Mohamed Uvaze Ahamed Ayoobkhan

@newuu.uz

Professor and Software Engineering
New Uzbekistan University



                    

https://researchid.co/shaofkings

Mohamed Uvaze Ahamed Ayoobkhan currently working as Assistant Professor in Software Engineering Department, New Uzbekistan University, Tashkent, Uzbekistan. Area of research in Human-computer Interaction, Artificial Neural Network and Machine Learning.

EDUCATION

Bachelor of Engineering (CSE) - Anna University, India,
Master of Engineering (CSE) - Anna University, India,
Doctor of Philosophy (IT) - Multimedia University, Malaysia

RESEARCH INTERESTS

Machine Learning, Optimization, Medical Image Processing

38

Scopus Publications

461

Scholar Citations

12

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • Machine Learning Based Image Classification with COREL 1K Dataset
    Ibrohim Abdivokhidov and Mohamed Uvaze Ahamed Ayoobkhan
    Springer Nature Singapore

  • Transformative Pedagogy: Collaborative Learning with Cloud Architecture and Virtual Programming Labs
    Zulfiya Usmonova and Mohamed Uvaze Ahamed Ayoobkhan
    Chapman and Hall/CRC

  • Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images
    M. Maheswari, Mohamed Uvaze Ahamed Ayoobkhan, C. P. Shirley, and T. R. Vijaya Lakshmi
    Springer Science and Business Media LLC

  • Ensemble embedding and convolutional neural network-based big data framework for structure prediction of proteins
    Leo Dencelin Xavier, Ramkumar Thirunavukarasu, Rajganesh Nagarajan, and Mohamed Uvaze Ahamed Ayoobkhan


  • Hybrid Elephant Herding Optimization Approach for Cluster Head Selection and Secure Data Transmission in WSN Using Hybrid Approach Cryptography Techniques
    Yuvaraja M., Sumathi D., M. Rajeshkumar, and Mohamed Uvaze Ahamed Ayoobkhan
    Salud, Ciencia y Tecnologia
    Introduction: The wireless nature of sensor networks makes safe transfer of data from one node to another a major challenge in communications. Sensing tasks connect these sensor nodes which have limitations of memories and energies. Cryptography techniques are utilised to handle critical issues of security in these networks. The performance of large-scale networks is enhanced in this case by optimisation algorithm mimicking natural behaviours.Methods: This work uses H-EHO (Hybrid Elephant Herding Optimisation technique based on Individual strategies to enhance cluster head selections in WSNs (Wireless Sensor Networks) and thus extend networks’ lifetime. WSNs complete cluster head selection processes, and proposed optimisation approach which selects cluster heads based on tracking of sensor nodes for enhancements. The clan operators of optimisation algorithms are adjusted to handle random walk scale factors of elephants. Clusters of WSNs elect updated sensor nodes in principle. Hybrid algorithm HSR19, a novel security symmetric technique offers greater security during data transfers. It offers integrity, confidentiality, and authentication for cryptographic primary keys. Results: The output of the simulation demonstrates the energy consumption, network longevity, end to end delay, and secure data transfer metrics. The results for choosing an effective and time-efficient cluster head selection process for WSNs are improved by contrasting the two approaches. Conclusion: This comparison also shows the efficiency of communication devices in terms of calculation times for encoding, decoding and energies consumed for various file sizes

  • Machine Learning-Based Renewable Energy Systems Fault Mitigation and Economic Assessment
    Syed Ghyasuddin Hashmi, V Balaji, Mohamed Uvaze Ahamed Ayoobkhan, Mohammad Shabbir Alam, R. Anilkuamr, Neerav Nishant, Jyoti Prasad Patra, and A. Rajaram
    Informa UK Limited

  • Enhancing Energy Efficiency and Classification Modeling Through a Combined Approach of LightGBM and Stratified KFold Cross-Validation
    Sushma Kakkar, A. Jaya Lakshmi, Anand Deva Durai C, Wakeel Ahmad, Mohamed Uvaze Ahamed Ayoobkhan, P. Veeramanikandan, P. Nagasekhar Reddy, Vijay Kumar Dwivedi, and A. Rajaram
    Informa UK Limited

  • Multi-Class Classification of Skin Cancer Using Hybrid Inception-Residual Network


  • Comparative Analysis of the Advancement of Hydrogen Vehicles and Electric Vehicles: A Critical Review
    Mohamed Uzave Ahmed A., Ulugbek Raximov, Mehriniso Sayidvalieva, and Aneesh Pradeep
    IEEE
    Electric vehicles are rapidly gaining popularity these days. This is due to the air pollution problem since 55.2% of greenhouse gas emissions stem from conventional cars. In contrast, only 5.76% of total air emissions produced emanate from electric cars. Nevertheless, electric cars are not the only environmentally friendly vehicles. Hydrogen vehicles have a fast and cheap recharge and are also as eco-friendly as electric cars, but they have failed to achieve the same success. In this paper, we will analyze the characteristics and compare them and find out how these two types of cars work. And most importantly, we will try to answer the question, "Can hydrogen cars replace electric ones?"

  • Drowsy Driving Detection System Using Face Detection
    Ismoiljon Mirabdullayev, Mohamed Uvaze Ahamed Ayoobkhan, A.M. Jasmine Hashana, and Liayakath Ali Khan Subair Ali
    IEEE
    Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone.

  • Deep Learning in ChatGPT - A Survey
    A.M. Jasmine Hashana, P. Brundha, Mohamed Uvaze Ahamed Ayoobkhan, and Fazila S
    IEEE
    Abstract-As a subset of machine learning, deep learning makes use of multiple-layer neural networks to learn with available data and make decisions or predictions. A large language model called ChatGPT is based on deep learning, specifically a type of neural network called a transformer. ChatGPT's transformer architecture uses attention mechanisms to focus on the most important parts of the input, allowing it to process and comprehend a large amount of text data. In order for the model to comprehend the context and meaning of natural language text, it is trained on a huge database of text, including articles and books. One of the main importance of using deep learning in ChatGPT is its intelligence to understand relationships and patterns from the input text and generate or predict new text that is homogeneous to the input/training data. Because of this, ChatGPT is able to respond to questions and prompts in a manner that is comparable to that of a human, making it useful for a wide scope of natural language processing missions like translating languages, summarizing texts, and responding to questions. It's worth noting that, while deep learning has been highly effective in ChatGPT, it is not without its limitations. To train, deep learning models can be very complex and require a lot of data and computing power.

  • Medical image enhancement in health care applications using modified sun flower optimization
    S. Navaneetha Krishnan, D. Yuvaraj, Kakoli Banerjee, P Joel Josephson, T CH Anil Kumar, and Mohamed Uvaze Ahamed Ayoobkhan
    Elsevier BV

  • WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM
    Mohamed Uvaze Ahmed Ayoobkhan and Liayakath Ali Khan Subair Ali
    XLE Science

  • Implementation of multicloud strategies for healthcare organisations to avoid cloud sprawl
    A. Jayanthiladevi, Mohamed Uvaze Ahamed Ayoobkhan, R. ThamaraiSelvi, Laishram Jimmy, Piyush Mishra, and Nismon Rio Robert
    Inderscience Publishers

  • Classification and Segmentation of Mitotic Cells using Ant Colony Algorithm and TNM Classifier
    R.G. Vidhya, T.S. Sasikala, Ayoobkhan Mohamed Uvaze Ahamed, Subair Ali Liayakath Ali Khan, Kamlesh Singh, and M. Saratha
    IEEE
    Breast cancer develops from breast tissue and leads to abnormally growing cells in the chest. Doctors usually look for tumors on a mammogram, and some mammograms contain abnormal macrocalcifications and microcalcifications when the image quality is very poor. The presence of these abnormal amounts of calcium deposits in the breast is a sign of early breast cancer and should never be ignored. The image quality should be of the highest quality for an accurate interpretation of this mammographic deposit. Proposed research work is ongoing, exploring other screening methods and the stages of breast cancer. Improved Adaptive Fuzzy C-Means (IAFCM), Ant Colony Algorithm (ACA), and TNM (The size of the breast tumour (T), adjacent lymph nodes and Metastasized methods are used which builds the proposed medical image processing systems into an efficient way. Modified Poisson Inverse Gradient, Metastasized classifier (MPIG) has been used for classification. More than 500 image modalities are involved in all of the approaches. Clinical practitioners who make decisions based on photographs are predicted to benefit from the findings of this study.

  • Data Footprinting in Big Data
    Sathana Venkadasubbiah, D. Yuvaraj, Subair Ali, and Mohamed Uvaze Ahamed Ayoobkhan
    Springer International Publishing

  • IMPLYING FUZZY SET FOR COMPUTING AGRICULTURAL VULNERABILITY


  • Fine-Tuned Convolutional Neural Networks for Bangladeshi Vehicle Classification
    Minhajur Rahman, Saimunur Rahman, and Mohamed Uvaze Ahamed Ayoobkhan
    IEEE
    Classification of vehicles plays an important role in the intelligent transport system. In this paper, we propose a framework for the classification of Bangladeshi vehicle images based on fine-tuned convolutional neural networks (CNNs). Using the proposed framework and an unified experimental setting, we fine-tuned fifteen popular CNN architectures, namely, AlexNet, Inception-V3, VGG-II, VGG-13, VGG-16, VGG-19, ResNet18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, DenseNet121, DenseNet-161, DenseNet-169 and DenseNet-201 pretrained on ImageNet dataset on a public Bangladeshi vehicle image dataset. We conduct a systematic and comprehensive analysis on the performance of the fine-tuned CNNs which leads to new insights. Our experimental results show that ResNet-152 and DenseNet-201 fine-tuned with our proposed strategy provide excellent performance for the classification of Bangladeshi vehicle images. Our implementation is available at https://github.con MinhajurRFahad/bd-vehicle-cnn-benchmark.

  • On the Effectiveness of Deep Transfer Learning for Bangladeshi Meat Based Curry Image Classification
    Minhajur Rahman, Saimunur Rahman, and Mohamed Uvaze Ahamed Ayoobkhan
    IEEE
    Food image classification has received significant attention from researchers in recent years. A large number of methods have been shown to be working well on generic image food classification and dealing with problems like scale and appearance. However, their applicability to the classification of food images from a specific country and culture is still unknown. In this paper, we focus on Bangladeshi meat based curry image classification. We experiment with multiple transfer learning strategies to perform curry image classification with popular pretrained CNNs. Our proposed transferlearning strategies can be useful for achieving better classification performance when full fine-tuning and training from scratch of a CNN is not possible. We also proposed a new dataset for the task and performed a relevant ablation study. Our source code and dataset are available at https://github.com/MinhajurRFahad/bd-curry-image.

  • Novel DoS Attack Detection Based on Trust Mode Authentication for IoT
    D. Yuvaraj, S. Shanmuga Priya, M. Braveen, S. Navaneetha Krishnan, S. Nachiyappan, Abolfazl Mehbodniya, A. Mohamed Uvaze Ahamed, and M. Sivaram
    Computers, Materials and Continua (Tech Science Press)

  • Deep learning model for deep fake face recognition and detection
    Suganthi ST, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, Nebojsa Bacanin, Venkatachalam K, Hubálovský Štěpán, and Trojovský Pavel
    PeerJ
    Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.

  • Dynamic Time Tracking and Task Monitoring Agent Service
    Elbek Normurodov and Mohamed Uvaze Ahamed Ayoobkhan
    IEEE
    Since a large number of heterogeneous tasks are used in service-oriented cloud computing frameworks. The problem of random hardware failures of the processors is turning out to be a progressively noticeable task. In Uzbekistan, the method of managing these tasks is mostly done using a “pen and paper” approach. Business owners from other countries have already implemented digital management systems. It is high time to introduce a national task and time management system, which is cheaper, suits better for national business operations as well as better designed for business needs based on the analysis of other foreign competitor applications. In this work, we advise a fast task assignment system to fulfill unwavering quality prerequisite for a parallel application in a heterogeneous service-oriented cloud computing framework. This is the platform, where workers can enter their action/activity during a specific timeframe. This permits employers to have a clear view of the time required by the task to be completed by the employee.

  • Analysis of Surface Quality Measurement with Classification Approach
    Laith R. Flaih, Shaimaa Awadh Baha al_Deen, and Mohamed Uvaze Ahamed Ayoobkhan
    IOP Publishing
    Abstract This investigation provides a methodology for surface quality measurement. In machine based vision, an optical inspection is validated to identify defects over materials. As well, normalization approach is used to process homogeneous thickness. With compensation procedures flaws are identified and analyzed. However, after defect identification, decision rules are defected for appropriate classification which offers optimal performance and diminishes tuning complexity. The anticipated approach is effectual and fulfils inspection requirements. Experimental outcomes may validate performance of anticipated approach to recognition rate and inspection speed.

  • An Efficient Lion Optimization Based Cluster Formation and Energy Management in WSN Based IoT
    D. Yuvaraj, M. Sivaram, A. Mohamed Uvaze Ahamed, and S. Nageswari
    Springer International Publishing

  • Earth Voltage Monitoring and Alert System for Indian ATM System
    M. Deva Brinda, Mohamed Uvaze Ahamed. A, R. Sundar, M. Padmapriya, V. Porkodi, and Sangeetha Krishnan
    IEEE
    The environment changes like increase in moisture level, humidity, fall in temperature and everyday changes in the voltage levels leads to the generation of fault current. This high value of fault current has to be monitored continuously. The need for a monitoring and alert system is to protect the electronic device. A proper grounding for equipment is must if not it leads to the induction of fault current. This fault current has a larger probability in providing a proper zero resistance path this leakage current. also provides safety to both humans as well as machines. This device is specifically designed for ATM system across the country, is capable of measuring the earth-neutral voltage up to 10 volts. When there occurs any isolation in the voltage levels due to the above mentioned changes, this microcontroller based alert system sends information to the authorised person through SMS and E- MAIL.As this device is not connected with the main system any surges in the ATM will not affect this device and has the ability to disconnect the device from fault in emergency cases.

RECENT SCHOLAR PUBLICATIONS

  • Machine Learning Based Image Classification
    I Abdivokhidov, MUA Ayoobkhan
    Proceedings of the ICSDI 2024 Volume 3: Proceedings of the 2nd International 2025

  • Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images
    M Maheswari, MU Ahamed Ayoobkhan, CP Shirley, TRV Lakshmi
    Medical & Biological Engineering & Computing 62 (11), 3311-3325 2024

  • Hybrid Elephant Herding Optimization Approach for Cluster Head Selection And Secure Data Transmission In Wsn Using Hybrid Approach Cryptography Techniques
    M Yuvaraja, D Sumathi, M Rajeshkumar, MUA Ayoobkhan
    Data and Metadata 3, . 366-. 366 2024

  • Machine Learning-Based Renewable Energy Systems Fault Mitigation and Economic Assessment
    S Ghyasuddin Hashmi, V Balaji, MU Ahamed Ayoobkhan, ...
    Electric Power Components and Systems, 1-24 2024

  • Enhancing Energy Efficiency and Classification Modeling Through a Combined Approach of LightGBM and Stratified KFold Cross-Validation
    S Kakkar, AJ Lakshmi, AD Durai C, W Ahmad, MUA Ayoobkhan, ...
    Electric Power Components and Systems 2024

  • Artificial Intelligence-Based Skin Cancer Detection Device
    DAKM Puneet Kumar, Dr. Balaji Viswanathan, Mohamed Uvaze Ahamed Ayoobkhan ...
    GB Patent 6,347,562 2024

  • Machine Learning Based Image Classification with COREL 1K Dataset
    I Abdivokhidov, MUA Ayoobkhan
    Proceedings of the International Conference on Sustainability: Developments 2024

  • Multi-Class Classification of Skin Cancer Using Hybrid Inception-Residual Network
    AMU Ahamed, VR Yella, PV Krishna, M Subramanian, AK Dey, ...
    International Journal of Intelligent Systems and Applications in Engineering 2024

  • Deep learning and optimization-based task scheduling algorithms for fog-cloud computing environment
    AMU Ahamed, DJ Joel Devadass Daniel, D Seenivasan, ...
    Journal of Intelligent & Fuzzy Systems, 1-14 2023

  • Drowsy Driving Detection System Using Face Detection
    I Mirabdullayev, MUA Ayoobkhan, AMJ Hashana, LAKS Ali
    2023 3rd International Conference on Technological Advancements in 2023

  • Camera system for subject identification
    VKSCNS Sultanuddin Sayed Jamaluddin., Sebastian George., Ayoobkhan, Mohamed ...
    GB Patent 6,291,777 2023

  • Smart Device for Mentally Alerting Alzheimer's Patients
    SKSVKS Ayoobkhan, Mohamed Uvaze Ahamed., Nidhi Pathak., Pushpraj Singh ...
    GB Patent 6,291,776 2023

  • Deep learning in ChatGPT-A survey
    AMJ Hashana, P Brundha, MUA Ayoobkhan
    2023 7th international conference on trends in electronics and informatics 2023

  • Comparative Analysis of the Advancement of Hydrogen Vehicles and Electric Vehicles: A Critical Review
    U Raximov, M Sayidvalieva, A Pradeep
    2023 International Conference on Computational Intelligence and Knowledge 2023

  • Medical image enhancement in health care applications using modified sun flower optimization
    SN Krishnan, D Yuvaraj, K Banerjee, PJ Josephson, TCHA Kumar, ...
    Optik 271, 170051 2022

  • Classification and segmentation of mitotic cells using ant colony algorithm and tnm classifier
    RG Vidhya, TS Sasikala, AMU Ahamed, SALA Khan, K Singh, M Saratha
    2022 International Conference on Augmented Intelligence and Sustainable 2022

  • Data Footprinting in Big Data
    S Venkadasubbiah, D Yuvaraj, S Ali, M Uvaze Ahamed Ayoobkhan
    Big Data Analytics and Computational Intelligence for Cybersecurity, 203-218 2022

  • Vehicle ad hoc network to avoid traffic accidents
    AN Contractor, AK Shukla, AK Saini, AMU Ahamed, B Varadharajan, ...
    DE Patent DE202022102153U1 2022

  • Web page recommendation system by integrating ontology and stemming algorithm
    MUA Ayoobkhan, LAKS Ali
    international journal of advances in signal and image sciences 8 (1), 9-16 2022

  • On the effectiveness of deep transfer learning for bangladeshi meat based curry image classification
    M Rahman, S Rahman, MUA Ayoobkhan
    2022 International Conference on Innovations in Science, Engineering and 2022

MOST CITED SCHOLAR PUBLICATIONS

  • Deep learning model for deep fake face recognition and detection
    ST Suganthi, MUA Ayoobkhan, N Bacanin, K Venkatachalam, H Štěpn, ...
    PeerJ Computer Science 8, e881 2022
    Citations: 86

  • Lossy image compression based on prediction error and vector quantisation
    MUA Ayoobkhan, E Chikkannan, K Ramakrishnan
    EURASIP Journal on Image and Video Processing 2017, 1-13 2017
    Citations: 51

  • Feed-forward neural network-based predictive image coding for medical image compression
    MUA Ayoobkhan, E Chikkannan, K Ramakrishnan
    Arabian Journal for Science and Engineering 43, 4239-4247 2018
    Citations: 36

  • Prediction-based lossless image compression
    MUA Ayoobkhan, E Chikkannan, K Ramakrishnan, SB Balasubramanian
    Proceedings of the International Conference on ISMAC in Computational Vision 2019
    Citations: 31

  • CBIR System based on Prediction Errors
    AMU AHAMED, C ESWARAN, R KANNAN
    Journal of Information Science and Engineering 33 (2), 347-365 2017
    Citations: 28

  • An efficient lion optimization based cluster formation and energy management in WSN based IoT
    D Yuvaraj, M Sivaram, A Mohamed Uvaze Ahamed, S Nageswari
    Intelligent Computing and Optimization: Proceedings of the 2nd International 2020
    Citations: 20

  • Web page recommendation system by integrating ontology and stemming algorithm
    MUA Ayoobkhan, LAKS Ali
    international journal of advances in signal and image sciences 8 (1), 9-16 2022
    Citations: 19

  • Classification and segmentation of mitotic cells using ant colony algorithm and tnm classifier
    RG Vidhya, TS Sasikala, AMU Ahamed, SALA Khan, K Singh, M Saratha
    2022 International Conference on Augmented Intelligence and Sustainable 2022
    Citations: 16

  • Deep learning in ChatGPT-A survey
    AMJ Hashana, P Brundha, MUA Ayoobkhan
    2023 7th international conference on trends in electronics and informatics 2023
    Citations: 15

  • Biometric Security and Performance Metrics: FAR, FER, CER, FRR
    M Sivaram, MUA A, D Yuvaraj, G Megala, V Porkodi, M Kandasamy
    2019 International Conference on Computational Intelligence and Knowledge 2019
    Citations: 14

  • Some investigation on DDOS attack models in mobile networks
    D Yuvaraj, M Sivaram, S Nageswari
    International Association of Online Engineering 2019
    Citations: 13

  • Lossy Image Compression based on Vector Quantization using Artificial Bee Colony and Genetic Algorithms
    RK A.Mohamed Uvaze Ahamed, C. Eswaran
    Advanced Science Letters 24 (2), 1134-1137 2018
    Citations: 13

  • Medical image enhancement in health care applications using modified sun flower optimization
    SN Krishnan, D Yuvaraj, K Banerjee, PJ Josephson, TCHA Kumar, ...
    Optik 271, 170051 2022
    Citations: 11

  • Hybrid Method for Moving Object Exploration in Video Surveillance
    RSB Krishna, MUA A, B Bharathi, B Ankayarkanni
    2019 International Conference on Computational Intelligence and Knowledge 2019
    Citations: 11

  • An Automated Grading System for Diabetic Retinopathy using Curvelet Transform and Hierarchical Classification
    FA Mukti, C Eswaran, N Hashim, HC Ching, MUA Ayoobkhan
    International Journal of Engineering & Technology (UAE) 7 (2), 154-157 2018
    Citations: 10

  • Enhancing Energy Efficiency and Classification Modeling Through a Combined Approach of LightGBM and Stratified KFold Cross-Validation
    S Kakkar, AJ Lakshmi, AD Durai C, W Ahmad, MUA Ayoobkhan, ...
    Electric Power Components and Systems 2024
    Citations: 8

  • Fine-tuned convolutional neural networks for bangladeshi vehicle classification
    M Rahman, S Rahman, MUA Ayoobkhan
    2022 International Conference on Innovations in Science, Engineering and 2022
    Citations: 8

  • Smart connected digital products and IoT platform with the digital twin
    MUA Ayoobkhan, D Yuvaraj, A Jayanthiladevi, B Easwaran, ...
    Research advancements in smart technology, optimization, and renewable 2021
    Citations: 8

  • Novel DoS attack detection based on trust mode authentication for IoT
    D Yuvaraj, SS Priya, M Braveen, SN Krishnan, S Nachiyappan, ...
    Intelligent Automation & Soft Computing 34 (3), 1505-1522 2022
    Citations: 7

  • WITHDRAWN: A study on the role of natural language processing in the healthcare sector
    D Yuvaraj, AMU Ahamed, M Sivaram
    Materials Today: Proceedings 2021
    Citations: 7