Chitosan has become the most known and second abundantly available recyclable, non-hazardous and eco-friendly biopolymer after cellulose with several advantageous biomedical, agriculture, and wastewater treatment applications. As nanotechnology has progressed, researchers have begun incorporating chitosan-based carbon compounds into various compounds, elements, and carbonaceous materials to increase their efficiency and biocompatibility. Chitosan carbon compounds have also been used directly in many applications due to their inherent chelating and antibacterial features and the presence of customizable functional groups. In this review, synthesis technologies and microstructure of chitosan composites and its carbon materials in biomedical, agriculture, and wastewater treatment concerning the administration of abiotic stress within plants, water accessibility for crops, scheming food bear pathogens, photothermal cancer rehabilitation, and heavy water pollutants absorption and removal methods are widely deliberated upon, with a relevant discussion of the techniques that can be used to put these into action. Chitosan is also utilized in miscellaneous applications, including the food sector and cosmetics. Overall, chitosan-based carbon compounds promise to extend agricultural practices while also addressing health concerns in an environmentally friendly manner.

Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.

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