Dr. Abu Taha Zamani is a dedicated academician and researcher, currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science, Northern Border University, Arar, Kingdom of Saudi Arabia. With a deep passion for technology and research, he has significantly contributed to the advancement of knowledge in various fields within computer science. He has an extensive research portfolio, with numerous articles published in prestigious international journals. His research interests span several cutting-edge areas of computer science, including Cloud Computing, Ad hoc Networks, Cyber Security, Artificial Intelligence (AI), the Internet of Things (IoT), Machine Learning, and Data Mining. His actively contributes to the academic community as a reviewer for prominent international journals and is a respected member of prestigious organizations like IEEE and ACM. He is an editorial board member of several respected computer science journals, where his work as a resea
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence, Computer Networks and Communications, Computer Engineering
21
Scopus Publications
480
Scholar Citations
12
Scholar h-index
15
Scholar i10-index
Scopus Publications
UNet with self-adaptive Mamba-like attention and causal-resonance learning for medical image segmentation Saqib Qamar, Mohd Fazil, Parvez Ahmad, Shakir Khan, Abu Taha Zamani Scientific Reports, 2026 Medical image segmentation plays an important role in various clinical applications, but existing deep learning models face trade-offs between efficiency and accuracy. Convolutional Neural Networks (CNNs) capture local details well but miss the global context, whereas transformers handle the global context but at a high computational cost. Recently, State Space Sequence Models (SSMs) have shown potential for capturing long-range dependencies with linear complexity, but their direct use in medical image segmentation remains limited due to incompatibility with image structures and autoregressive assumptions. To overcome these challenges, we propose SAMA-UNet, a novel U-shaped architecture that introduces two key innovations. First, the Self-Adaptive Mamba-like Aggregated Attention (SAMA) block adaptively integrates local and global features through dynamic attention weighting, enabling an efficient representation of complex anatomical patterns. Second, the causal resonance multi-scale module (CR-MSM) improves encoder-decoder interactions by adjusting feature resolution and causal dependencies across scales, enhancing the semantic alignment between low- and high-level features. Extensive experiments on MRI, CT, and endoscopy datasets demonstrate that SAMA-UNet consistently outperforms CNN, Transformer, and Mamba-based methods. It achieves 85.38% DSC and 87.82% NSD on BTCV, 92.16% and 96.54% on ACDC, 67.14% and 68.70% on EndoVis17, and 84.06% and 88.47% on ATLAS23, establishing new benchmarks across modalities. These results confirm the effectiveness of SAMA-UNet in combining efficiency with accuracy, making it a promising solution for real-world clinical segmentation tasks. The source code is available on https://github.com/sqbqamar/SAMA-UNet .
Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems Wang Jian, Amin Ul Haq, Noman Afzal, Shakir Khan, Hadeel Alsolai, Sultan M. Alanazi, Abu Taha Zamani Scientific Reports, 2025 Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems. Various deep learning-based methods have been proposed for Lung cancer diagnosis. In this study, we proposed a Deep learning techniques-based integrated model (CNN-GRU) for Lung cancer detection. In the proposed model development Convolutional neural networks (CNNs), and gated recurrent units (GRU) models are integrated to design an intelligent model for lung cancer detection. The CNN model extracts spatial features from lung CT images through convolutional and pooling layers. The extracted features from data are embedded in the GRUs model for the final prediction of LC. The model (CNN-GRU) was validated using LC data using the holdout validation technique. Data augmentation techniques such as rotation, and brightness were used to enlarge the data set size for effective training of the model. The optimization techniques Stochastic Gradient Descent(SGD) and Adaptive Moment Estimation(ADAM) were applied during model training for model training parameters optimization. Additionally, evaluation metrics were used to test the model performance. The experimental results of the model presented that the model achieved 99.77% accuracy as compared to previous models. The (CNN-GRU) model is recommended for accurate LC detection in AI-based healthcare systems due to its improved diagnosis accuracy.
Delineation and evaluation of management zones for site-specific nutrient management using a geostatistical and fuzzy C mean cluster approach Pandit Vaibhav Bhagwan, Theerthala Anjaiah, Chitteti Ravali, Darshanoju Srinivasa Chary, Abu Taha Zamani, Sajid Ullah, Nazih Y. Rebouh, Aqil Tariq Scientific Reports, 2025 Expansive soil spatial variability plays a key role in the over- and under-application of fertilizers, contributing to environmental pollution. Assess soil variability and delineate it into management zones to adopt site-specific nutrient management for balanced fertilization and sustainable agriculture. To assess spatial variability by geostatistical methods and delineate and evaluate nutrient management zones for site-specific nutrient management and variable rate fertilizer application using fuzzy c-means clustering. Overall, 200 soil samples (0–15 cm depth) with geographical coordinates were collected with a grid size of 14.2 m × 14.2 m from a 4-ha maize cultivated 4-ha of Mahagoan village of Bhainsa Mandal, Nirmal district, Telangana, India. The collected samples were tested with different reagents to determine the soil reaction and available nutrient status. Soil spatial variability was assessed by the geostatistical method, and delineation of nutrient management zones was carried out by integrating principal component analysis and fuzzy c-means clustering. Geostatistical analysis revealed spherical (pH, electrical conductivity, organic carbon, available sulfur, and available Zn) and Gaussian (available nitrogen, available P 2 O 5 , available K 2 O, available Fe, available Zn, and available Cu) as the best-fit semivariogram model with strong spatial dependence. Five management zones were delineated by principal component analysis and fuzzy c-means clustering based on fuzzy performance index (FPI) and normalized classification entropy (NCE) indices. Variable rates of fertilizer recommendations in different management zones were calculated using a soil test crop response equation. The results show the highest grain yield and fertilizer saving in MZ −5 , followed by MZ −4 , MZ −3 , MZ −2 , and MZ −1 , compared to farmer fertilizer practices. The study aims to delineate the management zone to reduce fertilizer application, ensure balanced fertilizer application, minimize environmental pollution, and increase crop grain yield and profitability.
An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli Results in Engineering, 2025 Blast induced ground vibrations (BIGV) pose critical challenges in surface mining , threatening structural integrity, worker safety, and environmental compliance. This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. A multiobjective optimization scheme is employed to balance competing goals: minimizing peak particle velocity (PPV), maximizing fragmentation efficiency, and reducing operational costs. Crucially, the framework incorporates Explainable AI (XAI) techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME) and uncertainty quantification (UQ) methods based on Bayesian Neural Networks to provide insight into model decisions and confidence in predictions. Validation across five operational mines in the Godavari Valley Coalfields (India) demonstrates strong generalizability, achieving up to a 20% reduction in RMSE compared to empirical baselines. The improvement is statistically significant ( p < 0.01 ) as confirmed through a paired t-test across cross-validation folds. These findings highlight that a physics informed, explainable, and uncertainty aware AI framework can substantially improve vibration prediction, ensure regulatory compliance, and support safer, more sustainable blasting operations in modern surface mining.
Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen Array, 2025 Leukemia is identified by an excess of immature white blood cells (WBC) being formed in the bone marrow, leading to cancer. It is divided into two main types: acute, which stems from early cell growth ab- normalities and involves rapid immature cell proliferation, and chronic, which progresses more slowly due to a blockage in the later stages of the cell life cycle. Detecting acute lymphoblastic leukemia (ALL) at an early stage is critical to reducing its associated mortality rate. This study presents an empirical analysis of various pre-trained deep learning models, including VGG16, VGG19, ResNet50, Xception, ResNet152, EfficientNet- B0, NASNetMobile, DenseNet169, DenseNet121, and EfficientNetV2B0, for the detection and classification of ALL. A comprehensive evaluation highlights the effectiveness of deep learning in distinguishing different types of ALL, demonstrating its potential as a reliable diagnostic tool in medical imaging. Additionally, we evaluated the performance of these models using different optimization techniques, including Adadelta, SGD, RMSprop, and Adam, to determine the most effective optimization strategy for improving classifica-tion accuracy. Our results demonstrate that EfficientNet-B0 achieved a classification accuracy of 72 %, while NASNetMobile attained 81 %. Notably, DenseNet121 outperformed these models with an accuracy of 99 %. Furthermore, the remaining seven models VGG16, VGG19, ResNet50, Xception, ResNet152, DenseNet169, and EfficientNetV2B achieved a perfect classification accuracy of 100 %, highlighting their robustness and effectiveness in our experimental setup. To improve the interpretability of the leukemia detection process, explainable AI techniques, including Grad-CAM, Score-CAM, and Grad-CAM++, were integrated to vi-sualize critical regions influencing model predictions. These techniques enhance transparency by providing visual explanations of classification decisions. A detailed comparative analysis was conducted, examining key parameters such as learning rate, optimization algorithms, and the number of training epochs to determine the most effective approach. The study leveraged a publicly available acute lymphoblastic leukemia dataset to ensure comprehensive model evaluation. By offering insights into model performance and interpretability.
DCT-based Robust Reversible Watermarking Technique based on histogram Modification Soumitra Roy, Naushad Varish, Md Shamsul Haque Ansari, Abu Taha Zamani, Syed Irfan Yaqoob International Journal of Electrical and Computer Engineering Systems, 2025 In this paper, a strong, reversible image watermarking technique based on discrete cosine transform (DCT) and histogram shifting is proposed, where it overcomes the following concerns: (i) Reversing the cover object to its starting appearance is the primary goal of the reversible watermarking system. (ii) Military, medical, and standard law enforcement images are the main types of images that require distortion and reinstatement of the cover object following the watermark extraction. (iii) Lack of robustness and cover image-dependent embedding capacity are the primary concerns about reversible watermarking. Decompose the cover object into blocks that don't overlap in the first stage to insert a binary watermark bit into every block that is converted. These binary bits of watermark are embedded by altering a single set of middle substantial AC coefficients. To restore the cover image, subsequently using the histogram bin shifting method, a location map is created and integrated within the cover image. On the extracting side, at first, a location map is extracted from the image using the histogram bin shifting technique. In the following step, the image's watermark is recovered, and a reversed image has been generated using a location map. To verify the robustness property, several image processing attacks are tested with the suggested reversible watermarking approach, and favorable results are attained. The proposed scheme using the Lena image achieved 46.62 imperceptibility for 4096 embedding capacities. To methodically evaluate the proposed approach, it is compared with two current reversible watermarking systems, where they achieved 39.10 and 37.90 imperceptibility with 4.4 × 103 and 256 embedding capacities, respectively. The experimental results affirmed that the suggested method exhibits superior performance relative to these existing techniques.
Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach Debendra Muduli, Syed Irfan Yaqoob, Santosh Kumar Sharma, Anuradha S. Kanade, Mohammad Shameem, Harendra S. Jangwan, P.M. Ashok Kumar, Abu Taha Zamani Results in Engineering, 2025 Glaucoma, a progressive eye disease, can cause irreversible vision loss if not detected early. Timely diagnosis is crucial, especially in underserved areas where machine learning and cloud technology can offer a viable solution for remote glaucoma screening. This study presents an automated eHealth system designed to enhance early glaucoma detection and mitigate its impact. The proposed model follows a multi-stage approach, beginning with the application of a stationary wavelet transform (SWT) for image preprocessing and augmentation. The augmented fundus images are subsequently processed through four convolutional neural network (CNN) models—ResNet50, InceptionV3, VGG16, and Xception—to extract deep features, which are then combined into a final feature matrix. To optimize the data for the next stage, principal component analysis (PCA) is applied to reduce the feature dimensions. Finally, an improved gray wolf optimization algorithm integrated with an extreme learning machine (IMGWO-ELM) classifies the images as either healthy or glaucomatous. This optimization enhances generalization and reduces overfitting, making the model a promising tool for advancing glaucoma diagnosis. The model's performance was evaluated in a cloud-based environment using two datasets: ORIGA and G1020. These datasets contain fundus images of individuals with and without glaucoma. We compared a stand-alone system with a cloud-based setup utilizing three virtual machines (4 vCPU–16 GB RAM, 8 vCPU–32 GB RAM, and 16 vCPU–64 GB RAM). A five-fold, ten-run cross-validation was employed across both configurations. The cloud setup with 16 vCPU–64 GB RAM achieved superior classification accuracies of 93.8% on the G1020 dataset and 96.74% on ORIGA. The deep CNN classifiers demonstrated exceptional performance, achieving a recall of 0.99 and an ROC score of 1.00, indicating perfect classification metrics. This study advances glaucoma detection by showcasing the efficacy of the ELM and CNN models, offering a promising direction for future research and improved patient outcomes.
Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli Results in Engineering, 2025 Credit card fraud is an emerging global issue that causes substantial financial losses and undermines consumer trust in digital transactions. With the increase in online payment volumes, conventional fraud detection technologies are increasingly confronted by the complexity of fraudulent strategies that require intelligent and scalable alternatives. This study introduces an innovative machine learning-based fraud detection framework that incorporates sophisticated preprocessing methods like SMOTE-ENN for class imbalance mitigation, autoencoder for dimensionality reduction, and TOPSIS for optimal feature selection. A stacking ensemble model is developed with support vector machine (SVM), K-nearest neighbors (KNN), and extreme learning machine (ELM) to enhance forecast accuracy. The particle swarm optimization (PSO) algorithm is employed to optimize ELM parameters, enhancing generalization and model convergence. Extensive tests with standard datasets show outstanding results, achieving 99.95% accuracy, 99.93% precision, and 99.97% recall in detecting fraud. The outcomes highlight the model's proficiency in properly identifying fraudulent transactions while reducing false positives. The proposed method provides a viable alternative for secure and efficient credit card fraud detection in the contemporary digital economy, characterized by high accuracy and real-time scalability. • Propose a novel ML framework using advanced preprocessing and optimization to boost fraud detection accuracy. • Uses SMOTE-ENN, Autoencoder, and TOPSIS to balance data, reduce dimensions, and select optimal features. • Combines SVM, KNN, and PSO-optimized ELM to boost robustness, prevent overfitting, and speed up convergence.
A Multi-Layered AI-Driven Cybersecurity Architecture: Integrating Entropy Analytics, Fuzzy Reasoning, Game Theory, and Multi-Agent Reinforcement Learning for Adaptive Threat Defense Eram Fatima Siddiqui, Mohd Haleem, Sheikh Fahad Ahmad, Amina Salhi, Abu Taha Zamani, Naushad Varish IEEE Access, 2025 In the face of increasingly sophisticated cyberattacks, including adaptive adversaries and stealthy anomalies, key features of defense mechanisms should be effective, interpretable, and theoretically rooted. Conventional intrusion detection systems are typically based on a single-paradigm machine learning model which can be effective (because it is optimized for conditions), but fail in generalizability and falling back on an explanation of its prediction. This paper outlines a multi-layered AI-enabled cyber defense framework that integrates entropy analytics, fuzzy inference, game-theoretic defense, and multi-agent reinforcement learning (MARL) inside a closed-loop adaptive architecture. In its simplest form, the novelty of the paper is that, four functional paradigms - uncertainty quantification, interpretability, strategic adversarial thinking, and live policy adaptation - are placed into a single coherent system. The framework operates as sequential and feedback salients - entropy analytics quantify the uncertainty in are states, fuzzy inference end maps the uncertainty into qualitative decision rules, game theory shapes defender - attacker towards equilibrium strategies, and MARL dynamically updates those strategies for convergence and long term adaptation. The empirical work on appropriate benchmark intrusion detection datasets consistently Thoutperformed baseline systems including the DDN, Fed-ID, AG-IDS, DL-FL systems producing a 6-12% increase in detection accuracy, lower false positive rates from non-intrusions, and a faster convergence, with adversarial examples across multiple epochs. Also, practical case studies reveal a level of improved explainability in threat classification and anomaly detection, which equates to practical interpretability for security analysts from the framework. The major contributions of the work are threefold: (i) an integrated multi-layered AI-based cybersecurity framework, (ii) theoretical robustness results in bounded adversarial models, and (iii) performance and interpretability form the systematic empirical evaluations over multiple datasets.
AI-Driven Automation and Industrial Peace in Indonesia: Mediation Effects of Employability and Organizational Support AR Syahfitri, Z Polkowski, MA Nawaz, AT Zamani Blockchain, Artificial Intelligence, and Future Research 2 (1), 1-19 , 2026 2026
UNet with self-adaptive Mamba-like attention and causal-resonance learning for medical image segmentation S Qamar, M Fazil, P Ahmad, S Khan, AT Zamani Scientific Reports , 2025 2025 Citations: 1
A multi-layered AI-driven cybersecurity architecture: Integrating entropy analytics, Fuzzy reasoning, game theory and multi-agent reinforcement learning for adaptive threat defense EF Siddiqui, M Haleem, SF Ahmad, A Salhi, AT Zamani, N Varish IEEE Access , 2025 2025 Citations: 4
An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining CK Ala, ZL Mayaluri, A Kaushik, N Parveen, S Saxena, AT Zamani, ... Results in Engineering 27, 106046 , 2025 2025 Citations: 6
A Two-Stage Ensemble Feature Selection with Particle Swarm Optimization for Microarray Data Classification in Distributed Computing Environments A Adhikari, S Bhatta, HS Jangwan, A Mishra, KU Nisa, AT Zamani, ... arXiv preprint arXiv:2507.04251 , 2025 2025 Citations: 1
Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems W Jian, AU Haq, N Afzal, S Khan, H Alsolai, SM Alanazi, AT Zamani Scientific Reports 15 (1), 22945 , 2025 2025 Citations: 21
Delineation and evaluation of management zones for site-specific nutrient management using a geostatistical and fuzzy C mean cluster approach PV Bhagwan, T Anjaiah, C Ravali, DS Chary, AT Zamani, S Ullah, ... Scientific Reports 15 (1), 20991 , 2025 2025 Citations: 9
Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques D Muduli, S Parija, S Kumari, A Hassan, HS Jangwan, AT Zamani, ... Array 26, 100397 , 2025 2025 Citations: 20
DCT-based Robust Reversible Watermarking Technique based on histogram Modification S Roy, V Naushad, SI Yaqoob, MSH Ansari, AT Zamani International journal of electrical and computer engineering systems 16 (6 … , 2025 2025 Citations: 1
Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach RK Gupta, A Hassan, SK Majhi, N Parveen, AT Zamani, R Anitha, B Ojha, ... Results in Engineering 26, 105084 , 2025 2025 Citations: 29
Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach D Muduli, SI Yaqoob, SK Sharma, AS Kanade, M Shameem, HS Jangwan, ... Results in Engineering 26, 104682 , 2025 2025 Citations: 12
Detection of driver drowsiness using adaptive eye characteristic ratio for enhanced road safety PS Lamba, R Jain, S Khan, SM Alanazi, A Jain, AT Zamani, A Panwar, ... IEEE Access , 2025 2025 Citations: 7
Bi-Directional hybrid attention feature pyramid network for detecting diabetic macular edema in retinal fundus Images N Mukherjee, S Sengupta, MN Ahmed, SI Yaqoob, MR Hussain, ... IEEE access , 2025 2025 Citations: 9
Enhancing anomaly detection in attributed networks using proximity preservation and advanced embedding techniques W Khan, M Ishrat, MN Ahmed, S Abidin, M Husain, M Izhar, AT Zamani, ... IEEE Access , 2025 2025 Citations: 10
FIDNet: A Deep Convolution Neural Network Model for Enhanced Fake Image Detection MH Shantanu Shookdeb, Debendra Muduli, Abu Taha Zamani, Syed Irfan Yaqoob ... https://ssrn.com/abstract=5194324 , 2025 2025
SIDNet: A SQL injection detection network for enhancing cybersecurity D Muduli, S Shookdeb, AT Zamani, S Saxena, AS Kanade, N Parveen, ... Ieee Access 12, 176511-176526 , 2024 2024 Citations: 10
Empirical Evaluation of Deep Learning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer Learning and Fusion-Based Approach S Biswas, D Muduli, MA Islam, AS Kanade, AT Zamani, SP Kanade IEEE Access 12 , 2024 2024 Citations: 33
Brain tumor classification using an ensemble of deep learning techniques SGK Patro, N Govil, S Saxena, BK Mishra, AT Zamani, AB Miled, ... IEEE Access 12, 162094-162106 , 2024 2024 Citations: 32
Refined software defect prediction using enhanced jaya optimization and extreme learning machine D Pradhan, D Muduli, AT Zamani, SI Yaqoob, SM Alanazi, RR Kumar, ... IEEE Access 12, 141559-141579 , 2024 2024 Citations: 35
Empirical Evaluation of Deep Learning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer Learning and Fusion-Based Approach AS KANADE, ABUT ZAMANI 2024
MOST CITED SCHOLAR PUBLICATIONS
A diabetes monitoring system and health-medical service composition model in cloud environment SK Sharma, AT Zamani, A Abdelsalam, D Muduli, AA Alabrah, N Parveen, ... IEEE Access 11, 32804-32819 , 2023 2023 Citations: 78
Genetic algorithm based probabilistic model for agile project success in global software development M Shameem, M Nadeem, AT Zamani Applied Soft Computing 135, 109998 , 2023 2023 Citations: 37
Refined software defect prediction using enhanced jaya optimization and extreme learning machine D Pradhan, D Muduli, AT Zamani, SI Yaqoob, SM Alanazi, RR Kumar, ... IEEE Access 12, 141559-141579 , 2024 2024 Citations: 35
Empirical Evaluation of Deep Learning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer Learning and Fusion-Based Approach S Biswas, D Muduli, MA Islam, AS Kanade, AT Zamani, SP Kanade IEEE Access 12 , 2024 2024 Citations: 33
Factor prioritization for effectively implementing DevOps in software development organizations: a SWOT-AHP approach NM Noorani, AT Zamani, M Alenezi, M Shameem, P Singh Axioms 11 (10), 498 , 2022 2022 Citations: 33
Brain tumor classification using an ensemble of deep learning techniques SGK Patro, N Govil, S Saxena, BK Mishra, AT Zamani, AB Miled, ... IEEE Access 12, 162094-162106 , 2024 2024 Citations: 32
Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach RK Gupta, A Hassan, SK Majhi, N Parveen, AT Zamani, R Anitha, B Ojha, ... Results in Engineering 26, 105084 , 2025 2025 Citations: 29
Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems W Jian, AU Haq, N Afzal, S Khan, H Alsolai, SM Alanazi, AT Zamani Scientific Reports 15 (1), 22945 , 2025 2025 Citations: 21
Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques D Muduli, S Parija, S Kumari, A Hassan, HS Jangwan, AT Zamani, ... Array 26, 100397 , 2025 2025 Citations: 20
BDDTPA: Blockchain-driven deep traffic pattern analysis for enhanced security in cognitive radio ad-hoc networks D Dansana, PK Behera, AA Darem, Z Ashraf, AT Zamani, MN Ahmed, ... Ieee Access 11, 98202-98216 , 2023 2023 Citations: 17
Qos-aware cloud service recommendation using metaheuristic approach SS Mohapatra, RR Kumar, M Alenezi, AT Zamani, N Parveen Electronics 11 (21), 3469 , 2022 2022 Citations: 16
Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach D Muduli, SI Yaqoob, SK Sharma, AS Kanade, M Shameem, HS Jangwan, ... Results in Engineering 26, 104682 , 2025 2025 Citations: 12
Enhancing anomaly detection in attributed networks using proximity preservation and advanced embedding techniques W Khan, M Ishrat, MN Ahmed, S Abidin, M Husain, M Izhar, AT Zamani, ... IEEE Access , 2025 2025 Citations: 10
SIDNet: A SQL injection detection network for enhancing cybersecurity D Muduli, S Shookdeb, AT Zamani, S Saxena, AS Kanade, N Parveen, ... Ieee Access 12, 176511-176526 , 2024 2024 Citations: 10
Secure and efficient key management scheme in MANETs AT Zamani, S Zubair IOSR Journal of Computer Engineering (IOSR-JCE) 16 (2), 146-158 , 2014 2014 Citations: 10
Delineation and evaluation of management zones for site-specific nutrient management using a geostatistical and fuzzy C mean cluster approach PV Bhagwan, T Anjaiah, C Ravali, DS Chary, AT Zamani, S Ullah, ... Scientific Reports 15 (1), 20991 , 2025 2025 Citations: 9
Bi-Directional hybrid attention feature pyramid network for detecting diabetic macular edema in retinal fundus Images N Mukherjee, S Sengupta, MN Ahmed, SI Yaqoob, MR Hussain, ... IEEE access , 2025 2025 Citations: 9
Critical factors affecting Oracle E-Business Suite enterprise resource planning (ERP) R12 implementation: A case study of Saudi Arabia’s university S Zubair, AT Zamani information systems 7 (3) , 2014 2014 Citations: 9
Key management scheme in mobile Ad Hoc networks SZ Abu Taha Zamani International Journal of Emerging Research in Management &Technology 3 (4 … , 2014 2014 Citations: 8
Detection of driver drowsiness using adaptive eye characteristic ratio for enhanced road safety PS Lamba, R Jain, S Khan, SM Alanazi, A Jain, AT Zamani, A Panwar, ... IEEE Access , 2025 2025 Citations: 7
