K.LAKSHMINADH

@professor

PROFESSOR
Dr K LakshmiNadh



                 

https://researchid.co/lakshminadh

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Artificial Intelligence, Computer Science, Information Systems

9

Scopus Publications

54

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models
    Popuri Mohana Siva Lakshmi, K. LakshmiNadh, K.V. Narasimha Reddy, and Dodda Venkata Reddy
    IEEE
    In the world, plant diseases pose a serious threat to agricultural productivity and food security. Early, accurate, and rapid identification of plant diseases is important for con-trolling loss of crops. In the following research, transfer learning models VGG16, ResNet50, and Xception are applied to attempt overcoming this challenge of multiclass plant disease detection. To improve classification accuracy, we propose an ensemble model that combines the strengths of these pre-trained networks. Multiple plant species and disease categories were experimented on extensively on publicly available plant disease datasets. The results show that ensemble model achieves better precision, precision and recall than individual models and therefore presents a robust solution for identifying several plant diseases together as a pack. Results from the experiment demonstrate that the proposed method could be deployed in real-time agricultural systems and have potential to provide a scalable and efficient diagnostic tool for farmers and agronomists to detect plant diseases and reduce their impact. This work is among the growing body of work in AI based agricultural solutions and indicates that transfer learning and ensemble techniques are promising in precision farming.

  • Enhancing Profanity Detection in Textual Data Using Bidirectional Long Short-Term Memory Networks
    Kagithala Lakshminadh, Velavolu Sravanthi, Kollipara Koushik, and Chavatapalli Surya Bhaskar
    IEEE
    The proliferation of offensive language and content, often referred to as 'profanity,’ poses significant challenges across various digital platforms. Profanity detection in textual data plays a crucial rolein various domains such as social media monitoring, online content moderation, and cyberbullying prevention. This approach aims to enhance profanity detection in textual data using Bidirectional Long Short-Term Memory (BiLSTM) networks to prevent offensive content. The performance of profanity detection is improved in this work by utilizing a novel approach that makes use of cutting-edge deep learning techniques. To be more precise, This approach uses a Bidirectional Long Short-Term Memory network, a powerful deep learning architecture for sequence classification, to model the intricate relationships between words in textual data. Furthermore, this incorporates fine-tuned pre-trained word embeddings to capture semantic information and contextual cues, thereby augmenting the performance of the model. Through extensive experimentation and evaluation of exemplary datasets, the approach achieves a remarkable accuracy rate of 88.3%. Modern methodologies are outperformed by the suggested approach in terms of memory, accuracy, recall, and precision. The efficiency of the method opens up avenues for its application in real-world scenarios, facilitating more effective social media monitoring, online content moderation, and proactive cyberbullying prevention. The findings contribute to advancing the field of profanity detection and hold promise for future research in this domain.

  • Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos
    Chepuri Rajyalakshmi, K. LakshmiNadh, and M Sathyam Reddy
    IEEE
    The rapid development of computer vision and machine learning in recent years has led to fruitful accomplishments in a variety of tasks, including the classification of objects, the identification of actions, and the recognition of faces, among other things. Nevertheless, identifying human emotions remains one of the most difficult tasks to do. To find a solution to this issue, a significant amount of work has been put in. In order to achieve higher accuracy in this reactivity towards a variety of speeches and vocal -based methods, computer intelligence, natural language modelling systems, and other similar technologies have been used. The examination of the emotions has the potential to be useful in a number of different settings. Cooperation with human computers is one example of such a field. Computers can help customers recognize emotions, make wiser decisions, and create more lifelike human-robot interactions. In recent times, there has been a lot of focus placed on the ability to forecast dynamic facial emotion expressions in videos. Therefore, this work proposes a deep convolutional neural networks (CNNs) model for emotion prediction from speech samples, facial expression images, and videos with enhanced prediction accuracy and reduced loss. In addition, the speech CNN model also utilizes mel-frequency Cepstrum coefficients (MFCC) as feature extraction from given speech samples. The proposed MFCC-CNN model resulted in superior performance than traditional models.

  • A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection
    Lakshmi Narayana Gumma, Ramalingam Thiruvengatanadhan, Pattusamy Dhana Lakshmi, and Kurakula LakshmiNadh
    International Information and Engineering Technology Association
    ABSTRACT

  • An efficient spatial temporal provenance mechanism for adhoc mobile users
    K. Divya, S. N. Rao and K. LakshmiNadh
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    Location-based services rectangle measures quickly changing into vastly in different style. Additionally to services supported users' current location, several potential services believe users' currentlocation history, or their spatial-temporal place of origin.Malicious users might idle their spatial-temporal place of originwhile not a rigorously designed security system for users toprove their past locations. during this system, we tends to gift theSpatial-Temporal place of origin Assurance with Mutual Proofs theme. STAMP is meant for ad-hoc mobile usersgenerating location system proofs for every different in an exceedinglydistributed settings. However, it will simply accommodate trustyworthy mobile users and wireless access points. STAMP ensures theintegrity and non-transferability of the placement proofs andprotects users' privacy. A semi-trusted Certification Authority isemployed to distribute specific keys in addition as guard users against collusion by a light-weight entropy-based trust analysisapproach. Our image implementation is based on the Andriod platformshows that STAMP is low cost in terms of procedure and storageresources. Intensive simulation experiments show that ourentropy-based trust model is in a position to attains high collusion to detects the accuracy.

  • A binary feedback schemes for detecting failure of node in mobile wireless network
    P.Naga Priyanka*, , Dr.K.Lakshmi Nadh, Dr. S.Siva Nageswara Rao, , and
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    The network topology of association was always active but the association between them may not be always connected and properties are restricted. On the time there is a chance of node failures and detecting the node failure is important. Two node failure detection schemes are implemented which are binary and non-binary feedback schemes. These schemes unite locality estimation, localized monitoring and node association. These results are applicable to both attached and detached networks. The schemes accomplish high disappointment discovery rates, low forged positive rates, and low correspondence overhead.

  • DDSRC: Algorithm for improving QOS in VANET


  • Feedback enabled transmission control protocol for next generation networks


  • Improving TCP performance with delayed acknowledgments over wireless networks: A receiver side solution
    K. LakshmiNadh, K.N. Rao, and Y.K.S. Krishna
    Institution of Engineering and Technology
    The earlier studies have shown that TCP distresses from performance diminution due to lean wireless channel characteristics and host mobility. For such networks generating acknowledgement for each data packet reduces TCP throughput. By sharing the same path for data and acknowledgement, it creates competition and collision, resulting in reduced TCP throughput. The TCP throughput is improved when one acknowledgement acknowledges out-of-order packets or full congestion window. This paper propose a solution "Improved Delay the Duplicate Acknowledgement" (IDDA) for a certain time period in order to avoid unnecessary fast retransmissions.

RECENT SCHOLAR PUBLICATIONS

  • Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models
    PMS Lakshmi, K LakshmiNadh, KVN Reddy, DV Reddy
    2024 International Conference on IoT Based Control Networks and Intelligent 2024

  • An Improved and More Effective FSPC-Based Cloud Consumer Legality Process for Protected Data
    D Priyanka, P Anjaneyulu, KL Nadh, SKKM Basha
    International Conference on Computing, Communication and Learning, 413-424 2024

  • Enhancing Profanity Detection in Textual Data Using Bidirectional Long Short-Term Memory Networks
    K Lakshminadh, V Sravanthi, K Koushik, CS Bhaskar
    2023 International Conference on Self Sustainable Artificial Intelligence 2023

  • Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos
    C Rajyalakshmi, K LakshmiNadh, MS Reddy
    2023 5th International Conference on Smart Systems and Inventive Technology 2023

  • A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection
    K Gumma, L.N. , Thiruvengatanadhan, R. , Lakshmi, P.D. , LakshmiNadh
    International Information and Engineering Technology Association, 657-664 2022

  • LUNG DISORDER DETECTION USING CORRELATED PIXEL DENOISING MODEL WITH TAGGED FEATURE SELECTION USING CONVOLUTION NEURAL NETWORKS
    KLN Lakshmi Narayana Gumma, Ramalingam Thiruvengatanadhan, Pattusamy Dhana ...
    MATERIAL SCIENCE AND TECHNOLOGY 21, 53-63 2022

  • CUcovid: U-Net incorporated CNN based Deep-learning system of chest X-ray image classification for COVID-19 detection
    LN Gumma, R Thiruvengatanadhan, K LakshmiNadh, PD Lakshmi
    NeuroQuantology 20 (6), 6188 2022

  • A survey on convolutional neural network (deep-learning technique)-based lung cancer detection
    LN Gumma, R Thiruvengatanadhan, LN Kurakula, T Sivaprakasam
    SN Computer Science 3, 1-7 2022

  • Brain tumour detection using cnn
    SL Jagannadham, KL Nadh, M Sireesha
    2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile 2021

  • AN EFFICIENT SPATIAL TEMPORAL PROVENANCE MECHANISM FOR ADHOC MOBILE USERS
    KLN K Sai Divya, S.Siva Nageswara Rao
    International Journal of Innovative Technology and Exploring Engineering 2019

  • A Binary Feedback Schemes for Detecting Failure of Node in Mobile Wireless Network
    SSNR P.Naga Priyanka, K.LakshmiNadh
    International Journal of Innovative Technology and Exploring Engineering 2019

  • DDSRC: Algorithm for improving QOS in VANET
    G Parimala, S Nageswararao, K LakshmiNadh
    Int. J. Recent Technol. Eng.(IJRTE) 7, 1327-1331 2019

  • SECURITY ENHANCEMENT FOR CLOUDLET BASED MEDICAL DATA SHARING
    DSSN K.Lavanya,Dr.K.Lakshminadh
    International Journal of Management, Technology And Engineering 9 (1), 3569-3576 2019

  • An Efficient Location Detection Mechanism for VANETS Using Smart Phone
    DKLN Dr. Siva Nageswara Rao S ,G.Parimala
    International Journal of Pure and Applied Mathematics, 118, 215-219 2018

  • MULTI INTERFACE TCP FOR HANDOVER IN NEXT GENERATION WIRELESS NETWORKS
    DK Lakshminadh
    International Journal of Pure and Applied Mathematics, 4533-4546 2018

  • Efficient Services for Cloud Computing Enabled Vehicle Networks
    DK Lakshminadh
    International Journal of Creative Research Thoughts (IJCRT) 6 (2), 1470-1477 2018

  • Mobile ad hoc network integrated wireless networks: a survey
    DK Lakshminadh
    International Journal of Engineering & Technology, 7, 217-220 2018

  • ANALYSIS OF TCP ISSUES IN INTERNET OF THINGS
    DK Lakshminadh
    International Journal of Pure and Applied Mathematics 118, 163-166 2018

  • Distributed Opportunistic Routing Mechanism for Wireless Ad-Hoc Networks
    KL K. LOHITHA, S. SIVA NAGESWARA RAO
    International Journal of Scientific Engineering and Technology Research 6 2017

  • Distributed Client Tracking Mechanism for Mobile Mesh Networks
    P SOWJANYA, SS NAGESWARAO, KL NADH
     2017

MOST CITED SCHOLAR PUBLICATIONS

  • A survey on convolutional neural network (deep-learning technique)-based lung cancer detection
    LN Gumma, R Thiruvengatanadhan, LN Kurakula, T Sivaprakasam
    SN Computer Science 3, 1-7 2022
    Citations: 18

  • Markova Scheme for Credit Card Fraud Detection
    BS Gandhi, RL Naik, SG Krishna, K Lakshminadh
    International Conference on Advanced Computing, Communication and Networks 2011
    Citations: 12

  • Brain tumour detection using cnn
    SL Jagannadham, KL Nadh, M Sireesha
    2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile 2021
    Citations: 9

  • Improving TCP performance with delayed acknowledgments over wireless networks: A receiver side solution
    KL Nadh, YKS Krishna, KN Rao
    Fifth International Conference on Advances in Recent Technologies in 2013
    Citations: 6

  • ANALYSIS OF TCP ISSUES IN INTERNET OF THINGS
    DK Lakshminadh
    International Journal of Pure and Applied Mathematics 118, 163-166 2018
    Citations: 4

  • DDSRC: Algorithm for improving QOS in VANET
    G Parimala, S Nageswararao, K LakshmiNadh
    Int. J. Recent Technol. Eng.(IJRTE) 7, 1327-1331 2019
    Citations: 3

  • Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos
    C Rajyalakshmi, K LakshmiNadh, MS Reddy
    2023 5th International Conference on Smart Systems and Inventive Technology 2023
    Citations: 1

  • A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection
    K Gumma, L.N. , Thiruvengatanadhan, R. , Lakshmi, P.D. , LakshmiNadh
    International Information and Engineering Technology Association, 657-664 2022
    Citations: 1