Joel Devadass Daniel

@veltech.edu.in

Assistant professor
Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

Joel Devadass Daniel


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https://researchid.co/joeldaniel
Dr. D. J. Joel Devadass Daniel*, Presently working as Assistant Professor in the Department of Electronics and Communication Engineering,(Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology( Deemed to be University), He had completed his Graduation in the year 2009 and Post Graduation with a Specialization of Applied Electronics in the year 2011 from Annauniversity Chennai. He has completed Ph.D. in Information and Communication Engineering, under Anna University, Chennai. He had a teaching Experience of about 11 years. His area of Interest are IoT, Networking, Image Processing. Email: drjoelmephd@
10

Scopus Publications

57

Scholar Citations

5

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • IoT-Naive Bayes and Random Forest Hybrid Framework for Cyclone Prediction and Early Warning in Smart Cities
    Swathi Tejah Yalla, Kottamidde Adisekhar Babu, Thangadurai. M, Ajay S. Bhongade, Prathima Gamini, D. J. Joel Devadass Daniel
    2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2025, 2025
    Economic loss, infrastructure protection, and lives saved are all outcomes of smart city cyclone prediction and early warning systems. Because cyclonic systems are complex and ever-changing, conventional weather models aren't very good at predicting how strong a cyclone will be. The built environment and people living in coastal areas have been greatly affected by the increasing frequency and intensity of storms in recent decades. Principal Component Analysis (PCA) for feature extraction and data normalisation to guarantee data quality are two examples of the advanced data-driven approaches needed for this assignment. They offer a hybrid approach called NB-PSO that merges the probabilistic classification of Naïve Bayes with an improved version of the PSO algorithm. The cyclone prediction probability (CP) is computed by Naïve Bayes, and for smart city early warning signal accuracy, PSO optimises the selection of CP. Based on experimental results, the NB-PSO model has a superior prediction accuracy of 95.60 percent compared to its competitors. The results demonstrate that smart cities can be well-prepared for and resilient in the face of disasters when optimisation for cyclone prediction and early warning is combined with machine learning.
  • A Naive Bayes Approach for Improving Heart Disease Detection on Healthcare Monitoring through IoT and WSN
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Prediction of gender from structural MRI images using Multiscale ShuffleNet Extreme Learning Machine
    Berakhah.F. Stanley, R Jeen Retna Kumar, V Gnanaprakash, P Bini Palas, J Gold Beaulah Patturose, D J Joel Devadass Daniel
    2024 3rd International Conference on Artificial Intelligence for Internet of Things Aiiot 2024, 2024
    In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MRI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme Learning Machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme Learning Machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MRI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare.
  • A Low Complexity Gabor Based CNN Model for Defect Detection in Fabrics
    Gnanaprakash V, Jeen Retna Kumar R, Preethi D, Shoukath Ali K, Saranya N, Joel Devadass Daniel D J
    2024 4th International Conference on Artificial Intelligence and Signal Processing Aisp 2024, 2024
    In the textile industry, automatic fabric defect detection is the most important process to ensure the quality of the fabric. The current technology uses a learning-based methodology to identify defects in fabrics with simple patterns. In the current system, Convolutional Neural Network (CNN) models are used to identify the defects present in the fabric image with maximum accuracy. However, the complexity of the CNN becomes more and it takes much time to process the data. To cater to this problem, a novel Gabor based CNN (GCNN) architectures namely Gabor based VGG-16 (GVGG-16) and Gabor based MobileNet (GMN) models are developed for detecting defects in simple and complex patterned fabrics. In this case, the initial convolution layer uses the Gabor filter bank rather than traditional filters. Gabor filters are more beneficial for examining textures with varying sizes and orientations. The analysis of the texture and the information extracted from it are influenced differently by each of the Gabor filter's parameters. Thus, this paper examines how Gabor filter parameters are applied in GCNN architecture. The TILDA textile image database shows that the suggested model for fabric defect detection can detect defects with an accuracy of 99.17% using the fewest trainable parameters possible.
  • A Novel Weber Cross Information Sharing Deep Learning Encoder Decoder Model for Emotion Recognition Using Facial Expression
    Jeen Retna Kumar R, Berakhah.F. Stanley, Gnanaprakash V, Bini Palas P, Purusothaman K E, Joel Devadass Daniel D J
    2024 4th International Conference on Artificial Intelligence and Signal Processing Aisp 2024, 2024
    This study introduces an innovative deep learning framework, the Weber Cross Information Sharing Deep Learning Encoder-Decoder (WCISD-ED) model, designed for emotion recognition through facial expression analysis. Recognition of emotion is a pivotal aspect of man-machine interaction, offering profound implications in areas ranging from mental health assessment to customer service and entertainment. However, because human expressions are so subtle and varied, accurately deducing emotions from facial expressions is a sophisticated task. The WCISD-ED model is crafted to address these complexities by incorporating principles derived from Weber's Law, which relates to the perception of changes in visual stimuli. This integration enhances the model's sensitivity to the minute yet critical variations in facial expressions associated with different emotions. The model features a novel cross information sharing structure within an encoder-decoder architecture, enabling the effective processing of facial features at multiple scales and depths. The encoder segment of the model focuses on the detailed extraction of facial features, while the decoder reconstructs these features into recognizable emotion categories. The cross information sharing mechanism allows for the interaction between different layers of the network, facilitating a more comprehensive and nuanced understanding of facial expressions. Extensive testing on diverse datasets demonstrates that the WCISD-ED model significantly outperforms existing emotion recognition models in terms of accuracy and reliability.
  • Optimized MSVM-RFE Model for Enhanced Urban Air Quality Management and Decision Support
    Jaidev Kumar, Rajlaxmi Pujar, S. Thenmozhi, Aaliyah Siddiqui, Mujahid Siddiqui, D. J. Joel Devadass Daniel
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
    A useful instrument for keeping urban air quality at an acceptable level is the Urban Air Quality Management Plan (UAQMP). However, UAQM techniques are tailored to meet the unique requirements of each country. In developed countries, regulatory management systems often include UAQMPs. However, developing nations still don't know how to implement UAQMPs that are both effective and practicable to limit the worsening of their urban air quality. The approach consists of three phases, which include preprocessing, feature extraction, and training the model. As a technique for offline frequency-domain filtering, the Hampel filter is employed in preprocessing to eliminate spectral outliers. There are three types of characteristics used in feature extraction: those pertaining to traffic, human mobility, and road networks. It employed an Improved MSVM-RFE for the model's training. The proposed approach surpasses RFE and MSVM with an average accuracy of 93.57%.
  • Effective Facial Emotion Recognition Using Bi-wavelet Bi-directional Gated Recurrent Unit Neural Network
    Jeen Retna Kumar R, Berakhah F. Stanley, Joel Devadass D J Daniel
    2023 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2023, 2023
    When communicating with others, one of the best ways to express human emotions is through facial expression. Emotion recognition through face expression find good place in researchers mind in the field of affect computing. For various interactive applications facial expressions must be recognized automatically for better performance. Here an efficient emotion recognition system using face images is presented reckoned on wavelet transform. The effective extraction of spectral and spatial domain features makes wavelet transform seems to a major technique used in extracting localized features for facial emotion recognition. In this work the extraction of features is accomplished using proposed Bi-wavelet transform method. The temporal correlation in the extracted features is learned using bi-directional gated recurrent unit. The SoftMax classifier is utilized to get the classified output. Experiments were carried out on JAFEE, CK+ and SFEW database which provides a promising result with better accuracy.
  • Spectrum Sensing Channel Allocation Based on Flower Pollination Algorithm in Cognitive Radio - VANET
    S. Esakki Rajavel, T. Sivaprakasam, P. Sasikala, Joel Devadass Daniel D.J, E. J. Priyadharsini, Shiva Shankari L
    1st International Conference on Emerging Research in Computational Science Icercs 2023 Proceedings, 2023
    Vehicular Ad-hoc Networks (VANETs) are formed through the extemporaneous formation of a wireless network of portable devices in the vehicle area, also known as mobile ad hoc networks (MANET). VANETs are a main element of inventive transport schemes (ITS) and their appearance has delivered traffic safety and additional roadside amenities. The trouble of a shortage of spectrum was developed due to the request for several VANET amenities. To overcome the shortage of spectrum, we use the cognitive radio (CR) technology in VANET has developed a study in recent years. The present spectrum division policies cannot excellently crack difficulties such as network security, Channel allocation, and throughput. In some networks, the cognitive radio VANET is into two types: huge capacity CR-VANET (HCRVANET) and low-capacity CR-VANET (LCRVANET). Make a fresh model for exploiting throughput and plan a channel distribution system based on the Flower pollination algorithm (FPA). To maximize the acceptance probability of safe application service and throughput, we introduce a flower pollination algorithm (FPA). Simulation outcomes verify that the flower pollination algorithm considerably improves the system throughput and the probability of acceptance of safe application services
  • Business Process Automation using Robotic Process Automation (RPA) and AI Algorithm's on Various Tasks
    M. N. Dandale, Mazharunnisa, D. J. Joel Devadass Daniel, R. Sathya Priya, Md. Abul Ala Walid, Thulasimani T
    Proceedings of the 8th International Conference on Communication and Electronics Systems Icces 2023, 2023
    Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous researches are less efficient, slowly working algorithms when classification is performed in a large set of databases. The existing methods could be doing better while comparing the error factors, and in the cross-verification process, they have made inappropriate results, leading to wrong classifications. Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous research on email automation and invoice process automation have needed to improve classification model efficiency and they have less efficient, slowly working algorithms when doing classification in a large set of databases. In this work, the Random Forest algorithm is used for classification, and the Quest method is used to segment texts in emails and invoices, both of which can be automated more effectively. The results of existing categorization algorithms have been less than ideal, especially when used to huge datasets, and are often completely inaccurate. The suggested method outperforms previous ML/AI approaches because it produces highly accurate outcomes with little resource investment. There are a number of benefits to utilizing RPA with AI, such as cost reduction, increased output, and streamlined operations. The advantages of this automation, challenges that must be met, and potential answers to those questions are discussed in this study.
  • AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling
    International Journal of Communication Networks and Information Security, 2022

RECENT SCHOLAR PUBLICATIONS

  • Semantic-Preserved Generative Adversarial Network with Portia Spider Optimization for Anomaly Detection in Cloud Datacenter Security
    G Dinesh, DJ Daniel, S Aghalya, M Elangovan
    Journal of Vibration Engineering & Technologies 14 (4), 178 , 2026
    2026.0
  • Stock Price Trend Prediction in the Banking Sector for Market Analysis and Investment Decision-Making Using a Hybrid Random Forest-LSTM Model
    RM Kani, KS Kumar, JY Gasimov, RVS Praveen, DJJD Daniel, KS Kumari
    2025 3rd World Conference on Communication & Computing (WCONF) , 2025
    2025.0
    Citations: 2
  • IoT–Naive Bayes and Random Forest Hybrid Framework for Cyclone Prediction and Early Warning in Smart Cities
    ST Yalla, KA Babu, T M, AS Bhongade, P Gamini, DJJD Daniel
    2025 2nd International Conference on Intelligent Algorithms for … , 2025
    2025.0
  • A Secure IOT-Based Cluster Formation and Optimal Path Selection Using Fuzzy Rules and Metaheuristic Optimization
    SEJ D. J. Joel Devadass Daniel, S. Jagadeesh, L. Brighty Ebenezer
    Security and Privacy 8 (6) , 2025
    2025.0
  • A Secure Data Storage Architecture for Internet of Things Using a Hybrid Whale-Based Harris Hawk Optimization Algorithm and Fuzzy-Based Secure Clustering
    SJ D. J. Joel Devadass Daniel
    Security and Privacy 8 (2) , 2025
    2025.0
    Citations: 6
  • Prediction of gender from structural MRI images using Multiscale ShuffleNet Extreme Learning Machine
    F Stanley, RJR Kumar, V Gnanaprakash, PB Palas, JGB Patturose, ...
    2024 3rd International Conference on Artificial Intelligence For Internet of … , 2024
    2024.0
    Citations: 1
  • Optimized MSVM-RFE Model for Enhanced Urban Air Quality Management and Decision Support
    J Kumar, R Pujar, S Thenmozhi, A Siddiqui, M Siddiqui, DJJD Daniel
    2024 International Conference on Data Science and Network Security (ICDSNS) , 2024
    2024.0
    Citations: 4
  • A Naive Bayes Approach for Improving Heart Disease Detection on Healthcare Monitoring through IoT and WSN
    CPD E. Aarthi1*, Joel Devadass Daniel2 , G. Merlin Suba3 , N. P. Dharani4
    International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN … , 2023
    2023.0
    Citations: 10
  • A Survey on Feature Selection, Classification, and Optimization Techniques for EEG-Based Brain–Computer Interface
    SC Subramanian, D Daniel
    Congress on Intelligent Systems, 79-93 , 2023
    2023.0
  • Business Process Automation using Robotic Process Automation (RPA) and AI Algorithm’s on Various Tasks
    MN Dandale, Mazharunnisa, DJJD Daniel, RS Priya, MAA Walid
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023.0
    Citations: 16
  • Affordable and Energy-Efficient Smart Security System with Information Stamping for IoT Networks
    DJJDD Shrinivas Sirdeshpande1* , P. Roshni Mol2 , Soundararajan S3 , B ...
    European Chemical Bulletin 12 (Special issue 4), 15813-15828 , 2023
    2023.0
  • Effective Facial Emotion Recognition Using Bi-wavelet Bi-directional Gated Recurrent Unit Neural Network
    BF Stanley, JDDJ Daniel
    2023 International Conference on Recent Advances in Electrical, Electronics … , 2023
    2023.0
    Citations: 2
  • A Deep Learning Method for Classification in Brain-Computer Interface
    SC Subramanian, D Daniel
    2023 Second International Conference on Electrical, Electronics, Information … , 2023
    2023.0
    Citations: 1
  • AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling
    J Jeba Sonia, DJJD Daniel, DRS Begum, A Pathan, DV Talukdar, ...
    Jan , 2023
    2023.0
    Citations: 5
  • AI techniques for efficient healthcare systems in ECG wave based cardiac disease detection by high performance modelling
    JJ Sonia, DJJD Daniel, RS Begum, AKNK Pathan, V Talukdar, ...
    International Journal of Communication Networks and Information Security 14 … , 2022
    2022.0
    Citations: 5
  • A Framework for Enhancing Classification in Brain–Computer Interface
    SC Subramanian, D Daniel
    Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 2, 651-665 , 2022
    2022.0
  • A secure data storage architecture for internet of medical things (iomt) using an adaptive Gaussian mutation based sine cosine optimization algorithm and fuzzy-based secure …
    DJ Joel Devadass Daniel, S Ebenezer Juliet
    Journal of Medical Imaging and Health Informatics 11 (12), 2883-2890 , 2021
    2021.0
    Citations: 4
  • A Survey on Security Issues in IoT
    DJJD Daniel, IDSE Juliet
    International Journal of Emerging Trends in Engineering Research 7 (12), 2-3 , 2019
    2019.0
  • Activity classifier: a novel approach using Naïve Bayes classification
    G Muneeswari, D Daniel, K Natarajan
    International Conference on Inventive Computation Technologies, 323-330 , 2019
    2019.0
    Citations: 1
  • Discriminated-SDS: A Novel Hybrid Approach for Optimizing EEG Based Brain-Computer Interface Signals Faced by Metaheuristic Algorithms
    SC Subramanian, D Daniel

MOST CITED SCHOLAR PUBLICATIONS

  • Business Process Automation using Robotic Process Automation (RPA) and AI Algorithm’s on Various Tasks
    MN Dandale, Mazharunnisa, DJJD Daniel, RS Priya, MAA Walid
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023.0
    Citations: 16
  • A Naive Bayes Approach for Improving Heart Disease Detection on Healthcare Monitoring through IoT and WSN
    CPD E. Aarthi1*, Joel Devadass Daniel2 , G. Merlin Suba3 , N. P. Dharani4
    International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN … , 2023
    2023.0
    Citations: 10
  • A Secure Data Storage Architecture for Internet of Things Using a Hybrid Whale-Based Harris Hawk Optimization Algorithm and Fuzzy-Based Secure Clustering
    SJ D. J. Joel Devadass Daniel
    Security and Privacy 8 (2) , 2025
    2025.0
    Citations: 6
  • AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling
    J Jeba Sonia, DJJD Daniel, DRS Begum, A Pathan, DV Talukdar, ...
    Jan , 2023
    2023.0
    Citations: 5
  • AI techniques for efficient healthcare systems in ECG wave based cardiac disease detection by high performance modelling
    JJ Sonia, DJJD Daniel, RS Begum, AKNK Pathan, V Talukdar, ...
    International Journal of Communication Networks and Information Security 14 … , 2022
    2022.0
    Citations: 5
  • Optimized MSVM-RFE Model for Enhanced Urban Air Quality Management and Decision Support
    J Kumar, R Pujar, S Thenmozhi, A Siddiqui, M Siddiqui, DJJD Daniel
    2024 International Conference on Data Science and Network Security (ICDSNS) , 2024
    2024.0
    Citations: 4
  • A secure data storage architecture for internet of medical things (iomt) using an adaptive Gaussian mutation based sine cosine optimization algorithm and fuzzy-based secure …
    DJ Joel Devadass Daniel, S Ebenezer Juliet
    Journal of Medical Imaging and Health Informatics 11 (12), 2883-2890 , 2021
    2021.0
    Citations: 4
  • Stock Price Trend Prediction in the Banking Sector for Market Analysis and Investment Decision-Making Using a Hybrid Random Forest-LSTM Model
    RM Kani, KS Kumar, JY Gasimov, RVS Praveen, DJJD Daniel, KS Kumari
    2025 3rd World Conference on Communication & Computing (WCONF) , 2025
    2025.0
    Citations: 2
  • Effective Facial Emotion Recognition Using Bi-wavelet Bi-directional Gated Recurrent Unit Neural Network
    BF Stanley, JDDJ Daniel
    2023 International Conference on Recent Advances in Electrical, Electronics … , 2023
    2023.0
    Citations: 2
  • Prediction of gender from structural MRI images using Multiscale ShuffleNet Extreme Learning Machine
    F Stanley, RJR Kumar, V Gnanaprakash, PB Palas, JGB Patturose, ...
    2024 3rd International Conference on Artificial Intelligence For Internet of … , 2024
    2024.0
    Citations: 1
  • A Deep Learning Method for Classification in Brain-Computer Interface
    SC Subramanian, D Daniel
    2023 Second International Conference on Electrical, Electronics, Information … , 2023
    2023.0
    Citations: 1
  • Activity classifier: a novel approach using Naïve Bayes classification
    G Muneeswari, D Daniel, K Natarajan
    International Conference on Inventive Computation Technologies, 323-330 , 2019
    2019.0
    Citations: 1
  • Semantic-Preserved Generative Adversarial Network with Portia Spider Optimization for Anomaly Detection in Cloud Datacenter Security
    G Dinesh, DJ Daniel, S Aghalya, M Elangovan
    Journal of Vibration Engineering & Technologies 14 (4), 178 , 2026
    2026.0
  • IoT–Naive Bayes and Random Forest Hybrid Framework for Cyclone Prediction and Early Warning in Smart Cities
    ST Yalla, KA Babu, T M, AS Bhongade, P Gamini, DJJD Daniel
    2025 2nd International Conference on Intelligent Algorithms for … , 2025
    2025.0
  • A Secure IOT-Based Cluster Formation and Optimal Path Selection Using Fuzzy Rules and Metaheuristic Optimization
    SEJ D. J. Joel Devadass Daniel, S. Jagadeesh, L. Brighty Ebenezer
    Security and Privacy 8 (6) , 2025
    2025.0
  • A Survey on Feature Selection, Classification, and Optimization Techniques for EEG-Based Brain–Computer Interface
    SC Subramanian, D Daniel
    Congress on Intelligent Systems, 79-93 , 2023
    2023.0
  • Affordable and Energy-Efficient Smart Security System with Information Stamping for IoT Networks
    DJJDD Shrinivas Sirdeshpande1* , P. Roshni Mol2 , Soundararajan S3 , B ...
    European Chemical Bulletin 12 (Special issue 4), 15813-15828 , 2023
    2023.0
  • A Framework for Enhancing Classification in Brain–Computer Interface
    SC Subramanian, D Daniel
    Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 2, 651-665 , 2022
    2022.0
  • A Survey on Security Issues in IoT
    DJJD Daniel, IDSE Juliet
    International Journal of Emerging Trends in Engineering Research 7 (12), 2-3 , 2019
    2019.0
  • Discriminated-SDS: A Novel Hybrid Approach for Optimizing EEG Based Brain-Computer Interface Signals Faced by Metaheuristic Algorithms
    SC Subramanian, D Daniel