An analytical characterization study on biofuel obtained from pyrolysis of Madhuca longifolia residues.

The current study focuses on examining the characteristics of biofuel obtained from the pyrolysis of Madhuca longifolia residues, since the selected forest residue was primarily motivated by its greater volatile matter content. The study used several analytical techniques to describe pyrolysis oil, char, and gas obtained from slow pyrolysis process conducted between 350 and 600 °C in a fixed-bed reactor. Initially, the effect of process temperature on product distribution was assessed to motivate maximum pyrolysis oil yield and found to be 44.2 wt% at pyrolysis temperature of 475 °C, while the yields of char and gas were 22.1 wt% and 33.7 wt%, respectively. In order to determine the suitability of the feedstock, the Madhuca longifolia residues were analyzed by TGA and FT-IR, which revealed that the feedstock could be a feasible option as an energy source. The characterization of pyrolysis oil, char, and gas has been done through various analytical methods like FT-IR, GC-MS, and gas chromatography. The physicochemical characteristics of the pyrolysis oil sample were examined, and the results showed that the oil is a viscous liquid with a lower heating value than conventional diesel. The FT-IR and GC-MS analysis of pyrolysis oil revealed the presence of increased levels of oxygenated chemicals, acids, and phenol derivatives. The findings of the FT-IR analysis of char indicated the existence of aromatic and aliphatic hydrocarbons. The increased carbon content in the char indicated the possibility of using solid fuel. Gas chromatography was used to examine the chemical structure of the pyrolysis gas, and the results showed the existence of combustible elements.

An Interpretable Approach with Explainable AI for Heart Stroke Prediction.

Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model's comprehensibility for healthcare practitioners. This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. Applying these techniques, including model interpretability measures such as permutation importance and explainability methods like LIME, has achieved impressive results. While permutation importance provides insights into feature importance globally, LIME complements this by offering local and instance-specific explanations. Together, they contribute to a comprehensive understanding of the Artificial Neural Network (ANN) model. The combination of these techniques not only aids in understanding the features that drive overall model performance but also helps in interpreting and validating individual predictions. The ANN model has achieved an outstanding accuracy rate of 95%.