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The Impact of Career Adaptability and Social Support on Job Search Self-Efficacy
Ibrahim AL-JUBARI,Siti Nurzulaika Binti SHAMSOL ANUAR,Ahmad Alif Bin AHMAD SUHAIMI,Aissa MOSBAH 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.6
The objective of the current study is to examine the effect of career adaptability (conceptualized as a multidimensional construct: concern, control, curiosity, and confidence) and social support on job search self-efficacy and career outlook among students in higher education institutions in Malaysia. Graduates’ employability is of great concern to policymakers as it impacts the economic and social development. It is crucial to enhance students’ career-related skills as well as their adaptability competencies towards the fast-paced changing dynamic of the job market demand. Data were collected from a convenient sample of 358 respondents from final and second final year students representing several disciplines. To validate the model we used the covariance structural equation modeling with maximum likelihood estimation. Several fit indices have been used: chi-square index (χ2), normed-fit chi-square, root mean square error of approximation (RMSEA), Tucker–Lewis Index (TLI), and Comparative Fit Index (CFI). The analysis revealed that career adaptability and social support have positive effects on job search self-efficacy and career outlook. The findings suggest that students’ perceived concern, control, curiosity and confidence as dimensions of career adaptability and social support from family, friends and peers, are critical factors in predicting their career outlook and their ability to find career opportunities.
Amin Dastanpour,Suhaimi Ibrahim,Reza Mashinchi,Ali Selamat 한국산학기술학회 2014 SmartCR Vol.4 No.6
Currently network security researchers are focusing on intrusion detection systems. The effectiveness of a Gravitational Search Algorithm in optimizing the results of an Artificial Neural Network is investigated for attack detection in an intrusion detection system. The KDD CUP ‘99 dataset is used in this study for achieving the ANN results. The results are presented before applying the GSA, and they are compared with optimal results after the GSA has been applied.
Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
Hassan Amer Algaifi,Suhaimi Abu Bakar,Rayed Alyousef,Abdul Rahman Mohd. Sam,Ali S. Alqarni,M.H. Wan Ibrahim,Shahiron Shahidan,Mohammed Ibrahim,Babatunde Abiodun Salami 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.4
In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, <i>R</i><sup>2</sup>, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.