ARCHANA URITI

@gmrit.edu.in

Assistant Professor and Information Technology
GMR Institute of Technology



                 

https://researchid.co/archanauriti

EDUCATION

M.Tech(

12

Scopus Publications

36

Scholar Citations

4

Scholar h-index

Scopus Publications

  • PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines
    Praveen Kumar Sekharamantry, Marada Srinivasa Rao, Yarramalle Srinivas, and Archana Uriti
    MDPI AG
    In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due to the changes in the effects of image light, stance, and orientation. Further, it is difficult to identify the medicinal plants due to factors like variations in leaf shape with age and changing leaf color in response to varying weather conditions. The proposed work uses machine learning techniques and deep neural networks to choose appropriate leaf features to determine if the leaf is a medicinal or non-medicinal plant. This study presents a neural network design based on PSR-LeafNet (PSR-LN). PSR-LeafNet is a single network that combines the P-Net, S-Net, and R-Net, all intended for leaf feature extraction using the minimum redundancy maximum relevance (MRMR) approach. The PSR-LN helps obtain the shape features, color features, venation of the leaf, and textural features. A support vector machine (SVM) is applied to the output achieved from the PSR network, which helps classify the name of the plant. The model design is named PSR-LN-SVM. The advantage of the designed model is that it suits more considerable dataset processing and provides better results than traditional neural network models. The methodology utilized in the work achieves an accuracy of 97.12% for the MalayaKew dataset, 98.10% for the IMP dataset, and 95.88% for the Flavia dataset. The proposed models surpass all the existing models, having an improvement in accuracy. These outcomes demonstrate that the suggested method is successful in accurately recognizing the leaves of medicinal plants, paving the way for more advanced uses in plant taxonomy and medicine.

  • Evaluating Object Detection Approaches for Fruit Detection in Precision Agriculture: A Comprehensive Review
    Archana Uriti and Naga Jyothi Pothabathula
    IEEE
    Agricultural automation has become increasingly vital in addressing the growing demand for food and the need for efficient farming practices. Fruit harvesting is crucial in agriculture due to its labor intensity and the challenges of timely, accurate picking under occlusions and varying illumination. Various methods have been developed for accurate fruit detection, but traditional techniques often struggle with occlusions by branches or leaves, as well as challenges posed by climate and fruit maturity. This study reviews existing literature, compares various fruit detection approaches and discusses their strengths and weaknesses. Building on the work of various authors, this paper aims to offer a thorough understanding of the current state of fruit detection technologies in agricultural automation. Additionally, the survey addresses the objective of the proposed work, which is to identify the most promising techniques for improving fruit detection accuracy and efficiency.

  • A Systematic Analysis of Multi-Property Prediction Using Deep Learning in the Field of Drug Development
    Archana Uriti, Abhisek Sethy, and Surya Prakash Yalla
    IEEE
    In the rapidly changing field of pharmaceutical development, it is crucial to speed up clinical trials in order to introduce effective drugs in a timely manner. Conventional methods of forecasting molecular characteristics are often slow and time consuming, causing difficulties in the drug discovery process. However, Advancements in deep learning have streamlined pharmaceutical testing, enabling precise prediction of key drug properties like solubility, toxicity, reactivity, and bioactivity before synthesis. By employing different Simplified Molecular Input Line Entry System (SMILES) representations and training them with various deep learning algorithms like Graph Neural Networks (GNN) and Graph Convolution Networks (GCN). In this, a comparative study done on various technologies used in predicting molecular properties that helps to develop faster clinical trials in pharmaceutical industry. By identifying the limitations in the existing technologies, proposed an improved GCN which helps in better estimation of molecular properties.

  • An Analysis of Identification of Plant Leaf Diseases and Classification Using Machine Learning and Computer Vision
    Abhisek Sethy, Y. Surya Prakash, U. Archana, Soumya Ranjan Nayak, and Raghvendra Kumar
    Springer Nature Singapore

  • Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
    J. Lekha, K. Sandhya, Uriti Archana, Chunduru Anilkumar, Saini Jacob Soman, and S. Satheesh
    Elsevier BV


  • A comprehensive machine learning framework for automated book genre classifier
    Abhisek Sethy, Ajit Kumar Rout, Archana Uriti, and Surya Prakash Yalla
    International Information and Engineering Technology Association
    ABSTRACT

  • An in-depth Analysis of the Elements Shaping Organic Farmers: A Systematic Review
    A Ravi Kishore, K Niveditha, Archana Uriti, Chunduru Anilkumar, Ashok Sarabu, and K V L Prasanna
    IEEE
    In pursuit of sustainable and eco-friendly agricultural practices, organic farming has emerged as a promising solution to address the challenges posed by conventional agriculture. This paper presents a comprehensive survey of organic farming techniques, aiming to shed light on the diversity of approaches employed by farmers worldwide. Organic farming emphasizes the use of natural processes, avoiding synthetic chemicals and genetically modified organisms, to promote soil health, biodiversity, and overall ecosystem balance. Through a systematic review, this study explores various organic farming techniques, including soil management practices that enhance soil fertility and structure, pest and disease control strategies based on natural predators and biological agents, crop rotation systems to reduce pest pressure and nutrient depletion, and the role of composting in recycling organic matter to enrich soil nutrients. Moreover, this survey investigates the impact of organic farming on ecosystem resilience and its potential to mitigate climate change by sequestering carbon in the soil. As organic farming continues to gain momentum, it becomes essential to understand its potential benefits, challenges, and implications for sustainable agriculture. By highlighting different techniques used in organic farming, this research aims to contribute valuable insights and foster a collective mission towards a more sustainable and healthier future for the nation.

  • A Survey on Generating Audio with Captions for a Live Video using Neural Networks
    Archana Uriti, Durga Prasad Kakarla, Deepika Jinugu, Kanaka Nihitha Silla, K.T. V.N.S. Sai Krishna, and Chunduru Anilkumar
    IEEE
    Live video caption generation is a challenging task where the generated output should be accurate generalized captions for a video content and also providing audio will also get more attention. It is used in various applications like surveillance, military operations etc. Generating captions for a real time video is very difficult where the caption generator module has to semantically understand video content and translate it into meaningful captions to make the visually impaired people understand. As there are various approaches available to implement the live video captioning, this research study discusses about the various methodologies used for live video captioning and also works to overcome the existing challenges. This research study analyses the complete state-of-art models, datasets and evaluation metrics used for video captioning in the existing models. The main goal of this research study is to provide a clear understanding on the already existed works and the future scope.

  • GUI Implementation of Modified and Secure Image Steganography Using Least Significant Bit Substitution
    Surya Prakash Yalla, Archana Uriti, and Abhisek Sethy
    International Information and Engineering Technology Association
    Due to swift improvement of information innovation in recent times, providing security to data has become major concern and threat to Information Privacy has become inevitable. Data Hiding technology is an efficient way to solve the problems of data leakage and loss of information. Data hiding called steganography is a security method to provide security to secret data which is transferred from sender to receiver from harmful attacks. Steganography is an interaction of concealing a mysterious message inside a cover object which is not secret. There are many cover media like images, audio, video, text files etc. There are many ways to approach steganography like spatial domain, transformation domain, masking and filtering. This technique is helpful because the human eye is quite insensitive to the minute changes that help the embedded data stay safe and secure. The main motive of steganography is to get high stego image quality, low computational complexity, more embedding capability, visually unnoticeable, invisibility, and improved security. A capable steganographic technique must be resistant to any steganalysis approach the secret data is prone to. In this proposed system, implement the GUI implementation image steganography in spatial domain using Least Significant Bit (LSB) where the modified high capacity cover image undergoes the Discrete Wavelet Transformation (DWT) and propose an Advanced Encryption Standard (AES) secret key stego system such that the data is secure. The distortion between the two images in identified with the help of MSE and Histogram analysis.

  • An Approach of Understanding Customer Behavior with an Emphasis on Rides
    Archana Uriti, Surya Prakash Yalla, and Koteswara Rao Chintada
    IEEE
    Nowadays data is increases so fast due to varies of information in everyday life. Large amount of data is taken from different organizations which are very complex to analyze and utilize. With the availability of internet-connected devices, data is created in real-time. Examples of huge data include social networking sites, healthcare databases, ride-hailing companies, and so on. Nowa days the main challenge is to discover all the hidden information from the large amount of data collected from a mixed collection of sources. Ride-hailing firms get immerse with vast customer data. Processing, analyzing and visualization of this vast data is a complex task. As a result, a method is required to effectively analyze and visualize the data. Customer analytics refers to the study of customer behavior. Customer analytics helps the business people to turn huge data into big value by allowing them to identify customer behavior and thus improve the ride of their services. In this, mainly focus on a approach that will analyze and visualize the ride data using tableau and Jupyter notebook, helps the organizations in increasing their intelligence in business, generating revenue, decision making, managing various business operations, and trace the task status.

  • A Deep Belief Network Based Land Cover Classification
    Koteswara Rao Chintada, Surya Prakash Yalla, and Archana Uriti
    IEEE
    Land cover is the noticed actual cover on the Earth's surface which fills in as an ideal info boundary for various agricultural, hydrological and biological models. Thus, to foster feasible land use frameworks, there is a need to arrange the land cover. The principle objective of this work is to perform land cover arrangement by utilizing Hyper Spectral Image information and by applying Deep Belief Network. In this work, a combination approach is utilized to join the spatial and unearthly data in the arrangement cycle. An epic significant plan is proposed to get high portrayal accuracy in the wake of checking the capability of Restricted Boltzmann machine and Deep Belief Network. Trial results with broadly utilized hyper spectral information show those classifiers, inherent this profound learning-based system give serious execution. Also, the work reports to resolve the issues looked in utilizing 1-D information of the first Deep Belief Networks. To conquer this limit, another system is proposed for 3-D hyper unearthly picture which joins Principal Component Analysis; various leveled learning-based element extraction with calculated relapse.

RECENT SCHOLAR PUBLICATIONS

  • PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines
    PK Sekharamantry, MS Rao, Y Srinivas, A Uriti
    Big Data and Cognitive Computing 8 (12), 176 2024

  • A Systematic Analysis of Multi-Property Prediction Using Deep Learning In The Field Of Drug Development
    A Uriti, A Sethy, SP Yalla
    2024 International Conference on Intelligent Computing and Sustainable 2024

  • Evaluating Object Detection Approaches for Fruit Detection in Precision Agriculture: A Comprehensive Review
    A Uriti, NJ Pothabathula
    2024 International Conference on Intelligent Computing and Sustainable 2024

  • Secure method of communication using Quantum Key Distribution
    SP Yalla, A Uriti, A Sethy, S VE
    Applied and computational engineering 30, 32-37 2024

  • An in-depth Analysis of the Elements Shaping Organic Farmers: A Systematic Review
    AR Kishore, K Niveditha, A Uriti, C Anilkumar, A Sarabu, KVL Prasanna
    2023 International Conference on Energy, Materials and Communication 2023

  • An Analysis of Identification of Plant Leaf Diseases and Classification Using Machine Learning and Computer Vision
    A Sethy, Y Surya Prakash, U Archana, SR Nayak, R Kumar
    International conference on smart computing and cyber security: strategic 2023

  • Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
    J Lekha, K Sandhya, U Archana, C Anilkumar, SJ Soman, S Satheesh
    Measurement: Sensors 30, 100929 2023

  • A Survey on Generating Audio with Captions for a Live Video using Neural Networks
    A Uriti, DP Kakarla, D Jinugu, KN Silla, KS Krishna, C Anilkumar
    2023 International Conference on Sustainable Communication Networks and 2023

  • Exploration on Quick Response (QR) Code Behaviour in Commerce based Platforms Using Machine Learning
    SPY Archana Uriti
    I.J. Information Engineering and Electronic Business, 15 (5), 1-12 2023

  • Understand the working of Sqoop and hive in Hadoop
    A Uriti, SP Yalla, C Anilkumar
    Applied and Computational Engineering 6, 225-230 2023

  • A comprehensive machine learning framework for automated book genre classifier
    A Sethy, AK Rout, A Uriti, SP Yalla
    Revue d'Intelligence Artificielle 37 (3), 745 2023

  • GUI Implementation of Modified and Secure Image Steganography Using Least Significant Bit Substitution.
    SP Yalla, A Uriti, A Sethy
    International Journal of Safety & Security Engineering 12 (5) 2022

  • E-Commerce Data Analysis Using Hadoop
    PS U.Archana, S.Sai Vamsi , K.Deeksha , S.Amruta Varshini , J.Srijaa , S ...
    International Journal of Research Publication and Reviews 3 (4), 2235-2240 2022

  • A Survey on Enhancement of digital business using QR
    U Archana, Y Santosh, P kumar Tavva, P Rojitha, S Jitendra
    NeuroQuantology 20 (13), 930 2022

  • Wheel Chair Movement through Eyeball Recognition Using Raspberry Pi.
    SP Yalla, A Uriti, A Sethy, KR Chintada
    Special Education 1 (43) 2022

  • An Approach of Understanding Customer Behavior with an Emphasis on Rides
    A Uriti, SP Yalla, KR Chintada
    2021 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-5 2021

  • A deep belief network based land cover classification
    KR Chintada, SP Yalla, A Uriti
    2021 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-5 2021

  • An Exploration of Sentiment Analysis using Twitter Dataset
    PA Uriti Archana, Dr.Jyothi Mandala
    PSYCHOLOGY AND EDUCATION 58 (2), 10443-10447 2021

  • An Approach to Analyze YouTube Data using Hadoop
    U Archana
    International Journal of Advanced Science and Technology 29 (6), 4910 - 4918 2020

  • ROUTING TECHNIQUES ON INTERVEHICULAR COMMMMUNICATION SYSTEM
    U Archana
    IJARSE 7 (2), 486-496 2018

MOST CITED SCHOLAR PUBLICATIONS

  • GUI Implementation of Modified and Secure Image Steganography Using Least Significant Bit Substitution.
    SP Yalla, A Uriti, A Sethy
    International Journal of Safety & Security Engineering 12 (5) 2022
    Citations: 7

  • Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
    J Lekha, K Sandhya, U Archana, C Anilkumar, SJ Soman, S Satheesh
    Measurement: Sensors 30, 100929 2023
    Citations: 6

  • Exploration on Quick Response (QR) Code Behaviour in Commerce based Platforms Using Machine Learning
    SPY Archana Uriti
    I.J. Information Engineering and Electronic Business, 15 (5), 1-12 2023
    Citations: 4

  • A comprehensive machine learning framework for automated book genre classifier
    A Sethy, AK Rout, A Uriti, SP Yalla
    Revue d'Intelligence Artificielle 37 (3), 745 2023
    Citations: 4

  • A deep belief network based land cover classification
    KR Chintada, SP Yalla, A Uriti
    2021 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-5 2021
    Citations: 3

  • A Novel Quantization Approach for Approximate Nearest Neighbor Search to Minimize the Quantization Error
    U Archana, U Sridhar
    International Journal of Innovative Research in Science, Engineering and 2017
    Citations: 3

  • PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines
    PK Sekharamantry, MS Rao, Y Srinivas, A Uriti
    Big Data and Cognitive Computing 8 (12), 176 2024
    Citations: 2

  • Secure method of communication using Quantum Key Distribution
    SP Yalla, A Uriti, A Sethy, S VE
    Applied and computational engineering 30, 32-37 2024
    Citations: 2

  • Wheel Chair Movement through Eyeball Recognition Using Raspberry Pi.
    SP Yalla, A Uriti, A Sethy, KR Chintada
    Special Education 1 (43) 2022
    Citations: 2

  • An in-depth Analysis of the Elements Shaping Organic Farmers: A Systematic Review
    AR Kishore, K Niveditha, A Uriti, C Anilkumar, A Sarabu, KVL Prasanna
    2023 International Conference on Energy, Materials and Communication 2023
    Citations: 1

  • An Analysis of Identification of Plant Leaf Diseases and Classification Using Machine Learning and Computer Vision
    A Sethy, Y Surya Prakash, U Archana, SR Nayak, R Kumar
    International conference on smart computing and cyber security: strategic 2023
    Citations: 1

  • Understand the working of Sqoop and hive in Hadoop
    A Uriti, SP Yalla, C Anilkumar
    Applied and Computational Engineering 6, 225-230 2023
    Citations: 1