    IEEE
    This research focuses on the development of an advanced cryptocurrency monitoring platform with a primary emphasis on its distinctive "watchlist" feature. This feature empowers users to add their preferred digital currencies, enabling a smooth and user-friendly experience for tracking their selected coins, accessing current market values, and conducting in-depth analyses of their historical performance. The platform is constructed using the MERN tech stack, which combines MongoDB for flexible data storage, Express.js for efficient handling of HTTP requests, React.js for dynamic user interfaces, and Node.js for high- performance server- side execution. To ensure the provision of real-time and comprehensive data and to enhance the overall functionality of the platform, it heavily relies on two key APIs: CryptoCompare and CoinGecko. These APIs play a crucial role in delivering accurate information to users, and their integration has been instrumental in achieving the platform's objectives.

  • Groundwater Chemistry Of Umred Taluka, Nagpur District, Maharashtra
    Alpashi L Sadawarti, Shubham P Masurkar, N. Anusha, J. Refonaa, and Ramesh Cheripelli
    IEEE
    15 groundwater samples were gathered from distinct villages within the Umred taluka. A range of parameters were scrutinized for each of the collected samples. To ascertain the groundwater’s appropriateness for drinking, the specifications outlined by the Bureau of Indian Standards (BIS) in 2012 were employed as a benchmark. With the exception of nitrate (NO 3-) levels exceeding the permissible limit in four samples; all other parameters adhered to the recommended BIS standards. The study also investigated the suitability of groundwater for irrigation based on various parameters, and the results suggest that the groundwater is indeed suitable for this purpose. The hydro chemical facies of the groundwater were determined through chemical analysis, and a Piper’s trilinear diagram was utilized for this purpose. The diagram revealed a predominance of the combined cations of calcium and magnesium over the combined cations of sodium and potassium. Additionally, the weak acid bicarbonate outweighed the strong acids sulfate and chloride. The majority of the samples were situated in the Ca-HCO3 region.

  • Enforcement of CNN Model in Drone Detection System
    A. Viji Amutha Mary, N Anusha, Mercy Paul Selvan, R. Rajalakshmi, S. Jancy, and L K Joshila Grace
    IEEE
    Drones play a crucial role across various applications. However, their widespread use has raised concerns about potential misuse, includi surveillance, solicitation, and terrorism. Consequently, there is a growing demand for effective drone detection systems. A promising solution for this is the application of Convolutional Neural Networks (CNNs). Rhynchus is a system designed specifically for image recognition tasks. CNNs have proven highly effective in detecting drones in images, even under challenging conditions such as thick fog. In this paper, the objective is to develop a drone detection system with various applications. The proposed system consists of two main components: a feature extraction module and a classification module. The feature extraction module identifies relevant characteristics, such as shape, texture, and movement, from input images. The classification module uses these features to classify an object as either a drone or a non-drone. We will utilize a large dataset of drone images for training and evaluating the system, which includes a diverse array of drones and environments, ensuring robustness across different scenarios. Once developed, the system will be implemented on a real-time platform to showcase its effectiveness in real-world settings. It will be capable of detecting drones in real time using video feeds from cameras. The proposed drone detection system offers several advantages: it is accurate and resilient under various conditions, cost-effective to implement, and scalable for use in a wide range of scenarios.

  • AN APPROACH FOR MOVIE RECOMMENDATION USING COLLABORATIVE FILTERING WITH SINGULAR VALUE DECOMPOSITION
    N. Anusha, Darmoju Deekshitha, Ghadiyaram Bhavya and Buchupalli Mohitha
    Asian Research Publishing Network
    Movie recommendation systems help movie enthusiasts by suggesting movies to watch without the hassle of having to go through the time-consuming process of deciding from a large collection of movie streaming platforms that recommend movies and TV episodes. News organizations that suggest articles to readers, and online stores that suggest products to customers all benefit from these recommendation systems. The algorithms implemented in this research train their models on the MovieLens dataset and provide users with tailored movie recommendations. The study compares different machine learning algorithms, which include a Content-based model, item-item and user-user collaborative filtering (CF), Collaborative filtering with Singular Value Decomposition (SVD), K Nearest Neighbors, and Non-negative Factorization. The algorithms are evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to measure their accuracy and performance. While the proposed system which is based on a collaborative approach using SVD determines the connection between various users and, depending on their ratings, recommends movies to others with similar tastes, subsequently allowing users to explore more. The proposed approach using collaborative filtering with SVD performs better with a minimal RMSE of 0. 880258 by giving accurate and appropriate recommendations to the user. The model is further evaluated using performance metrics like Precision, Recall, and f1 score. So, CF with the SVD recommendation model is chosen for implementation and is integrated into a web application that allows the platform users to rate and review the available digital content as well as allows them to restrict screen time using a parental control system. The results of the study in this paper are presented in the form of tables, graphs, and statistical analyses, and can be used to guide the development of new and improved recommendation algorithms.

  • Groundwater Quality Assessment for Villages Around Umred Coal Mines, Nagpur District, Maharashtra
    Shubham P Masurkar, Alpashi L Sadawarti, N. Anusha, T. Swapna, and S. L. Jany Shabu
    IEEE
    Groundwater is the principal source of water in the area of research. The area covers six villages surrounding the Umred coal mines. Fifteen water samples in total were meticulously collected and a comprehensive investigation was undertaken to study the potential influence of mining activities on the quality of groundwater. Wide range of parameters was investigated and the results were precisely compared to the standards set by the BIS (Bureau of Indian Standards) in 2012 and 2015. All the parameters meet the required standards, except for NO3-, It exceeds the acceptable limits in four samples. The findings show that the samples are well-suited for irrigation as per U.S. Salinity Laboratory Diagram. The Piper’s trilinear diagram for the samples shows the dominance of calcium (Ca2+), magnesium(Mg2+) over sodium (Na+), and potassium (K+), with the weak acid (HCO3-) outweighing strong acids (SO42- + Cl-). Majority of samples in this area lie in the Ca-HCO region. The study also evaluated the water quality of this area, by accessing the following key parameters - pH (Potential of Hydrogen), Mg2+ (Magnesium), TDS (Total Dissolved Solids), TH (Total Hardness), Na+ (Sodium), HCO3- (Bicarbonate), SO42- (Sulphates), Ca2+ (Calcium), Cl- (Chlorides), K+ (Potassium), EC (Electrical Conductivity),NO3- (Nitrates), and F- (Fluorides).

  • Deep Learning-Based Aerial Object Detection for Unmanned Aerial Vehicles
    N. Anusha and T. Swapna
    IEEE
    The proliferation of drones and unmanned aerial vehicles (UAVs) across various sectors, including civil, military, and business applications, has underscored the need for effective collision prevention measures and enhanced surveillance capabilities. Military deployments and civilian applications increasingly rely on UAVs for swift reconnaissance and various tasks. However, the surge in UAV numbers has heightened collision risks, necessitating robust collision prevention measures. In the context of the research's questions and purposes, this research aims to enhance UAV capabilities for efficient and dependable operations in dynamic environments. Specifically, it seeks to improve collision avoidance and surveillance through Charge-Coupled Device (CCD) sensors and deep learning techniques. The research leverages deep learning architectures, including You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (R-CNN), and EfficientDet, in conjunction with CCD sensors for object detection. It conducts a comprehensive comparative analysis to evaluate the performance of these architectures, with a particular focus on YOLOv5, using the UAVDT dataset. The comparative analysis reveals that YOLOv5 outperforms other architectures in terms of accuracy and speed for aerial object detection, especially when applied to the UAVDT dataset. YOLOv5 demonstrates remarkable realtime object detection capabilities, including the identification of diverse objects. While Faster R-CNN and EfficientDet models offer competitive accuracy, they require longer inference times and more training epochs to achieve comparable results. This study showcases the potential of deep learning algorithms to enhance UAV capabilities, making them more efficient and reliable in dynamic environments, thus contributing to the advancement of UAV technology. The research findings presented herein have significant implications for the ongoing development and deployment of UAVs across various sectors.

  • Studies on the Functionality of On-Board Computer in 1U CubeSat
    Anusha N, Md Fardeen, Chandana K, Md Abdullah, Balaram Mishra, and Vasanth K
    IEEE
    The paper studies the various functionality of the On-Board Computer (OBC) used in 1U CubeSat for Leo orbit. The OBC in CubeSat is responsible for controlling and managing all the subsystems of the Spacecraft. The functionality includes modes of operations of the satellite, socket programming that is used to communicate between subsystems, user datagram protocol as transportation layer protocol between different subsystems, and hardware to visualize the functional working of the satellite. The modes of operation is timer based and implemented using Java in Visual Studio (VS) code platform that acts as a real-time operating system (RTOS) of the satellite. The Nested socket programming between various subsystems was developed in Java finally the functionality of Lora was implemented through Arduino board. The packet transfer between each subsystem is analyzed using packet tracer software. The functionalities were successfully studied implemented and analyzed about the onboard computer


  • LAND USE LAND COVER CLASSIFICATION USING MULTI-SPECTRAL SENTINEL-2B SATELLITE IMAGE


  • DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE
    Anusha NALLAPAREDDY
    Politechnika Lubelska
    Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive.  In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possibilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified.


  • Automatic flood detection in multi-temporal sentinel-1 synthetic aperture radar imagery using ANN algorithms
    Anusha Nallapareddy and Bharathi Balakrishnan
    Agora University of Oradea
    Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection.

  • Weather prediction using neural network backpropagation


  • Weather Prediction Using Multi Linear Regression Algorithm
    N Anusha, M Sai Chaithanya, and Guru Jithendranath Reddy
    IOP Publishing
    Abstract Weather forecasting is one of the applications of science and technology, used to predict the weather condition depending on the input attributes. Most of the existing systems are implemented using statistical approaches for Support Vector Machine (SVM), which are incapable of giving the accurate prediction as they cannot capture sudden changes in weather conditions. The proposed technique uses the concept of Multi-Linear regression which can produce better results than existing methods.

  • An overview on change detection and a case study using multi-temporal satellite imagery
    N. Anusha and B. Bharathi
    IEEE
    Satellite imagery based change detection plays an important role in analyzing the after effects of natural disasters, detecting the changes in city limits due to rapid urbanization, updating the map database, monitoring the factors impacting agriculture, etc., The remote sensors mounted on satellites or aircrafts absorb the light reflected by the earth’s surface. The output of these sensors will be a digital image which represents the scene being perceived. In order to extract the useful information from these images, various image processing techniques need to be employed. In this paper, a detailed outline of the steps and various techniques used for detecting the changes in multi temporal remote sensing images is discussed and a case study is done by taking multi-temporal Landsat-8 images covering Hyderabad city. Image differencing method is applied in order to find the changes in the Hyderabad city limits over 2013December and 2017 December time periods.

  • Despeckling of synthetic aperture radar satellite imagery using various filtering techniques


  • Change detection and flood water mapping using sentinel-1A synthetic aperture radar images
    N. Anusha and B. Bharathi
    American Scientific Publishers
    Prodigious flooding in the state of Uttar Pradesh, India during the month of August 2017 was induced by heavy rainfall, causing water levels in several rivers to cross the danger mark bringing normal life to a standstill. The peculiar rainfall pattern in India makes it highly vulnerable to floods. Demand for crisis information, for instance, natural disasters like severe flood events has increased. A simple but effective method is proposed in this study to find the areas that are affected due to floods, to detect the changes and for flood mapping. These indicators were derived from the Sentinel-1A Synthetic Aperture Radar (SAR) data by taking the crisis and archive images. An open flood surface can be detected easily in SAR data as it acts as a specular reflector that scatters the energy away from the sensor, causing relatively dark pixels of low backscattered SAR data. In contrast, the surrounding non-water areas usually exhibit a higher return due to surface roughness. Red, Green, Blue (RGB) composite is made for highlighting the flooded areas and for detecting changes by combining both archive and crisis images. Finally the flood map is compared with the optical imagery on the Google earth by integrating the resultant RGB composite image on the Google earth. Identification of the flood-prone areas is crucial to action the appropriate control measures in the flood-affected regions.

  • Image segmentation using tozero method and tozero inverse methods


  • Segmentation of multi-temporal images using Gaussian Mixture Model (GMM)


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