Connect
- +1 234 567 8901
- +1 234 567 8902
- izzatdin@utp.edu.my
AP Dr Ts Izzatdin Abdul Aziz
Center head
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Morbi volutpat justo sed efficitur cursus. Mauris fringilla quam vitae lacinia viverra. Mauris eros dolor, pellentesque sed luctus dapibus, lobortis a orci. Cras pulvinar lorem elit, vel laoreet.
Education
B.Sc. (Computer Science)
Master of Computer Applications
PhD
Experience
X Years of teaching experience
area of expertise
Lorem Ipsum
profiles
SCOPUS | Google Scholar
Joined UTP
November, 2014
2022
Üsmani, Usman Ahmad; Roy, Arunava; Watada, Junzo; Jaafar, Jafreezal; Aziz, Izzatdin Abdul
Enhanced reinforcement learning model for extraction of objects in complex imaging Incollection
In: Lecture Notes in Networks and Systems, pp. 946–964, Springer International Publishing, Cham, 2022.
BibTeX | Tags:
@incollection{Usmani2022-fq,
title = {Enhanced reinforcement learning model for extraction of objects in complex imaging},
author = {Usman Ahmad Üsmani and Arunava Roy and Junzo Watada and Jafreezal Jaafar and Izzatdin Abdul Aziz},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Lecture Notes in Networks and Systems},
pages = {946--964},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture notes in networks and systems},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2021
Muneer, Amgad; Taib, Shakirah Mohd; Naseer, Sheraz; Ali, Rao Faizan; Aziz, Izzatdin Abdul
Data-driven deep learning-based attention mechanism for remaining useful life prediction: Case study application to turbofan engine analysis Journal Article
In: Electronics (Basel), vol. 10, no. 20, pp. 2453, 2021.
@article{Muneer2021-qn,
title = {Data-driven deep learning-based attention mechanism for
remaining useful life prediction: Case study application to
turbofan engine analysis},
author = {Amgad Muneer and Shakirah Mohd Taib and Sheraz Naseer and Rao Faizan Ali and Izzatdin Abdul Aziz},
year = {2021},
date = {2021-10-01},
journal = {Electronics (Basel)},
volume = {10},
number = {20},
pages = {2453},
publisher = {MDPI AG},
abstract = {Äccurately predicting the remaining useful life (RUL) of the
turbofan engine is of great significance for improving the
reliability and safety of the engine system. Due to the high
dimension and complex features of sensor data in RUL prediction,
this paper proposes four data-driven prognostic models based on
deep neural networks (DNNs) with an attention mechanism. To
improve DNN feature extraction, data are prepared using a
sliding time window technique. The raw data collected after
normalizing is simply fed into the suggested network, requiring
no prior knowledge of prognostics or signal processing and
simplifying the proposed method's applicability. In order to
verify the RUL prediction ability of the proposed DNN
techniques, the C-MAPSS benchmark dataset of the turbofan engine
system is validated. The experimental results showed that the
developed long short-term memory (LSTM) model with attention
mechanism achieved accurate RUL prediction in both scenarios
with a high degree of robustness and generalization ability.
Furthermore, the proposed model performance outperforms several
state-of-the-art prognosis methods, where the LSTM-based model
with attention mechanism achieved an RMSE of 12.87 and 11.23 for
FD002 and FD003 subset of data, respectively."},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Äccurately predicting the remaining useful life (RUL) of the
turbofan engine is of great significance for improving the
reliability and safety of the engine system. Due to the high
dimension and complex features of sensor data in RUL prediction,
this paper proposes four data-driven prognostic models based on
deep neural networks (DNNs) with an attention mechanism. To
improve DNN feature extraction, data are prepared using a
sliding time window technique. The raw data collected after
normalizing is simply fed into the suggested network, requiring
no prior knowledge of prognostics or signal processing and
simplifying the proposed method's applicability. In order to
verify the RUL prediction ability of the proposed DNN
techniques, the C-MAPSS benchmark dataset of the turbofan engine
system is validated. The experimental results showed that the
developed long short-term memory (LSTM) model with attention
mechanism achieved accurate RUL prediction in both scenarios
with a high degree of robustness and generalization ability.
Furthermore, the proposed model performance outperforms several
state-of-the-art prognosis methods, where the LSTM-based model
with attention mechanism achieved an RMSE of 12.87 and 11.23 for
FD002 and FD003 subset of data, respectively."
turbofan engine is of great significance for improving the
reliability and safety of the engine system. Due to the high
dimension and complex features of sensor data in RUL prediction,
this paper proposes four data-driven prognostic models based on
deep neural networks (DNNs) with an attention mechanism. To
improve DNN feature extraction, data are prepared using a
sliding time window technique. The raw data collected after
normalizing is simply fed into the suggested network, requiring
no prior knowledge of prognostics or signal processing and
simplifying the proposed method's applicability. In order to
verify the RUL prediction ability of the proposed DNN
techniques, the C-MAPSS benchmark dataset of the turbofan engine
system is validated. The experimental results showed that the
developed long short-term memory (LSTM) model with attention
mechanism achieved accurate RUL prediction in both scenarios
with a high degree of robustness and generalization ability.
Furthermore, the proposed model performance outperforms several
state-of-the-art prognosis methods, where the LSTM-based model
with attention mechanism achieved an RMSE of 12.87 and 11.23 for
FD002 and FD003 subset of data, respectively."
Üsmani, Usman Ahmad; Watada, Junzo; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Roy, Arunava"
Ä reinforcement learning algorithm for automated detection of skin lesions" Journal Article
In: Äppl. Sci. (Basel)", vol. 11, no. 20, pp. 9367, 2021.
@article{Usmani2021-yl,
title = {Ä reinforcement learning algorithm for automated detection of
skin lesions"},
author = {Usman Ahmad Üsmani and Junzo Watada and Jafreezal Jaafar and Izzatdin Abdul Aziz and Arunava" Roy},
year = {2021},
date = {2021-10-01},
journal = {Äppl. Sci. (Basel)"},
volume = {11},
number = {20},
pages = {9367},
publisher = {MDPI AG},
abstract = {Skin cancers are increasing at an alarming rate, and detection
in the early stages is essential for advanced treatment. The
current segmentation methods have limited labeling ability to
the ground truth images due to the numerous noisy expert
annotations present in the datasets. The precise boundary
segmentation is essential to correctly locate and diagnose the
various skin lesions. In this work, the lesion segmentation
method is proposed as a Markov decision process. It is solved by
training an agent to segment the region using a deep
reinforcement-learning algorithm. Our method is similar to the
delineation of a region of interest by the physicians. The agent
follows a set of serial actions for the region delineation, and
the action space is defined as a set of continuous action
parameters. The segmentation model learns in continuous action
space using the deep deterministic policy gradient algorithm.
The proposed method enables continuous improvement in
performance as we proceed from coarse segmentation results to
finer results. Finally, our proposed model is evaluated on the
International Skin Imaging Collaboration (ISIC) 2017 image
dataset, Human against Machine (HAM10000), and PH2 dataset. On
the ISIC 2017 dataset, the algorithm achieves an accuracy of
96.33% for the naevus cases, 95.39% for the melanoma cases,
and 94.27% for the seborrheic keratosis cases. The other
metrics are evaluated on these datasets and rank higher when
compared with the current state-of-the-art lesion segmentation
algorithms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Skin cancers are increasing at an alarming rate, and detection
in the early stages is essential for advanced treatment. The
current segmentation methods have limited labeling ability to
the ground truth images due to the numerous noisy expert
annotations present in the datasets. The precise boundary
segmentation is essential to correctly locate and diagnose the
various skin lesions. In this work, the lesion segmentation
method is proposed as a Markov decision process. It is solved by
training an agent to segment the region using a deep
reinforcement-learning algorithm. Our method is similar to the
delineation of a region of interest by the physicians. The agent
follows a set of serial actions for the region delineation, and
the action space is defined as a set of continuous action
parameters. The segmentation model learns in continuous action
space using the deep deterministic policy gradient algorithm.
The proposed method enables continuous improvement in
performance as we proceed from coarse segmentation results to
finer results. Finally, our proposed model is evaluated on the
International Skin Imaging Collaboration (ISIC) 2017 image
dataset, Human against Machine (HAM10000), and PH2 dataset. On
the ISIC 2017 dataset, the algorithm achieves an accuracy of
96.33% for the naevus cases, 95.39% for the melanoma cases,
and 94.27% for the seborrheic keratosis cases. The other
metrics are evaluated on these datasets and rank higher when
compared with the current state-of-the-art lesion segmentation
algorithms.
in the early stages is essential for advanced treatment. The
current segmentation methods have limited labeling ability to
the ground truth images due to the numerous noisy expert
annotations present in the datasets. The precise boundary
segmentation is essential to correctly locate and diagnose the
various skin lesions. In this work, the lesion segmentation
method is proposed as a Markov decision process. It is solved by
training an agent to segment the region using a deep
reinforcement-learning algorithm. Our method is similar to the
delineation of a region of interest by the physicians. The agent
follows a set of serial actions for the region delineation, and
the action space is defined as a set of continuous action
parameters. The segmentation model learns in continuous action
space using the deep deterministic policy gradient algorithm.
The proposed method enables continuous improvement in
performance as we proceed from coarse segmentation results to
finer results. Finally, our proposed model is evaluated on the
International Skin Imaging Collaboration (ISIC) 2017 image
dataset, Human against Machine (HAM10000), and PH2 dataset. On
the ISIC 2017 dataset, the algorithm achieves an accuracy of
96.33% for the naevus cases, 95.39% for the melanoma cases,
and 94.27% for the seborrheic keratosis cases. The other
metrics are evaluated on these datasets and rank higher when
compared with the current state-of-the-art lesion segmentation
algorithms.
Kumar, Rajnish; Khan, Farhat Ullah; Sharma, Anju; Siddiqui, Mohammed Haris; Aziz, Izzatdin Abdul; Kamal, Mohammad Amjad; Ashraf, Ghulam Md; Alghamdi, Badrah S; Uddin, Md Sahab
Ä deep neural network-based approach for prediction of mutagenicity of compounds Journal Article
In: Environ. Sci. Pollut. Res. Int., vol. 28, no. 34, pp. 47641–47650, 2021.
Abstract | BibTeX | Tags: Deep learning; Deep neural network; Environmental exposure; Machine learning; Mutagen; Prediction
@article{Kumar2021-ky,
title = {Ä deep neural network-based approach for prediction of mutagenicity of compounds},
author = {Rajnish Kumar and Farhat Ullah Khan and Anju Sharma and Mohammed Haris Siddiqui and Izzatdin Abdul Aziz and Mohammad Amjad Kamal and Ghulam Md Ashraf and Badrah S Alghamdi and Md Sahab Uddin},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
journal = {Environ. Sci. Pollut. Res. Int.},
volume = {28},
number = {34},
pages = {47641--47650},
publisher = {Springer Science and Business Media LLC},
abstract = {We are exposed to various chemical compounds present in the
environment, cosmetics, and drugs almost every day. Mutagenicity
is a valuable property that plays a significant role in
establishing a chemical compound's safety. Exposure and handling
of mutagenic chemicals in the environment pose a high health
risk; therefore, identification and screening of these chemicals
are essential. Considering the time constraints and the pressure
to avoid laboratory animals' use, the shift to alternative
methodologies that can establish a rapid and cost-effective
detection without undue over-conservation seems critical. In
this regard, computational detection and identification of the
mutagens in environmental samples like drugs, pesticides, dyes,
reagents, wastewater, cosmetics, and other substances is vital.
From the last two decades, there have been numerous efforts to
develop the prediction models for mutagenicity, and by far,
machine learning methods have demonstrated some noteworthy
performance and reliability. However, the accuracy of such
prediction models has always been one of the major concerns for
the researchers working in this area. The mutagenicity
prediction models were developed using deep neural network
(DNN), support vector machine, k-nearest neighbor, and random
forest. The developed classifiers were based on 3039 compounds
and validated on 1014 compounds; each of them encoded with 1597
molecular feature vectors. DNN-based prediction model yielded
highest prediction accuracy of 92.95% and 83.81% with the
training and test data, respectively. The area under the
receiver's operating curve and precision-recall curve values
were found to be 0.894 and 0.838, respectively. The DNN-based
classifier not only fits the data with better performance as
compared to traditional machine learning algorithms, viz.,
support vector machine, k-nearest neighbor, and random forest
(with and without feature reduction) but also yields better
performance metrics. In current work, we propose a DNN-based
model to predict mutagenicity of compounds.},
keywords = {Deep learning; Deep neural network; Environmental exposure; Machine learning; Mutagen; Prediction},
pubstate = {published},
tppubtype = {article}
}
We are exposed to various chemical compounds present in the
environment, cosmetics, and drugs almost every day. Mutagenicity
is a valuable property that plays a significant role in
establishing a chemical compound's safety. Exposure and handling
of mutagenic chemicals in the environment pose a high health
risk; therefore, identification and screening of these chemicals
are essential. Considering the time constraints and the pressure
to avoid laboratory animals' use, the shift to alternative
methodologies that can establish a rapid and cost-effective
detection without undue over-conservation seems critical. In
this regard, computational detection and identification of the
mutagens in environmental samples like drugs, pesticides, dyes,
reagents, wastewater, cosmetics, and other substances is vital.
From the last two decades, there have been numerous efforts to
develop the prediction models for mutagenicity, and by far,
machine learning methods have demonstrated some noteworthy
performance and reliability. However, the accuracy of such
prediction models has always been one of the major concerns for
the researchers working in this area. The mutagenicity
prediction models were developed using deep neural network
(DNN), support vector machine, k-nearest neighbor, and random
forest. The developed classifiers were based on 3039 compounds
and validated on 1014 compounds; each of them encoded with 1597
molecular feature vectors. DNN-based prediction model yielded
highest prediction accuracy of 92.95% and 83.81% with the
training and test data, respectively. The area under the
receiver's operating curve and precision-recall curve values
were found to be 0.894 and 0.838, respectively. The DNN-based
classifier not only fits the data with better performance as
compared to traditional machine learning algorithms, viz.,
support vector machine, k-nearest neighbor, and random forest
(with and without feature reduction) but also yields better
performance metrics. In current work, we propose a DNN-based
model to predict mutagenicity of compounds.
environment, cosmetics, and drugs almost every day. Mutagenicity
is a valuable property that plays a significant role in
establishing a chemical compound's safety. Exposure and handling
of mutagenic chemicals in the environment pose a high health
risk; therefore, identification and screening of these chemicals
are essential. Considering the time constraints and the pressure
to avoid laboratory animals' use, the shift to alternative
methodologies that can establish a rapid and cost-effective
detection without undue over-conservation seems critical. In
this regard, computational detection and identification of the
mutagens in environmental samples like drugs, pesticides, dyes,
reagents, wastewater, cosmetics, and other substances is vital.
From the last two decades, there have been numerous efforts to
develop the prediction models for mutagenicity, and by far,
machine learning methods have demonstrated some noteworthy
performance and reliability. However, the accuracy of such
prediction models has always been one of the major concerns for
the researchers working in this area. The mutagenicity
prediction models were developed using deep neural network
(DNN), support vector machine, k-nearest neighbor, and random
forest. The developed classifiers were based on 3039 compounds
and validated on 1014 compounds; each of them encoded with 1597
molecular feature vectors. DNN-based prediction model yielded
highest prediction accuracy of 92.95% and 83.81% with the
training and test data, respectively. The area under the
receiver's operating curve and precision-recall curve values
were found to be 0.894 and 0.838, respectively. The DNN-based
classifier not only fits the data with better performance as
compared to traditional machine learning algorithms, viz.,
support vector machine, k-nearest neighbor, and random forest
(with and without feature reduction) but also yields better
performance metrics. In current work, we propose a DNN-based
model to predict mutagenicity of compounds.
Naulia, Pradeep S; Watada, Junzo; Aziz, Izzatdin Abdul; Roy, Arunava
A GA approach to Optimization of Convolution Neural Network Inproceedings
In: 2021 International Conference on Computer & Information Sciences (ICCOINS), IEEE, 2021.
@inproceedings{Naulia2021,
title = {A GA approach to Optimization of Convolution Neural Network},
author = {Pradeep S Naulia and Junzo Watada and Izzatdin Abdul Aziz and Arunava Roy},
url = {//doi.org/10.1109/iccoins49721.2021.9497147},
doi = {10.1109/iccoins49721.2021.9497147},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
booktitle = {2021 International Conference on Computer & Information Sciences (ICCOINS)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ngo, Son Tung; Jaafar, Jafreezal B; Aziz, Izzatdin Abdul; Nguyen, Giang Hoang; Bui, Anh Ngoc
Genetic Algorithm for solving multi-objective optimization in examination timetabling problem Journal Article
In: Int. J. Emerg. Technol. Learn., vol. 16, no. 11, pp. 4, 2021.
@article{Ngo2021-xj,
title = {Genetic Algorithm for solving multi-objective optimization in
examination timetabling problem},
author = {Son Tung Ngo and Jafreezal B Jaafar and Izzatdin Abdul Aziz and Giang Hoang Nguyen and Anh Ngoc Bui},
year = {2021},
date = {2021-06-01},
journal = {Int. J. Emerg. Technol. Learn.},
volume = {16},
number = {11},
pages = {4},
publisher = {International Association of Online Engineering (IAOE)},
abstract = {Examination timetabling is one of 3 critical timetabling jobs
besides enrollment timetabling and teaching assignment. After a
semester, scheduling examinations is not always an easy job in
education management, especially for many data. The timetabling
problem is an optimization and Np-hard problem. In this study,
we build a multi-objective optimizer to create exam schedules
for more than 2500 students. Our model aims to optimize the
material costs while ensuring the dignity of the exam and
students' convenience while considering the rooms' design, the
time requirement of each exam, which involves rules and policy
constraints. We propose a programmatic compromise to approach
the maximum tar-get optimization model and solve it using the
Genetic Algorithm. The results show the effectiveness of the
introduced algorithm.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Examination timetabling is one of 3 critical timetabling jobs
besides enrollment timetabling and teaching assignment. After a
semester, scheduling examinations is not always an easy job in
education management, especially for many data. The timetabling
problem is an optimization and Np-hard problem. In this study,
we build a multi-objective optimizer to create exam schedules
for more than 2500 students. Our model aims to optimize the
material costs while ensuring the dignity of the exam and
students' convenience while considering the rooms' design, the
time requirement of each exam, which involves rules and policy
constraints. We propose a programmatic compromise to approach
the maximum tar-get optimization model and solve it using the
Genetic Algorithm. The results show the effectiveness of the
introduced algorithm.
besides enrollment timetabling and teaching assignment. After a
semester, scheduling examinations is not always an easy job in
education management, especially for many data. The timetabling
problem is an optimization and Np-hard problem. In this study,
we build a multi-objective optimizer to create exam schedules
for more than 2500 students. Our model aims to optimize the
material costs while ensuring the dignity of the exam and
students' convenience while considering the rooms' design, the
time requirement of each exam, which involves rules and policy
constraints. We propose a programmatic compromise to approach
the maximum tar-get optimization model and solve it using the
Genetic Algorithm. The results show the effectiveness of the
introduced algorithm.
Prakash, J; Naidu, S Harshavardhan; Aziz, Izzatdin Abdul; Jaafar, Jafreezal
Reward-based residential wireless sensor optimization approach for appliance monitoring Journal Article
In: Soft Comput., vol. 25, no. 10, pp. 6947–6956, 2021.
BibTeX | Tags:
@article{Prakash2021-kk,
title = {Reward-based residential wireless sensor optimization approach
for appliance monitoring},
author = {J Prakash and S Harshavardhan Naidu and Izzatdin Abdul Aziz and Jafreezal Jaafar},
year = {2021},
date = {2021-05-01},
journal = {Soft Comput.},
volume = {25},
number = {10},
pages = {6947--6956},
publisher = {Springer Science and Business Media LLC},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kumar, Rajnish; Khan, Farhat Ullah; Sharma, Anju; Siddiqui, Mohammed Haris; Aziz, Izzatdin Abdul; Kamal, Mohammad Amjad; Ashraf, Ghulam Md; Alghamdi, Badrah S.; Uddin, Md. Sahab
A deep neural network–based approach for prediction of mutagenicity of compounds Journal Article
In: Environmental Science and Pollution Research, vol. 28, no. 34, pp. 47641–47650, 2021.
@article{Kumar2021,
title = {A deep neural network–based approach for prediction of mutagenicity of compounds},
author = {Rajnish Kumar and Farhat Ullah Khan and Anju Sharma and Mohammed Haris Siddiqui and Izzatdin Abdul Aziz and Mohammad Amjad Kamal and Ghulam Md Ashraf and Badrah S. Alghamdi and Md. Sahab Uddin},
url = {//doi.org/10.1007/s11356-021-14028-9},
doi = {10.1007/s11356-021-14028-9},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
journal = {Environmental Science and Pollution Research},
volume = {28},
number = {34},
pages = {47641--47650},
publisher = {Springer Science and Business Media LLC},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ngo, Son Tung; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Anh, Bui Ngoc
Ä compromise programming for multi-objective task assignment problem" Journal Article
In: Computers, vol. 10, no. 2, pp. 15, 2021.
@article{Ngo2021-og,
title = {Ä compromise programming for multi-objective task assignment
problem"},
author = {Son Tung Ngo and Jafreezal Jaafar and Izzatdin Abdul Aziz and Bui Ngoc Anh},
year = {2021},
date = {2021-01-01},
journal = {Computers},
volume = {10},
number = {2},
pages = {15},
publisher = {MDPI AG},
abstract = {The problem of scheduling is an area that has attracted a lot of
attention from researchers for many years. Its goal is to
optimize resources in the system. The lecturer's assigning task
is an example of the timetabling problem, a class of scheduling.
This study introduces a mathematical model to assign constrained
tasks (the time and required skills) to university lecturers.
Our model is capable of generating a calendar that maximizes
faculty expectations. The formulated problem is in the form of a
multi-objective problem that requires the trade-off between two
or more conflicting objectives to indicate the optimal solution.
We use the compromise programming approach to the
multi-objective problem to solve this. We then proposed the new
version of the Genetic Algorithm to solve the introduced model.
Finally, we tested the model and algorithm with real scheduling
data, including 139 sections of 17 subjects to 27 lecturers in
10 timeslots. Finally, a web application supports the
decision-maker to visualize and manipulate the obtained results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The problem of scheduling is an area that has attracted a lot of
attention from researchers for many years. Its goal is to
optimize resources in the system. The lecturer's assigning task
is an example of the timetabling problem, a class of scheduling.
This study introduces a mathematical model to assign constrained
tasks (the time and required skills) to university lecturers.
Our model is capable of generating a calendar that maximizes
faculty expectations. The formulated problem is in the form of a
multi-objective problem that requires the trade-off between two
or more conflicting objectives to indicate the optimal solution.
We use the compromise programming approach to the
multi-objective problem to solve this. We then proposed the new
version of the Genetic Algorithm to solve the introduced model.
Finally, we tested the model and algorithm with real scheduling
data, including 139 sections of 17 subjects to 27 lecturers in
10 timeslots. Finally, a web application supports the
decision-maker to visualize and manipulate the obtained results.
attention from researchers for many years. Its goal is to
optimize resources in the system. The lecturer's assigning task
is an example of the timetabling problem, a class of scheduling.
This study introduces a mathematical model to assign constrained
tasks (the time and required skills) to university lecturers.
Our model is capable of generating a calendar that maximizes
faculty expectations. The formulated problem is in the form of a
multi-objective problem that requires the trade-off between two
or more conflicting objectives to indicate the optimal solution.
We use the compromise programming approach to the
multi-objective problem to solve this. We then proposed the new
version of the Genetic Algorithm to solve the introduced model.
Finally, we tested the model and algorithm with real scheduling
data, including 139 sections of 17 subjects to 27 lecturers in
10 timeslots. Finally, a web application supports the
decision-maker to visualize and manipulate the obtained results.
Son, Ngo Tung; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Anh, Bui Ngoc; Binh, Hoang Duc; Aftab, Muhammad Umar
Ä compromise programming to task assignment problem in software development project" Journal Article
In: Comput. mater. contin., vol. 69, no. 3, pp. 3429–3444, 2021.
BibTeX | Tags:
@article{Tung_Son2021-rp,
title = {Ä compromise programming to task assignment problem in software
development project"},
author = {Ngo Tung Son and Jafreezal Jaafar and Izzatdin Abdul Aziz and Bui Ngoc Anh and Hoang Duc Binh and Muhammad Umar Aftab},
year = {2021},
date = {2021-01-01},
journal = {Comput. mater. contin.},
volume = {69},
number = {3},
pages = {3429--3444},
publisher = {Computers, Materials and Continua (Tech Science Press)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Son, Ngo Tung; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Anh, Bui Ngoc
Meta-heuristic algorithms for learning path recommender at MOOC Journal Article
In: IEEE Access, vol. 9, pp. 59093–59107, 2021.
BibTeX | Tags:
@article{Son2021-jz,
title = {Meta-heuristic algorithms for learning path recommender at
MOOC},
author = {Ngo Tung Son and Jafreezal Jaafar and Izzatdin Abdul Aziz and Bui Ngoc Anh},
year = {2021},
date = {2021-01-01},
journal = {IEEE Access},
volume = {9},
pages = {59093--59107},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Khan, Farhat Ullah; Aziz, Izzatdin Abdul; Akhir, Emilia Akashah P
Pluggable micronetwork for layer configuration relay in a dynamic deep neural surface Journal Article
In: IEEE Access, vol. 9, pp. 124831–124846, 2021.
BibTeX | Tags:
@article{Khan2021-ye,
title = {Pluggable micronetwork for layer configuration relay in a dynamic deep neural surface},
author = {Farhat Ullah Khan and Izzatdin Abdul Aziz and Emilia Akashah P Akhir},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Access},
volume = {9},
pages = {124831--124846},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Suboh, Syahirah; Aziz, Izzatdin Abdul
Anomaly Detection with Machine Learning in the Presence of Extreme Value - A Review Paper Inproceedings
In: 2020 IEEE Conference on Big Data and Analytics (ICBDA), IEEE, 2020.
@inproceedings{Suboh2020,
title = {Anomaly Detection with Machine Learning in the Presence of Extreme Value - A Review Paper},
author = {Syahirah Suboh and Izzatdin Abdul Aziz},
url = {//doi.org/10.1109/icbda50157.2020.9289798},
doi = {10.1109/icbda50157.2020.9289798},
year = {2020},
date = {2020-11-01},
booktitle = {2020 IEEE Conference on Big Data and Analytics (ICBDA)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ghani, Nur Laila Ab; Aziz, Izzatdin Abdul; Mehat, Mazlina
Concept drift detection on unlabeled data streams: A systematic literature review Inproceedings
In: 2020 IEEE Conference on Big Data and Analytics (ICBDA), IEEE, Kota Kinabalu, Malaysia, 2020.
BibTeX | Tags:
@inproceedings{Ghani2020-fi,
title = {Concept drift detection on unlabeled data streams: A
systematic literature review},
author = {Nur Laila Ab Ghani and Izzatdin Abdul Aziz and Mazlina Mehat},
year = {2020},
date = {2020-11-01},
booktitle = {2020 IEEE Conference on Big Data and Analytics (ICBDA)},
publisher = {IEEE},
address = {Kota Kinabalu, Malaysia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Baseer, Faisal; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Habib, Asad
Refined Urdu lexicon development K-means clustering based computational model using colloquial romanized Urdu dataset Inproceedings
In: 2020 International Conference on Computational Intelligence (ICCI), IEEE, Bandar Seri Iskandar, Malaysia, 2020.
BibTeX | Tags:
@inproceedings{Baseer2020-zc,
title = {Refined Urdu lexicon development K-means clustering based computational model using colloquial romanized Urdu dataset},
author = {Faisal Baseer and Jafreezal Jaafar and Izzatdin Abdul Aziz and Asad Habib},
year = {2020},
date = {2020-10-01},
urldate = {2020-10-01},
booktitle = {2020 International Conference on Computational
Intelligence (ICCI)},
publisher = {IEEE},
address = {Bandar Seri Iskandar, Malaysia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hossain, Touhid Mohammad; Watada, Junzo; Aziz, Izzatdin Abdul; Hermana, Maman
Machine learning in electrofacies classification and subsurface lithology interpretation: A rough set theory approach Journal Article
In: Äppl. Sci. (Basel), vol. 10, no. 17, pp. 5940, 2020.
@article{Hossain2020-bq,
title = {Machine learning in electrofacies classification and subsurface lithology interpretation: A rough set theory approach},
author = {Touhid Mohammad Hossain and Junzo Watada and Izzatdin Abdul Aziz and Maman Hermana},
year = {2020},
date = {2020-08-01},
urldate = {2020-08-01},
journal = {Äppl. Sci. (Basel)},
volume = {10},
number = {17},
pages = {5940},
publisher = {MDPI AG},
abstract = {Initially, electrofacies were introduced to define a set of
recorded well log responses in order to characterize and
distinguish a bed from the other rock units, as an advancement
to the conventional application of well logs. Well logs are
continuous records of several physical properties of drilled
rocks that can be related to different lithologies by
experienced log analysts. This work is time consuming and likely
to be imperfect because human analysis is subjective. Thus, any
automated classification approach with high promptness and
accuracy is very welcome by log analysts. One of the crucial
requirements in petroleum engineering is to interpret a bed's
lithology, which can be done by grouping a formation into
electrofacies. In the past, geophysical modelling,
petro-physical analysis, artificial intelligence and several
statistical method approaches have been implemented to interpret
lithology. In this research, important well log features are
selected by using the Extra Tree Classifier (ETC), and then five
individual electrofacies are constructed by using the selected
well log features. Finally, a rough set theory (RST)-based
whitebox classification approach is proposed to classify the
electrofacies by generating decision rules. These rules are
later on used to determine the lithology classes and we found
that RST is beneficial for performing data mining tasks such as
data classification and rule extraction from uncertain and vague
well log datasets. A comparison study is also provided, where we
use support vector machine (SVM), deep learning based on
feedforward multilayer perceptron (MLP) and random forest
classifier (RFC) to compare the electrofacies classification
accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Initially, electrofacies were introduced to define a set of
recorded well log responses in order to characterize and
distinguish a bed from the other rock units, as an advancement
to the conventional application of well logs. Well logs are
continuous records of several physical properties of drilled
rocks that can be related to different lithologies by
experienced log analysts. This work is time consuming and likely
to be imperfect because human analysis is subjective. Thus, any
automated classification approach with high promptness and
accuracy is very welcome by log analysts. One of the crucial
requirements in petroleum engineering is to interpret a bed's
lithology, which can be done by grouping a formation into
electrofacies. In the past, geophysical modelling,
petro-physical analysis, artificial intelligence and several
statistical method approaches have been implemented to interpret
lithology. In this research, important well log features are
selected by using the Extra Tree Classifier (ETC), and then five
individual electrofacies are constructed by using the selected
well log features. Finally, a rough set theory (RST)-based
whitebox classification approach is proposed to classify the
electrofacies by generating decision rules. These rules are
later on used to determine the lithology classes and we found
that RST is beneficial for performing data mining tasks such as
data classification and rule extraction from uncertain and vague
well log datasets. A comparison study is also provided, where we
use support vector machine (SVM), deep learning based on
feedforward multilayer perceptron (MLP) and random forest
classifier (RFC) to compare the electrofacies classification
accuracy.
recorded well log responses in order to characterize and
distinguish a bed from the other rock units, as an advancement
to the conventional application of well logs. Well logs are
continuous records of several physical properties of drilled
rocks that can be related to different lithologies by
experienced log analysts. This work is time consuming and likely
to be imperfect because human analysis is subjective. Thus, any
automated classification approach with high promptness and
accuracy is very welcome by log analysts. One of the crucial
requirements in petroleum engineering is to interpret a bed's
lithology, which can be done by grouping a formation into
electrofacies. In the past, geophysical modelling,
petro-physical analysis, artificial intelligence and several
statistical method approaches have been implemented to interpret
lithology. In this research, important well log features are
selected by using the Extra Tree Classifier (ETC), and then five
individual electrofacies are constructed by using the selected
well log features. Finally, a rough set theory (RST)-based
whitebox classification approach is proposed to classify the
electrofacies by generating decision rules. These rules are
later on used to determine the lithology classes and we found
that RST is beneficial for performing data mining tasks such as
data classification and rule extraction from uncertain and vague
well log datasets. A comparison study is also provided, where we
use support vector machine (SVM), deep learning based on
feedforward multilayer perceptron (MLP) and random forest
classifier (RFC) to compare the electrofacies classification
accuracy.
Qaiyum, Sana; Aziz, Izzatdin Abdul; Hasan, Mohd Hilmi; Khan, Asif Irshad; Almalawi, Abdulmohsen
Incremental Interval Type-2 fuzzy clustering of data Streams using single pass method Journal Article
In: Sensors (Basel), vol. 20, no. 11, pp. 3210, 2020.
Abstract | BibTeX | Tags: änt colony optimization; data stream; incremental learning; interval type-2 fuzzy c-means"
@article{Qaiyum2020-nu,
title = {Incremental Interval Type-2 fuzzy clustering of data Streams using single pass method},
author = {Sana Qaiyum and Izzatdin Abdul Aziz and Mohd Hilmi Hasan and Asif Irshad Khan and Abdulmohsen Almalawi},
year = {2020},
date = {2020-06-01},
urldate = {2020-06-01},
journal = {Sensors (Basel)},
volume = {20},
number = {11},
pages = {3210},
publisher = {MDPI AG},
abstract = {Data Streams create new challenges for fuzzy clustering
algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM).
One problem associated with IT2FCM is that it tends to be
sensitive to initialization conditions and therefore, fails to
return global optima. This problem has been addressed by
optimizing IT2FCM using Ant Colony Optimization approach.
However, IT2FCM-ACO obtain clusters for the whole dataset which
is not suitable for clustering large streaming datasets that may
be coming continuously and evolves with time. Thus, the clusters
generated will also evolve with time. Additionally, the incoming
data may not be available in memory all at once because of its
size. Therefore, to encounter the challenges of a large data
stream environment we propose improvising IT2FCM-ACO to generate
clusters incrementally. The proposed algorithm produces clusters
by determining appropriate cluster centers on a certain
percentage of available datasets and then the obtained cluster
centroids are combined with new incoming data points to generate
another set of cluster centers. The process continues until all
the data are scanned. The previous data points are released from
memory which reduces time and space complexity. Thus, the
proposed incremental method produces data partitions comparable
to IT2FCM-ACO. The performance of the proposed method is
evaluated on large real-life datasets. The results obtained from
several fuzzy cluster validity index measures show the enhanced
performance of the proposed method over other clustering
algorithms. The proposed algorithm also improves upon the run
time and produces excellent speed-ups for all datasets.},
keywords = {änt colony optimization; data stream; incremental learning; interval type-2 fuzzy c-means"},
pubstate = {published},
tppubtype = {article}
}
Data Streams create new challenges for fuzzy clustering
algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM).
One problem associated with IT2FCM is that it tends to be
sensitive to initialization conditions and therefore, fails to
return global optima. This problem has been addressed by
optimizing IT2FCM using Ant Colony Optimization approach.
However, IT2FCM-ACO obtain clusters for the whole dataset which
is not suitable for clustering large streaming datasets that may
be coming continuously and evolves with time. Thus, the clusters
generated will also evolve with time. Additionally, the incoming
data may not be available in memory all at once because of its
size. Therefore, to encounter the challenges of a large data
stream environment we propose improvising IT2FCM-ACO to generate
clusters incrementally. The proposed algorithm produces clusters
by determining appropriate cluster centers on a certain
percentage of available datasets and then the obtained cluster
centroids are combined with new incoming data points to generate
another set of cluster centers. The process continues until all
the data are scanned. The previous data points are released from
memory which reduces time and space complexity. Thus, the
proposed incremental method produces data partitions comparable
to IT2FCM-ACO. The performance of the proposed method is
evaluated on large real-life datasets. The results obtained from
several fuzzy cluster validity index measures show the enhanced
performance of the proposed method over other clustering
algorithms. The proposed algorithm also improves upon the run
time and produces excellent speed-ups for all datasets.
algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM).
One problem associated with IT2FCM is that it tends to be
sensitive to initialization conditions and therefore, fails to
return global optima. This problem has been addressed by
optimizing IT2FCM using Ant Colony Optimization approach.
However, IT2FCM-ACO obtain clusters for the whole dataset which
is not suitable for clustering large streaming datasets that may
be coming continuously and evolves with time. Thus, the clusters
generated will also evolve with time. Additionally, the incoming
data may not be available in memory all at once because of its
size. Therefore, to encounter the challenges of a large data
stream environment we propose improvising IT2FCM-ACO to generate
clusters incrementally. The proposed algorithm produces clusters
by determining appropriate cluster centers on a certain
percentage of available datasets and then the obtained cluster
centroids are combined with new incoming data points to generate
another set of cluster centers. The process continues until all
the data are scanned. The previous data points are released from
memory which reduces time and space complexity. Thus, the
proposed incremental method produces data partitions comparable
to IT2FCM-ACO. The performance of the proposed method is
evaluated on large real-life datasets. The results obtained from
several fuzzy cluster validity index measures show the enhanced
performance of the proposed method over other clustering
algorithms. The proposed algorithm also improves upon the run
time and produces excellent speed-ups for all datasets.
Hossain, Touhid Mohammad; Wataada, Junzo; Hermana, Maman; Aziz, Izzatdin Abdul
Supervised machine learning in electrofacies classification: A rough set theory approach Journal Article
In: J. Phys. Conf. Ser., vol. 1529, no. 5, pp. 052048, 2020.
@article{Hossain2020-aq,
title = {Supervised machine learning in electrofacies classification: A rough set theory approach},
author = {Touhid Mohammad Hossain and Junzo Wataada and Maman Hermana and Izzatdin Abdul Aziz},
year = {2020},
date = {2020-05-01},
urldate = {2020-05-01},
journal = {J. Phys. Conf. Ser.},
volume = {1529},
number = {5},
pages = {052048},
publisher = {IOP Publishing},
abstract = {Äbstract Electrofacies were initially introduced for defining a
set of recorded log responses in order to characterize a bed and
permitted it to be distinguished from the other rock units as an
improvement to the traditional use of well logs. Grouping a
formation into electrofacies can be used in lithology
prediction, reservoir characterization and discrimination.
Usually Multivariate statistical analyses, such as principal
component analysis `PCA' and cluster analysis are used for this
purpose. In this study Extra Tree Classifier (ETC) based feature
selection method is used to select the important attributes and
three distinctive electrofacies were extracted from the
dendrogram plot using the selected attributes. Finally, we
proposed a rough set theory (RST) based white box classification
approach to extract the pattern of the electrofacies in the form
of decision rules which will allow the geosciences researchers
to correlate the electrofacieses with the lithofacies from the
extracted rough set (RS) rules."},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Äbstract Electrofacies were initially introduced for defining a
set of recorded log responses in order to characterize a bed and
permitted it to be distinguished from the other rock units as an
improvement to the traditional use of well logs. Grouping a
formation into electrofacies can be used in lithology
prediction, reservoir characterization and discrimination.
Usually Multivariate statistical analyses, such as principal
component analysis `PCA' and cluster analysis are used for this
purpose. In this study Extra Tree Classifier (ETC) based feature
selection method is used to select the important attributes and
three distinctive electrofacies were extracted from the
dendrogram plot using the selected attributes. Finally, we
proposed a rough set theory (RST) based white box classification
approach to extract the pattern of the electrofacies in the form
of decision rules which will allow the geosciences researchers
to correlate the electrofacieses with the lithofacies from the
extracted rough set (RS) rules."
set of recorded log responses in order to characterize a bed and
permitted it to be distinguished from the other rock units as an
improvement to the traditional use of well logs. Grouping a
formation into electrofacies can be used in lithology
prediction, reservoir characterization and discrimination.
Usually Multivariate statistical analyses, such as principal
component analysis `PCA' and cluster analysis are used for this
purpose. In this study Extra Tree Classifier (ETC) based feature
selection method is used to select the important attributes and
three distinctive electrofacies were extracted from the
dendrogram plot using the selected attributes. Finally, we
proposed a rough set theory (RST) based white box classification
approach to extract the pattern of the electrofacies in the form
of decision rules which will allow the geosciences researchers
to correlate the electrofacieses with the lithofacies from the
extracted rough set (RS) rules."
Äfridi, Sharjeel; Ahmed, Usman; Gilal, Abdul Rehman; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Sandhu, Muhammad Yameen"
High stop band rejection for ceramic loaded waveguide filters Journal Article
In: IEEE Access, vol. 8, pp. 109309–109314, 2020.
BibTeX | Tags:
@article{Afridi2020-mg,
title = {High stop band rejection for ceramic loaded waveguide filters},
author = {Sharjeel Äfridi and Usman Ahmed and Abdul Rehman Gilal and Jafreezal Jaafar and Izzatdin Abdul Aziz and Muhammad Yameen" Sandhu},
year = {2020},
date = {2020-01-01},
journal = {IEEE Access},
volume = {8},
pages = {109309--109314},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Älabri, Shafiq Darwish; Kamaruddin, Suzilawati; Gilal, Abdul Rehman; Jaafar, Jafreezal; Aziz, Izzatdin Abdul
The moderation influence of power distance on the relationship between technological factors and the successful implementation of citizen relationship management in the public sector Journal Article
In: IEEE Access, vol. 8, pp. 132446–132465, 2020.
BibTeX | Tags:
@article{Alabri2020-lr,
title = {The moderation influence of power distance on the relationship between technological factors and the successful implementation of citizen relationship management in the public sector},
author = {Shafiq Darwish Älabri and Suzilawati Kamaruddin and Abdul Rehman Gilal and Jafreezal Jaafar and Izzatdin Abdul Aziz},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {IEEE Access},
volume = {8},
pages = {132446--132465},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}