Analisis ramalan pelemahan hujan Semenanjung Malaysia menggunakan peta rekaan berkontur

Rain weaken the signal wave through the absorption and dispersion of signal reception causing disturbance in satellite systems operating above 10 GHz frequencies. This phenomenon resulting rain attenuation occurs. Malaysia is a tropical country and experiencing a high rate of rainfal hence facing a huge disturbancel. The source of rainfall data used by this research are from the Department of Irrigation and Drainage Malaysia (DID) using a rain gauge Tipping Bucket method with rain gauge size diameter 0.25 mm per tip for measuring droplets of rainfall. One minute rain precipitate data were used to calculate the rainfall rate. precipitation rate calculation of rainfall. ITU-R model was selected according to the climate and levels of rainfall throughout the year as well as it is widely accepted on satellite communication systems research in the tropics. The rain attenuation in PenisularMalaysia were found in the range of 40-51 dB at 0.01% of time and was illustrated through contoured map. The result of this rain attenuation prediction can be used as reference to other researchers for continued research to improve and enhance the quality of transmission and reception of radio signals on satellite systems which operate over 10 GHz in tropical climate and high rainfall

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Background and Aims A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning

Pterygium classification using deep patch region-based anterior segment photographed images

Early pterygium screening is crucial to avoid blurred vision caused by cornea and pupil encroachment. However, medical assessment and conventional screening could be laborious and time-consuming to be implemented. This constraint seeks an advanced yet efficient automated pterygium screening to assist the current diagnostic method. Patch region-based anterior segment photographed images (ASPIs) focus the feature on a particular region of the pterygium growth. This work addresses the data limitation on deep neural network (DNN) processing with large-scale data requirements. It presents an automated pterygium classification of patch region-based ASPI using our previous re-establish network, “VggNet16-wbn”, the VggNet16, with the addition of batch normalisation layer after each convolutional layer. During an image pre-processing step, the pterygium and nonpterygium tissue are extracted from ASPI, followed by the generation of a single and three-by-three image patch region-based on the size of the 85×85 dataset. Data preparation with 10-fold cross-validation has been conducted to ensure the data are well generalised to minimise the probability of underfitting and overfitting problems. The proposed experimental work has successfully classified the pterygium tissue with more than 99% accuracy, sensitivity, specificity, and precision using appropriate hyperparameters values. This work could be used as a baseline framework for pterygium classification using limited data processing

Deep learning for an automated image-based stem cell classification

Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.Keywords: Automated stem cell classification; Colony-forming unit (CFU); Deep learning; Convolutional neural network (CNN) Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiabOptometry and Vision Sciences Programme, Faculty of Health Sciences, School of Healthcare Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia*proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research