http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Dilemma of Saudi Arabian Construction Industry
Albogamy, Abdullah,Scott, Darren,Dawood, Nashwan Korea Institute of Construction Engineering and Ma 2013 Journal of construction engineering and project ma Vol.3 No.4
Currently, the Kingdom of Saudi Arabia (KSA) is the epicentre of building services engineering encapsulating the construction industry. On rise of technological advancements, engineers have the ease to thoroughly investigate engineering aspects. Not only engineers, but other stakeholders, tender related people, financial analysts work in parallel as well. However, there are some factors that are stumbling blocks in the way of progression including delaying factors in the construction industry. The paper provides deep insights of delaying factors regarding public building projects of the KSA. Collection of primary data was carried out by conducting a survey which comprised of 63 chief delay factors. Professionals related to construction industry were asked for ranking the factors in terms of their frequency of occurrence and degree of impact. Seven groups of risk factors are categorized and a correlation analysis is performed by identifying the correlation amongst the variables. Finally, 31 leading delay factors are extracted and reported.
A Deep Convolutional Neural Network with Batch Normalization Approach for Plant Disease Detection
Albogamy, Fahad R. International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.9
Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.
M. M. Hessien,Mahdi Albogamy,Mohammed Alsawat,Abdulrahman Alhadhrami 한국자기학회 2023 Journal of Magnetics Vol.28 No.2
Hexagonal M-type strontium ferrite (SrFe12O19) has been fabricated through a simple self-combustion tartrate precursor approach to producing a homogenous powder with a homogeneous shape and limited size distribution at low-processing temperatures. The impacts of the Sr2+:Fe3+ molar ratio and the annealing temperature on formation, morphological structure, crystallite size, and magnetic performance were studied. The powders were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) profile, and vibrating sample magnetometer (VSM). The development of crystalline single-phase Sr-hexagonal ferrite occurred at ≥ 1100 °C and Sr2+:Fe3+ molar ratios 1.1:12-1.3:12. Existence of α-Fe2O3 and impurities in the hexagonal powders increases the lattice parameters while higher annealing temperature decreases it. The c/a ratios of as-prepared samples (~3.911-3.920) are comfortably within the range of ratios for M-type structures. The platelet-like structure has appeared at an annealing temperature ≥ 1000 °C. The wide saturation magnetization (37.26-66.19 emu/g) and coercivity (275.09-2107.8Oe) were accomplished at diverse synthesis conditions and reached the greatest values at 1350 °C. The squareness ratios (Mr/Ms) for all studied samples are <0.5, which is for multimagnetic domains.
Image compression using K-mean clustering algorithm
Munshi, Amani,Alshehri, Asma,Alharbi, Bayan,AlGhamdi, Eman,Banajjar, Esraa,Albogami, Meznah,Alshanbari, Hanan S. International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.9
With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.
Almaleki, Deyab A.,Alzahrani, Abdulrahman J.,Alkhairi, Mohammed A.,Albalawi, Farhan A.,Albogami, Hosin A.,Alhajory, Easa S.,Readi, Wadea A.,Idrees, Mohammed A.,Alshamrani, Saleh M.,Alwusaidi, Osama A. International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.8
This study aimed to test the factor structure of the measure of student participation in distance education. The study population consisted of all teachers in public education and faculty members in higher education in the Kingdom of Saudi Arabia by applying it to a sample of bachelor's and graduate students at the college of Education at umm al-Qura University. The (ESE) was applied to a random sample representing the study population consisting of (216) respondents. The results of the study showed that the scale consists of three main factors, with showed a high degree of construct validity through fit indices of the confirmatory factor analysis. The results have shown a gradual consistency of the measure's invariance that reaches the high level of the Measurement Invariance across the gender and study groups variables.