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Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.

The present world is badly affected by the novel coronavirus (COVID-19). Using medical kits to identify coronavirus-affected persons is very slow. What happens in the next, nobody knows. The world is facing an erratic problem and doesn't know what will happen in near future. This paper is trying to make a prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data from 258 regions, their latitude and longitude, and the number of death in 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect in extracting highly essential features for time series data (TSD) analysis. There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.

This study examined whether participants’ adherence to an algorithmic aid was related to the degree of control they were provided at decision point and their attitudes toward new technologies and algorithms. It also tested the influence of control on participants’ subjective reports of task demands whilst using the aid. A total of 159 participants completed an online experiment centred on a simulated forecasting task, which required participants to predict the performance of school students on a standardized mathematics test. For each student, participants also received an algorithm-generated forecast of their score. Participants were randomly assigned to either the ‘full control’ (adjust forecast as much as they wish), ‘moderate control’ (adjust forecast by 30%) or ‘restricted control’ (adjust forecast by 2%) group. Participants then completed an assessment of subjective task load, a measure of their explicit attitudes toward new technologies, demographic and experience items (age, gender and computer literacy) and a novel version of the Go/No-Go Association Task, which tested their implicit attitudes toward algorithms. The results revealed that participants who were provided with more control over the final forecast tended to deviate from it more greatly and reported lower levels of frustration. Furthermore, participants showing more positive implicit attitudes toward algorithms were found to deviate less from the algorithm’s forecasts, irrespective of the degree of control they were given. The findings allude to the importance of users’ control and preexisting attitudes in their acceptance of, and frustration in using a novel algorithmic aid, which may ultimately contribute to their intention to use them in the workplace. These findings can guide system developers and support workplaces implementing expert system technology.

In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is used to design the corresponding quantum image circuit. Due to the limitation of qubits, a total of 16 numbers of qubits is used to represent the coefficient and its position of the grayscale image. Among those, 8 number of qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio), and bit rate.