In today's world anything almost everything related to literature can be achieved by LLMs. Be it summarization, abstraction, translation, transformation, etc. But not always is it possible to do those operations on extremely large content. Even with the large token output limits of newly launched ad...

Presents the introductory editorial for the inaugural issue of this title.

Early diagnosis of cardiac abnormalities depends on accurate classification of heart sounds, but centralized training methods run the danger of violating patient privacy. We thus propose a privacy-preserving and reliable heart sound abnormality detection system combining Blockchain Technology with F...

This paper provides a comprehensive analysis of the economic implications of implementing advanced loss-reduction measures in electrical distribution networks, as observed across developed economies. The study evaluates a set of technical and infrastructural interventions that have demonstrated sign...

The classification of Capsicum spp. varieties is often hindered by their morphological similarities, making accurate identification a challenging task. To address this issue, this study applies a hybrid computational approach that combines data dimensionality reduction techniques using Principal Com...

The healthcare sector has both opportunities and challenges as a result of the rapid expansion of unstructured clinical text data in electronic health records (EHRs). Physician notes, reports from radiologists, and summaries of discharge are examples of narrative medical documents from which relevan...

Deep learning is a great success primarily because it encodes large amounts of data and manipulates billions of model parameters. Despite this, it is challenging to deploy these cumbersome deep models on devices with limited resources, such as mobile phones and embedded devices, due to the high comp...

Presents the introductory editorial for the inaugural issue of this title.

Presents the introductory editorial for the inaugural issue of this title.

Multimodal Sentiment Analysis (MSA), a pivotal task in affective computing, aims to enhance sentiment understanding by integrating heterogeneous data from modalities such as text, images, and audio. However, existing methods continue to face challenges in semantic alignment, modality fusion, and int...