Search results for: Shaira L. Kee

Authors: Shaira Karishma Sheriff, Zarinah Hamid

Abstract:

The Arab Spring, which commenced during the end of 2010 and accelerated during 2011, was caused primarily due to poverty, unemployment and a general recession in the Middle East and North African (MENA) region. The core motivation of this revolution could be said to be the need for political, economic and social reforms that the region desires to experience. Though GDP growth has been significant in the region, the income distribution mechanism in MENA countries has been ineffective. This results in low levels of education, substandard health care facilities, unemployment, and poverty. This paper argues that MENA countries have great potential for experiencing socioeconomic development by being less dependent on oil exports and enhancing their services sector through better education which would eventually lead to job creation. Furthermore, the region can encourage better trade and political integration by forming transparent and accountable governments. The notion of Nation-State needs to be addressed and the countries in the region need to look for ways to develop effective supra-national institutions for better political and economic integration that goes beyond geographical borders.

Keywords: political reforms, social reforms, economic development, nation-state, economic integration

Procedia PDF Downloads 439

Authors: Salina Budin, Nor Liawati Abu Othman, Shaira Ismail

Abstract:

This study attempts to determine kinesthetic, verbal and visual intelligences among mechanical engineering undergraduate students and explores any probable relation with students’ learning styles and academic performance. The questionnaire used in this study is based on Howard Gardner’s multiple intelligences theory comprising of five elements of learning style; environmental, sociological, emotional, physiological and psychological. Questionnaires are distributed amongst undergraduates in the Faculty of Mechanical Engineering. Additional questions on students’ perception of learning styles and their academic performance are included in the questionnaire. The results show that one third of the students are strongly dominant in the kinesthetic intelligent (33%), followed by a combination of kinesthetic and visual intelligences (29%) and 21% are strongly dominant in all three types of intelligences. There is a statistically significant correlation between kinesthetic, verbal and visual intelligences and students learning styles and academic performances. The ANOVA analysis supports that there is a significant relationship between academic performances and level of kinesthetic, verbal and visual intelligences. In addition, it has also proven a remarkable relationship between academic performances and kinesthetic, verbal and visual learning styles amongst the male and female students. Thus, it can be concluded that, academic achievements can be enhanced by understanding as well as capitalizing the students’ types of intelligences and learning styles.

Keywords: kinesthetic intelligent, verbal intelligent, visual intelligent, learning style, academic performances

Procedia PDF Downloads 298

Authors: Ravish K. Jain, Jatinder Kaur, Shaira Arora, Arun Kumar, Amit K. Chawla, Atul Khanna

Abstract:

Surface-dependent properties such as hydrophobicity can be modified significantly by oblique angle deposition technique. Bi thin films were studied for their hydrophobic nature. The effects of oblique angle deposition on structural, surface morphology, electrical and wettability properties of Bi thin films have been studied and a comparison of these physical properties of normally deposited and obliquely deposited Bi films has been carried out in this study. X-ray diffraction studies found that films have highly oriented hexagonal crystal structure and crystallite size is smaller for obliquely deposited (70 nm) film as compared to that of the normally deposited film (111 nm). Raman spectra of the films consist of peaks corresponding to E_g and A_1g first-order Raman modes of bismuth. The atomic force and scanning electron microscopy studies show that the surface roughness of obliquely deposited film is higher as compared to that of normally deposited film. Contact angle measurements revealed that both films are strongly hydrophobic in nature with the contact angles of 105ᵒ and 119ᵒ for normally and obliquely deposited films respectively. Oblique angle deposition enhances the hydrophobicity of the film. The electrical conductivity of the film is significantly reduced by oblique angle deposition. The activation energies for electrical conduction were determined by four-probe measurements and are 0.016 eV and 0.018 eV for normally and obliquely deposited films respectively.

Keywords: bi thin films, hydrophobicity, oblique angle deposition, surface morphology

Procedia PDF Downloads 259

Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

Abstract:

Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

Procedia PDF Downloads 80

Authors: Salina Budin, Shaira Ismail

Abstract:

Students learn effectively in a learning environment with a suitable teaching approach that matches their learning preferences. The main objective of the study is to examine the learning preferences amongst the students in the Science and Technology (S&T), and Social Science (SS) fields of study at the Universiti Teknologi Mara (UiTM), Pulau Pinang. The measurement instrument is based on the Dunn and Dunn Learning Styles which measure five elements of learning styles; environmental, sociological, emotional, physiological and psychological. Questionnaires are distributed amongst undergraduates in the Faculty of Mechanical Engineering and Faculty of Business Management. The respondents comprise of 131 diploma students of the Faculty of Mechanical Engineering and 111 degree students of the Faculty of Business Management. The results indicate that, both S&T and SS students share a similar learning preferences on the environmental aspect, emotional preferences, motivational level, learning responsibility, persistent level in learning and learning structure. Most of the S&T students are concluded as analytical learners and the majority of SS students are global learners. Both S&T and SS students are concluded as visual learners, preferred to be in an active mobility in a relaxing and enjoying mode with some light of refreshments during the learning process and exhibited reflective characteristics in learning. Obviously, the S&T students are considered as left brain dominant, whereas the SS students are right brain dominant. The findings highlighted that both categories of students exhibited similar learning preferences except on psychological preferences.

Keywords: learning preferences, Dunn and Dunn learning style, teaching approach, science and technology, social science

Procedia PDF Downloads 243

Authors: Mohammad Abu-Shaira, Weishi Shi

Abstract:

Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

Procedia PDF Downloads 10