Although concept-based approaches are widely used to explain a model's behavior and assess the contributions of different concepts in decision-making, identifying relevant concepts can be challenging for non-experts. This paper introduces a novel method that simplifies concept selection by leveraging the capabilities of a state-of-the-art large Vision-Language Model (VLM). Our method employs a VLM to select textual concepts that describe the classes in the target dataset. We then transform these influential textual concepts into human-readable image concepts using a text-to-image model. This process allows us to explain the targeted network in a post-hoc manner. Further, we use directional derivatives and concept activation vectors to quantify the importance of the generated concepts. We evaluate our method on a neonatal pain classification task, analyzing the sensitivity of the model's output for the generated concepts. The results demonstrate that the VLM not only generates coherent and meaningful concepts that are easily understandable by non-experts but also achieves performance comparable to that of natural image concepts without the need for additional annotation costs.

Deep Learning (DL)-based solutions have shown promising performance in assessing neonatal pain. However, the occlusion of the visual modality (face and body) is common in clinical settings due to several factors, including a prone sleeping position, low light, or swaddling. In such scenarios, other pain signals, such as audio signals, can be used as the major behavioral signs of pain. Although DL-based methods are proposed to assess pain from audio, these methods lack transparency and explainability (black box), which can decrease the user's trust in the automated decision. In this work, we visualize the neonate's audio signal as a spectrogram image to classify it as pain or no pain and present an instance-based approach for explaining the decision of the black-box model. Further, this work provides an analysis of the most helpful and harmful training instances using an influence score followed by assessing their impact on pain prediction. Experimental results demonstrate that the proposed approach can detect and remove harmful instances, eventually leading to a compressed dataset. Our results also show that the proposed work can add explainability to the current DL-based pain detection methods, which can enhance users' trust and provide a viable approach toward pain assessment in clinical settings.

This research paper explores and examines the performance of a private blockchain in a mobile ad hoc network (MANET). The adoption of blockchain in a mobile ad hoc network (MANET) presents distinct challenges due to the inherent mobility of the network. The dynamic nature of node connectivity and continuous changes due to mobility make the consensus process difficult. Due to their mobility, nodes in a MANET have limited time for rendezvous in order to exchange and verify new blocks while they are on the move. Previous studies have not thoroughly discussed the measurement of blockchain performance in a MANET. To address this gap, this research proposes an analysis that evaluates the performance of a private blockchain in a MANET environment using key metrics like throughput, latency, and scalability. The analysis considers different factors such as data rate, packet size, area coverage, and hashing ability.