The machine learning technique is studied to aid farmers in decision-making and analysing soil quality based on the nitrogen, phosphorus, and potassium NPK nutrients as the current soil in Malaysia experience degradation of soil organic that affect in production of the nutrient for the crops. The research aim is to study and analyse the Artificial Neural Network model in analysing the quality of soil based on the prediction of NPK level class, which the data collected from Smart Agri-Scan. Next objective is to evaluate the prediction and accuracy of the model. The ANN model is constructed in Neural Net Fitting App in MATLAB. A feedforward neural network is applied to the ANN model and trains it with two different training functions and a different number of neurons of hidden layers. The model with the smallest Mean Square Error is chosen for data analysis as it means the model has the best performance. From the prediction graph, the output of training and validation that corresponds to the prediction model is observed. The points of the output prediction close to the reference line are considered a good prediction model, which means it can analyse soil quality accurately. In future, the model might be able to do the analysis and decision directly at the monitoring platform based on the real-life prediction data.

In industrial process, pipeline leakage often only noticeable pipe condition is critical henceforth causing damages to its internal content. Therefore, pipe.line inspection and monitoring work is highly demanding to take early precautions. Electromagnetic Tomography (EMT) is capable to produce images of the internal structure of an object by using external sensors without disrupting it. The coil sensors are placed around the object where the source coil transmits on oscillating magnetic field while the rest acts as receiver and measure the received signals. The measured signal provides the magnetic field distribution of the pipe, and will differ according to the, materials' passive electrical properties. In this paper, we investigate the capability of using EMT to identify metallic pipe openings by conducting a finite element analysis simulation study. The design and parameters of the EMT system, as well as the results of using the EMT model to detect various degree of metallic pipe openings is presented. The results confirm that the EMT imaging as a promising tool for inspection of metallic pipelines where the magnetic field of the investigated region differs according to the pipe opening and material of the pipeline.

Velocity is one of the crucial parameters in the multiphase flow especially gas-solid two-phase flow. Electrical capacitance tomography (ECT) offers robust Velocity is one of the crucial parameters in the multiphase flow especially gas-solid two-phase flow. Electrical capacitance tomography (ECT) offers robust

Study on Influence of Different Gamma-ray Energy on Images of an Industrial CT System