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Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming Megat Nabil Irwan Megat Amerudin; Siti Khatijah Nor Abdul Rahim; Nasiroh Omar; Mohd Suffian Sulaiman; Amir Hamzah Jaafar; Raseeda Hamzah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp624-631

Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.

Visual analytics of 3D LiDAR point clouds in robotics operating systems Alia Mohd Azri; Shuzlina Abdul-Rahman; Raseeda Hamzah; Zalilah Abd Aziz; Nordin Abu Bakar
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on simultaneous localization and mapping (SLAM) using point cloud data derived from the light detection and ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as grid mapping and monte carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on real-time appearance-based (RTAB) and makes use of the red green blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the robot operating system (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition, the probability for the estimated location is improved which allows for better vehicle maneuverability.

Security issues and framework of electronic medical record: A review Jibril Adamu; Raseeda Hamzah; Marshima Mohd Rosli
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

The electronic medical record has been more widely accepted due to its unarguable benefits when compared to a paper-based system. As electronic medical record becomes more popular, this raises many security threats against the systems. Common security vulnerabilities, such as weak authentication, cross-site scripting, SQL injection, and cross-site request forgery had been identified in the electronic medical record systems. To achieve the goals of using EMR, attaining security and privacy is extremely important. This study aims to propose a web framework with inbuilt security features that will prevent the common security vulnerabilities in the electronic medical record. The security features of the three most popular and powerful PHP frameworks Laravel, CodeIgniter, and Symfony were reviewed and compared. Based on the results, Laravel is equipped with the security features that electronic medical record currently required. This paper provides descriptions of the proposed conceptual framework that can be adapted to implement secure EMR systems.

Evaluations of Internet of Things-based personal smart farming system for residential apartments Fatin Natasya Shuhaimi; Nursuriati Jamil; Raseeda Hamzah
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2496

Urban farming is popularly accepted by communities living in cities as they are more health-conscious and to help support the high cost of living. Unfortunately, farming takes a considerable amount of time specially to monitor the plant’s growth. Therefore, smart farming using Internet of Things (IoT) should be adopted to realize urban farming. In this study, two IoT-based smart farming system designs for personal usages in a residential apartment were proposed and evaluated. As the design was meant for beginners, two utmost parameters for maintaining plant growth was evaluated, that are humidity and temperature. The humidity and temperature readings of design A using DHT 11 sensor and design B using DHT 22 sensor were recorded for 3 days and were compared against the actual humidity and temperature of the environment. After comparing the sum of absolute difference (SAD) of both designs, the implementation costs, and the consumption power, there is an inconclusive finding in terms of accuracy and costs. However, the basic design and cost of implementing a personal IoT-based smart farming system were proposed. The factors to be considered in constructing a personal smart farming system were also described.

Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores Mastura Md Saad; Nursuriati Jamil; Raseeda Hamzah
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.

Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores Mastura Md Saad; Nursuriati Jamil; Raseeda Hamzah
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.

Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores Mastura Md Saad; Nursuriati Jamil; Raseeda Hamzah
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.

A linear regression approach to predicting salaries with visualizations of job vacancies: a case study of Jobstreet Malaysia Khyrina Airin Fariza Abu Samah; Nurqueen Sayang Dinnie Wirakarnain; Raseeda Hamzah; Nor Aiza Moketar; Lala Septem Riza; Zainab Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1130-1142

This study explicitly discusses helping job seekers predict salaries and visualize job vacancies related to their future careers. Jobstreet Malaysia is an ideal platform for discovering jobs across the country. However, it is challenging to identify these jobs, which are organized according to their respective and specific courses. Therefore, the linear regression approach and visualization techniques were applied to overcome the problem. This approach can provide predicted salaries, which is useful as this enables job seekers to choose jobs more easily based on their salary expectations. The extracted Jobstreet data runs the pre-processing, develops the model, and runs on real-world data. A web-based dashboard presents the visualization of the extracted data. This helps job seekers to gain a thorough overview of their desired employment field and compare the salaries offered. The system’s reliability as tested using mean absolute error, the functionality test was performed according to the use case description, and the usability test was performed using the system usability scale. The reliability results indicate a positive correlation with the actual values. The functionality test produced a successful result, and a score of 96.58% was achieved for the system usability scale result, proving the system grade is ‘A’ and usable.

Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant? Nursuriati Jamil; Ali Abd Almisreb; Syed Mohd Zahid Syed Zainal Ariffin; N. Md Din; Raseeda Hamzah
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp558-566

Current deep convolution neural network (CNN) has shown to achieve superior performance on a number of computer vision tasks such as image recognition, classification and object detection. The deep network was also tested for view-invariance, robustness and illumination invariance. However, the CNN architecture has thus far only been tested on non-uniform illumination invariant. Can CNN perform equally well for very underexposed or overexposed images or known as uniform illumination invariant? This is the gap that we are addressing in this paper. In our work, we collected ear images under different uniform illumination conditions with lumens or lux values ranging from 2 lux to 10,700 lux. A total of 1,100 left and right ear images from 55 subjects are captured under natural illumination conditions. As CNN requires considerably large amount of data, the ear images are further rotated at every 5o angles to generate 25,300 images. For each subject, 50 images are used as validation/testing dataset, while the remaining images are used as training datasets. Our proposed CNN model is then trained from scratch and validation and testing results showed recognition accuracy of 97%. The results showed that 100% accuracy is achieved for images with lumens ranging above 30 but having problem with lumens less than 10 lux

Simulation of simultaneous localization and mapping using 3D point cloud data Shuzlina Abdul-Rahman; Mohamad Soffi Abd Razak; Aliya Hasanah Binti Mohd Mushin; Raseeda Hamzah; Nordin Abu Bakar; Zalilah Abd Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp941-949

Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology. Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.

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