Dr. Mohd Dilshad Ansari is currently working as an Associate Professor in the Department of Computer Science & Engineering at Guru Nanak University, Hyderabad, India. He obtained his Ph.D. and M.Tech in Computer Science & Engineering from Jaypee University of Information Technology, Waknaghat, Solan, HP, India in 2018 and 2011 respectively. He received B.Tech in Information Technology from Uttar Pradesh Technical University, Lucknow, UP in 2009. He is having more than 12 years of Academic/Research Experience; He has published more than 80 papers in International Journals (SCIE/Scopus) and conferences (IEEE/Springer). He is the Member of various Technical/Professional societies such as IEEE, UACEE and IACSIT. He has been appointed as Editorial/Reviewer Board and Technical Programme Committee member in numerous reputed Journals/Conferences. He is also serving as Associate, Academic and Guest Editor in Reputed Journals and Organized Special Sessions in IEEE/Springer Conferences.
Neuroroute-GNNRL: A Hybrid Graph Neural and Reinforcement Learning Framework for Dynamic Node Classification and Speed Ravi Prakash Chaturvedi, Yashasvi Makin, Mohd Dilshad Ansari, Annu Mishra, Deepti Kushwaha, Kuldeep Chouhan, Rajneesh Kumar Singh International Journal on Smart Sensing and Intelligent Systems, 2026 Dynamic and complex networks like smart transportation systems, communication infrastructures, and sensor-based Internet of Things (IoT) contexts typically require proper routing and flexible node behaviors to ensure good performance and low latency. We present a new idea of NeuroRoute-GNNRL, the hybrid framework of graph neural networks (GNNs) and reinforcement learning (RL) to effectively implement real-time dynamic node categorization and speed optimization in an evolving network environment seamlessly. GNNs are utilized to learn what are known as the structural dependencies and feature distributions among the nodes in a network so that high-level representations of the graphs can be extracted. Such embeddings are then leveraged by a RL agent, which makes intelligent routing and speed alteration decisions. By interacting with the network, the RL agent learns an optimal policy to maximize throughput and to minimize delays. An experimental comparison with both synthetic and real-world dataset shows the advantage of NeuroRoute-GNNRL over conventional graph-based and machine learning-based methods. The comparison is made in terms of accuracy rate and adaptation capabilities, as well as the performance of the entire network.
Machine Learning Techniques for Diabetes Mellitus Based on Lifestyle Predictors Gufran Ahmad Ansari, Salliah Shafi Bhat, Mohd Dilshad Ansari Recent Advances in Electrical and Electronic Engineering, 2025 Background: Diabetes has been rising in recent years and prior research has demonstrated Machine Learning Techniques (MLTs) to be useful tools for predicting diabetes. This research has examined the accuracy of six different MLTs for predicting diabetes using lifestyle data gathered from UCI (University of California). To improve medical outcomes and prevent its onset, the prediction of diabetes is necessary. This research has proposed a new framework based on the early detection of diabetes using lifestyle factors. Various MLTs, such as Logistic Regression (LR), Decision Tree Classification (DTC), Random Forest Classification (RFC), Support Vector Classification (SVC), and K-Nearest Classification (KNC) have been used for tenfold cross-validation and the results obtained from different techniques have been verified. Among all classification techniques, LR has achieved the highest accuracy of 93%, the precision of 92%, the recall score of 94%, the F1 score of 93%, and the weighted average of 90%, respectively. The proposed framework is utilized by the healthcare sector to predict diabetes early. It can also be used with datasets from various sectors that share diabetes-related data. Methods: In this paper, we have used the proposed framework to predict diabetes mellitus in the healthcare system, diagnose various ailments, and assess if MLA performs well. The proposed system has been developed based on the MLT for the classification of DM. An intelligent framework for Diabetes Mellitus (DM) that has been developed using MLT illustrates the full workflow from data input to output. The five algorithms, Logistic Regression (LR), Decision Tree Classification (DTC), Random Forest Classification (RFC), Support Vector Classification (SVC), and K-Nearest Classification (KNC), have been compared in terms of accuracy, precision, recall, and F1 score. Results: Results from the experimental setting using MLTs for DM prediction based on lifestyle predictors have been obtained. Descriptive statistics of lifestyle characteristics have been displayed along with their corresponding metrics, such as mean, standard deviation, minimum, maximum, etc. For instance, the age parameters’ mean, standard, and minimum at 25%, 50%, 75%, and maximum values were as follows: 520.0, 48.02, 12.151, 16.0, 39.0, 47.5, 57.0, and 90.0 respectively, as shown in Fig. (10). Feature engineering is crucial to the process of constructing MLT. Insignificant or incorrect characteristics may have a negative impact on the way a model runs. The training time is drastically reduced and accuracy is increased with careful feature selection. In machine learning frameworks, some feature selection strategies include embedding, filter, wrapper, embedded, and hybrid techniques. An alarming number of people around the world suffer from the chronic and dangerous disease of diabetes. Using MLT, early DM prediction-based biological variables have been obtained in this research work. Data on patients’ lifestyles have been thoroughly examined in order to create a framework. The Canonical-correlation Analysis (CCA) has been used to select the ideal combination of lifestyle features. Finally, 10-fold cross-validations have been used to apply five alternative machine learning techniques for the prediction of disease. Conclusion: To our knowledge, it is the first time a framework has been proposed that has yielded prediction results so much better than those from earlier research. The results obtained in this suggested work have been found accurate and reliable by metrics evaluation.
Advanced supervised machine learning methods for precise diabetes mellitus prediction using feature selection Gufran Ahmad Ansari, Salliah Shafi, Mohd Dilshad Ansari, Azhar Shadab Frontiers in Medicine, 2025 BackgroundDiabetes mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting millions, many of whom remain undiagnosed in the early stages. If left untreated, diabetes can result in severe complications such as blindness, stroke, cancer, joint pain, and kidney failure. Accurate and early prediction is critical for timely intervention. Recent advancements in machine learning techniques (MLT) have shown promising potential in enhancing disease prediction due to their robust pattern recognition and classification capabilities.Materials and methodsThis study presents a comparative analysis of supervised MLT such as Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF) using the Pima Indian Diabetes dataset (PIDD) from the UCI repository. A 10-fold cross-validation approach was employed to mitigate class imbalance and ensure generalizability. Performance was evaluated using standard classification metrics: accuracy, precision, recall, and F1-score.ResultsAmong the evaluated models, SVM outperformed the others with an accuracy of 91.5%, followed by RF (90%), KNN (89%), and NB (83%). The study highlights the effectiveness of SVM in early diabetes prediction and demonstrates how model performance varies with algorithm selection.ConclusionUnlike many prior studies that focus on a single algorithm or overlook validation robustness, this research offers a comprehensive comparison of popular classifiers and emphasizes the value of cross-validation in medical prediction tasks. The proposed framework advances the field by identifying optimal models for real-world diabetes risk assessment.
A new local tetra pattern in composite planes (LTcP) technique for classifying brain tumors using partial least squares and super-pixel segmentation Ravi Prakash Chaturvedi, Annu Mishra, Mohd Dilshad Ansari, Ajay Shriram Kushwaha, Prakhar Mittal, Rajneesh Kumar Singh International Journal on Smart Sensing and Intelligent Systems, 2025 The classification of magnetic resonance imaging (MRI) images of brain tumors is challenging, and it is also important, as the number of cases of brain tumors is projected to increase by 1.2% in the year 2030 from the current scenario. This projection makes it necessary to explore medical image classification. The classification involves the extraction of features and the segmentation of the images. The overall accuracy depends upon the type and number of features attained from the image. Thus, we propose a novel descriptor called Local Tetra patterns, which is based on the composite plane (LTcP). The proposed LTcP method helps to extract the maximum number of features from the image. The MRI images are segmented via superpixel segmentation followed by LTcP to obtain the maximum number of features from the image. The 3D directions that are in the cardinalities of xy, yz, and xz are used for primary division of the images into three composite planes. The orientations of the neighborhood and center pixels in both the vertical and horizontal directions were identified via first-order derivatives. We then fused the selected optimal features via the partial least squares (PLS) approach, which in turn helps reduce the number of features required for accurate classification. These features are chosen via the filter method. The proposed approach can classify images with 42 features gained from LTcP, which is better than the existing techniques. The fused features were fed to the SVM classifier, which yielded an accuracy of 91.4%. The computational time for 35 features was observed to be 94.4 s for SVM, which itself proves that the proposed method has a fast computational speed. The F1-score and Recall for the proposed method were observed to be 95.90 and 96.80, respectively. It depicts that the approach can identify a reasonable number of true positives.
IOT for Healthcare G. Suryanarayana, L. N. C. Prakash K, Mohd Dilshad Ansari, Vinit Kumar Gunjan Internet of Things, 2024
Data Mining for Predictive Analytics Prakash Kuppuswamy, Mohd Dilshad Ansari, M. Mohan, Sayed Q.Y. Al Khalidi Intelligent Techniques for Predictive Data Analytics, 2024
IOT Based Smart Agriculture Using LIFI A Vijayakrishna, Gopichand G, Mohd Dilshad Ansari, G Suryanarayana 2022 5th International Conference on Multimedia Signal Processing and Communication Technologies Impact 2022, 2022
Analysis of Diabetes mellitus using Machine Learning Techniques Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari, Mohd Dilshad Ansari 2022 5th International Conference on Multimedia Signal Processing and Communication Technologies Impact 2022, 2022
Local Feature Methods Based Facial Recognition Mohammed Ahmed Talab, Neven Ali Qahraman, Mais Muneam Aftan, Alaa Hamid Mohammed, Mohd Dilshad Ansari Hora 2022 4th International Congress on Human Computer Interaction Optimization and Robotic Applications Proceedings, 2022
Impact of Covid-19 on Education P. Sunitha, Naeem Ahmad, Rejaul Karim Barbhuiya, Vinit Kumar Gunjan, Mohd Dilshad Ansari Lecture Notes in Electrical Engineering, 2022
Fuzzy and entropy based approach for feature extraction from digital image Pertanika Journal of Science and Technology, 2019
Security and privacy issue of big data over the cloud computing: A comprehensive analysis International Journal of Recent Technology and Engineering, 2019
Copy-move image forgery detection using ring projection and modi_ed fast discrete haar wavelet transform Department of Computer Science & Engineering Jaypee University of Information Technology, Waknaghat, Solan(HP), India., Mohd Dilshad Ansari, Satya Prakash Ghrera, Department of Computer Science & Engineering Jaypee University of Information Technology, Waknaghat, Solan(HP), India. International Journal on Electrical Engineering and Informatics, 2017
An approach for identification of copy-move image forgery based on projection profiling Pertanika Journal of Science and Technology, 2017
On Copy Move Image Forgery Detection using Modified Fast Discrete Haar Wavelet Transform 11th Indiacom 4th International Conference on Computing for Sustainable Global Development Indiacom 2017, 2017
On contrast enhancement techniques for medical images with edge detection: A comparative analysis Journal of Telecommunication Electronic and Computer Engineering, 2017
Quantum classical convolutional neural network using ResNet-18 and feature optimization for multiclass skin lesion classification G Suryanarayana, MD Ansari, LNCP K, V Biksham, N Swapna, ... Discover Artificial Intelligence , 2026 2026
Neuroroute-GNNRL: A Hybrid Graph Neural and Reinforcement Learning Framework For Dynamic Node Classification and Speed RP Chaturvedi, M Yashasvi, AM Dilshad, M Annu, K Deepti, C Kuldeep, ... International Journal on Smart Sensing and Intelligent Systems 19 (1) , 2026 2026
Advanced supervised machine learning methods for precise diabetes mellitus prediction using feature selection GA Ansari, S Shafi, MD Ansari, A Shadab Frontiers in Medicine 12, 1620268 , 2025 2025 Citations: 8
Machine learning techniques for diabetes mellitus based on lifestyle predictors GA Ansari, SS Bhat, MD Ansari Recent Advances in Electrical & Electronic Engineering 18 (7), 1060-1071 , 2025 2025 Citations: 5
Diabetes Prediction in Healthcare with Ensemble Learning S Shafi, GA Ansari, MD Ansari, VK Gunjan International Conference on Recent Trends in Machine Learning, IOT, Smart … , 2025 2025
Performance analysis of machine learning based on optimized feature selection for type II diabetes mellitus SS Bhat, GA Ansari, MD Ansari Multimedia tools and applications 84 (8), 4945-4964 , 2025 2025 Citations: 23
A new local tetra pattern in composite planes (LTcP) technique for classifying brain tumors using partial least squares and super-pixel segmentation RP Chaturvedi, A Mishra, MD Ansari, AS Kushwaha, P Mittal, RK Singh International Journal on Smart Sensing and Intelligent Systems 18 (1) , 2025 2025 Citations: 4
On Machine Learning Techniques to Improve Medical Assessment and Prediction for Diabetes Mellitus S Shafi, GA Ansari, MD Ansari, VK Gunjan International Conference on Cognitive and Intelligent Computing, 37-51 , 2024 2024
Exposing the Illusion: A Comprehensive Study on Fake Review Detection on Amazon S Shafi, GA Ansari, MD Ansari, VK Gunjan International Conference on Cognitive and Intelligent Computing, 105-112 , 2024 2024
A Framework for Predicting Diabetes Using Machine Learning Techniques GA Ansari, S Shafi, MD Ansari, VK Gunjan International Conference on Cognitive and Intelligent Computing, 181-195 , 2024 2024
Data mining for predictive analytics P Kuppuswamy, MD Ansari, M Mohan, SQY Al Khalidi Intelligent Techniques for Predictive Data Analytics, 1-24 , 2024 2024 Citations: 6
Intelligent Techniques for Predictive Data Analytics N Singh, S Birla, MD Ansari, NK Shukla 2024 Citations: 5
IOT for Healthcare G Suryanarayana, K Prakash, MD Ansari, VK Gunjan Internet of Things, 201-218 , 2024 2024
Enabling Technologies for Next Generation Wireless Communications MD Ansari, M Usman, M Wajid Taylor & Francis Limited , 2024 2024
Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques GA Ansari, S ShafiBhat, MD Ansari, S Ahmad, HAM Abdeljaber Data and Metadata 3 , 2024 2024 Citations: 11
IOT for Healthcare G Suryanarayana, K Prakash, MD Ansari, VK Gunjan Internet of Things, 201-218 , 2024 2024 Citations: 2
Phishing Email Mitigation Technique Using Back-Propagation Neural Network SP Goje, GA Ansari, MD Ansari, S Tharewal Proceedings of the 4th International Conference on Data Science, Machine … , 2023 2023
Secure and fast emergency road healthcare service based on blockchain technology for smart cities A Ksibi, H Mhamdi, M Ayadi, L Almuqren, MS Alqahtani, MD Ansari, ... Sustainability 15 (7), 5748 , 2023 2023 Citations: 22
Design and development of a data structure visualisation system using the ant colony algorithm X Li, M Khan, MD Ansari Recent Advances in Electrical & Electronic Engineering (Formerly Recent … , 2023 2023 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
Pixel-based image forgery detection: A review MD Ansari, SP Ghrera, V Tyagi IETE journal of education 55 (1), 40-46 , 2014 2014 Citations: 197
On K-means data clustering algorithm with genetic algorithm S Kapil, M Chawla, MD Ansari 2016 Fourth international conference on parallel, distributed and grid … , 2016 2016 Citations: 157
A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing L Ting, M Khan, A Sharma, MD Ansari Journal of Intelligent Systems 31 (1), 221-236 , 2022 2022 Citations: 132
Prevalence and early prediction of diabetes using machine learning in North Kashmir: a case study of district bandipora SS Bhat, V Selvam, GA Ansari, MD Ansari, MH Rahman Computational Intelligence and Neuroscience 2022 (1), 2789760 , 2022 2022 Citations: 117
New divergence and entropy measures for intuitionistic fuzzy sets on edge detection MD Ansari, AR Mishra, FT Ansari International Journal of Fuzzy Systems 20 (2), 474-487 , 2018 2018 Citations: 105
Credit card fraud detection using support vector machine S Kumar, VK Gunjan, MD Ansari, R Pathak Proceedings of the 2nd International Conference on Recent Trends in Machine … , 2022 2022 Citations: 82
A convolution neural network based approach to detect the disease in corn crop M Agarwal, VK Bohat, MD Ansari, A Sinha, SK Gupta, D Garg 2019 IEEE 9th international conference on advanced computing (IACC), 176-181 , 2019 2019 Citations: 76
A performance comparison of optimization algorithms on a generated dataset DKR Gaddam, MD Ansari, S Vuppala, VK Gunjan, MM Sati ICDSMLA 2020: Proceedings of the 2nd International Conference on Data … , 2021 2021 Citations: 74
A comparative study of edge detectors in digital image processing A Sharma, MD Ansari, R Kumar 2017 4th International Conference on Signal Processing, Computing and … , 2017 2017 Citations: 67
Rating‐Based Recommender System Based on Textual Reviews Using IoT Smart Devices M Ahmed, MD Ansari, N Singh, VK Gunjan, SK BV, M Khan Mobile Information Systems 2022 (1), 2854741 , 2022 2022 Citations: 66
Human facial emotion detection using deep learning DKR Gaddam, MD Ansari, S Vuppala, VK Gunjan, MM Sati ICDSMLA 2020: Proceedings of the 2nd International Conference on Data … , 2021 2021 Citations: 64
Prediction of agriculture yields using machine learning algorithms VK Gunjan, S Kumar, MD Ansari, Y Vijayalata Proceedings of the 2nd international conference on recent trends in machine … , 2022 2022 Citations: 62
Performance evaluation of machine learning techniques (MLT) for heart disease prediction GA Ansari, SS Bhat, MD Ansari, S Ahmad, J Nazeer, AEM Eljialy Computational and Mathematical Methods in Medicine 2023 (1), 8191261 , 2023 2023 Citations: 57
Enhanced security for electronic health care information using obfuscation and RSA algorithm in cloud computing P Gautam, MD Ansari, SK Sharma Research Anthology on Architectures, Frameworks, and Integration Strategies … , 2021 2021 Citations: 55
On classification of BMD images using machine learning (ANN) algorithm S Kumar, MD Ansari, VK Gunjan, VK Solanki ICDSMLA 2019: Proceedings of the 1st International Conference on Data … , 2020 2020 Citations: 54
A Traditional Analysis for Efficient Data Mining with Integrated Association Mining into Regression Techniques G SuryaNarayana, K Kolli, MD Ansari, VK Gunjan ICCCE 2020 698, 1393-1404 , 2021 2021 Citations: 53
Machine learning based support system for students to select stream (subject) K Sethi, V Jaiswal, MD Ansari Recent Advances in Computer Science and Communications (Formerly: Recent … , 2020 2020 Citations: 53
Enhancement in teaching quality methodology by predicting attendance using machine learning technique E Rashid, MD Ansari, VK Gunjan, M Khan Modern approaches in machine learning and cognitive science: A walkthrough … , 2020 2020 Citations: 50
Assessment of performance of telecom service providers using intuitionistic fuzzy grey relational analysis framework (IF-GRA) P. Rani et al. P Rani, AR Mishra, MD Ansari, J Ali Soft Computing 25 (3), 1983-1993 , 2021 2021 Citations: 48
On Security and Data Integrity Framework for Cloud Computing Using Tamper-Proofing MD Ansari, VK Gunjan, E Rashid ICCCE 2020 698, 1419-1427 , 2021 2021 Citations: 48
