An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based fuzzy systems. We also discuss the proposed approach (which won the competition) to attain efficiency in the XAI algorithm. We have explored the potential of the widely used Mamdani- and TSK-based fuzzy inference systems and investigated which model might have a more optimized implementation. Even though the TSK-based model outperforms Mamdani in several applications, no empirical evidence suggests this will also be applicable in implementing an XAI agent. The experimentations are then performed to find a better-performing inference system in a fast-paced environment. The thorough analysis recommends more robust and efficient TSK-based XAI agents than Mamdani-based fuzzy inference systems.
Background: TCD (Transcranial Doppler) is a well-established study to predict Cerebrovascular stroke in SCD (Sickle cell disease). We aim to establish baseline TCD findings in Indian children with SCD and compare the results with the available STOP (Stroke prevention trial in Sickle Cell Anemia) protocol. We would also compare TCD findings in homozygous sickle cell disease and heterozygous sickle cell trait.
Material and Methods: Seventy nine children with SCD were included in this study for one year period. TCD was performed and TAMMV (time-averaged maximum mean) velocity in the middle cerebral, anterior cerebral, posterior cerebral and internal carotid arteries was measured. Children were divided into two groups. Group I (56 homozygos–70.88%) and group II (23 heterozygos–29.11%).
Results: In group I, 50 children fall in normal range with average TAMM velocity of 127.59 ± 17.48 cm/s. There was 1 (1.78%) abnormal result and 5 (8.9%) conditional results in group I. All results were normal in group II with average TAMM velocity of 116.33 ± 12.412 cm/sec. Middle cerebral artery was the only affected vessels amongst all.
Conclusions: In our study, there was low prevalence of abnormal TCD results as compared to STOP protocol. The difference was significant in TAMM velocity between two groups, with all children being within normal range in group II. Result of this study differs from previous studies, done in western countries probably due to difference in haplotype.
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