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Robust Joint Graph Learning for Multi-View Clustering
IEEE Transactions on Big Data
Cited by: Papers (1)
In real-world applications, multi-view datasets often comprise diverse data sources or views, inevitably accompanied by noise. However, most existing graph-based multi-view clustering methods utilize fixed graph similarity matrices to handle noisy multi-view data, necessitating additional clustering steps for obtaining the final clustering. This paper proposes a Robust Joint Graph learning for Mul...Show More
Simulation of Crowd Evacuation in Asymmetrical Exit Layout Based on Improved Dynamic Parameters Model
Omar Alidmat;Hamza Abu Owida;Umi Kalsom Yusof;Ahmed Almaghthawi;Askar Altalidi;Rami S. Alkhawaldeh;Suhaila Abuowaida;Nawaf Alshdaifat;Jamil AlShaqsi
Evacuation systems are crucial for minimizing casualties and property losses during emergencies. Understanding evacuee behavior in overcrowded situations is essential for developing effective evacuation strategies. However, evacuating large crowds from buildings with multiple exits is a challenging problem, especially when the exits are asymmetrical and the crowds are dense. This paper proposes a ...Show More
Dynamic Exit Selection and Counting Area Adjustment for Efficient Crowd Evacuation in Asymmetrical Layouts
Omar Khair Alla Alidmat;Raed Alazaidah;Mohammad Arabiat;Hayel Khafajeh;Mohammad Aljaidi;Umi Kalsom Yusof;Ali Elrashidi
2024 25th International Arab Conference on Information Technology (ACIT)
Year: 2024 | Conference Paper |
This research introduces an innovative methodology to address the significant challenge of evacuating large crowds from buildings with multiple exits, particularly in scenarios with asymmetrical exit configurations and high population densities. The proposed model incorporates dynamic counting areas that adjust according to evacuee density, alongside a probabilistic exit selection mechanism influe...Show More
A Novel Cellular Automaton Model for Optimized Multi-Exit Crowd Evacuation in High-Density Environments
Omar Khair Alla Alidmat;Suhaila Abuowaida;Esraa Alhenawi;Hebatullah Khattab Awwad;Adai Al-Momani;Umi Kalsom Yusof;Ali Elrashidi
2024 25th International Arab Conference on Information Technology (ACIT)
Year: 2024 | Conference Paper |
Evacuation systems play a critical role in mitigating casualties and property damage during emergency situations. Comprehending evacuee behavior in overcrowded scenarios is vital for developing effective evacuation strategies. Nonetheless, evacuating large crowds from buildings with multiple exits presents a significant challenge, particularly when exits are asymmetrical and crowds are dense. This...Show More
ASFE Feature Selection Technique Integrated with SMOTE-ENN for Handling High-Dimensional and Highly Imbalanced Wafer Defect Classification Data
2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE)
Year: 2024 | Conference Paper |
In semiconductor manufacturing, the advancement of high-throughput sensors leads to the generation of high-dimensional and highly imbalanced data, presenting challenges like feature redundancy, increased computational costs, and bias in the classification models towards the majority class. These challenges complicate wafer defect classification, necessitating the integration of advanced feature se...Show More
In practical applications, the issue of data imbalance inevitably rises. In most studies, the predominant focus regarding long-tailed class imbalance pertains to a setting within a single domain in which the training and test samples are presumed to originate from the same feature space and possess identical data distributions. However, natural datasets can be derived from distinct domains in whic...Show More
Study on the Correlation of Trainable Parameters and Hyperparameters with the Performance of Deep Learning Models
Song-Quan Ong;Pradeep Isawasan;Gomesh Nair;Khairulliza Ahmad Salleh;Umi Kalsom Yusof
2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Trainable parameters and hyperparameters are critical to the development of a deep learning model. However, the components have typically been studied individually, and most studies have found it difficult to investigate the effects of their combination on model performance. We are interested in examining the correlation between the number of trainable parameters in a deep learning model and its p...Show More
An Automated Blastocyst Grading System Using Convolutional Neural Network and Transfer Learning
Yusuf Abas Mohamed;Umi Kalsom Yusof;Iza Sazanita Isa;Murizah Mohd Zain
2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
In vitro fertilization (IVF) involves collecting several mature egg samples fertilized with sperm in the IVF laboratory through a process of manual grading and selecting the healthiest blastocyst embryo through visual morphology assessment. Nevertheless, this manual process is highly subjective, prone to error and human bias, time-consuming, and can result in multiple pregnancies. Since the gradin...Show More
An Automated Day 3 Embryo Grading Based On Morphological Characteristics Using CNN with Transfer Learning Techniques
2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
In-Vitro fertilization (IVF) is the most successful form of assisted reproductive technology and one of the most used fertility treatments. According to a prior study, the day 3 embryo development stage is a crucial transitional time that significantly impacts the pregnancy and implantation rates of day 5 transfers. Most embryologists currently analyze the morphological characteristics of day 3 em...Show More
Forecasting of Outpatient Hospital Visits using A Bidirectional Long Short-Term Memory Model
2023 8th International Conference on Business and Industrial Research (ICBIR)
Year: 2023 | Conference Paper |
Recently, accurate predicting outpatient visits is crucial in healthcare for optimizing service delivery and resource allocation. Time series models, particularly Deep Learning (DL) methods, have gained popularity in predicting demand and can potentially be used to predict demand for medical services, including outpatient hospital visits. This study aims to assess the potential of Bidirectional Lo...Show More
Multiple perspectives can be used to explore rich and complex datasets that are widely used in many applications. However, in real-world applications, the multi-view data are often noisy because of various environmental factors. The key challenge of graph-based multi-view clustering is obtaining a consistent clustering structure. Most graph-based methods learn independently in one view how similar...Show More
Optimizing the Hyperparameter Tuning of YOLOv5 for Underwater Detection
Iza Sazanita Isa;Mohamed Syazwan Asyraf Rosli;Umi Kalsom Yusof;Mohd Ikmal Fitri Maruzuki;Siti Noraini Sulaiman
This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model performances based on quantitative metrics and image processing speed. The hyperparameter in the feature-extraction phase was configured based on the learning rate and momentum and further improved based on the adaptive mo...Show More
With the increasing pressure from the market and the surge of “Industry 4.0,” staying competitive and relevant is becoming more and more difficult. The assembly line, which represents a long-term investment of the manufacturing industry, needs to be efficiently utilized. While assembly line balancing (ALB) problem had been studied for decades, oversights on the bottleneck resources could significa...Show More
Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset
Haziqah Shamsudin;Umi Kalsom Yusof;Andal Jayalakshmi;Mohd Nor Akmal Khalid
2020 IEEE 16th International Conference on Control & Automation (ICCA)
Year: 2020 | Conference Paper |
Cited by: Papers (46)
Credit card fraud is a serious and growing problem. It is becoming more challenging with an issues of highly imbalanced class. In the field of data mining, prediction or typically known as data classification problem involved detecting events. Uncommon events are hard to identify on account of their inconsistency and casualness, however, misclassifying rare events can result in heavy costs. Thus, ...Show More
An Artificial Immune System Approach for Solving Type-E Assembly Line Balancing Problem with Problem-Specific Information
2020 IEEE 16th International Conference on Control & Automation (ICCA)
Year: 2020 | Conference Paper |
The advent of industrial 4.0 ushered a new era in the manufacturing industry where merely optimizing the available resources is not enough to fulfill the need of the growing market. Various approaches have addressed the assembly line balancing (ALB) problem for decades. Although solving the ALB problem is crucial, a bottleneck may still occur over the next operation stages. As such, two research d...Show More
Multi-objective Filter-based Feature Selection Using NSGAIII With Mutual Information and Entropy
Ali Muhammad Usman;Umi Kalsom Yusof;Sybirah Naim;Nehemiah Musa;Haruna Chiroma
2020 2nd International Conference on Computer and Information Sciences (ICCIS)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Feature selection (FS) aims to select the subsets of the most informative features by ignoring the redundant ones and consequently, improving the classification performance. Hence, consider as a two objective optimisation problem. Moreover, most of the existing work treats FS as single-objective by combining the two aims into a single fitness function. As such, there is a trade-off between the num...Show More
Feature selection aims to confiscate inappropriate features and yet improve classification performance. These aims are conflicting with one another, and a choice must be made in the presence of the trade-off between them. Numerous researches deal with feature selection problem but, they are mostly single-objective based. Nowadays, multi-objective optimisation approaches are becoming the most suita...Show More
Literature Survey: Statistical Characteristics of Google Cluster Trace
2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Usually, data sets used in computer network research are not widely shared because of the risk of security breach. However, with the release of Google Cluster trace (GCT), academics are able to validate their work with a common set of data. Furthermore, it helps researchers to understand the real nature of huge workload in cloud data centres. This paper aims to collect and summarize the characteri...Show More
Model student selection using fuzzy logic reasoning approach
2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
In an educational institution, various students criteria contributed to the main reason the student is nominated as a model student. This includes cumulative grade point average (CGPA) of academic courses taken, co-curriculum involvement, soft skills, hard work, leadership, attitude, time management, attendance, attire, and technical skill in order to make the selection decision. The distinctive s...Show More
Modelling crowd behaviour during emergency evacuation: A proposed framework
2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA)
Year: 2016 | Conference Paper |
Cited by: Papers (7)
Emergency often create panic and stress in crowd. The human emotional transition between normal to panic state is due to extreme event which is beyond control. In the past, crowd behavior models for emergency evacuation being studied are aircraft emergency evacuation, emotional ant based, behavior based real time evacuation and many more which is related to the behavior and life like. They are dif...Show More
Survey on crowd behaviour evacuation modelling during emergency event
2015 9th Malaysian Software Engineering Conference (MySEC)
Year: 2015 | Conference Paper |
In the event of natural disasters, catastrophes or war, efficient tools are needed to produce evacuation plans to evacuate the crowd safely. Disaster is an unplanned situation that can happen anywhere and anytime. The worst case is when disasters happen on large gatherings which can increase risk of death during egress evacuation. When an emergency arises, crowds will evacuate to the nearest exit ...Show More
Incorporating dynamism in emergency route planning problem using immune-based approach
2015 4th International Conference on Software Engineering and Computer Systems (ICSECS)
Year: 2015 | Conference Paper |
The occurrence of emergency events tends to spark a very chaotic reaction, inducing a large surge of demand (number of evacuees) which exceeds the available resources (pathway/network). Planning a suitable evacuation route and identifying the shortest evacuation route before the occurrence of extreme events are crucial for an effective evacuation process. Although evacuation plans can be orchestra...Show More
Content-based filtering algorithm for mobile recipe application
2014 8th. Malaysian Software Engineering Conference (MySEC)
Year: 2014 | Conference Paper |
Cited by: Papers (6)
In general, people like to cook but they have no idea on what to cook and how to cook. Furthermore, they also wish to know certain recipes that suit to their preference. There are many cooking recipe websites but most of them do not provide suggestions for the user based on user's food preferences. Current recipe suggestions developed by some researchers are mainly based on the collective interest...Show More
Preventive maintenance scheduling optimization in semiconductor assembly industry using harmony search algorithm
2013 IEEE Student Conference on Research and Developement
Year: 2013 | Conference Paper |
Highly competitive markets and rapidly changing customer demands have increased the importance of proper resource utilization in the semiconductor industry. Utilizing resources (i.e. machines) is vital in order to maintain continuous flow of manufacturing as well as their efficiency and productivity. However, machine maintenance is being overlooked in most cases of the semiconductor assembly proce...Show More
An artificial immune system algorithm for optimizing the distributed production scheduling in the semiconductor assembly industry
2013 13th International Conference on Intellient Systems Design and Applications
Year: 2013 | Conference Paper |
Cited by: Papers (2)
The semiconductor industry faces one of the most challenging and rapidly evolving problem in each passing time. The importance of satisfying customer demands and achieving higher profits have increased the attention towards production scheduling in semiconductor assembly areas. Combination of productivity and flexibility provided by semiconductor assembly areas have attracted many research efforts...Show More
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