Hashim Ali

@nu.edu.kz

Assistant Professor, Department of Computer Science
Nazarbayev University

Hashim Ali


                  Download Compact PDF  
https://researchid.co/hashim.ali
17

Scopus Publications

708

Scholar Citations

12

Scholar h-index

14

Scholar i10-index

Scopus Publications

  • Modality-Specific Sparse Autoencoders for Efficient Multimodal ICU Alignment: A Symmetry–Asymmetry Learning Framework
    Hashim Ali, Muhammad Tahir Akhtar
    Symmetry, 2026
    Intensive care units (ICUs) generate heterogeneous data streams, including structured electronic health records, physiological time series, and medical imaging, that describe the same patient state through different observational forms. Effective multimodal learning in this setting requires a principled balance between representation-level symmetry and architectural asymmetry. Clinically corresponding patient states should exhibit cross-modal representational symmetry, whereas each modality retains intrinsic asymmetry in dimensionality, temporal resolution, noise characteristics, and missingness. This study proposes a modality-specific sparse autoencoder framework for efficient multimodal ICU representation learning under this symmetry–asymmetry principle. Separate sparse encoders are assigned to each modality to preserve the modality-dependent structure while suppressing redundant latent activity through adaptive gating. Representation-level symmetry is encouraged through a sparsity-aware contrastive objective that aligns paired latent embeddings across modalities only on active informative dimensions. To further model inter-patient dependencies, the framework incorporates a graph neural network (GNN) whose message-passing operations respect modality-specific sparsity patterns. Experimental results indicate that the proposed framework improves predictive performance and computational efficiency relative to conventional multimodal baselines, while also exhibiting stronger robustness under missing-modality conditions and more selective latent representations. Overall, the method provides an effective and clinically relevant multimodal learning strategy for ICU decision support while offering a measurable symmetry-aware and asymmetry-preserving formulation for heterogeneous medical data.
  • Constrained LLM-Guided Refactoring of JavaScript: A Smell-Targeted Transformation Framework with Human-in-the-Loop Validation
    Emir Kuanyshev, Hashim Ali
    Computers Materials and Continua, 2026
  • Spectral Filtering Using Periodic Autoregressive Moving Average Graph Neural Networks for Heterophilic Graphs
    Baimyrza Kalmyrzayev, Hashim Ali, Muhammad Tahir Akhtar
    IEEE Access, 2026
  • Enhancing ML-based anomaly detection in data management for security through integration of IoT, cloud, and edge computing
    Sultan Baimukhanov, Hashim Ali, Adnan Yazici
    Expert Systems with Applications, 2025
  • Accurately assessing congenital heart disease using artificial intelligence
    Khalil Khan, Farhan Ullah, Ikram Syed, Hashim Ali
    Peerj Computer Science, 2024
    Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.
  • Driving drowsiness detection using spectral signatures of EEG-based neurophysiology
    Saad Arif, Saba Munawar, Hashim Ali
    Frontiers in Physiology, 2023
    Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks.Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics.Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen’s kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods.Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.
  • A Web-Based Platform for Real-Time Speech Emotion Recognition using CNN
    Damir Kabdualiyev, Askar Madiyev, Adil Rakhaliyev, Balgynbek Dikhan, Kassymzhan Gizhduan, et al.
    2023 International Conference on Smart Applications Communications and Networking Smartnets 2023, 2023
    This pilot study presents a web-based real-time speech emotion recognition platform using a convolutional neural network algorithm. The study aims to develop a reliable tool for predicting emotions in speech with a user-friendly design to enable easy access and display of recognition results. The platform recognizes seven emotions (angry, disgust, fear, happy, neutral, sad, and surprise) and has two functionalities: static and real-time speech signals analysis. The static analysis allows users to upload pre-recorded audio files for analysis, while the real-time analysis provides continuous audio processing as it is being recorded. The study also focuses on developing a reliable model with minimal features to predict emotions while accurately identifying various emotions detected in speech. The algorithmic performance of the model was evaluated using publicly available datasets (RAVDESS, TESS, and SAVEE). It achieved an accuracy of 86.46% in static analysis using the selected spectral feature: i.e., MFCC. The performance of the real-time analysis was validated through a user study involving 20 participants. It achieved an accuracy of 65% in recognizing emotions in real-time due to possible known factors. An interesting finding was the discrepancy between how individuals perceived their emotions and those detected by the ML model. The accuracy of the ML model was higher in pre-recorded audio recognition and about the same in real-time recognition compared to previous works. The user-friendly design and CNN algorithm make it a promising solution to address challenges in emotion recognition and highlight the importance of further research in this field.
  • HEVC's intra mode process expedited using Histogram of Oriented Gradients
    Junaid Tariq, Amir Ijaz, Ammar Armghan, Hameedur Rahman, Hashim Ali, et al.
    Journal of Visual Communication and Image Representation, 2022
  • Fast intra mode selection in HEVC using statistical model
    Junaid Tariq, Ayman Alfalou, Amir Ijaz, Hashim Ali, Imran Ashraf, et al.
    Computers Materials and Continua, 2022
    : Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost (rate-distortion). Firstly, the proposed work utilizes neighboring blocks to extract available information for the current block. Then this information is converted to the probability that tells which intra mode might be best in the current situation. The proposed model has a strong foundation as it is based on the probability rule-2 which says that the sum of probabilities of all outcomes should be 1. Moreover, it is also based on optimal stopping theory (OST). Therefore, the proposed model performs better than many existing OST and classical secretary-based models. The proposed algorithms expedited the encoding process by 30.22% of the HEVC with 1.35% Bjontegaard Delta Bit Rate (BD-BR).
  • Review of automated computerized methods for brain tumor segmentation and classification
    Umaira Nazar, Muhammad Attique Khan, Ikram Ullah Lali, Hong Lin, Hashim Ali, et al.
    Current Medical Imaging, 2020
    Recently, medical imaging and machine learning gained significant attention in the early detection of brain tumor. Compound structure and tumor variations, such as change of size, make brain tumor segmentation and classification a challenging task. In this review, we survey existing work on brain tumor, their stages, survival rate of patients after each stage, and computerized diagnosis methods. We discuss existing image processing techniques with a special focus on preprocessing techniques and their importance for tumor enhancement, tumor segmentation, feature extraction and features reduction techniques. We also provide the corresponding mathematical modeling, classification, performance matrices, and finally important datasets. Last but not least, a detailed analysis of existing techniques is provided which is followed by future directions in this domain.
  • Melanoma detection and classification using computerized analysis of dermoscopic systems: A review
    Muhammad Nasir, Muhammad Attique Khan, Muhammad Sharif, Muhammad Younus Javed, Tanzila Saba, et al.
    Current Medical Imaging, 2020
  • A unified design of ACO and skewness based brain tumor segmentation and classification from MRI scans
    Control Engineering and Applied Informatics, 2020
  • Intelligent human action recognition: A framework of optimal features selection based on Euclidean Distance and Strong Correlation
    Control Engineering and Applied Informatics, 2019
  • CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features
    Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Muhammad Awais, Kashif Javed, et al.
    Computers and Electronics in Agriculture, 2018
  • Internet of Things Shaping Smart Cities: A Survey
    Arsalan Shahid, Bilal Khalid, Shahtaj Shaukat, Hashim Ali, Muhammad Yasir Qadri
    Studies in Big Data, 2018
  • Demonstrating Contexta-CARE: A situation-aware system for supporting independent living
    Davide Merico, Roberto Bisiani, Fabio Malizia, Gabriele Rizzi, Hashim Ali
    Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops Pervasivehealth 2013, 2013
  • Subject-dependent physical activity recognition model framework with a semi-supervised clustering approach
    Hashim Ali, Enza Messina, Roberto Bisiani
    Proceedings Uksim Amss 7th European Modelling Symposium on Computer Modelling and Simulation EMS 2013, 2013

RECENT SCHOLAR PUBLICATIONS

  • Hybrid CNN–LSTM network for cross-subject EEG emotion recognition
    H Ali, Z Ermaganbet, MT Akhtar
    Discover Artificial Intelligence , 2026
    2026
  • Constrained LLM-Guided Refactoring of JavaScript: A Smell-Targeted Transformation Framework with Human-in-the-Loop Validation
    E Kuanyshev, H Ali
    Computers, Materials & Continua 88 (1), 78 , 2026
    2026
  • Advancing Explainable AI for Clinical Decision Support: A Multimodal Evaluation Framework
    H Ali
    Cloud Computing and Data Science 7 (2), 270-89 , 2026
    2026
  • Modality-Specific Sparse Autoencoders for Efficient Multimodal ICU Alignment: A Symmetry–Asymmetry Learning Framework
    H Ali, MT Akhtar
    Symmetry 18 (4) , 2026
    2026
  • Spectral filtering using periodic autoregressive moving average graph neural networks for heterophilic graphs
    B Kalmyrzayev, H Ali, MT Akhtar
    IEEE Access 14, 9696 - 9716 , 2026
    2026
    Citations: 1
  • Enhancing ML-based anomaly detection in data management for security through integration of IoT, cloud, and edge computing
    S Baimukhanov, H Ali, A Yazici
    Expert Systems with Applications 293 , 2025
    2025
    Citations: 15
  • Accurately Assessing Congenital Heart Disease using Artificial Intelligence
    K Khan​, F Ullah, I Syed, H Ali
    PeerJ Computer Science 10, 43 , 2024
    2024
    Citations: 15
  • A Web-Based Platform for Real-Time Speech Emotion Recognition using CNN
    D Kabdualiyev, A Madiyev, A Rakhaliyev, B Dikhan, K Gizhduan, H Ali
    2023 International Conference on Smart Applications, Communications and … , 2023
    2023
    Citations: 4
  • Driving drowsiness detection using spectral signatures of EEG-based neurophysiology
    S Arif, S Munawar, H Ali
    Frontiers in physiology 14, 1153268 , 2023
    2023
    Citations: 61
  • HEVC’s intra mode process expedited using histogram of oriented gradients
    J Tariq, A Ijaz, A Armghan, H Rahman, H Ali, F Alenezi
    Journal of Visual Communication and Image Representation 88, 103594 , 2022
    2022
    Citations: 8
  • Fast Intra Mode Selection in HEVC Using Statistical Model
    J Tariq, A Alfalou, A Ijaz, H Ali, I Ashraf, H Rahman, A Armghan, ...
    CMC-Computers, Materials & Continua 70 (2), 3903-3918 , 2022
    2022
    Citations: 23
  • Early Assessment of Student’s Learning Outcomes using Prediction Model under Outcome-Based Education System
    H Ali
    Eurasian Journal of Educational Research, 315-332 , 2021
    2021
    Citations: 8
  • A Unified Design of ACO and Skewness based Brain Tumor Segmentation and Classification from MRI Scans
    UN Hussain, MA Khan, IU Lali, K Javed, I Ashraf, J Tariq, H Ali, A Din
    Journal of Control Engineering and Applied Informatics 22 (2), 43-55 , 2020
    2020
    Citations: 55
  • Human Behavior Analysis Based on Multi-Types Features Fusion and Von Nauman Entropy Based Features Reduction
    K Aurangzeb, I Haider, MA Khan, T Saba, K Javed, T Iqbal, A Rehman, ...
    Journal of Medical Imaging and Health Informatics 9 (4), 662–669 , 2019
    2019
    Citations: 48
  • Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review
    M Nasir, MA Khan, M Sharif, MY Javed, T Saba, H Ali, J Tariq
    Current Medical Imaging 15 (10) , 2019
    2019
    Citations: 32
  • Review of Automated Computerized Methods for Brain Tumor Segmentation and Classification
    U Nazar, MA Khan, IU Lali, L Hong, H Ali, I Ashraf, J Tariq
    Current Medical Imaging 15 (10), 3-11 , 2019
    2019
    Citations: 35
  • Intelligent Human Action Recognition: A Framework of Optimal Features Selection based on Euclidean Distance and Strong Correlation
    A Sharif, MA Khan, K Javed, H Gulfam, T Iqbal, T Saba, H Ali, W Nisar
    Journal of Control Engineering and Applied Informatics 21 (3), 3-11 , 2019
    2019
    Citations: 34
  • CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features
    MA Khan, T Akram, M Sharif, M Awais, K Javed, H Ali, T Saba
    Computers and Electronics in Agriculture 155, 220-236 , 2018
    2018
    Citations: 285
  • An Optimized Risk Management Model Based on Software Risk Factors Analysis
    H Ali, N Akhtar, MY Javed
    Advanced Science Letters 24 (4), 2306-2311 , 2018
    2018
  • Internet of Things Shaping Smart Cities: A Survey
    A Shahid, B Khalid, S Shaukat, H Ali, MY Qadri
    Internet of Things and Big Data Analytics Toward Next-Generation … , 2018
    2018
    Citations: 20

MOST CITED SCHOLAR PUBLICATIONS

  • CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features
    MA Khan, T Akram, M Sharif, M Awais, K Javed, H Ali, T Saba
    Computers and Electronics in Agriculture 155, 220-236 , 2018
    2018
    Citations: 285
  • Driving drowsiness detection using spectral signatures of EEG-based neurophysiology
    S Arif, S Munawar, H Ali
    Frontiers in physiology 14, 1153268 , 2023
    2023
    Citations: 61
  • A Unified Design of ACO and Skewness based Brain Tumor Segmentation and Classification from MRI Scans
    UN Hussain, MA Khan, IU Lali, K Javed, I Ashraf, J Tariq, H Ali, A Din
    Journal of Control Engineering and Applied Informatics 22 (2), 43-55 , 2020
    2020
    Citations: 55
  • Human Behavior Analysis Based on Multi-Types Features Fusion and Von Nauman Entropy Based Features Reduction
    K Aurangzeb, I Haider, MA Khan, T Saba, K Javed, T Iqbal, A Rehman, ...
    Journal of Medical Imaging and Health Informatics 9 (4), 662–669 , 2019
    2019
    Citations: 48
  • Automated Segmentation of Hard Exudates Using Dynamic Thresholding to Detect Diabetic Retinopathy in Retinal Photographs
    M Zubair, H Ali, MY Javed
    International Conference on Innovative Computing , 2016
    2016
    Citations: 37
  • Review of Automated Computerized Methods for Brain Tumor Segmentation and Classification
    U Nazar, MA Khan, IU Lali, L Hong, H Ali, I Ashraf, J Tariq
    Current Medical Imaging 15 (10), 3-11 , 2019
    2019
    Citations: 35
  • Intelligent Human Action Recognition: A Framework of Optimal Features Selection based on Euclidean Distance and Strong Correlation
    A Sharif, MA Khan, K Javed, H Gulfam, T Iqbal, T Saba, H Ali, W Nisar
    Journal of Control Engineering and Applied Informatics 21 (3), 3-11 , 2019
    2019
    Citations: 34
  • Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review
    M Nasir, MA Khan, M Sharif, MY Javed, T Saba, H Ali, J Tariq
    Current Medical Imaging 15 (10) , 2019
    2019
    Citations: 32
  • Fast Intra Mode Selection in HEVC Using Statistical Model
    J Tariq, A Alfalou, A Ijaz, H Ali, I Ashraf, H Rahman, A Armghan, ...
    CMC-Computers, Materials & Continua 70 (2), 3903-3918 , 2022
    2022
    Citations: 23
  • Internet of Things Shaping Smart Cities: A Survey
    A Shahid, B Khalid, S Shaukat, H Ali, MY Qadri
    Internet of Things and Big Data Analytics Toward Next-Generation … , 2018
    2018
    Citations: 20
  • Enhancing ML-based anomaly detection in data management for security through integration of IoT, cloud, and edge computing
    S Baimukhanov, H Ali, A Yazici
    Expert Systems with Applications 293 , 2025
    2025
    Citations: 15
  • Accurately Assessing Congenital Heart Disease using Artificial Intelligence
    K Khan​, F Ullah, I Syed, H Ali
    PeerJ Computer Science 10, 43 , 2024
    2024
    Citations: 15
  • Demonstrating Contexta-CARE: A situation-aware system for supporting independent living
    D Merico, R Bisiani, H Ali
    7th International Conference on Pervasive Computing Technologies for … , 2013
    2013
    Citations: 10
  • Subject-Dependent Physical Activity Recognition Model Framework with a Semi-supervised Clustering Approach
    H Ali, E Messina, R Bisiani
    2013 European Modelling Symposium (EMS), 42 - 47 , 2013
    2013
    Citations: 10
  • HEVC’s intra mode process expedited using histogram of oriented gradients
    J Tariq, A Ijaz, A Armghan, H Rahman, H Ali, F Alenezi
    Journal of Visual Communication and Image Representation 88, 103594 , 2022
    2022
    Citations: 8
  • Early Assessment of Student’s Learning Outcomes using Prediction Model under Outcome-Based Education System
    H Ali
    Eurasian Journal of Educational Research, 315-332 , 2021
    2021
    Citations: 8
  • A Web-Based Platform for Real-Time Speech Emotion Recognition using CNN
    D Kabdualiyev, A Madiyev, A Rakhaliyev, B Dikhan, K Gizhduan, H Ali
    2023 International Conference on Smart Applications, Communications and … , 2023
    2023
    Citations: 4
  • Intelligent and Flexible Home Automation System
    U Ahmed, H Ali, F Ahsan
    International Conference on Innovative Computing , 2016
    2016
    Citations: 4
  • Physical Activity Recognition Using Single Sensor: A Novel Approach
    H Ali
    LAP LAMBERT Academic Publishing , 2015
    2015
    Citations: 2
  • Spectral filtering using periodic autoregressive moving average graph neural networks for heterophilic graphs
    B Kalmyrzayev, H Ali, MT Akhtar
    IEEE Access 14, 9696 - 9716 , 2026
    2026
    Citations: 1