Papers by Sofianita (Dr) Mutalib
Atmosphere, Mar 29, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Lecture Notes in Computer Science, 2012
ABSTRACT
Designing the algorithm to extract features in signature is a challenging task due to complex hum... more Designing the algorithm to extract features in signature is a challenging task due to complex human behavior which is developed through repetition. Features such as direction, slant, baseline, pressure, speed and numbers of pen ups and downs are some of the dynamic information signature that can be extracted from an online method. However, the variables for identifying the features is rarely discussed and notified. Therefore, this paper presents the variables that involve in designing the algorithm for extracting the slant features. Both local and global slant characteristics are considered in extracting the features. Local slant is the longest slant among the detected slant while the global slant represents the highest quantity of classified slant whether the slant are leftward, upright or rightward. Signature Extraction Features System (SEFS) is used to extract the slant features in signature automatically for analysis purposes. The images created by SEFS are used as samples for the questionnaire to identify the slant features and to be given to human expert for evaluation. The results from the SEFS are compared with the result from the questionnaire. The results demonstrate a competitive performance with 85% accuracy.
This study presents pre-processing methods for detecting lane detection using camera and Light De... more This study presents pre-processing methods for detecting lane detection using camera and Light Detection and Ranging (LiDAR) sensor technologies. Standard image processing methods are not suitable for complicated roads with various sign on the ground. Thus, determining the right techniques for pre-processing such data would be a challenge. The objectives of this study are to pre-process the scanned images and apply the image recognition algorithm for lane detection. The study employed Canny Edge Detection and Hough Transform algorithms on several sets of images. A different region of interest was experimented to find the optimal one. The experimental results showed that the proposed algorithms could be practical in terms of effectively detecting road lines and generate lane detection
This study presents pre-processing methods for detecting lane detection using camera and Light De... more This study presents pre-processing methods for detecting lane detection using camera and Light Detection and Ranging (LiDAR) sensor technologies. Standard image processing methods are not suitable for complicated roads with various sign on the ground. Thus, determining the right techniques for pre-processing such data would be a challenge. The objectives of this study are to pre-process the scanned images and apply the image recognition algorithm for lane detection. The study employed Canny Edge Detection and Hough Transform algorithms on several sets of images. A different region of interest was experimented to find the optimal one. The experimental results showed that the proposed algorithms could be practical in terms of effectively detecting road lines and generate lane detection
Nowadays, the history of Batu Aceh has been forgotten through the centuries. If this happens, fut... more Nowadays, the history of Batu Aceh has been forgotten through the centuries. If this happens, future generations will not know the absence of this creative heritage. Even to an expert, it takes time for them to recognize and memorize each type easily. To solve this difficulty, a prototype is devised to guide future generation to appreciate these precious cultural heritage artifacts in the Islamic-Malay civilization. A neural network approach is employed for supervised classification of this Batu Aceh object images. In this research, back propagation algorithm is applied. In order to classify the type, several images of each type of Batu Aceh are used as training samples. The image samples would be processed to extract useful information to be fed into each type of Batu Aceh. The network will be trained first with data samples that have been converted into binary forms. Then, network parameters such as momentum value, learning rate and number of hidden neuron will be set to ensure the performance of the system. After several experiments were conducted, 0.04 learning rate value with 40 hidden neurons in the hidden layer was found to be the optimal parameter values for the neural network. The learning curve is smooth and the performance goal is met.
Advanced Science Letters, Nov 1, 2013
ABSTRACT
International Journal of Intelligent Systems and Applications in Engineering, Mar 31, 2022
The year 2020 has been a tough year with the global pandemic situation, and the utmost priority i... more The year 2020 has been a tough year with the global pandemic situation, and the utmost priority is to live in a clean, green, and safe environment. One of the areas that the governments are emphasizing for the readiness of our ecosystem is autonomous and contactless environments in adapting to the new norm. Thus, Autonomous Vehicle (AV) is a promising technology to bring forward. One of the critical aspects of Autonomous Navigation is object detection. Most AV use multiple sensors to detect objects, such as cameras, radar and Light Detection and Ranging sensor (LiDAR). Nowadays, the LiDAR sensor is widely implemented due to the ability to detect objects in the form of pulsed lasers, benefiting in low-light object detection. However, even with advanced technology, poor programming can affect the performance of object detection system. Thus, the study explores the state-of-the-art of You Only Look Once (YOLO) algorithms namely Tiny-YOLO and Complex-YOLO for object detection on KITTI dataset. Their performances were compared based on accuracy, precision, and recall metrics. The results showed that the Complex-YOLO has better performance as the mean average precision is higher than the Tiny-YOLO model when tested with equal parameters.
... S., Armand, M., Blumenstein, and V., Muthukkumarasamy, Off-Line Signature Verificatio based ... more ... S., Armand, M., Blumenstein, and V., Muthukkumarasamy, Off-Line Signature Verificatio based on the ... line Signature Verification and Recognition by Support Vector Machine, DissertationComputer Engineering Department ... [23] RC Eberhart and J. Kennedy, "A New Optimizer ...
Lecture Notes in Computer Science, 2012
With the availability of biological data and the power of sharing, it produces many opportunities... more With the availability of biological data and the power of sharing, it produces many opportunities for computer scientists to perform researches in bioinformatics. Generally the researches propose methods for different tasks, mainly to develop algorithms in diagnosing and identification of diseases. One of the primary studies that relevant to health and diseases is genome wide association studies (GWAS). Normally the studies are conducted in different populations to replicate the risk loci of specific disease and the number of groups are keep on progressing, including those from Asian country. Computer scientists should be involved in GWAS due to certain problems and the complexity of the processes involved. The problems and past studies related to GWAS are presented in this paper.
... Abdul-Rahman, Yap May Lin, Intelligent Systems SIG, Study Centre of Systems Science, Faculty ... more ... Abdul-Rahman, Yap May Lin, Intelligent Systems SIG, Study Centre of Systems Science, Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam, Selangor, Malaysia {shuzlina, maylin}@fskm.uitm.edu.my Sofianita Mutalib, Azlinah Mohamed ...
International Journal of Advanced Computer Science and Applications, 2021
Tourism is among the significant source of income to Malaysia and Taman Negara Pahang is one of t... more Tourism is among the significant source of income to Malaysia and Taman Negara Pahang is one of the Malaysia's tourism spots and the heritage of Malaysia in achieving the Sustainable Development Goals (SDG). It has attracted many international and local tourists for its richness in flora and fauna. Currently, the information of tourists' visits is not properly analyzed. This study integrates the internal and public information to analyze the visits. The regression models used are multiple linear regression, support vector regression, and decision tree regression to predict the tourism demand for Taman Negara, Malaysia and the best model was deployed. Predictive analytics can support the decision-making process for tourism destinations management. When the management gets a head-up of the demand in the future, they can choose a strategic planning and be more aware about the factors influencing tourism demand, such as the tourists' web search engine behaviors for accommodation, facilities, and attractions. The factors affecting the tourism demand are determined as the first objective. The role of independent variable was set to the total number of visitors, subsequently being set as the target variable in the modeling process. A total of 30 models were generated by tuning the cross-validation parameters. This study concluded that the best model is the multiple linear regression due to lower root mean square error (RSME) value.
Lecture Notes in Computer Science, Aug 12, 2008
Due to difficulty in monitoring the attendance of members in the higher education institution (HE... more Due to difficulty in monitoring the attendance of members in the higher education institution (HEI), the HEI management has recognized the need to conduct a study on the adoption of fingerprint system. The study identified the appropriate indexing model and developed a questionnaire that suits for the environment. The higher education institution selected for this study is International Islamic University Malaysia (IIUM). The target groups were selected across a sample of the organisation's members. The returned questionnaires were analyzed using a standard statistical tool. Reliability and normality testing were deployed to ensure that the data is reliable and normal for further testing. Spearman's correlation and Kruskal Wallis were used to find the relation between elements of innovativeness, optimism, discomfort and insecurity. The findings have revealed that the innovativeness and optimism have a positive relation with the users' readiness. Meanwhile, discomfort and insecurity gave a negative relation to the faculty members'. This indicates that the faculty member is aware of the technology and optimist with the implementation of the technology in their organization. However, they still feel insecure and discomfort with the justification and impact of the implementation.
Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions,... more Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions, or feedback that relates to the usage of computational linguistics, text analytics, and natural language processing. With the rise of social media posts, it is becoming more challenging to evaluate brief, casual, and non-structured texts to optimize consumer feedback and spot patterns. Meanwhile, social commerce involves social media for social interaction in assisting customers and merchants to do business transactions. From a social media perspective, the informal Malay Text is less explored by the researchers. Thus, it will directly yield difficulties in conducting and preparing the SA processes. Cross-Industry Standard Process for Data Mining (CRISP-DM) was adapted as a reference model for the methodology of this work with machine learning approaches in classifying the informal Malay textual data based on sentiment. The dataset was extracted from the Facebook platform of Pos Laju Malaysia pages. The comparison of the classification technique performances was analyzed in identifying the most accurate classifier for SA, within three different machine learning classifiers was experimented by using 1200 instances from an informal Malay textual dataset. The results of Decision Tree (J48), Support Vector Machine (SVM), and Naïve Bayes (NB) were analyzed and discussed. The result of the highest accuracy of Ten-Fold Cross-Validation is 69.7% and meanwhile, for the Percentage Split method, the highest accuracy result is 70.9%. It shows that Support Vector Machine (SVM) is the best classifier compared to other classifiers of text classification based on sentiment.
Communications in computer and information science, 2019
In most universities, the number of students who graduated on time reflect tremendously on their ... more In most universities, the number of students who graduated on time reflect tremendously on their operation costs. In such cases, the high number of graduate-on-time or GOT students achievement will indirectly reduce the university’s annual operation cost per student. Not as trivial as it seems, to ensure most of the students able to GOT is challenging. It may vary in the perspective of university practises, academic programmes, and students’ background. At the university’s level, students’ data can be used to identify the achievement and ability of students, interests, and weaknesses. To build an accurate predictive model, it requires an extensive study on significant factors that may contribute to students’ ability to graduate on time. Consequently, this study aims to construct a predictive model that can predict students’ graduation status. We applied five different machine learning algorithms (classifiers) namely Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (PolyKernel), and Support Vector Machine (RBFKernel). These classifiers were evaluated with four different k folds of 5, 10, 15, and 20. The performance of these classifiers was compared based on different measurement subject to accuracy, precision, recall, and F-Score. The results indicated that Support Vector Machine (PolyKernel) outperformed other classifiers and the best numbers of k folds for this experiment are 5 and 20. This predictive model of GOT is hopefully will beneficial to university management and academicians to devise their strategies in helping and improving the weakness of students’ academic performance and to ensure they can graduate on time.
This paper attempts to predict the survival of patients using supervised machine learning techniq... more This paper attempts to predict the survival of patients using supervised machine learning techniques. To predict this task, the variables were identified and retrieved from the StatLib database. Both the artificial neural networks and linear regression models were used to perform the task. Experimental results, based on the classification accuracy were analysed from training and testing datasets. To increase the
IAES International Journal of Artificial Intelligence, Dec 1, 2020
Food delivery services have gained attention and become a top priority in developed cities by red... more Food delivery services have gained attention and become a top priority in developed cities by reducing travel time and waiting time by offering online food delivery options for a variety of dishes from a wide variety of restaurants. Therefore, customer analytics have been considered in business analysis by enabling businesses to collect and analyse customer feedback to make business decisions to be more advanced in the future. This paper aims to study the techniques used in customer analytics for food delivery services and identify the factors of customers' reviews for food delivery services especially in social media. A total of 53 papers reviewed, several techniques and algorithms on customer analytics for food delivery services in social media are Lexicon, machine learning, natural language processing (NLP), support vector machine (SVM), and text mining. The paper further analyse the challenges and factors that give impacts to the customers' reviews for food delivery services. These findings would be appropriate for development and enhancement of food delivery services in future works.
Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes a... more Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes an early experiment towards modelling a low-cost and robust centimetre-level localization for mobile robots in crowded indoor and outdoor environments. While a wide range of methods have been developed and tested on high-end hardware in autonomous vehicles, the work utilizes multiple sensor information to achieve robustness with different types of mobile robots. The application can be used by any group or organization, especially the frontliners, in managing the COVID-19 pandemic. Different Simultaneous Localization and Mapping (SLAM) algorithms, such as GMapping, Google Cartographer and Hector SLAM, are used to achieve better localization. Sensor fusion strategy is applied for these SLAM packages using Real-Time Kinematic (RTK) positioning, a precise Global Navigation Satellite System (GNSS)-based sensor, by applying both Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to estimate position, velocity and attitude (PVA). The performance of the proposed algorithm will be compared against the benchmark algorithm using different sets of data in crowded places in various settings.
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Papers by Sofianita (Dr) Mutalib