Neelakandan Subramani

Scopus Publications

Stage-aware disentangled conditional VAE for interpretable prediction of cognitive decline
Sergey Yarushev, S. Neelakandan, N. Banupriya, S. Velmurugan
Discover Computing, 2026
Neurodegenerative diseases like Alzheimer’s, Parkinson’s, vascular dementia, and frontotemporal dementia are difficult to detect early due to noisy or missing Mini-Mental State Examination (MMSE) data and uninterpretable prediction models. A Disentangled Conditional Variational Autoencoder (D-CVAE) Fusion model with a novel stage-aligned disentanglement loss addresses these issues. Clinically interpretable representations are achieved by separating cognitive decline components from other latent variations. The model reconstructs lost MMSE data and improves feature representation by conditioning the encoder-decoder architecture on cognitive stage. Using disentangled latent embeddings, an integrated Multi-Layer Perceptron (MLP) classifier accurately identifies cognitive impairment levels from no impairment to moderate impairment. Our D-CVAE Fusion model has 94.52% accuracy, resilience with 30% missing data, and clinically significant latent dimensions (e.g. z₁ shows strong correlation with MMSE score, r = − 0.94). Clinical decision support systems for cognitive evaluation and early-stage neurodegenerative disease diagnosis can use the suggested paradigm with confidence and interpretability.
Integrating Deep Learning and Signal Processing for Cybersickness Classification Using Electroencephalogram and Exploratory Factor Analysis Approach
Neelakandan Subramani, Reza Kazemi, Jeongeun Park, Sungkean Kim, Seul Chan Lee
International Journal of Human Computer Interaction, 2026
ERP Insights and Truncated SVD in Conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis
N. Banupriya, Neelakandan Subramani, M. Prakash, S. Velmurugan
International Journal of Computational Intelligence Systems, 2025
Multi-modal EEG data analysis requires sophisticated methods for accurate prediction in the critical area of cognitive depression study in neuroscience. With the help of Multi-View Hypergraph Neural Networks (MV-HGNN) and Dual-Tree Complex Wavelet Transform (DT-CWT), a novel framework for enhancing cognitive distortion analysis is provided today. The initial stage of the procedure, DT-CWT, captures EEG signals and extracts the crucial frequency characteristics (gamma, delta, theta, beta, and alpha). Truncated singular value decomposition, or SVD, thereby reduces noise although preserving significant features. To identify task-related cognitive responses, Event-Related Potential (ERP) is used. The data is arranged into a multi-view framework following processing, which records multiple perspectives such as task-specific responses, frequency patterns, and temporal trends. To enable MV-HGNN to recognize complex cognitive patterns, a hypergraph is then constructed to mimic the complex relationships between various viewpoints. The final category predicts cognitive distortion. According on experimental data, the proposed method outperforms traditional deep learning models and delivers improved accuracy. This work shows that integrating multi-resolution feature extraction, dimensionality reduction, and hypergraph learning is effective for EEG-based cognitive distortion analysis.
A knowledge-Aware NLP-Driven conversational model to detect deceptive contents on social media posts
Deepak Kumar Jain, S. Neelakandan, Ankit Vidyarthi, Anand Mishra, Ahmed Alkhayyat
Computer Speech and Language, 2025
Enhanced Stroke Detection in CT Imaging via DeepLabV3+ and Multi-Scale Feature Learning
T. Aparna, Neelakandan, T.Sethukarasi, D.PaulRaj, A.S.Thillai Valavan
Proceedings IEEE International Conference on Advanced Video and Signal Based Surveillance Avss, 2025
Strokes still stand as one of the most common, as well as fatal, causes of disability. Prompt, accurate diagnosis is essential for recovery, and effective treatment depends on it. With radiologists being the prime career involved in CT or MRI interpretation, this may take much time and be prone to inaccuracy. Deep learning mainly promotes the concept of a total automation boost to the diagnostic capabilities of medical imaging, proving to be a quick answer. The Stroke Detective framework, which utilizes deep learning algorithms, classifies whether a stroke on a CT scan is ischemic versus hemorrhagic, as well as the specific location of the damage within the brain that resulted in hemiplegia. In the pursuit of achieving pixel-wise segmentation of brain images (CT and MRI), the Deep Lab version 3 (DeepLabV3) continues its performance on precise segmentation of stroke-invoked areas. This segmentation, by measuring and analyzing the affected areas, is such a tool for stroke diagnosis, which can directly impact patient outcomes and therapeutic approaches. DeepLabV3: This encoder-decoder architecture, which employs ASPP and is built upon a deep convolutional neural network (CNN), is thought to be a very promising way to enhance stroke identification and segmentation in brain scans. To accurately segment stroke areas in complex brain images, DeepLabV3 recommends an encoder-decoder architecture structure that enables the model to identify details at the edges of lesions. The DeepLabV3+ with ASPP Modules permits the model to pay attention to both large and minute brain lesions. The ASPP introduced by the model enables successful retrieval of contextual information about multi-scales by using atrous (or dilated) convolutions with increasing rates. The ASPP captures image features at multiple scales, hence helping us segment those lesions that are challenging to identify owing to subtle differences in their size and texture. Various experiments have shown that the DeepLabV3 model outperforms the current best methods. The model demonstrates superior potential for lesion localization and boundary accuracy on stroke MRI and CT imaging datasets in comparison with patchy, dreary, repetitive models, thus assisting lesion segmentation. Advocates the integration of the DeepLabV3+ and ASPP for stroke detection and segmentation and, hence, will provide a functional tool to enhance diagnostic precision and facilitate timely intervention decisions by a physician.
Comparative analysis of basic operations of helib and microsoft seal libraries for cloud computing
Maria Lapina, Subramani Neelakandan, Maxim Donchenko, Dmitry Ardeev, Nikita Boykov
Nature Inspired Optimization Algorithms for Cyber Physical Systems, 2024
The following encryption types are considered: partially, somewhat and fully homomorphic. Each type differs in the arithmetic operations provided and the number of their execution. Basic operations in homomorphic encryption are considered: addition, multiplication, encryption and decryption. The three homomorphic encryption libraries OpenFHE, HElib, and Microsoft SEAl are examined. OpenFHE provides maximum flexibility by supporting a variety of homomorphic encryption methods and is therefore suitable for a variety of applications. HElib focuses on support for BGV schemes. Microsoft SEAL focuses on ease of use and high performance and supports BFV and CKKS schemes. Two benchmarks were implemented to analyze basic operations and solve the quadratic equation in encrypted form in HElib and Microsoft SEAL libraries. The results showed the advantage of HElib library in the execution time of all operations compared to Microsoft SEAL.
Provably secure data selective sharing scheme with cloud-based decentralized trust management systems
S. Velmurugan, M. Prakash, S. Neelakandan, Arun Radhakrishnan
Journal of Cloud Computing, 2024
The smart collection and sharing of data is an important part of cloud-based systems, since huge amounts of data are being created all the time. This feature allows users to distribute data to particular recipients, while also allowing data proprietors to selectively grant access to their data to users. Ensuring data security and privacy is a formidable task when selective data is acquired and exchanged. One potential issue that emerges is the risk that data may be transmitted by cloud servers to unauthorized users or individuals who have no interest in the particular data or user interests. The prior research lacks comprehensive solutions for balancing security, privacy, and usability in secure data selective sharing schemes inside Cloud-Based decentralized trust management systems. Motivating factors for settling this gap contain growing concerns concerning data privacy, the necessity for scalable and interoperable frameworks, and the increasing dependency on cloud services for data storage and sharing, which necessitates robust and user-friendly mechanisms for secure data management. An effective and obviously secure data selective sharing and acquisition mechanism for cloud-based systems is proposed in this work. We specifically start by important a common problematic related to the selective collection and distribution of data in cloud-based systems. To address these issues, this study proposes a Cloud-based Decentralized Trust Management System (DTMS)-connected Efficient, Provably Secure Data Selection Sharing Scheme (EPSDSS). The EPSDSS approach employs attribute-based encryption (ABE) and proxy re-encryption (PRE) to provide fine-grained access control over shared data. A decentralized trust management system provides participant dependability and accountability while mitigating the dangers of centralized trust models. The EPSDSS-PRE paradigm would allow data owners to regulate granular access while allowing users to customize data collection without disclosing their preferences. In our strategy, the EPSDSS recognizes shared data and generates short fingerprints for information that can elude detection before cloud storage. DTMS also computes user trustworthiness and improves user behaviour administration. Our research demonstrates that it’s able to deliver trustworthy and safe data sharing features in cloud-based environments, making it a viable option for enterprises seeking to protect sensitive data while maximizing collaboration and utilization of resources.
Robust multi-modal pedestrian detection using deep convolutional neural network with ensemble learning model
Deepak Kumar Jain, Xudong Zhao, Salvador Garcia, Subramani Neelakandan
Expert Systems with Applications, 2024
Mobility aware load balancing using Kho–Kho optimization algorithm for hybrid Li-Fi and Wi-Fi network
Meshal Alharbi, S. Neelakandan, Sachi Gupta, R. Saravanakumar, Siripuri Kiran, A. Mohan
Wireless Networks, 2024
A Novel Quantum Chimp Optimization with DNA Sequence Based Multihop Secure Routing Protocol for Wireless Sensor Networks
D. Paulraj, S. Neelakandan, M. Prakash
Wireless Personal Communications, 2024
Three-dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm
Harshavardhan Awari, Neelakandan Subramani, Avanija Janagaraj, Geetha Balasubramaniapillai Thanammal, Jackulin Thangarasu, Rachna Kohar
Expert Systems, 2024
Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
S. Deepa, A. Umamageswari, S. Neelakandan, Hanumanthu Bhukya, I. V. Sai Lakshmi Haritha, Manjula Shanbhog
International Journal of Cooperative Information Systems, 2024
Reinforcement Learning-Based Multidimensional Perception and Energy Awareness Optimized Link State Routing for Flying Ad-Hoc Networks
M. Prakash, S. Neelakandan, Bong-Hyun Kim
Mobile Networks and Applications, 2024
Slime Mold optimization with hybrid deep learning enabled crowd-counting approach in video surveillance
Zheng Xu, Deepak Kumar Jain, Pourya Shamsolmoali, Alireza Goli, Subramani Neelakandan, Amar Jain
Neural Computing and Applications, 2024
A novel efficient Rank-Revealing QR matrix and Schur decomposition method for big data mining and clustering (RRQR-SDM)
D. Paulraj, K.A. Mohamed Junaid, T. Sethukarasi, M. Vigilson Prem, S. Neelakandan, Adi Alhudhaif, Norah Alnaim
Information Sciences, 2024
Smart Contract-Driven Pandemic Management Using Blockchain Technology and Artificial Intelligence
Sathishkumar Veerappampalayam Easwaramoorthy, Neelakandan Subramani, Angela Lee Siew Hoong
IEEE International Conference on Data Mining Workshops Icdmw, 2024
Preface
Communications in Computer and Information Science, 2024
An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain
Velmurugan S., Prakash M., Neelakandan S., Eric Ofori Martinson
International Journal of Intelligent Systems, 2024
Accelerated Cancer Prediction in WBAN with Markov Transition Field and U-net+ Collaboration
Banupriya N, Sethukarasi T, Neelakandan S, Regina J
2024 International Conference on Integration of Emerging Technologies for the Digital World Icietdw 2024, 2024
An Approach to Reducing Device Uncertainty in Fog-Cloud Computing
Nikolay Kucherov, Egor Shiriaev, Dmitry Zolotariov, Subramani Neelakandan
Lecture Notes in Networks and Systems, 2024
A Deep Learning Modified Neural Network(DLMNN) based proficient sentiment analysis technique on Twitter data
Neelakandan S, D. Paulraj, P. Ezhumalai, M. Prakash
Journal of Experimental and Theoretical Artificial Intelligence, 2024
A multi-layer encryption with AES and Twofish encryption algorithm for smart assistant security
S. Neelakandan, S. Velmurugan, M. Prakash, D. Paulraj
Digital Image Security Techniques and Applications, 2024
Research and Modification of Montgomery Multiplication Algorithm
Maria Lapina, S. Neelakandan, Ivan Borodulin, Anton Koronskiy, Maxim Martemyanov
Lecture Notes in Networks and Systems, 2024
Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing
G. Saravanan, S. Neelakandan, P. Ezhumalai, Sudhanshu Maurya
Journal of Cloud Computing, 2023
A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition
S. R. Menaka, M. Prakash, S. Neelakandan, Arun Radhakrishnan
Scientific Reports, 2023
Optimal transfer learning based nutrient deficiency classification model in ridge gourd (Luffa acutangula)
Finney Daniel Shadrach, Gunavathi Kandasamy, S. Neelakandan, T. Bheema Lingaiah
Scientific Reports, 2023
Hunger games search optimization with deep learning model for sustainable supply chain management
Zheng Xu, Deepak Kumar Jain, S. Neelakandan, Jemal Abawajy
Discover Internet of Things, 2023
Correction: Improved wild horse optimization with levy fight algorithm for effective task scheduling in cloud computing (Journal of Cloud Computing, (2023), 12, 1, (24), 10.1186/s13677-023-00401-1)
G. Saravanan, S. Neelakandan, P. Ezhumalai, Sudhanshu Maurya
Journal of Cloud Computing, 2023
Deep learning-based intelligent system for fingerprint identification using decision-based median filter
Deepak Kumar Jain, S. Neelakandan, Ankit Vidyarthi, Deepak Gupta
Pattern Recognition Letters, 2023
Internet of things with nanomaterials-based predictive model for wastewater treatment using stacked sparse denoising auto-encoder
S. Neelakandan, N. V. RajaSekhar Reddy, Ayman A. Ghfar, Sadanand Pandey, Siripuri Kiran, P. Thillai Arasu
Water Reuse, 2023
Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare
Zhenwei Zhao, Xiaoming Li, Bing Luan, Weining Jiang, Weidong Gao, Subramani Neelakandan
Information Sciences, 2023
Retraction Note to: An automated exploring and learning model for data prediction using balanced CA-SVM (Journal of Ambient Intelligence and Humanized Computing, (2021), 12, 5, (4979-4990), 10.1007/s12652-020-01937-9)
S. Neelakandan, D. Paulraj
Journal of Ambient Intelligence and Humanized Computing, 2023
Quantum invasive weed optimization-based energy aware task scheduling for cyber-physical system environment
S. Neelakandan, K. Keerthika, P. Ilanchezhian, TamilSelvi Madeswaran, Vedanti B. Hardas, U. Sakthi
International Journal of Modeling Simulation and Scientific Computing, 2023
Multi-layer perceptron's neural network with optimization algorithm for greenhouse gas forecasting systems
Ashok Kumar Nanda, Neelakandan. S, Sachi Gupta, Angel Latha Mary Saleth, Ramya. S, Siripuri Kiran
Environmental Challenges, 2023
A Gradient Boosted Decision Tree-Based Influencer Prediction in Social Network Analysis
Neelakandan Subramani, Sathishkumar Veerappampalayam Easwaramoorthy, Prakash Mohan, Malliga Subramanian, Velmurugan Sambath
Big Data and Cognitive Computing, 2023
An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment
D. Paulraj, T. Sethukarasi, S. Neelakandan, M. Prakash, E. Baburaj
Plos One, 2023
Deep learning enabled cross-lingual search with metaheuristic web based query optimization model for multi-document summarization
Mahesh Gangathimmappa, Neelakandan Subramani, Velmurugan Sambath, Rengaraj Alias Muralidharan Ramanujam, Naresh Sammeta, Maheswari Marimuthu
Concurrency and Computation Practice and Experience, 2023
2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
Enhancing Human Emotion Recognition with Long Short-Term Memory (LSTM) and Adaptive Adam Optimization (AOA) of EEG Signals
Neelakandan Subramani, Suman Kalyan Sardar, Seul Chan Lee
2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
Deep Belief Network Algorithm-Based Intrusion Detection System in Internet of Things Environments
C. Geetha, A. Jasmine Gilda, S. Neelakandan
Lecture Notes in Electrical Engineering, 2023
Fuzzy adaptive learning control network (FALCN) for image clustering and content-based image retrieval on noisy dataset
S. Neelakandan, Sathishkumar Veerappampalayam Easwaramoorthy, A. Chinnasamy, Jaehyuk Cho
Aims Mathematics, 2023
Extreme Gradient Boosting based Fetal Health Classification
S. Neelakandan, A Brinda, S Dheekshitha, M S Divya Priya
International Conference on Applied Intelligence and Sustainable Computing Icaisc 2023, 2023
Forecasting of E-Commerce System for Sale Prediction Using Deep Learning Modified Neural Networks
S. Neelakandan, V. Prakash, M. S. PranavKumar, R. Balasubramaniam
International Conference on Applied Intelligence and Sustainable Computing Icaisc 2023, 2023
Modeling of human action recognition using hyperparameter tuned deep learning model
Deepak Kumar Jain, Xue Liu, Subramani Neelakandan, Mohan Prakash
Journal of Electronic Imaging, 2023
Forecasting Electricity Price using Convolutional Neural Networks with Chicken Swarm Optimization Algorithm
S. Neelakandan, M. Bharath Varma, K. Sanvith Reddy, N. Yeswanth Reddy
International Conference on Applied Intelligence and Sustainable Computing Icaisc 2023, 2023
An Efficient WBC Cancer Cells Prediction Using Dense Net Algorithm
S. Neelakandan, P. Rajesh, P. Mahesh, P. Karthik, M. Mukesh
International Conference on Applied Intelligence and Sustainable Computing Icaisc 2023, 2023
Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model
Mayuri Arul Vinayakam Rajasimman, Ranjith Kumar Manoharan, Neelakandan Subramani, Manimaran Aridoss, Mohammad Gouse Galety
Applied Sciences Switzerland, 2023
Artificial Neural Network (ANN) with Chan-Vese(CV) Algorithm-Based Plant Disease Detection and Classification
Avr Mayuri, V Rashmi, Bappaditya Das, Amitabha Mandal, S. Neelakandan
International Conference on Applied Intelligence and Sustainable Computing Icaisc 2023, 2023
A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks
Neelakandan Subramani, Abbas Mardani, Prakash Mohan, Arunodaya Raj Mishra, Ezhumalai P
Aims Mathematics, 2023
Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass
M. Prakash, S. Neelakandan, M. Tamilselvi, S. Velmurugan, S. Baghavathi Priya, Eric Ofori Martinson
International Journal of Intelligent Systems, 2023
Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
Sachin Upadhye, S. Neelakandan, K. Thangaraj, D. Vijendra Babu, N. Arulkumar, Kashif Qureshi
Journal of Mobile Multimedia, 2023
A novel convolutional neural network with gated recurrent unit for automated speech emotion recognition and classification
P. Ravi Prakash, D. Anuradha, Javid Iqbal, Mohammad Gouse Galety, Ruby Singh, S. Neelakandan
Journal of Control and Decision, 2023
Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation
Prakash Mohan, S. Neelakandan, Abbas Mardani, Sudhanshu Maurya, N. Arulkumar, K. Thangaraj
Enterprise Information Systems, 2023
Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images
Prakash Mohan, Sathishkumar Veerappampalayam Easwaramoorthy, Neelakandan Subramani, Malliga Subramanian, Sangeetha Meckanzi
Electronics Switzerland, 2022
Artificial humming bird with data science enabled stability prediction model for smart grids
Raghavendra S, S. Neelakandan, M. Prakash, B.T. Geetha, S. Mary Rexcy Asha, Michaelraj Kingston Roberts
Sustainable Computing Informatics and Systems, 2022
Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data
Kogilavani Shanmugavadivel, V. E. Sathishkumar, Sandhiya Raja, T. Bheema Lingaiah, S. Neelakandan, Malliga Subramanian
Scientific Reports, 2022
Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management
S. Neelakandan, M. Prakash, B.T. Geetha, Ashok Kumar Nanda, Ahmed Mohammed Metwally, Madhappan Santhamoorthy, M. Satyanarayana Gupta
Chemosphere, 2022
Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks
Neelakandan Subramani, Santhosh Kumar Perumal, Jagadish Shivappa Kallimani, Sakthi Ulaganathan, Sanjay Bhargava, Sangeetha Meckanizi
Concurrency and Computation Practice and Experience, 2022
Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems
Deepak Kumar Jain, S. Neelakandan, T. Veeramani, Surbhi Bhatia, Fida Hussain Memon
Computers and Electrical Engineering, 2022
Multilayer Stacked Probabilistic Belief Network-Based Brain Tumor Segmentation and Classification
S. Raghavendra, A. Harshavardhan, S. Neelakandan, R. Partheepan, Ranjan Walia, V. Chandra Shekhar Rao
International Journal of Foundations of Computer Science, 2022
Green energy aware and cluster based communication for future load prediction in IoT
B.T. Geetha, P. Santhosh Kumar, B. Sathya Bama, S. Neelakandan, Chiranjit Dutta, D. Vijendra Babu
Sustainable Energy Technologies and Assessments, 2022
Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model
S. Neelakandan, J. Rene Beulah, L. Prathiba, G. L. N. Murthy, E. Fantin Irudaya Raj, N. Arulkumar
International Journal of Modeling Simulation and Scientific Computing, 2022
Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment
Deepak Kumar Jain, Sumarga Kumar Kumar Sah Tyagi, S. Neelakandan, M. Prakash, L. Natrayan
IEEE Transactions on Industrial Informatics, 2022
Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks
Kuruva Lakshmanna, Neelakandan Subramani, Youseef Alotaibi, Saleh Alghamdi, Osamah Ibrahim Khalafand, Ashok Kumar Nanda
Sustainability Switzerland, 2022
Intelligent deep learning based ethnicity recognition and classification using facial images
Gurram Sunitha, K. Geetha, S. Neelakandan, Aditya Kumar Singh Pundir, S. Hemalatha, Vinay Kumar
Image and Vision Computing, 2022
Artificial intelligence based quality of transmission predictive model for cognitive optical networks
Harinder Singh, D. Ramya, R. Saravanakumar, Nayani Sateesh, Rohit Anand, Swarnjit Singh, S. Neelakandan
Optik, 2022
Chaotic Search-and-Rescue-Optimization-Based Multi-Hop Data Transmission Protocol for Underwater Wireless Sensor Networks
Durairaj Anuradha, Neelakandan Subramani, Osamah Ibrahim Khalaf, Youseef Alotaibi, Saleh Alghamdi, Manjula Rajagopal
Sensors, 2022
IoT enabled environmental toxicology for air pollution monitoring using AI techniques
P. Asha, L. Natrayan, B.T. Geetha, J. Rene Beulah, R. Sumathy, G. Varalakshmi, S. Neelakandan
Environmental Research, 2022
Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images
T. Kavitha, Paul P. Mathai, C. Karthikeyan, M. Ashok, Rachna Kohar, J. Avanija, S. Neelakandan
Interdisciplinary Sciences Computational Life Sciences, 2022
An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication
D. Venu, A.V.R. Mayuri, S. Neelakandan, G.L.N. Murthy, N. Arulkumar, Nilesh Shelke
Optik, 2022
Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks
Prakash Mohan, Neelakandan Subramani, Youseef Alotaibi, Saleh Alghamdi, Osamah Ibrahim Khalaf, Sakthi Ulaganathan
Sensors, 2022
A Secured Healthcare Medical System Using Blockchain Technology
Shilpa Itnal, K. S. Kannan, K. G. Suma, S. Neelakandan
Lecture Notes in Electrical Engineering, 2022
An Efficient Metaheuristic‐Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks
Neelakandan Subramani, Prakash Mohan, Youseef Alotaibi, Saleh Alghamdi, Osamah Ibrahim Khalaf
Sensors, 2022
Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
S. Neelakandan, M. Prakash, Sanjay Bhargava, Kumar Mohan, Nismon Rio Robert, Sachin Upadhye
Studies in Systems Decision and Control, 2022
Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers
S. Parthiban, A. Harshavardhan, S. Neelakandan, Vempaty Prashanthi, Abdul-Rasheed Akeji Alhassan Alolo, S. Velmurugan
Computational Intelligence and Neuroscience, 2022
Capsule Network-Based Deep Transfer Learning Model for Face Recognition
Keshetti Sreekala, C. Pretty Diana Cyril, S. Neelakandan, Saravanan Chandrasekaran, Ranjan Walia, Eric Ofori Martinson
Wireless Communications and Mobile Computing, 2022
An Automated Word Embedding with Parameter Tuned Model for Web Crawling
S. Neelakandan, A. Arun, Raghu Ram Bhukya, Bhalchandra M. Hardas, T. Ch. Anil Kumar, M. Ashok
Intelligent Automation and Soft Computing, 2022
LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications
A. Harshavardhan, Prasanthi Boyapati, S. Neelakandan, Alhassan Alolo Abdul-Rasheed Akeji, Aditya Kumar Singh Pundir, Ranjan Walia
Journal of Healthcare Engineering, 2022
Artificial Intelligence Based Customer Churn Prediction Model for Business Markets
J. Faritha Banu, S. Neelakandan, B.T Geetha, V. Selvalakshmi, A. Umadevi, Eric Ofori Martinson
Computational Intelligence and Neuroscience, 2022
Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media
Neelakandan S, Sridevi M, Saravanan Chandrasekaran, Murugeswari K, Aditya Kumar Singh Pundir, Sridevi R, T.Bheema Lingaiah
Computational Intelligence and Neuroscience, 2022
Deep learning-based skin lesion diagnosis model using dermoscopic images
G. Reshma, Chiai Al-Atroshi, Vinay Kumar Nassa, B.T. Geetha, Gurram Sunitha, Mohammad Gouse Galety, S. Neelakandan
Intelligent Automation and Soft Computing, 2022
Convolutional Neural Networks Based Video Reconstruction and Computation in Digital Twins
M. Kavitha, B. Sankara Babu, B. Sumathy, T. Jackulin, N. Ramkumar, A. Manimaran, Ranjan Walia, S. Neelakandan
Intelligent Automation and Soft Computing, 2022
An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM
C Pretty Diana Cyril, J Rene Beulah, Neelakandan Subramani, Prakash Mohan, A Harshavardhan, D Sivabalaselvamani
Concurrent Engineering Research and Applications, 2021
Internet of things and wearables-enabled Alzheimer detection and classification model using stacked sparse autoencoder
Siripuri Kiran, S. Neelakandan, A. Pratapa Reddy, Sonali Goyal, Balajee Maram, V. Chandra Shekhar Rao
Wearable Telemedicine Technology for the Healthcare Industry Product Design and Development, 2021
Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm
R. Kamalraj, S. Neelakandan, M. Ranjith Kumar, V. Chandra Shekhar Rao, Rohit Anand, Harinder Singh
Measurement Journal of the International Measurement Confederation, 2021
IoT-based traffic prediction and traffic signal control system for smart city
S. Neelakandan, M. A. Berlin, Sandesh Tripathi, V. Brindha Devi, Indu Bhardwaj, N. Arulkumar
Soft Computing, 2021
An automated exploring and learning model for data prediction using balanced CA-SVM
S. Neelakandan, D. Paulraj
Journal of Ambient Intelligence and Humanized Computing, 2021
A gradient boosted decision tree-based sentiment classification of twitter data
S. Neelakandan, D. Paulraj
International Journal of Wavelets Multiresolution and Information Processing, 2020
Social media networks owing to disruptions for effective learning
S. Neelakandan, R. Annamalai, Sonia Jenifer Rayen, J. Arunajsmine
Procedia Computer Science, 2020
Large scale optimization to minimize network traffic using MapReduce in big data applications
S. Neelakandan, S. Divyabharathi, S. Rahini, G. Vijayalakshmi
2016 International Conference on Computation of Power Energy Information and Communication Iccpeic 2016, 2016
Trust based optimal routing in MANET's
S. Neelakandan, J. Gokul Anand
2011 International Conference on Emerging Trends in Electrical and Computer Technology Icetect 2011, 2011

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