Mohd Abdul Hameed

Scopus Publications

Retraction note: An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network (Wireless Personal Communications, (2025), 140, 1-2, (143-161), 10.1007/s11277-024-11604-3)
Ramdas Vankdothu, Mohd Abdul Hameed
Wireless Personal Communications, 2026
Multicast Scaling in Heterogeneous Wireless Sensor Networks for Security and Time Efficiency
Ramdas Vankdothu, Mohd Abdul Hameed, Husnah Fatima, Akkala Subbarao
Wireless Personal Communications, 2025
Heterogeneous wireless sensor networks (HWSNs) satisfy researchers' requirements for developing real-world solutions that handle unattended challenges. However, the primary constraint of researchers is the privacy of the sensor nodes. It safeguards the sensor nodes and extensions in the HWSNs. Therefore, it is necessary to develop secure operational systems. Multicast scaling with security and time efficiency is described in heterogeneous wireless sensor networks to maximize network performance while also successfully protecting network privacy. This study evaluates the initial security and time efficiency measures, such as execution time, transmission delay, processing delay, congestion level, and trust measure. Subsequently, the optimal location of the heterogeneous nodes is determined using sigmoid-based fuzzy c-means clustering. Finally, successful cluster routing was achieved via support-value-based particle swarm optimization. The experimental results indicate that the proposed strategy surpasses existing strategies in terms of network delivery ratio, end-to-end delay, throughput, packet delivery, and node remaining energy level.
Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning
T. Swathi Priyadarshini, Mohd Abdul Hameed
Measurement Sensors, 2025
Our research aims to present a comprehensive study of machine learning algorithms and deep learning advancements in medical field systems for decision making. Present study examines the idea of extracting most important risk factors from given medical data, which has major impact in the increase of severity condition of heart stroke. Three experimental prediction models are developed when k-means clustering is collaborated with classification which includes machine learning algorithms like Naïve Bayes, Decision Tree and a deep learning algorithm Artificial Neural Network. A detailed comparison analysis is done by evaluating performance metrics like sensitivity, specificity, accuracy, and AUC-ROC scores. Out of the three, k-means with Artificial Neural Network model outperformed with sensitivity 0.89, specificity 0.89, and accuracy of 0.90 in comparison with machine learning classifiers. The challenges of perfect balancing of sensitivity and specificity is achieved by AUC-ROC score of 0.96, which is the best possible result till now.
An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network
Ramdas Vankdothu, Mohd Abdul Hameed
Wireless Personal Communications, 2025
This paper provides an effective Wireless Sensor Network (WSN) routing solution for Internet of Things (IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.
Correction to: An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network (Wireless Personal Communications, (2025), 140, 1-2, (143-161), 10.1007/s11277-024-11604-3)
Ramdas Vankdothu, Mohd Abdul Hameed
Wireless Personal Communications, 2025
A Hybrid CNN with Boosting - Soft Voting Ensemble Technique for Diabetic Retinopathy Diagnosis
Vishnesh Reddy Patlolla, Mohd Abdul Hameed
Proceedings of the 2025 IEEE International Conference on Machine Learning and Applied Network Technologies Icmlant 2025, 2025
Diabetic retinopathy (DR) is a progressive retinal disorder and one of the leading causes of vision loss among diabetic patients. Early and accurate detection is critical for timely intervention, yet manual screening methods are time-consuming and prone to subjectivity. This paper proposes a novel framework "Hybrid CNN with Boosting" which integrates hybrid Convolutional Neural Network (CNN) feature extractor with a boosting-soft voting ensemble classification model to accurately identify DR severity stages from fundus images. The proposed approach employs DenseNet121 and ResNet50V2 as parallel feature extractors to capture both high-level semantic features and fine-grained retinal structures from fundus images. Feature vectors from both models are combined and further reduced using Linear Discriminant Analysis (LDA) to enhance class separability while reducing dimensionality. The extracted features were divided into 5 subsets, and individual subset is trained with boosting algorithm-XGBoost, the output of these classifiers are aggregated using soft voting for the final decision. To assess robustness, the proposed hybrid CNN with boosting model was compared against bagging and stacking ensembles using diverse base learners. The framework was evaluated on the Messidor-2 dataset, covering five DR severity stages: No DR, Mild, Moderate, Severe, and Proliferative DR. Among all classifiers, the proposed model achieved the best performance, reaching an accuracy of 98.57%, AUC of 99.98, recall of 98.75%, precision of 99.10%, and an F1-score of 98.12%, and Cohen’s Kappa score of 97.58. The implementation of the proposed model is available at: https://github.com/Vishnesh-Reddy/Hybrid_CNN_Boosting-Voting/blob/main/diabeticretinopathy-1-final-2.ipynbDataset: https://www.kaggle.com/datasets/abdhendi/messifor2 https://www.kaggle.com/datasets/mariaherrerot/aptos2019
A Hybrid CNN with Bagging-Hard Voting Ensemble Technique for Diabetic Retinopathy Diagnosis
Vishnesh Reddy Patlolla, Mohd Abdul Hameed
2025 10th International Conference on Research in Intelligent Computing in Engineering Rice 2025, 2025
Diabetic Retinopathy (DR) is a major cause of vision loss in diabetes patients, early detection is crucial for timely clinical intervention. Consequently, several Deep Learning with AI methods have been implemented to automatically detect anomalies in DR from distinct phases, from photographs of the retina. In this study, we propose a deep learning-based framework that integrates a hybrid Convolutional Neural Network (CNN) feature extractor with a two-stage ensemble classification model to accurately identify DR severity stages from fundus images. The hybrid CNN architecture combines DenseNet121 and ResNet50V2, pretrained on ImageNet, to capture both fine-grained retinal textures and global structural features. An attention mechanism is incorporated to emphasize lesion-relevant areas. The resulting high-dimensional features are reduced using Linear Discriminant Analysis (LDA) to enhance class separability. A soft-voting classifier comprising Gaussian Naïve Bayes, Logistic Regression, and SVM is used as the base learner, which is further wrapped in a bagging ensemble to improve generalization. The proposed framework is evaluated on the Messidor-2 and APTOS2019 dataset. It achieved an accuracy of 99.71 %, F1-score of 99.75 %, AUC of 99.99 %, and a Cohen's Kappa score of 99.52 % on Messidor-2 and an accuracy of 98.24 % on the APTOS2019 dataset, outperforming baseline stacking and bagging classifiers. The results demonstrate the potential of combining hybrid deep features with ensemble learning to develop scalable and clinically reliable DR screening tools. The implementation of the proposed model is available at https://github.com/Vishnesh-Reddy/Hybrid_CNN_2_stage_ensemble/blob/main/diabetic-retinopathy_2_stage_ensemble.ipynb
Assessing the Impact of Data Imbalance on the Performance of Random Forest Classifier for Diabetes Prediction
Juveria Mohammed Siddiqui, Mohd. Abdul Hameed
Proceedings of the 2025 International Conference on Emerging Techniques in Computational Intelligence Icetci 2025, 2025
The purpose of this study was to explore the influence of class distribution on the performance of machine learning (ML) models for predicting diabetes outcomes. A publicly available cleaned and preprocessed US Diabetes Health Indicators Dataset was used to train a Random Forest classifier. The model's ability to generalize to unseen data was significantly improved through hyperparameter tuning and the use of the Synthetic Minority Oversampling Technique (SMOTE) for generating synthetic minority class instances. Two data amplification strategies were evaluated: proportional scaling, which maintains the original class ratios, and balanced resampling, which equalizes class distributions. Our findings demonstrate that proportional scaling consistently outperforms balanced resampling, yielding higher accuracy, precision, and recall across multiple experiments. While balanced resampling led to reduced model performance in certain scenarios, proportional scaling showed greater stability, producing reliable results even with smaller sample sizes. The model's performance was evaluated based on its accuracy across multiple experiments. These initial findings indicate that maintaining the natural skew in class distribution can lead to better predictive performance, especially when dealing with imbalanced datasets such as those prevalent in medical research. This work lays the groundwork for future advancements in applying ML to predict healthcare-related outcomes where class imbalance is prevalent. Further research is needed to validate these results and further refine ML model development.
A Novel Approach on Modularized MLP-Mixer for Automated Histopathological Classification of Lung and Colon Cancers
Mohd Abdul Hameed, Narender Kota, Manoj Kumar N
Proceedings of the 2025 International Conference on Emerging Techniques in Computational Intelligence Icetci 2025, 2025
This paper describes a model referred to as the MLP-Mixer, which is designed to classify images of lung and colon tissue into five classes: lung benign, lung adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and colon benign tissue. The model processes high-resolution images by resizing them to a 224x224 size. It then transforms these images into 196 patches, 512 features per patch through patch embedding using Conv2D, and proceeds with eight consecutive MLP-Mixer blocks. This is the first study to employ a modularized MLP-Mixer to classify lung and colon cancer images. It incorporates some key improvements. The initial version of the MLP-Mixer achieved an accuracy of 86.00%. It employed a standard Adam optimizer, a prevalent error measurement technique known as Cross-Entropy Loss, and straightforward image enhancement methods. But it was less efficient than the later versions. The improved MLP-Mixer model obtained a better accuracy of 92.40% by using the AdamW optimizer with weight decay, a learning rate schedule of Cosine Annealing LR, and class-weighted loss to minimize the data imbalance. The best performance is obtained by the optimized modularized MLP-Mixer model, which achieved a 98.76% accuracy. It employed cutting-edge approaches such as Cosine Annealing LR Scheduler, Mixed Precision Training with GradScaler, Class Weights for dealing with unbalanced data, and state-of-the-art image improving techniques, going beyond the regular fixed learning rates and elementary procedures. These improvements greatly enhanced the performance of the model, making the modularized MLP-Mixer a powerful and efficient image classifier for images of lung and colon cancer tissues. The implementation of the proposed model is available at: https://github.com/kota2580/ModularizedMLP-mixer.
Learning Brain MRI Representations via GraphSAGE on ViT and ResNET
Mohd Abdul Hameed, Gone Preethi, Pavani Padigala, Cheekati Ram Sujay
Proceedings of the 2025 International Conference on Emerging Techniques in Computational Intelligence Icetci 2025, 2025
A brain tumor is an abnormal mass originating from brain tissue that can create pressure, affecting mobility, memory and health in general. Brain tumors can cause severe neurological defects based on size and location. This research develops an original model that combines Vision Transformers (ViT) to Graph SAGE, which is graph-based deep learning algorithm. We didn't find reports on the internet for neuroscience research papers for brain tumor classification using this model of a hybrid algorithm. This model detects and characterized our dataset as either a tumor or healthy MRI. We applied the Graph SAGE algorithm to our brain tumor MRI dataset, achieving an accuracy of 96.04%, after feature extraction using a ResNET-18 deep neural network (DNN). As ResNET-18 is a convolutional neural network, while it extracts spatial local features from the datasets, it does not identify spatial relationships in a 3D format as necessary for MRI slices. In addition, to determine whether we see limited partial correlations with long range dependencies inherent in self attention mechanisms, and the ability to recognize and learn them, we introduced Vision Transformers within Graph SAGE. This hybrid approach resulted in an accuracy of regarding 97.03%. The method described demonstrates a higher accuracy and robustness in the classification from glioma, meningioma and from pituitary tumors in the dataset, as learned from Graph SAGE and localized features from ResNet-18 and self-attention mechanisms from Vision Transformers. The implementation of proposed model is available at: https://github.com/PavaniPadigala123/Tumor-detection-with-GraphSAGE
Machine Learning on Geospatial Data
Ayesha Ameen, Tanveer Sultana, Ayesha Banu, Syed Mohd Ali, Mohd Abdul Hameed
2025 IEEE International Conference on Next Gen Technologies of Artificial Intelligence and Geoscience Remote Sensing Earthsense 2025, 2025
Malware Detection Using Transfer Learning with ResNet-50: An Image-Based Approach
Mohammed Hameed, Malak Korami, Abdulahakim Al-Wahabi, Amr Al-wajih, Mohammed Rageh, Adham Waleed, Roqaiah Mokhtar
2025 5th International Conference on Emerging Smart Technologies and Applications Esmarta 2025, 2025
Enhancing Wireless Sensor Network lifetime through hierarchical chain-based routing and horizontal network partitioning techniques
V.Rama Krishna, Vuppala Sukanya, Mohd Abdul Hameed
Measurement Sensors, 2024
Optimizing Wireless Sensor Network longevity with hierarchical chain-based routing and vertical network partitioning techniques
V. Rama Krishna, Vuppala Sukanya, Mohd Abdul Hameed
Measurement Sensors, 2024
Retraction Note: Entropy and sigmoid based K-means clustering and AGWO for effective big data handling (Multimedia Tools and Applications, (2023), 82, 10, (15287-15304), 10.1007/s11042-022-13929-2)
Ramdas Vankdothu, Mohd Abdul Hameed, Raju Bhukya, Gaurav Garg
Multimedia Tools and Applications, 2024
Brain MRI Images for Tumour Detection Using Storage Optimisation Technique
Ramdas Vankdothu, Mohd Abdul Hameed
Lecture Notes in Networks and Systems, 2024
A Study of Medical Image Analysis using Deep Learning Approaches
Syed Saba Raoof, M. A. Saleem Durai, C. Srimathi, Robin Doss, M. A. Mohammed Sahul Hameed
Deep Learning for Smart Healthcare Trends Challenges and Applications, 2024
Clothing Image Classification Using VGG-19 Deep Learning Model for E-commerce Web Application
Mohammed Hameed, Amr Al- Wajih, Mohammed Shaiea, Mohammed Rageh, Fahd A. Alqasemi
4th International Conference on Emerging Smart Technologies and Applications Esmarta 2024, 2024
Moving object detection using modified GMM based background subtraction
S. Rakesh, Nagaratna P. Hegde, M. Venu Gopalachari, D. Jayaram, Bhukya Madhu, Mohd Abdul Hameed, Ramdas Vankdothu, L.K. Suresh Kumar
Measurement Sensors, 2023
Entropy and sigmoid based K-means clustering and AGWO for effective big data handling
Ramdas Vankdothu, Mohd Abdul Hameed, Raju Bhukya, Gaurav Garg
Multimedia Tools and Applications, 2023
Mining for Enthalpy-Based Average High-Utility Patterns with Tighter Upper Bounds
Ramdas Vankdothu, Mohd Abdul Hameed
SN Computer Science, 2023
NDeep Learning Heart Stroke Prediction Model Integration of MMAM with NB(MMAM-NB) and DT(MMAM-DT)
T. Swathi Priyadarshini, Mohd Abdul Hameed, Shadan Amatul Qadeer
5th IEEE International Conference on Cybernetics Cognition and Machine Learning Applications Icccmla 2023, 2023
Developing a Deep Learning Heart Stroke Prediction Model Using Combination of Fixed Row Initial Centroid Method with Navie Bayes and Decision Tree Classifiers
T. Swathi Priyadarshini, Mohd Abdul Hameed, Shadan Amatul Qadeer
5th IEEE International Conference on Cybernetics Cognition and Machine Learning Applications Icccmla 2023, 2023
Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning
Ramdas Vankdothu, Mohd Abdul Hameed
Measurement Sensors, 2022
Brain tumor MRI images identification and classification based on the recurrent convolutional neural network
Ramdas Vankdothu, Mohd Abdul Hameed
Measurement Sensors, 2022
COVID-19 detection and classification for machine learning methods using human genomic data
Mohd Thousif Ahemad, Mohd Abdul Hameed, Ramdas Vankdothu
Measurement Sensors, 2022
Adaptive features selection and EDNN based brain image recognition on the internet of medical things
Ramdas Vankdothu, Mohd Abdul Hameed
Computers and Electrical Engineering, 2022
Image compression of brain MRI images using an autoencoder and restricted Boltzmann machine
Ramdas Vankdothu, Mohd Abdul Hameed
Neuroscience Informatics, 2022
Brain image identification and classification on Internet of Medical Things in healthcare system using support value based deep neural network
Ramdas Vankdothu, Mohd Abdul Hameed, Ayesha Ameen, Raheem Unnisa
Computers and Electrical Engineering, 2022
A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method
Ramdas Vankdothu, Mohd Abdul Hameed, Husnah Fatima
Computers and Electrical Engineering, 2022
Efficient detection of brain tumor using unsupervised modified deep belief network in big data
Ramdas Vankdothu
Journal of Advanced Research in Dynamical and Control Systems, 2020
A proposal: High-throughput robust architecture for log analysis and data stream mining
Adnan Rashid Hussain, Mohd Abdul Hameed, Sana Fatima
Advances in Intelligent Systems and Computing, 2016
Detecting communities in social networks using unnormalized spectral clustering incorporated with Bisecting K-means
E. Raju, M. A. Hameed, K. Sravanthi
Proceedings of 2015 IEEE International Conference on Electrical Computer and Communication Technologies Icecct 2015, 2015
Supervised opinion mining of social network data using a bag-of-words approach on the cloud
S. Fouzia Sayeedunnissa, Adnan Rashid Hussain, Mohd Abdul Hameed
Advances in Intelligent Systems and Computing, 2013
Information gain clustering through prototypeembedded genetic K-mean algorithm (IGCPGKA) A novel heuristic approach for personalisation of cold start problem
Mohd Abdul Hameed, S. Ramachandram, Omar Al Jadaan
ACM International Conference Proceeding Series, 2012
Measuring influence in social networks using a network amplification score-an analysis using cloud computing
Adnan Rashid Hussain, Mohd Abdul Hameed, S. Fouzia Sayeedunnissa
Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems His 2012, 2012
An effective hybrid algorithm in recommender systems based on fast genetic k-means and information gain
Mohd Abdul Hameed, M.A. Malik, Syeda Fouzia Sayeedunnisa, Husna Imroze
Proceedings 4th International Conference on Computational Intelligence and Communication Networks Cicn 2012, 2012
Mining twitter using cloud computing
Adnan Rashid Hussain, Mohd Abdul Hameed, Nagaratna P. Hegde
Proceedings of the 2011 World Congress on Information and Communication Technologies Wict 2011, 2011
Information gain clustering through roulette wheel genetic algorithm (IGCRWGA): A novel heuristic approach for personalisation of cold start problem
Mohd. Abdul Hameed, S. Ramachandram, Omar Al Jadaan
Proceedings 2011 International Conference on Computational Intelligence and Communication Systems Cicn 2011, 2011
IGCRGA: A novel heuristic approach for personalization of cold start problem
Mohammed Abdul Hameed, S. Ramachandram, Omar Al Jadaan
Proceedings Ams 2011 Asia Modelling Symposium 2011 5th Asia International Conference on Mathematical Modelling and Computer Simulation, 2011
IGCEGA: A novel heuristic approach for personalisation of cold start problem
Mohd Abdul Hameed, S. Ramachandram, Omar Al Jadaan
Proceedings 2011 International Conference on Communication Systems and Network Technologies Csnt 2011, 2011
Information theoretic approach to cold start problem using genetic algorithms
M A Hameed, O Al Jadaan, S Ramchandram
Proceedings 2010 International Conference on Computational Intelligence and Communication Networks Cicn 2010, 2010
Enhancing data selection using genetic algorithm
O A Jadaan, W Abdulal, M A Hameed, A Jabas
Proceedings 2010 International Conference on Computational Intelligence and Communication Networks Cicn 2010, 2010

RECENT SCHOLAR PUBLICATIONS

A Hybrid CNN with Boosting-Soft Voting Ensemble Technique for Diabetic Retinopathy Diagnosis
VR Patlolla, MA Hameed
2025 IEEE International Conference on Machine Learning and Applied Network … , 2025
2025
Machine Learning on Geospatial Data
A Ameen, T Sultana, A Banu, SM Ali, MA Hameed
2025 IEEE International Conference on Next-Gen Technologies of Artificial … , 2025
2025
Feature-Enriched Brain Tumor Classification Using ViT in GCN
MA Hameed, P Padigala, G Preethi, MM Boodidha
2025 International Conference on Emerging Techniques in Computational … , 2025
2025
Assessing the Impact of Data Imbalance on the Performance of Random Forest Classifier for Diabetes Prediction
JM Siddiqui, MA Hameed
2025 International Conference on Emerging Techniques in Computational … , 2025
2025
A Novel Approach on Modularized MLP-Mixer for Automated Histopathological Classification of Lung and Colon Cancers
MA Hameed, N Kota, M Kumar
2025 International Conference on Emerging Techniques in Computational … , 2025
2025
Learning Brain MRI Representations via GraphSAGE on ViT and ResNET
MA Hameed, G Preethi, P Padigala, CR Sujay
2025 International Conference on Emerging Techniques in Computational … , 2025
2025
Brain Tumor Classification Leveraging Feature Optimization and Advanced Regularization Techniques Based on CNN
MA Hameed, CR Sujay, J Vishwesh, MM Boodidha
2025 International Conference on Emerging Techniques in Computational … , 2025
2025
Multicast scaling in heterogeneous wireless sensor networks for security and time efficiency
R Vankdothu, MA Hameed, H Fatima, A Subbarao
Wireless Personal Communications, 1-17 , 2025
2025
Citations: 3
Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning
TS Priyadarshini, MA Hameed
Measurement: Sensors 37, 101405 , 2025
2025
Citations: 6
Correction to: An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network
R Vankdothu, MA Hameed
Wireless Personal Communications 140 (1), 163-163 , 2025
2025
RETRACTED ARTICLE: An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network
R Vankdothu, MA Hameed
Wireless Personal Communications 140 (1), 143-161 , 2025
2025
Citations: 16
Optimizing Wireless Sensor Network longevity with hierarchical chain-based routing and vertical network partitioning techniques
VR Krishna, V Sukanya, MA Hameed
Measurement: Sensors 36, 101390 , 2024
2024
Citations: 6
Enhancing Wireless Sensor Network lifetime through hierarchical chain-based routing and horizontal network partitioning techniques
VR Krishna, V Sukanya, MA Hameed
Measurement: Sensors 36, 101300 , 2024
2024
Citations: 11
Retraction Note: Entropy and sigmoid based K-means clustering and AGWO for effective big data handling
R Vankdothu, MA Hameed, R Bhukya, G Garg
Multimedia Tools and Applications 83 (39), 87383-87383 , 2024
2024
Citations: 1
Developing heart stroke prediction model using deep learning with combination of fixed row initial centroid method with Navie Bayes, Decision Tree, and Artificial Neural Network
TS Priyadarshini, MA Hameed
Measurement: Sensors 34, 101237 , 2024
2024
Citations: 10
Obtaining an accurate estimate of the COVID-19 mutation rate via coronavirus sequence analysis preeminent themes using convolutional neural networks
MT Ahemad, MA Hameed
Measurement: Sensors 33, 101171 , 2024
2024
Citations: 5
Brain MRI Images for Tumour Detection Using Storage Optimisation Technique Check for updates
R Vankdothu, MA Hameed
Mobile Radio Communications and 5G Networks: Proceedings of Fourth MRCN 2023 … , 2024
2024
Enhancements in Random Forest Algorithms for Improving Healthcare Applications
SH Mohammed, S Ahamad, MA Hameed
Proceedings of the International Conference on Industrial Engineering and … , 2024
2024
Citations: 2
Moving object detection using modified GMM based background subtraction
S Rakesh, NP Hegde, MV Gopalachari, D Jayaram, B Madhu, ...
Measurement: Sensors 30, 100898 , 2023
2023
Citations: 72
NDeep learning heart stroke prediction model integration of MMAM with NB (MMAM-NB) and DT (MMAM-DT)
TS Priyadarshini, MA Hameed, SA Qadeer
2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine … , 2023
2023
Citations: 4

Mohd Abdul Hameed

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Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS