Nurul Aida Mohd Mortar
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Preferred name
Nurul Aida Mohd Mortar
Official Name
Nurul Aida, Mohd Mortar
Alternative Name
Mortar, Nurul Aida Mohd
Mohd Mortar, Nurul Aida
Aida Mohd Mortar, Nurul
Main Affiliation
Scopus Author ID
57216407516
Researcher ID
DHB-8250-2022

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Optimization performance of unmanned aerial vehicle in wireless sensor network

2020 , Yumin Shakira Deraman , Izanoordina Ahmad , Aizat Faiz Ramli , Nurul Aida Mohd Mortar

An unmanned aerial vehicle (UAV) has been widely used in the Wireless Sensor Network for remote surveillance with number of advantages such as flexibility, mobility and ease of realization. The UAV can acts as a data collector or a relay between the central gateway and WSNs. The smart system in UAV are able to capture valuable data by using the right acquisition equipment and translate the information to better and faster data-driven decision. In despite of its success in many applications and field of studies, there are still open issues regarding the collaborative of UAV in Wireless Sensor Network such as an optimal trajectory design for the UAV, height between UAV and ground sensor nodes and energy management of the networks. This paper analyses the most common used algorithms to improve the performance of UAV-WSN. Each of the techniques are studied and a comprehensive analysis is presented.

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The development of arm rehabilitation for muscle contraction monitoring by using Labview

2020 , Izanoordina Ahmad , Muhammad Fadil Zaidi , Nurul Aida Mohd Mortar

Muscle paralysis can be defined as the loss of muscle function. This is caused by the failure of the nerves that send signals for muscle movement. It is often caused by the damage of nervous system especially at the spinal cord. Other major conditions of muscle paralysis are stroke, trauma and muscular dystrophy. This muscle function loss can be a partial or total muscle function loss. Partial muscle function loss or paralysis affects a part of the body whereas total muscle function loss affects the whole body. Arm paralysis is a partial paralysis of muscle function. It can be detected by feeling loss at the arm and unable to lift up the arm as there is sensory damage or motor damage of the arm. The only treatment to treat partial paralysis is through convalescence or rehabilitation. This includes physical therapy and occupational therapy for recuperation. Therefore, the development of arm rehabilitation for muscle contraction monitoring by using LabVIEW is in the scope of physical therapy where it is focuses on mobility, help to maintain and build any strength that remained in affected muscle. The distinctive part about this monitoring of muscle arm contraction rehabilitation development by using LabVIEW is the patients and doctors are able to see the progress in real time of the patient's arm muscle contraction. By using LabVIEW, it displays the data of recorded parameters on how much force is exerted from the patient's grip referring to index finger, middle finger and ring finger and rotation angle of the arm at the wrist and elbow when they move upward or downward. The sensors used are electromyogram sensor to detect muscle contraction, variable resistor for arm rotation angle and force sensitive resistor to detect the forces grip. From this, the activities of the patients? arm can be recognized

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The development of galvanic skin response for depressed people

2020 , M. N. Naszariah , K. Nur‘ Aina Khaleeda , Nurul Aida Mohd Mortar

University education and its environment are totally different from the nature of learning at the high school. The university students are facing a lot of challenges such as academic stress, financial problem, independent living, and future career planning. These factors predispose them to depression. Depression can be said as a serious medical condition and it is totally impossible to avoid it. However, with some preventive actions might be able to overcome the problem. The threat of depression caused the nervous system to respond by releasing a flood of stress hormones, including adrenaline and cortisol. The Galvanic Skin Response (GSR) is described as change in the electrical resistance of the skin that is physiochemical response to emotional arousal which increases sympathetic nervous system activity. The aim of this research is to investigate the waveform pattern of a depressed patient by using Galvanic Skin Response (GSR). The research was conducted on the university students with a range of gestational age 20-25 years old regardless of gender. The GSR measurements are performed by detecting changes in electrical activity as a result of changes in sweat gland activity during depression and at rest (sleep). The data from GSR and pulse rate are then transmitted to the serial plotter and organic light-emitting diode display. The GSR readings for no emotional arousal and physiological occurred were 600mω. According to the results of the analysis, it was found that the readings of depression patients were below 600mω with pulse rate of 210bpm compared with those were sleeping of 700mω with mean pulse rate of 79bpm. Hence, pulse Galvanic Skin Resistance is a useful tool to indicate the waveform pattern in depressed patients.

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Heterogeneous ensemble classifiers for Malay syllables classification

2020 , Zaridah Mat Zain , Zulkhairi Mohd Yusuf , Hariharan Muthusamy , Kushsairy Abd Kader , Nurul Aida Mohd Mortar

Traditionally, the approach that has been used in the design of pattern classification was to experimentally assess the performance of several classifiers so that the best one would be chosen. Ensemble classifiers is developed to design a such reliable system with improved accuracy. Recognising that each classifier may make different or complementary errors, the idea is to pool the results from all classifiers to find a composite system that would out-perform any individual (base) classifier. In this way, a single complex classifier may be replaced by a set of relatively simple classifiers. Similarly, the idea of ensemble learning is to employ multiple learners and combine their predictions. Most ensemble methods use a single base learning algorithm to produce homogeneous base learners to produce homogeneous ensembles. Some methods use heterogeneous learners to create heterogeneous ensembles. For ensemble methods to be more accurate than any of its members, the base learners must be as accurate and as diverse as possible. Motivated by this ensemble approach, a heterogeneous ensemble model with different algorithms for members' training is proposed. More specifically, three types of classifiers namely the ELM, SVM and RF have been adopted for classification of Malay syllables. In the process of improving ensemble accuracy and stability, different combination methods are explored. These methods vary in their approach to treat the training data, the type of algorithms used, and the combination methods followed. Diversity among the performance of each single classifier in the ensemble classifiers is also analysed and compared with SD, SI, FI, FD, MI and MD configurations. Based on the obtained results, heterogeneous ensemble method has been successfully proven in improving recognition accuracy on the selected Malay syllables.

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