Faktor-Faktor Pemilihan Kerjaya Pelajar

This study was aimed to analyse the association between the family, the school and the academic achievement factors with the choice of students’ career. This study was carried out on 227 students from two secondary schools in the town of Taiping, Perak by using a questionnaire. x2 test was used to show these relationships. It was found that for the family factor, only the aspects of discussion between brothers and sisters as well as encouragement by them have encouraged the students to choose their career in the higher skilled and technical field. Meanwhile, the factors of parents’ and family members’ advice, careers liked by the parents and family members, careers aspired by the parents, discussion and support from the parents, and brothers’ and sisters’ hope did not have any relationship with the choice of students’ career. For school factors, advice from the principal, teachers, the role of school councillor, career talks and exhibitions, discussion with the teachers and the school councillor was found not to have any relationship with the choice of students’ career. For the factor of academic achievement, only the results in the English and Biology were associated with the choice of students’ career compared to the achievement in the subjects like Malay Language, Mathematics, Science, History, Principles of Accounts, Physics and Chemistry which have no association with the choice of students career

Investigation of human-centered transportation (walking and biking) for low-income workers in Kabul, Afghanistan

Human-centered transportation (walking and biking) has been the cheapest, healthiest, and most convenient mode of transportation throughout history. In the new global economy, walking and biking have become common modes of transportation for low-income groups of people. Kabul is the biggest city in Afghanistan with scattered space organizations and currently is unfavorable for walking and biking due to insufficient attention to pedestrian and bicycle routes in city planning and poor road network and sidewalk conditions, which are among the issues that affect this 4-5 million population city. The purpose of this research is to analyze the current traffic situation in Kabul and identify the role and share of citizens' use of human-centered transportation (walking and biking) for transportation. This research also aims to investigate the relationship between the economic scope of low-income workers and the use of walking and biking for transportation. The statistical population of the current study was selected from three municipal districts as travel zones. Using cluster sampling, a sample participant of 929 people was obtained. It was observed that in the broad context, due to increasing cost and insufficient public transportation, low-income workers use bicycles and walking as a reliable mode of transportation. Finally, it is suggested that the spatial organization of Kabul is redefined and designed based on the new space organization, and the local organization and formulation of urban transportation strategies in urban strategic plans for pedestrian and bicycle transportation systems are strengthened, especially for roads leading to employment locations. Furthermore, in planning, priority is to be shifted to human-centered transportation (walking and biking)

Stochastic Computing Correlation Utilization in Convolutional Neural Network Basic Functions

In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group

Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

Due to their low power consumption and computing power, Internet of Things (IoT) devices are difficult to secure, and the rapid growth of IoT devices in the home increases the risk of cyber-attacks. One method of preventing cyberattacks is to employ an intrusion detection system (IDS), which detects incoming attacks and notifies the user, allowing for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning, but these efforts have been unsuccessful. In this paper, we classify network attacks using two Convolutional Neural Networks (CNN) models i.e., MyCNN and IoTCNN to automatically detect various kind malignant and benign intrusion in IoT network. The purpose of this research is to evaluate the use of deep learning in IoT IDS. The neural network was trained in this experiment using the NF-UNSW-NB15-v2 dataset, which contains nine distinct types of attacks. The data from the network stream was converted to Red Green and Blue (RGB) images, which were then used to train the neural network. To establish baseline models, we proposed two models with the name of MyCNN and IoTCNN. When compared the proposed MyCNN convolutional neural network for network attack classification, the IoTCNN was outperformed by the MyCNN model. Additionally, it demonstrates that both networks achieve higher accuracy in the majority of categories but the IoTCNN achieved lower than the proposed MyCNN model for network attack detection. We discovered that the MyCNN is generally more suitable to be deployed for intrusion detection in IoT devices

Analysis of nonlinear convection-radiation in chemically reactive Oldroyd-B nanoliquid configured by a stretching surface with Robin conditions: applications in nano-coating manufacturing

Motivated by emerging high-temperature manufacturing processes deploying nanopolymeric coatings, the present communication studies nonlinear thermally radiative OldroydB viscoelastic nanoliquid stagnant-point flow from a heated vertical stretching permeable surface. Robin (mixed derivative) conditions are utilized to better represent coating fabrication conditions. The nanoliquid analysis is based on Buongiorno's two-component model which elaborates Brownian movement and thermophoretic attributes. Nonlinear buoyancy force and thermal radiation formulations are included. Chemical reaction (constructive and destructive) is also considered since coating synthesis often features reactive transport phenomena. Via a similarity approach, an ordinary differential equation model is derived from the primitive partial differential boundary value problem. Analytical solutions are achieved employing homotopy analysis scheme. The influence of emerging dimensionless quantities on transport characteristics is comprehensively elaborated with appropriate data. The obtained analytical outcomes are compared with available limiting studies and good correlation is achieved. The computations show that the velocity profile is diminished with increasing relaxation parameter whereas it is enhanced when retardation parameter is increased. Larger thermophoresis parameter induces temperature and concentration enhancement. The heat and mass transfer rates at the wall are increased with an increment in temperature ratio and first order chemical reaction parameters while contrary effects are observed for larger thermophoresis, fluid relaxation and Brownian motion parameters. The simulations find applications in stagnation nano-polymeric coating of micromachines, robotic components and sensor

Persepsi masyarakat parlimen Pekan terhadap gagasan 1Malaysia

Malaysia merupakan negara yang terdiri daripada masyarakat yang berbilang etnik, kaum dan agama. Perbezaan ini memerlukan kepada satu gagasan yang mampu mengeratkan hubungan sedia ada. Justeru, pihak kerajaan melancarkan gagasan 1Malaysia dengan matlamat meningkatkan tahap hubungan sedia ada. Oleh itu, kajian ini dijalankan untuk mengetahui sejauh manakah penerimaan rakyat terhadap gagasan 1Malaysia yang dilancarkan oleh kerajaan, mengetahui halangan dan cabaran dalam merealisasikan gagasan 1Malaysia, mengenal pasti medium yang efektif dalam proses penyampaian gagasan 1Malaysia ke akar umbi dan menghasilkan model yang komprehensif untuk memajukan gagasan 1Malaysia. Bagi mencapai objektif kajian tersebut, kumpulan penyelidik telah menjalankan kajian kuantitatif dan kualitatif iaitu menggunakan instrumen soal selidik dan temu bual. Data yang diperoleh dianalisis menggunakan perisian SPSS dan N’Vivo 8.0. Dapatan kajian ini menunjukkan penerimaan rakyat terhadap gagasan 1Malaysia berada pada tahap yang tinggi. Kajian ini memberi manfaat kepada banyak pihak terutama pihak kerajaan dalam usaha memajukan gagasan 1Malaysia dan meningkatkan keharmonian hubungan antara kaum di negara ini

Factors associated with nonalcoholic fatty liver disease grades detected by ultrasound at a screening center in Klang Valley, Malaysia

Background: Non-alcoholic fatty liver disease (NAFLD) is a very common liver disease in the world, particularly in Western and developed countries. It is rapidly growing in the Asia- Pacific region. Objectives: This study was designed to determine the association between risk factors and non-alcoholic fatty liver disease grades among Malaysian adults. Patients and Methods: A cross-sectional observational study design was prospectively carried out in this study. Consecutive 628 respondents who attended for a medical checkup at urban health center had been recruited for the study. All respondents had the physical examination, blood tests, clinical assessments, and abdominal ultrasound. A structured self-administered questionnaire has been also used in this study in this survey. Results: From a total of 628 “health screened” subjects, 235 subjects (37.4%) were diagnosed to have non-alcoholic fatty liver disease. Female gender and Chinese race were predominant in our study population. Of those with NAFLD, more than half subjects (63.4) had a moderate grade of non-alcoholic fatty liver disease. The mean age of the study population was 54.54 ±6.69 years. Differences of the mean body mass index (BMI) and waist to hip ratio (WHR) were found to be significant among non-alcoholic fatty liver disease grades (P< 0.001). Similarly, mean triglycerides (TG) and high-densiy lipoprotein-cholesterol (HDL-C) levels had significant differences among non-alcoholic fatty liver disease grades (P< 0.001 and P= 0.016, respectively). Conclusion: the non-alcoholic fatty liver disease is common among urban Malaysian adult population. Anthropometric measurements were closely correlated with non-alcoholic fatty liver disease grades

Thermal growth in solar water pump using Prandtl-Eyring hybrid nanofluid: a solar energy application

Nowadays, with the advantages of nanotechnology and solar radiation, the research of Solar Water Pump (SWP) production has become a trend. In this article, Prandtl-Eyring hybrid nanofluid (P-EHNF) is chosen as a working fluid in the SWP model for the production of SWP in a parabolic trough surface collector (PTSC) is investigated for the case of numerous viscous dissipation, heat radiations, heat source, and the entropy generation analysis. By using a well-established numerical scheme the group of equations in terms of energy and momentum have been handled that is called the Keller-box method. The velocity, temperature, and shear stress are briefly explained and displayed in tables and figures. Nusselt number and surface drag coefficient are also being taken into reflection for illustrating the numerical results. The first finding is the improvement in SWP production is generated by amplification in thermal radiation and thermal conductivity variables. A single nanofluid and hybrid nanofluid is very crucial to provide us the efficient heat energy sources. Further, the thermal efficiency of MoS2-Cu/EO than Cu-EO is between 3.3 and 4.4% The second finding is the addition of entropy is due to the increasing level of radiative flow, nanoparticles size, and Prandtl-Eyring variable