Hashim Ali

@nu.edu.kz

Assistant Professor, Department of Computer Science
Nazarbayev University

https://researchid.co/hashim.ali

Scopus Publications

368

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Accurately assessing congenital heart disease using artificial intelligence
Khalil Khan, Farhan Ullah, Ikram Syed, and Hashim Ali
PeerJ
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.

A Web-Based Platform for Real-Time Speech Emotion Recognition using CNN
Damir Kabdualiyev, Askar Madiyev, Adil Rakhaliyev, Balgynbek Dikhan, Kassymzhan Gizhduan, and Hashim Ali
IEEE
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.

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology
Saad Arif, Saba Munawar, and Hashim Ali
Frontiers Media SA
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.

HEVC's intra mode process expedited using Histogram of Oriented Gradients
Junaid Tariq, Amir Ijaz, Ammar Armghan, Hameedur Rahman, Hashim Ali, and Fayadh Alenezi
Elsevier BV

Fast intra mode selection in HEVC using statistical model
Junaid Tariq, Ayman Alfalou, Amir Ijaz, Hashim Ali, Imran Ashraf, Hameedur Rahman, Ammar Armghan, Inzamam Mashood, and Saad Rehman
Computers, Materials and Continua (Tech Science Press)
: 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).

Melanoma detection and classification using computerized analysis of dermoscopic systems: A review
Muhammad Nasir, Muhammad Attique Khan, Muhammad Sharif, Muhammad Younus Javed, Tanzila Saba, Hashim Ali, and Junaid Tariq
Bentham Science Publishers Ltd.
Malignant melanoma is considered as one of the most deadly cancers, which has broadly increased worldwide since the last decade. In 2018, around 91,270 cases of melanoma were reported and 9,320 people died in the US. However, diagnosis at the initial stage indicates a high survival rate. The conventional diagnostic methods are expensive, inconvenient and subject to the dermatologist’s expertise as well as a highly equipped environment. Recent achievements in computerized based systems are highly promising with improved accuracy and efficiency. Several measures such as irregularity, contrast stretching, change in origin, feature extraction and feature selection are considered for accurate melanoma detection and classification. Typically, digital dermoscopy comprises four fundamental image processing steps including preprocessing, segmentation, feature extraction and reduction, and lesion classification. Our survey is compared with the existing surveys in terms of preprocessing techniques (hair removal, contrast stretching) and their challenges, lesion segmentation methods, feature extraction methods with their challenges, features selection techniques, datasets for the validation of the digital system, classification methods and performance measure. Also, a brief summary of each step is presented in the tables. The challenges for each step are also described in detail, which clearly indicate why the digital systems are not performing well. Future directions are also given in this survey.

Review of automated computerized methods for brain tumor segmentation and classification
Umaira Nazar, Muhammad Attique Khan, Ikram Ullah Lali, Hong Lin, Hashim Ali, Imran Ashraf, and Junaid Tariq
Bentham Science Publishers Ltd.
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.

A unified design of ACO and skewness based brain tumor segmentation and classification from MRI scans

Intelligent human action recognition: A framework of optimal features selection based on Euclidean Distance and Strong Correlation

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, Hashim Ali, and Tanzila Saba
Elsevier BV

Internet of Things Shaping Smart Cities: A Survey
Arsalan Shahid, Bilal Khalid, Shahtaj Shaukat, Hashim Ali, and Muhammad Yasir Qadri
Springer International Publishing

Demonstrating Contexta-CARE: A situation-aware system for supporting independent living
Davide Merico, Roberto Bisiani, Fabio Malizia, Gabriele Rizzi, and Hashim Ali
IEEE
In this paper we propose the demonstration of an Independent Living system that exploits an approach based on situation awareness and ambient intelligence in order to improve the quality of life of users that want to live independently. This approach aims at extending "awareness" bringing to bear new ambient data and opening the possibility of "reasoning" on complex sequences of events in relation to the context they occur in, i.e. particular situations such as falls. Contexta-CARE, the situation-aware independent-living system we implemented in order to validate the approach, enables close monitoring of a person as well as of the environment where the person lives. Contexta-CARE has the ability to both highlight, preventively, situations that might evolve into potentially dangerous events, and to promptly signal critical events, like falls. The demonstration will highlight all the details of the particular approach we are proposing.

Subject-dependent physical activity recognition model framework with a semi-supervised clustering approach
Hashim Ali, Enza Messina, and Roberto Bisiani
IEEE
Activity recognition systems have been found to bevery effective for tracking users' activities in research areas like healthcare and assisted living. Wearable accelerometers that can help in classifying Physical Activities (PA) have been made available by MEMS technology. State-of-the-art PAclassification systems use threshold-based techniques and Machine Learning (ML) algorithms. Each PA may exhibitinter-subject and intra-subject variability which is a major drawback for threshold and machine learning based techniques. Due to lack of empirical data in order to train classifier for ML clustering algorithms, there is a need to develop a mechanism which requires less training data for PA clustering. This paper describes a novel personalized PArecognition model framework based on a semi-supervised clustering approach to avoid fixed threshold techniques and traditional clustering methods by using a single accelerometer. The proposed methodology requires limited amount of data to compute (initial) centroids for PA clusters and achieved an accuracy of about 93% on average, moreover it has the potential capability of recognizing subjects' behavioral shifts and exceptional events, falls, etc.

RECENT SCHOLAR PUBLICATIONS

Accurately Assessing Congenital Heart Disease using Artificial Intelligence
K Khan, F Ullah, I Syed, H Ali
PeerJ Computer Science 10, 43 2024

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

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology
S Arif, S Munawar, H Ali
Frontiers in physiology 14, 1153268 2023

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

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

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

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

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

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

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

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

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

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

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

Intelligent and Flexible Home Automation System
U Ahmed, H Ali, F Ahsan
International Conference on Innovative Computing 2016

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

Parameter Estimation & Error Analysis Using Bootstrap Technique for Physical Activity Recognition
H Ali, MY Javed
3rd International Conference on Engineering & Emerging Technologies (ICEET) 2016

Subject-dependent Physical Activity Recognition Using Single Sensor Accelerometer
H Ali
University of Milan-Bicocca, Italy 2015

Physical Activity Recognition Using Single Sensor: A Novel Approach
H Ali
LAP LAMBERT Academic Publishing 2015

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

MOST CITED SCHOLAR PUBLICATIONS

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
Citations: 33

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology
S Arif, S Munawar, H Ali
Frontiers in physiology 14, 1153268 2023
Citations: 29

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
Citations: 26

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
Citations: 21

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
Citations: 19

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
Citations: 10

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
Citations: 8

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
Citations: 8

Intelligent and Flexible Home Automation System
U Ahmed, H Ali, F Ahsan
International Conference on Innovative Computing 2016
Citations: 4

Physical Activity Recognition Using Single Sensor: A Novel Approach
H Ali
LAP LAMBERT Academic Publishing 2015
Citations: 2

Subject-dependent Physical Activity Recognition Using Single Sensor Accelerometer
H Ali
University of Milan-Bicocca, Italy 2015
Citations: 1