Physical Classification of ripe fruits is an expensive affair in the agriculture industry and human error can lead to inaccurate results. This paper introduces the concept of an intelligent AI-based system using spectrophotometry and computer vision for automated fruit segregation based on their grade. When the fruit is fed into the proposed system, the fruit is identified with 95% accuracy, using a cloud-computing platform provided by Microsoft Azure. After that, using spectroscopy and ensemble machine learning approaches, fruit grade is predicted. This ensemble model is trained using 1366 apple readings taken from Unitec's Apple Sorting and Grading Machine from an industrial plant. With the help of H2O's Driverless.AI, the proposed ensemble provides an overall approximate validation accuracy of 82%. The model is also tested on an unseen test dataset containing real-life spectral values and the accuracy of fruit segregation into different classes peaked at 72%.

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

Unmanned aerial vehicles (UAVs) allow formation of wide range ad hoc networks. These ad hoc formations with unmanned vehicles provide coverage of vast areas of applications involving mission dependent activities. Such networks can solve various issues related to civilian and military activities. One of the main applications of these networks is continuous surveillance. Surveillance by multiple nodes in ad hoc mode is directly dependent upon the continuous data sharing, cooperative decision making and stabilized network formation. Failures in network can hinder the performance and can decrease its operability. It is difficult to aloof network from discrete failures. Therefore, stabilized model is required which can provide stability to the whole network. For this, a self-healing neural model is developed which is capable of handling uncertain failures. It also provides provision for recovery of nodes from failure to stabilized state.