Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.
<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Twitter is an online microblogging and social-networking platform which allows users to write short messages called tweets. It has over 330 million registered users generating nearly 250 million tweets per day. As Malay is the national language in Malaysia, there is a significant number of users tweeting in Malay. Tweets have a maximum length of 140 characters which forces users to stay focused on the message they wish to disseminate. This characteristic makes tweets an interesting subject for sentiment analysis. Sentiment analysis is a natural language processing (NLP) task of classifying whether a tweet has a positive or negative sentiment. Tweets in Malay are chosen in this study as limited research has been done on this language. In this work, sentiment analysis applied to Malay tweets using the deep learning model. We achieved 77.59% accuracy which exceeds similar work done on Bahasa Indonesia.</p></td></tr></tbody></table>
This study focused on developing software for image reconstruction system of optical image data obtained from four projections of CMOS linear image sensors by using MATLAB. Four projections of collimated light beams at least must be used in order to avoid aliasing and smear effect that may be appeared on the reconstructed image obtained. The image reconstruction is based on linear back-projection method where transpose matrix, and pseudo-inverse matrix are used to solve inverse matrix problems in MATLAB. Results were compared between both inverse problem calculation methods selected. It was discovered that transpose matrix method performs better than pseudo–inverse matrix for a high resolution images. A graphical user interface has been implemented, it is capable to reconstruct image from raw data collected from sensor. Reconstruction results demonstrated in most cases are able to produce an adequate image for further analysis by user.
Soil spectroscopy measurement is widely used to determine the macronutrients content in the soil. Spectrometer is costly equipment and commonly used to determine the transmittance, absorbance or reflectance level of various liquids and opaque solids by measuring the intensity of light as a light source passes through a sample chemical substance. This paper is reported on a low cost experimental assessment of soil macronutrient for soil spectroscopy utilizing Raspberry Pi (RPI) module in visible and near-infrared (NIR) wavelength. The sensitivity measurements are mainly due to the concentration level and the intensity of light emitting diode (LED) light source. The work is focusing on the absorbance spectroscopy particularly on linear relationship to determine the Nitrogen (N), Phosphorus (P) and Potassium (K) content level in soil using colour-developing reagent. The development of low cost and portable RPI based spectrophotometer has created new possibilities to measure the concentration level of the existed soil macronutrient within visible and infrared light wavelength of light sources. The absorbance of light was computed based on Beer-Lambert Law. The low cost RPI based spectrometer costs 80% less than the spectrometer available in the market and is capable of recording the absorbance measurements up to 5 samples. The performance of this prototype shows that it is possible to build the spectrometer using open-source software and hardware by considering the limiting factors such as light transfer to the sample, spectral filtering and the sensitivity due to the signal-to-noise ratio.
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