M SUCHITHRA

@srmist.edu.in

Associate Professor , Department of Computing Technologies , School of Computing
SRM Institute of Science and Technology , Kattankulathur



                 

https://researchid.co/suchithram

EDUCATION

BTech, ME, PhD

RESEARCH INTERESTS

Computer Science Engineering - AI, IoT, Vision

26

Scopus Publications

Scopus Publications

  • Detection and Classification of Cervical Cancer Using Deep Learning Techniques
    Keshav Rao, M. P. Nanda, and M. Suchithra
    AIP Publishing

  • Real-time Deep Learning Speech Output Based Object Recognition
    Sanskar Shrivastava, Jhalak Mahendra, and M. Suchithra
    AIP Publishing


  • Preface



  • EEG-based multimodal emotion recognition with optimal trained hybrid classifier
    G. Kalyana Chakravarthy, M. Suchithra, and Satish Thatavarti
    Springer Science and Business Media LLC

  • Fisher Mask Nodes for Language Model Merging



  • Automation in Natural Rubber Latex Harvesting Field: A Review
    Hanna Shukoor, M. S. Suchithra, and Jayakrushna Sahoo
    Springer Nature Switzerland



  • Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems
    M. Baskar, R. Renuka Devi, J. Ramkumar, P. Kalyanasundaram, M. Suchithra, and B. Amutha
    Springer Science and Business Media LLC

  • Apple Leaf Disease Detection: Machine Learning & Deep Learning Techniques
    Merin Sebastian, Suchithra M S, and Chippy Maria Antony
    IEEE
    Early leaf disease detection is essential to mitigate potential crop loss and reduce reliance on chemical treatments. Here we have tried out several Machine Learning and Deep Learning Techniques for automatically detecting apple leaf diseases. Machine Learning techniques like Support Vector Machine, Random Forest, Naive Bayes, K–Nearest Neighbor, Decision Tree, and some Deep Learning methods like Sequential, and VGG-16. We have compared all these models and found that VGG-16 has more accurate results than the other models we used. The recommended model proposes profound figuring out how to improve the arrangement of apple leaf illness. Additionally, it demonstrates the highest validation accuracy of 97.23 percent on the dataset for apple leaf disease. Contrasted with some present futuristic, this approach performs better. Therefore, farmers can use the suggested system to streamline the process of classifying apple leaf disease and aid in the disease's early diagnosis and treatment.


  • Potato Leaf Diseases Detection Using Machine Learning And Deep Learning
    Shiffa T.S., Suchithra M S, and Aiswarya Vijayakumar
    IEEE
    Agriculture plays a vital role in sustaining human life, providing the food and resources needed for survival. With the advent of digitalization, technology has permeated every aspect of our lives, including agriculture. The potato, a versatile tuber, is a widely enjoyed and commonly consumed staple in diets across the globe. To ensure a prosperous potato production, establishing a robust food security system becomes imperative, given its high nutritional value in terms of vitamins and minerals. Nonetheless, it is crucial to address diseases affecting potatoes including early blight and late blight. The manual assessment of these leaf diseases is labor-intensive and inconvenient. In the present investigation, we have implemented a system that utilizes a combination of machine learning and deep learning methodologies to classify two distinct types of diseases in potato plants, relying on the assessment of their leaf conditions. We utilized a range of models including support vector machine, naive bayes, K-Nearest Neighbor, Decision Tree, Random Forest, convolutional neural network, Sequential2, and VGG 16 to establish a highly accurate classification system. Notably, our experiment yielded an impressive accuracy of 95.36% within the initial 10 epochs of VGG 16 training, affirming the viability of the deep neural network approach. This research has made a substantial contribution to the agricultural sector and has provided valuable insights for farmers to effectively classify Potato leaf Disease, yielding optimal results.

  • Implementing a land registration system using non-fungible tokens to represent land in the system and side-chain for data storage
    Cyril Kunjumon, Aditya Mutharia, Salemula Shareef, and Suchithra M S
    IEEE
    Blockchain Technology has been increasing in popularity in recent times due to its decentralized nature, immutability, transparency, high security and transparency. Several private and public organisations have been using it to develop new and innovative projects like cryptocurrency, NFTs, Metaverse, etc., and also to upgrade and improve old technologies like supply chains in agriculture and government voting systems. Land Registration is one such sector that can be upgraded using blockchain because in the current system there are a lot of middlemen involved and data is centralized and not secure, these things make the current system vulnerable to frauds and corruption. In this paper we have proposed a new implementation for the existing land registration system in the country that uses Non-Fungible tokens in the blockchain to represent a property and a sidechain like IPFS for storing its data in a decentralized manner. With our implementation we can overcome the flaws in the old system and make land registration secure, transparent and trustless. Here by "trustless" we mean that users do not have to trust any centralized authority or intermediary to verify transactions or maintain the integrity of the system. Instead, trust is placed in the system’s consensus mechanism and cryptographic protocols.

  • A Novel Deep Learning based Improved Cluster based Region Classifier Algorithm to Recognize and Categorize Emotions using EEG Signals
    G.Kalyana Chakravarthy and M Suchithra
    IEEE
    Nowadays deep learning plays vital role in emotion recognition. It distinguishes emotions as easy or multi-models for visual capturing. This works to provide an automatic version for identifying feelings primarily based on EEG signals. The proposed version specializes in developing an effective model, which combines the basic ranges of EEG signal handling and feature extraction. A system is developed based on Independent component analysis (ICA) algorithm to overcome the recognition task which removes noise object and to extract the independent components, for the obtaining components. The channels were selected based on the threshold average activity value. K-Nearest Neighbor(KNN) and Artificial Neural Network (ANN) are used to categorize emotional states and extracted the features, together with the unconventional improved Cluster-based region Classifier (ICBRC). Based on EEG signals Average recognition rate up to 94% for three emotional states and 95% for binary states can be achieved with this system.

  • Application of Abstractive Summarization in Multiple Choice Question Generation
    Ayushi Mathur and M Suchithra
    IEEE
    This research aims to provide a solution to automate multiple choice question generation using natural language processing supported by abstractive summarization. The text or chapter imported by the user is transformed with the help of PEGASUS for Abstractive Summarization developed by Google AI in 2020 enabling us to get important in a paraphrased manner. These sentences will be used as questions. The key-value or word which will be the answer and will be removed from the sentence is determined with the help of KeyBERT which uses Bidirectional Encoder Representations from Transformers (BERT) embeddings and was developed by Maarten Grootendorst. KeyBERT considers the semantics of a word while performing keyword extraction. To generate the incorrect options pertaining to the respective question, we use sense2vec which was trained on Reddit comments and returns distractors (word similar to our keyword/answer). We generate questions along with the options based on the text received from the user at the end of the process.

  • Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business
    Shruti Aggarwal, M. Suchithra, N. Chandramouli, Macha Sarada, Amit Verma, D. Vetrithangam, Bhaskar Pant, and Biruk Ambachew Adugna
    Hindawi Limited
    Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain.

  • A Trusted Blockchain Based Traceability System for Fruits and Vegetable Agricultural Products
    E Keerthivasan, K Raja Vignesh, D Shinthan, S Naveen Kumaar, R Subha, and Ms Suchithra
    IEEE
    Blockchain is an arising computerized innovation permitting universal monetary exchanges among disseminated untrusted parties, without the need of middle people like banks. This article analyses the effect of blockchain innovation in agribusiness and food production network, presents existing continuous tasks and drives, and examines generally suggestions, difficulties and potential, with a basic view over the development of these activities. Our discoveries show that blockchain is a promising innovation towards a straightforward inventory network of food, with numerous continuous drives in different food items and food-related issues, yet numerous hindrances challenges actually exist, which frustrate its more extensive prevalence among ranchers and frameworks. These difficulties include specialized viewpoints, instruction, strategies and administrative structures.

  • Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS
    M. Suchithra, M. Baskar, J. Ramkumar, P. Kalyanasundaram, and B. Amutha
    Springer Science and Business Media LLC

  • QoS aware trusted service discovery architecture for optimization in MANETs
    Suchithra M and M. Ramakrishnan
    ACM
    During service discovery architecture designing in MANET, some of the critical services requested by the clients should be provided within short deadlines. Moreover, the QoS constraints of the service providersuch as; battery power, stability, trust, load, capacity, etc., should also be considered while selecting the servers. In this QoS aware trusted service discovery architecture, a set of backbone nodes are selected based on the relative mobility, residual energy and available bandwidth. The selected backbone node collects the information related to server and its services. When the service is availed by a client, the backbone node then updates the trust value of that server. By simulation results, we show that the proposed technique enhances quality of services through minimizing the delay.


  • Non functional QoS criterion based web service ranking
    M. Suchithra and M. Ramakrishnan
    Springer India

  • A survey on different web service discovery techniques
    M. Suchithra and M. Ramakrishnan
    Indian Society for Education and Environment
    Web services allow application to communicate using standardized protocols with low cost. With the development of SOA, web services have gained wide popularity. Since many web services are available in internet, finding the most appropriate for the user request is difficult. The paper presents a study on various web service discovery approaches and its features. Agent based discovery with QoS ranks web service accurately and fast.

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