NAHIER ALDHAFFERI

@iau.edu.sa

Computer Information Systems
Imam Abdulrahman Bin Faisal University

NAHIER ALDHAFFERI


               Download Compact PDF  
https://researchid.co/naldhafeeri

RESEARCH, TEACHING, or OTHER INTERESTS

Information Systems, Artificial Intelligence, Computer Networks and Communications, Computer Science
37

Scopus Publications

923

Scholar Citations

16

Scholar h-index

19

Scholar i10-index

Scopus Publications

  • Urdu-NERD: Urdu named entity recognition with BiGRU-based deep learning architecture
    Zainab Rafiq, Muhammad Wasim, Fatema Sabeen Shaikh, Nahier Aldhafferi, Abdullah Alqahtani
    Peerj Computer Science, 2026
    Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), focusing on identifying and extracting entities such as names, locations, organizations, and other specific labels from unstructured text data. It plays a crucial role in various NLP applications, including information retrieval, question answering, and sentiment analysis. However, while NER systems have been extensively developed for English, adapting them to languages like Urdu poses unique challenges due to linguistic differences and the scarcity of annotated data. In this research, we enhance data diversity and accessibility for Urdu NER by introducing the ZUNERA corpus , the most extensive Urdu NER dataset to date, comprising 1,189,614 tokens and 89,804 named entities. Additionally, we classify the entities into twenty-three different named entities types. We meticulously annotate the corpus , providing clear guidelines and employing the Kappa coefficient to ensure high-quality annotations. Furthermore, we propose the Urdu-Named Entity Recognition with BiGRU-based Deep Learning Architecture (NERD) framework, which facilitates efficient entity recognition in Urdu text. The proposed framework achieves an impressive F1-score of 94.6%. Comparing ZUNERA with the MK-PUCIT dataset underscores its robustness in accurately recognizing entities. Although this study centers on Urdu, the proposed NER framework and annotation pipeline are designed to be language-agnostic. They can be extended to other morphologically rich or low-resource languages, providing a replicable foundation for future cross-lingual research. Overall, our contributions significantly advance Urdu NER research by providing a comprehensive dataset, evaluating state-of-the-art techniques, and introducing a novel framework for efficient Urdu entity recognition.
  • Time and Memory Trade-Offs in Shortest-Path Algorithms Across Graph Topologies: A*, Bellman–Ford, Dijkstra, AI-Augmented A* and a Neural Baseline
    Nahier Aldhafferi
    Computers, 2025
    This study presents a comparative evaluation of Dijkstra’s algorithm, A*, Bellman-Ford, AI-Augmented A* and a neural AI-based model for shortest-path computation across diverse graph topologies, with a focus on time efficiency and memory consumption under standardized experimental conditions. We analyzed grids, random graphs, and scale-free graphs of sizes up to 103,103 nodes, specifically examining 100- and 1000-node grids, 100- and 1000-node random graphs, and 100-node scale-free graphs. The algorithms were benchmarked through repeated runs per condition on a server-class system equipped with an Intel Xeon Gold 6248R processor, NVIDIA Tesla V100 GPU (32 GB), 256 GB RAM, and Ubuntu 20.04. A* consistently outperformed Dijkstra’s algorithm when paired with an informative admissible heuristic, exhibiting faster runtimes by approximately 1.37× to 1.91× across various topologies. In comparison, Bellman-Ford was slower than Dijkstra’s by approximately 1.50× to 1.92×, depending on graph type and size; however, it remained a robust option in scenarios involving negative edge weights or when early-termination conditions reduced practical iterations. The AI model demonstrated the slowest performance across conditions, incurring runtimes that were 2.60× to 3.23× higher than A* and 1.62× to 2.15× higher than Bellman-Ford, offering limited gains as a direct solver. These findings underscore topology-sensitive trade-offs: A* is preferred when a suitable heuristic is available; Dijkstra’s serves as a strong baseline in the absence of heuristics; Bellman-Ford is appropriate for handling negative weights; and current AI approaches are not yet competitive for exact shortest paths but may hold promise as learned heuristics to augment A*. We provide environmental details and comparative results to support reproducibility and facilitate further investigation into hybrid learned-classical strategies.
  • Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis
    Nahier Aldhafferi
    Information Switzerland, 2024
    Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from Android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model’s superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model’s capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing Android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges.
  • Alternative cancer therapy through modeling pteridines photosensitizer quantum yield singlet oxygen production using swarm-based support vector regression and extreme learning machine
    Nahier Aldhafferi
    Cogent Engineering, 2024
    AbstractPhotodynamic cancer therapy circumvents the major side effects associated with the conventional cancer treatment methods, such as chemotherapy, surgery and exposure to radiation. Experimental measurement of photosensitizer quantum yield (PQY) singlet production of oxygen through either sensitive laser spectroscopy or luminescence detection at the wavelength of 1270 nm is costly; time consuming and intensive while unreliability of chemical traps experimental approach is of serious concern. Quantitative structure–activity relationship (QSAR) computational method proposed in the literature for computing PQY of singlet oxygen production has characteristics deviation from the measured values. PQY singlet oxygen production of twenty-nine pteridines photosensitizer compounds is modeled and predicted in this present contribution using extreme learning machine (ELM) and support vector regression (SVR) with hybridized particle swarm optimization (PSO) method for ensuring combinatory parameter selection. The performances of the developed SVR-PSO computational method are assessed using mean absolute error (MAE), correlation coefficient (CC), root mean square error (RMSE) and mean absolute percentage deviation (MAPD). The developed SVR-PSO model outperforms QSAR (2016) model with performance superiority of 34.78%, 3.65%, 17.64% and 42.16% on the basis of RMSE, CC, MAE and MAPD performance measuring parameters, respectively. The developed ELM-SINE (with sine activation function) and ELM-SIG (with sigmoid activation function) respectively outperform the existing QSAR (2016) model with improvement of 6.54% and 4.70% using R-squared metric. The demonstrated outstanding performance of the present predictive models is immensely meritorious in strengthening the potentials of alternative cancer therapy and circumventing the experimental challenges of PQY singlet oxygen production determination.
  • Modeling the magnetocaloric effect of spinel ferrites for magnetic refrigeration technology using extreme learning machine and genetically hybridized support vector regression computational methods
    Wasiu Adeyemi Oke, Nahier Aldhafferi, Saibu Saliu, Taoreed O. Owolabi, Abdullah Alqahtani, Abdullah Almurayh, Talal F. Qahtan
    Cogent Engineering, 2023
    AbstractSpinel ferrites are magnetic oxide materials with potentials to promote green technology in magnetic refrigeration which is known to be economically clean, energy saving and efficient. Maximum magnetic entropy change of spinel ferrites decides and controls the applicability as well as the strength of spinel ferrite magnetic oxide since it measures the hugeness of magnetocaloric effect. However, experimental determination of maximum magnetic entropy change requires intensive procedures, costly equipment and consumes appreciable time. Intelligent models are presented in this work using spinel-ferrite-molecular-based descriptors such as the ionic radii of spinel ferrites constituents, applied magnetic field and their concentrations. The developed intelligent models for prediction of spinel ferrite maximum magnetic entropy change include extreme learning machine (ELM) and hybrid genetic-algorithm-coupled support vector regression (GSVR). The developed ELM model has correlation coefficient (CC) and mean absolute error (MAE) of 98.45% and 0.117 J/kg/K, respectively, while the developed GSVR model has CC of 80.87% and MAE of 0.129 J/kg/J. The developed ELM model which is based on empirical risk minimization principle shows better performance over GSVR model that premises on structural minimization risk principle with improvement of 0.06%, 17.86% and 8.765% using root mean square error, CC and MAE yardsticks, respectively. Closeness of the estimates of the developed models with the experimental values is a strong indication of the potentials of the proposed intelligent methods in facilitating practical implementation of magnetic cooling refrigeration to solve energy crisis which promote efficiency and environmental friendliness.
  • Improved Whale Optimization with Local-Search Method for Feature Selection
    Malek Alzaqebah, Mutasem K. Alsmadi, Sana Jawarneh, Jehad Saad Alqurni, Mohammed Tayfour, Ibrahim Almarashdeh, Rami Mustafa A. Mohammad, Fahad A. Alghamdi, Nahier Aldhafferi, Abdullah Alqahtani, Khalid A. Alissa, Bashar A. Aldeeb, Usama A. Badawi, Maram Alwohaibi, Hayat Alfagham
    Computers Materials and Continua, 2023
    Various feature selection algorithms are usually employed to improve classification models’ overall performance. Optimization algorithms typically accompany such algorithms to select the optimal set of features. Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics. The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD). GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search. Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm. In addition, disruptive selection (DS) is employed to select the solutions from the population for local search. DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions. Fifteen (15) standard benchmark datasets provided by the University of California Irvine (UCI) repository were used in evaluating the proposed approaches’ performance. Next, a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature. The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods. Hence, the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.
  • Sustainable Education Quality Improvement Using Academic Accreditation: Findings from a University in Saudi Arabia
    Abdullah Almurayh, Saqib Saeed, Nahier Aldhafferi, Abdullah Alqahtani, Madeeha Saqib
    Sustainability Switzerland, 2022
    Accreditation is widely considered to be a vital tool for quality assurance in higher education; however, there is disagreement in the academic community on the intended benefits of accreditation. Preparing for accreditation requires extensive financial and human resources to complete the required documentation. All accreditation agencies require improvements in institutional infrastructure, enhanced student support, appropriate learning environments, and faculty development, which can directly improve students’ learning experiences. In this paper, we explore the impact of accreditation on students’ learning by using a case study-based approach. We selected four degree programs from a University in Saudi Arabia and compared the performances of students in different courses before and after acquiring local program accreditation (NCAAA). The results highlight that although there is no direct relationship between increased student performance and acquiring accreditation, there is a significant impact on the performance of student learning. However, there is a need for sustained efforts to continuously adopt accreditation-aligned practices to gain a sustained advantage. We have presented a model that can enable academic institutions to continuously adhere to best practices even if no accreditation visit has been scheduled in the near future. This way, academic programs can consistently improve their processes and enhance student learning.
  • Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods
    Abdullah Alqahtani, Saibu Saliu, Taoreed O. Owolabi, Nahier Aldhafferi, Abdullah Almurayh, Oluwatoba Emmanuel Oyeneyin
    Materials Today Communications, 2022
  • Learning trends in customer churn with rule-based and kernel methods
    Nahier Aldhafferi, Abdullah Alqahtani, Fatema Sabeen Shaikh, Sunday Olusanya Olatunji, Abdullah Almurayh, Fahad A. Alghamdi, Ghalib H. Alshammri, Amani K. Samha, Mutasem Khalil Alsmadi, Hayat Alfagham, Abderrazak Ben Salah
    International Journal of Electrical and Computer Engineering, 2022
    <span>In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.</span>
  • Extreme learning machine computational method of modeling energy gap of doped zinc selenide nano-material semiconductor
    Nahier Aldhafferi
    Materials Today Communications, 2022
  • Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning
    Atta-ur Rahman, Abdullah Alqahtani, Nahier Aldhafferi, Muhammad Umar Nasir, Muhammad Farhan Khan, Muhammad Adnan Khan, Amir Mosavi
    Sensors, 2022
  • Specific Surface Area Characterization of Spinel Ferrite Nanostructure Based Compounds for Photocatalysis and Other Applications Using Extreme Learning Machine Method
    Miloud Souiyah, Taoreed O. Owolabi, Saibu Saliu, Talal F. Qahtan, Nahier Aldhafferi, Abdullah Alqahtani
    Mathematical Problems in Engineering, 2022
  • Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method
    Talal F. Qahtan, Nahier Aldhafferi, Abdullah Alqahtani, Olawusi Richard Abidemi, Miloud Souiyah, Abdullah Almurayh, Fahad A. Alghamdi, Taoreed O. Owolabi
    Cogent Engineering, 2022
  • An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling
    Mutasem K. Alsmadi, Ghaith M. Jaradat, Malek Alzaqebah, Ibrahim ALmarashdeh, Fahad A. Alghamdi, Rami Mustafa A. Mohammad, Nahier Aldhafferi, Abdullah Alqahtani
    Computers Materials and Continua, 2022
  • Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method
    Olusayo Olubosede, Mohd Abd Rahman, Abdullah Alqahtani, Miloud Souiyah, Mouftahou Latif, Wasiu Oke, Nahier Aldhafferi, Taoreed Owolabi
    Crystals, 2022
  • Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
    Nahier Aldhafferi
    Aip Advances, 2021
  • Representations of generalized inverses via full-rank QDR decomposition
    Nahier Aldhafeeri, Dimitrios Pappas, Ivan P. Stanimirović, Milan Tasić
    Numerical Algorithms, 2021
  • Memory based cuckoo search algorithm for feature selection of gene expression dataset
    Malek Alzaqebah, Khaoula Briki, Nashat Alrefai, Sami Brini, Sana Jawarneh, Mutasem K. Alsmadi, Rami Mustafa A. Mohammad, Ibrahim ALmarashdeh, Fahad A. Alghamdi, Nahier Aldhafferi, Abdullah Alqahtani
    Informatics in Medicine Unlocked, 2021
  • Digitalization of learning in Saudi Arabia during the COVID-19 outbreak: A survey
    Mutasem K. Alsmadi, Ibrahim Al-Marashdeh, Malek Alzaqebah, Ghaith Jaradat, Fahad A. Alghamdi, Rami Mustafa A Mohammad, Muneerah Alshabanah, Daniah Alrajhi, Hanouf Alkhaldi, Nahier Aldhafferi, Abdullah Alqahtani, Usama A. Badawi, Mohammed Tayfour
    Informatics in Medicine Unlocked, 2021
  • Comorbidities and risk factors for severe outcomes in covid-19 patients in saudi arabia: A retrospective cohort study
    Fatema S Shaikh, Nahier Aldhafferi, Areej Buker, Abdullah Alqahtani, Subhodeep Dey, Saema Abdulhamid, Dalal Ali Mahaii AlBuhairi, Raha Saud Abdulaziz Alkabour, Waad Sami O Atiyah, Sara Bachar Chrouf, Abdussalam Alshehri, Sunday Olusanya Olatunji, Abdullah M Almuhaideb, Mohammed S Alshahrani, Yousof AlMunsour, Vahitha B Abdul-Salam
    Journal of Multidisciplinary Healthcare, 2021
  • Ensemble-Based Support Vector Regression with Gravitational Search Algorithm Optimization for Estimating Magnetic Relative Cooling Power of Manganite Refrigerant in Magnetic Refrigeration Application
    Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji, Nahier Aldhafferi, Abdullah Alqahtani
    Journal of Superconductivity and Novel Magnetism, 2019
  • Support Vector Regression Ensemble for Effective Modeling of Magnetic Ordering Temperature of Doped Manganite in Magnetic Refrigeration
    Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji, Nahier Aldhafferi, Abdullah Alqahtani
    Journal of Low Temperature Physics, 2019
  • The Dark Side of Digital Technologies
    Nahier Aldhafferi
    21st Saudi Computer Society National Computer Conference Ncc 2018, 2018
  • Educational data mining for enhanced teaching and learning
    Journal of Theoretical and Applied Information Technology, 2018
  • Adaptive communication: A systematic review
    , Abdullah Alqahtani, Nahier Aldhafferi, Atta-ur-Rahman Atta-ur-Rahman, Kiran Sultan, Mohammad Aftab Alam Khan
    Journal of Communications, 2018
  • Intelligent multiple relay selection and transmit power-saving with abc optimization for underlay relay-assisted crns
    , Kiran Sultan, Atta-ur-Rahman Atta-ur-Rahman, Bassam A. Zafar, Nahier Aldhafferi, Abdullah Alqahtani
    Journal of Communications, 2018
  • A review of wellness detection techniques using complex activities association in smart-homes
    Nahier Aldhafferi
    Journal of Computational and Theoretical Nanoscience, 2018
  • Adaptive communication for capacity enhancement: A hybrid intelligent approach
    Atta-ur-Rahman, Dhiaa Musleh, Nahier Aldhafferi, Abdullah Alqahtani, Hassan Alfifi
    Journal of Computational and Theoretical Nanoscience, 2018
  • Constraint based rule mining in patient claim data
    Nahier Aldhafferi, Abdullah Alqahtani, Atta-ur-Rahman, Muhammad Azam
    Journal of Computational and Theoretical Nanoscience, 2018
  • Differential evolution assisted MUD for MC-CDMA systems using non-orthogonal spreading codes
    Atta-ur-Rahman, Kiran Sultan, Nahier Aldhafferi, Abdullah Alqahtani
    Advances in Intelligent Systems and Computing, 2018
  • Development of hybrid computational intelligence model for Estimating Relative Cooling Power of manganite-based materials for magnetic refrigeration enhancement
    Journal of Engineering and Applied Sciences, 2018
  • Reversible and Fragile Watermarking for Medical Images
    Atta-ur-Rahman, Kiran Sultan, Nahier Aldhafferi, Abdullah Alqahtani, Maqsood Mahmud
    Computational and Mathematical Methods in Medicine, 2018
  • Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories
    Atta Ur Rahman, Kiran Sultan, Dhiaa Musleh, Nahier Aldhafferi, Abdullah Alqahtani, Maqsood Mahmud
    Journal of Healthcare Engineering, 2018
  • Modeling energy band gap of doped TiO2 semiconductor using homogeneously hybridized support vector regression with gravitational search algorithm hyper-parameter optimization
    Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji, Nahier Aldhafferi, Abdullah Alqahtani
    Aip Advances, 2017
  • Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines
    Reem Alyami, Jinan Alhajjaj, Batool Alnajrani, Ilham Elaalami, Abdullah Alqahtani, Nahier Aldhafferi, Taoreed O. Owolabi, Sunday O. Olatunji
    2017 International Conference on Informatics Health and Technology Iciht 2017, 2017
  • Incorporation of GSA in SBLLM-based neural network for enhanced estimation of magnetic ordering temperature of manganite
    Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji, Abdullah Alqahtani, Nahier Aldhafferi
    Journal of Intelligent and Fuzzy Systems, 2017
  • Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression
    Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji, Abdullah Alqahtani, Nahier Aldhafferi
    Aip Advances, 2016

RECENT SCHOLAR PUBLICATIONS

  • Urdu-NERD: Urdu named entity recognition with BiGRU-based deep learning architecture
    Z Rafiq, M Wasim, FS Shaikh, N Aldhafferi, A Alqahtani
    PeerJ Computer Science 12, e3678 , 2026
    2026
  • Time and Memory Trade-Offs in Shortest-Path Algorithms Across Graph Topologies: A*, Bellman–Ford, Dijkstra, AI-Augmented A* and a Neural Baseline
    N Aldhafferi
    Computers 14 (12), 545 , 2025
    2025
    Citations: 2
  • Alternative cancer therapy through modeling pteridines photosensitizer quantum yield singlet oxygen production using swarm-based support vector regression and extreme learning …
    N Aldhafferi
    Cogent Engineering 11 (1), 2301638 , 2024
    2024
    Citations: 3
  • Android malware detection using support vector regression for dynamic feature analysis
    N Aldhafferi
    Information 15 (10), 658 , 2024
    2024
    Citations: 30
  • Modeling the magnetocaloric effect of spinel ferrites for magnetic refrigeration technology using extreme learning machine and genetically hybridized support vector regression …
    WA Oke, N Aldhafferi, S Saliu, TO Owolabi, A Alqahtani, A Almurayh, ...
    Cogent Engineering 10 (2), 2257955 , 2023
    2023
    Citations: 5
  • Improved whale optimization with local-search method for feature selection
    M Alzaqebah, MK Alsmadi, S Jawarneh, JS Alqurni, M Tayfour, ...
    Computers, Materials & Continua 75 (1), 1371-1389 , 2023
    2023
    Citations: 7
  • Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method
    TF Qahtan, N Aldhafferi, A Alqahtani, OR Abidemi, M Souiyah, ...
    Cogent Engineering 9 (1), 2137936 , 2022
    2022
    Citations: 1
  • Sustainable education quality improvement using academic accreditation: Findings from a university in Saudi Arabia
    A Almurayh, S Saeed, N Aldhafferi, A Alqahtani, M Saqib
    Sustainability 14 (24), 16968 , 2022
    2022
    Citations: 44
  • Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods
    A Alqahtani, S Saliu, TO Owolabi, N Aldhafferi, A Almurayh, OE Oyeneyin
    Materials Today Communications 33, 104310 , 2022
    2022
    Citations: 21
  • Learning trends in customer churn with rule-based and kernel methods
    N Aldhafferi, A Alqahtani, FS Shaikh, SO Olatunji, A Almurayh, ...
    International Journal of Electrical and Computer Engineering (IJECE) 12 (5 … , 2022
    2022
    Citations: 7
  • An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling.
    MK Alsmadi, GM Jaradat, M Alzaqebah, I ALmarashdeh, FA Alghamdi, ...
    Computers, Materials & Continua 72 (1) , 2022
    2022
    Citations: 9
  • Extreme learning machine computational method of modeling energy gap of doped zinc selenide nano-material semiconductor
    N Aldhafferi
    Materials Today Communications 31, 103626 , 2022
    2022
    Citations: 8
  • Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning
    A Rahman, A Alqahtani, N Aldhafferi, MU Nasir, MF Khan, MA Khan, ...
    Sensors 22 (10), 3833 , 2022
    2022
    Citations: 185
  • Specific surface area characterization of spinel ferrite nanostructure based compounds for photocatalysis and other applications using extreme learning machine method
    M Souiyah, TO Owolabi, S Saliu, TF Qahtan, N Aldhafferi, A Alqahtani
    Mathematical Problems in Engineering 2022 (1), 1259131 , 2022
    2022
    Citations: 8
  • Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method
    O Olubosede, MA Abd Rahman, A Alqahtani, M Souiyah, MB Latif, ...
    Crystals 12 (1), 36 , 2021
    2021
    Citations: 17
  • Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
    N Aldhafferi
    AIP Advances 11 (11) , 2021
    2021
  • Comorbidities and risk factors for severe outcomes in COVID-19 patients in Saudi Arabia: a retrospective cohort study
    FS Shaikh, N Aldhafferi, A Buker, A Alqahtani, S Dey, S Abdulhamid, ...
    Journal of Multidisciplinary Healthcare, 2169-2183 , 2021
    2021
    Citations: 39
  • Representations of generalized inverses via full-rank QDR decomposition
    N Aldhafeeri, D Pappas, IP Stanimirović, M Tasić
    Numerical Algorithms 86 (3), 1327-1337 , 2021
    2021
    Citations: 6
  • Tailoring the Energy Harvesting Capacity of Zinc Selenide Semiconductor Nanomaterial through Optical Band Gap Modeling Using Genetically Optimized Intelligent Method. Crystals …
    O Olubosede, MA Abd Rahman, A Alqahtani, M Souiyah, MB Latif, ...
    s Note: MDPI stays neutral with regard to jurisdictional claims in published … , 2021
    2021
  • Memory based cuckoo search algorithm for feature selection of gene expression dataset
    M Alzaqebah, K Briki, N Alrefai, S Brini, S Jawarneh, MK Alsmadi, ...
    Informatics in Medicine Unlocked 24, 100572 , 2021
    2021
    Citations: 65

MOST CITED SCHOLAR PUBLICATIONS

  • Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning
    A Rahman, A Alqahtani, N Aldhafferi, MU Nasir, MF Khan, MA Khan, ...
    Sensors 22 (10), 3833 , 2022
    2022
    Citations: 185
  • Personal information privacy settings of online social networks and their suitability for mobile internet devices
    N Aldhafferi, C Watson, AS Sajeev
    arXiv preprint arXiv:1305.2770 , 2013
    2013
    Citations: 77
  • Digitalization of learning in Saudi Arabia during the COVID-19 outbreak: A survey
    MK Alsmadi, I Al-Marashdeh, M Alzaqebah, G Jaradat, FA Alghamdi, ...
    Informatics in Medicine Unlocked 25, 100632 , 2021
    2021
    Citations: 74
  • Memory based cuckoo search algorithm for feature selection of gene expression dataset
    M Alzaqebah, K Briki, N Alrefai, S Brini, S Jawarneh, MK Alsmadi, ...
    Informatics in Medicine Unlocked 24, 100572 , 2021
    2021
    Citations: 65
  • Reversible and fragile watermarking for medical images
    K Sultan, N Aldhafferi, A Alqahtani, M Mahmud
    Computational and mathematical methods in medicine 2018 (1), 3461382 , 2018
    2018
    Citations: 46
  • Sustainable education quality improvement using academic accreditation: Findings from a university in Saudi Arabia
    A Almurayh, S Saeed, N Aldhafferi, A Alqahtani, M Saqib
    Sustainability 14 (24), 16968 , 2022
    2022
    Citations: 44
  • Robust and fragile medical image watermarking: a joint venture of coding and chaos theories
    AU Rahman, K Sultan, D Musleh, N Aldhafferi, A Alqahtani, M Mahmud
    Journal of healthcare engineering 2018 (1), 8137436 , 2018
    2018
    Citations: 40
  • Comorbidities and risk factors for severe outcomes in COVID-19 patients in Saudi Arabia: a retrospective cohort study
    FS Shaikh, N Aldhafferi, A Buker, A Alqahtani, S Dey, S Abdulhamid, ...
    Journal of Multidisciplinary Healthcare, 2169-2183 , 2021
    2021
    Citations: 39
  • Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines
    R Alyami, J Alhajjaj, B Alnajrani, I Elaalami, A Alqahtani, N Aldhafferi, ...
    2017 International Conference on Informatics, Health & Technology (ICIHT), 1-7 , 2017
    2017
    Citations: 37
  • Educational data mining for enhanced teaching and learning
    K Sultan, N Aldhafferi, A Alqahtani
    Journal of Theoretical and Applied Information Technology 96 (14), 4417-4427 , 2018
    2018
    Citations: 35
  • Android malware detection using support vector regression for dynamic feature analysis
    N Aldhafferi
    Information 15 (10), 658 , 2024
    2024
    Citations: 30
  • Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression
    TO Owolabi, KO Akande, SO Olatunji, A Alqahtani, N Aldhafferi
    AIP Advances 6 (10) , 2016
    2016
    Citations: 23
  • Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods
    A Alqahtani, S Saliu, TO Owolabi, N Aldhafferi, A Almurayh, OE Oyeneyin
    Materials Today Communications 33, 104310 , 2022
    2022
    Citations: 21
  • Ensemble-based support vector regression with gravitational search algorithm optimization for estimating magnetic relative cooling power of manganite refrigerant in magnetic …
    TO Owolabi, KO Akande, SO Olatunji, N Aldhafferi, A Alqahtani
    Journal of Superconductivity and Novel Magnetism 32 (7), 2107-2118 , 2019
    2019
    Citations: 19
  • Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method
    O Olubosede, MA Abd Rahman, A Alqahtani, M Souiyah, MB Latif, ...
    Crystals 12 (1), 36 , 2021
    2021
    Citations: 17
  • Incorporation of GSA in SBLLM-based neural network for enhanced estimation of magnetic ordering temperature of manganite
    TO Owolabi, KO Akande, SO Olatunji, A Alqahtani, N Aldhafferi
    Journal of Intelligent & Fuzzy Systems 33 (2), 1225-1233 , 2017
    2017
    Citations: 16
  • Modeling of Curie temperature of manganite for magnetic refrigeration application using manual search and hybrid gravitational-based support vector regression
    TO Owolabi, KO Akande, SO Olatunji, A Alqahtani, N Aldhafferid
    Soft Computing 22 (9), 3023-3032 , 2018
    2018
    Citations: 14
  • Support vector regression ensemble for effective modeling of magnetic ordering temperature of doped manganite in magnetic refrigeration
    TO Owolabi, KO Akande, SO Olatunji, N Aldhafferi, A Alqahtani
    Journal of Low Temperature Physics 195 (1), 179-201 , 2019
    2019
    Citations: 12
  • Medical Image Watermarking for Fragility and Robustness: A Chaos, Error Correcting Codes and Redundant Residue Number System Based Approach
    A Rahman, M Mahmud, K Sultan, N Aldhafferi, D Musleh
    Journal of Medical Imaging and Health Informatics 8 (1), 1192-1200 , 2018
    2018
    Citations: 11
  • An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling.
    MK Alsmadi, GM Jaradat, M Alzaqebah, I ALmarashdeh, FA Alghamdi, ...
    Computers, Materials & Continua 72 (1) , 2022
    2022
    Citations: 9