# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 4 Results
### Application Domains
In the area of water resources and climate analysis, research has been conducted on groundwater quality monitoring [131], predicting ocean circulation regimes [132], water resources management through snowmelt-driven streamflow prediction [133], and analyzing the impact of land cover changes on climate [134]. Additionally, studies have addressed predicting spatiotemporal distributions of lake surface temperature in the Great Lakes [135] and soil moisture prediction [136]. Environmental monitoring and resource management applications also include predicting heavy metals in groundwater [137], detection and quantification of isotopes using gamma-ray spectroscopy [138], and recognizing bark beetle-infested forest areas [139]. Agricultural applications have similarly leveraged XAI techniques for plant breeding [140], disease detection in agriculture [141], diagnosis of plant stress [142], prediction of nitrogen requirements in rice [143], grape leaf disease identification [144], and plant genomics [145]. Urban and industrial applications are also prominent, with studies on urban growth modeling and prediction [146], building energy performance benchmarking [147], and optimization of membraneless microfluidic fuel cells for energy production [148]. Furthermore, predicting product gas composition and total gas yield [149], wastewater treatment [150], and the prediction of undesirable events in oil wells [151] have been significant areas of research. Lastly, environmental studies have also focused on predicting drought conditions in the Canadian prairies [152]. In the manufacturing sector, XAI techniques have been employed for a variety of predictive and diagnostic tasks. For instance, research has focused on prognostic lifetime estimation of turbofan engines [153], fault prediction in 3D printers [154], and modeling hydrocyclone performance [155]. Moreover, the prediction and monitoring of various manufacturing processes have seen substantial research efforts. These include predictive process monitoring [156; 157], average surface roughness prediction in smart grinding processes [158], and predictive maintenance in manufacturing systems [159]. Additionally, modeling refrigeration system performance [160] and thermal management in manufacturing processes [161] have been explored. Concrete-related studies include predicting the strength characteristics of concrete [162] and the identification of concrete cracks [163]. In the realm of industrial optimization and fault diagnosis, research has addressed the intelligent system fault diagnosis of the robotic strain wave gear reducer [164] and the optimization of injection molding processes [165]. The prediction of pentane content [166] and the hot rolling process in the steel industry [167] have also been areas of focus. Studies have further examined job cycle time [168] and yield prediction [169]. In the realm of security and defense, XAI techniques have been widely applied to enhance cybersecurity measures. Several studies have focused on intrusion detection systems [170; 171; 172], as well as trust management within these systems [173]. Research has also explored detecting vulnerabilities in source code [174]. Cybersecurity applications include general cybersecurity measures [175], the use of XAI methods in cybersecurity [176], and specific studies on malware detection [177]. In the context of facial and voice recognition and verification, XAI techniques have been employed for face verification [178] and deepfake voice detection [179]. Additionally, research has addressed attacking ML classifiers in EEG signal-based human emotion assessment systems using data poisoning attacks [180]. Emerging security concerns in smart cities have led to studies on attack detection in IoT infrastructures [181]. Furthermore, aircraft detection from synthetic aperture radar (SAR) imagery has been a significant area of research [182].