Stochastic calculus-guided reinforcement learning: A probabilistic framework for optimal decision-making.

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL's 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL's short-term risk value was 0.64, and its long-term risk value was 0.78. SRL's short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests.

TopoGeoFusion: Integrating object topology based feature computation methods into geometrical feature analysis to enhance classification performance.

This study used smartphone captured RGB images of gooseberries to automatically sort into standard, premium, or rejected categories based on topology. Main challenges addressed include, separation of touching or overlapping fruits into individual entities and new method called 'TopoGeoFusion' that combines basic geometrical features with topology aware features computed from the fruits to assess the grade or maturity. Quality assessment helps in grading the fruit to determine market suitability and intelligent camera applications. Computer Vision-based techniques have been applied to automatically grade the quality of gooseberries as standard, premium, or rejected according to fruit maturity. Smartphone-captured images of 1697 Indian Star Gooseberries are contributed to the study. This work acquired images consisting multiple fruits with overlapping and non-overlapping boundaries for concurrent quality assessment. Multiple classifiers such as Random Forest, SVM, Naive Bayes, Decision Tree, and KNN were applied to grade the gooseberry fruit. Random Forest classification with a fusion feature model resulted in an accuracy of 100 % towards reject, standard, and premium classes for test sets with four training strategies. The proposed segmentation model proves reliable in fruit detection & extraction with an average mAP of 0.56, resulting in an acceptable model for grade assessment.•The study highlights the effectiveness of TopoGeoFusion in automating the grading process of gooseberry fruits using topologically computed features.•The developed models exhibit high accuracy and reliability, even in challenging scenarios such as overlapping and touching fruits.•The method provides the technique to detect and extract the occluded objects and compute the features based on the partial object's topology.