An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005

Optical Character Recognition for Brahmi Script Using Geometric Method

Optical character recognition (OCR) system has been widely used for conversion of images of typed, handwritten or printed text into machine-encoded text (digital character). Previous researches on character recognition of South Asian scripts focus on modern scripts such as Sanskrit, Hindi, Tamil, Malayalam, and Sinhala etc. but little work is traceable to Brahmi script which is referred to as the origin of many scripts in south Asian. This study proposes a method for recognition of both handwritten and printed Brahmi characters which involve preprocessing, segmentation, feature extraction, and classification of Brahmi script characters. The geometric method was used for feature extraction into six different entities, followed by a newly developed classification rules to recognize the Brahmi characters based on the features. The method obtains accuracy of 91.69% and 89.55% for handwritten vowels and consonants character respectively and 93.30% and 94.90% for printed vowel and consonants character respectively

Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model

The deteriorating quality of natural water resources like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data is analyzed and water quality is predicted beforehand. This issue has been addressed in many previous researches, however, more work needs to be done in terms of effectiveness, reliability, accuracy as well as usability of the current water quality management methodologies. The goal of this study is to develop a water quality prediction model with the help of water quality factors using Artificial Neural Network (ANN) and time-series analysis. This research uses the water quality historical data of the year of 2014, with 6-minutes time interval. Data is obtained from the United States Geological Survey (USGS) online resource called National Water Information System (NWIS). For this paper, the data includes the measurements of 4 parameters which affect and influence water quality. For the purpose of evaluating the performance of model, the performance evaluation measures used are Mean-Squared Error (MSE), Root Mean-Squared Error (RMSE) and Regression Analysis. Previous works about Water Quality prediction have also been analyzed and future improvements have been proposed in this paper

Input parameters selection for soil moisture retrieval using an artificial neural network

Factors other than soil moisture which influence the intensity of microwave emission from the soil include surface temperature, surface roughness, vegetation cover and soil texture which make this a non-linear and ill-posed problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions to this type of problem. Since an ANN is a data driven model, proper input selection is a crucial step in its implementation as the presence of redundant or unnecessary inputs can severely impair the ability of the network to learn the target patterns. In this paper, the input parameters are chosen in combination with the brightness temperatures and are based on the use of incremental contributions of the variables towards soil moisture retrieval. Field experiment data obtained during the National Airborne Field Experiment 2005 (NAFE'05) are used. The retrieval accuracy with the input parameters selected is compared with the use of only brightness temperature as input and the use of brightness temperature in conjunction with a range of available parameters. Note that this research does not aim at selecting the best features for all ANN soil moisture retrieval problems using passive microwave. The paper shows that, depending on the problem and the nature of the data, some of the data available are redundant as the input of ANN for soil moisture retrieval. Importantly the results show that with the appropriate choice of inputs, the soil moisture retrieval accuracy of ANN can be significantly improved

A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development

Looking into the Freedom of Partner Choosing in Pair Programming

The published research studies to date indicate that pair programming has a positive impact on some aspects of students’ performance. In the normal practice of pairing programming in the academic field, the students were paired by assigning partners according to their level of programming skill. In other words, students were paired according to their programming compatibility that was perceived by their lecturers.However,research studies did not attempt to identify the main element that the student sarelookingintowhentheyaregiventhefreedomtoselecttheirpartnerinpairprogrammingpractice. Anexperimentwith76studentsduringaone-weekprogrammingworkshopshowsthat59.2%will choose their partner according to gender while 30.3% will choose their partner based on the ethnics group. The study shows that only 5.2% of the students focus on the skills of their choice of partner. At the end of the workshop, 96% of the students agree that pairing with a partner helps them in solving a programming problem. However, only 89.2% of the students prefer to work in pairs when solving programming while 5.4% prefer to work as an individual. This initial finding tallies with the other research whereby it shows that pair programming benefits the students in solving a programming problem.Despite the normal belief that the pairs are compatible if they are almost the same level in terms of technical competency in programming,students tend to choose according to gender when they are given a choice

Towards semantic user query: A review

This paper attempts to discuss the image query mechanisms and user needs for image retrieval. The explosive growth of image data leads to the need of research and development of Image retrieval. Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users’ mind. This paper also highlights both the already addressed and outstanding issues