Multilingual multimodal (MM) summarization, involving the processing of multimodal input (MI) data across multiple languages to generate corresponding multimodal summaries (MS) using a single model, has been under explored. MI data consists of text and associated images, while MS incorporates text alongside relevant images aligned with the MI context. In this paper, we propose an MM summarization model by fine-tuning transformers (MMSFT), focusing on low-resource languages (LRLs) such as the Indian languages. MMSFT comprises multilingual learning for encoder training, incorporating multilingual attention with a forget gate mechanism, followed by MS generation using a decoder. In the proposed approach, we use publicly available multilingual multimodal summarization dataset (M3LS). Evaluation utilizing ROUGE metrics and the language-agnostic target summary metric (LaTM) illustrates MMSFT’s significant enhancement over existing MM summarization models like mT5 and VG-mT5. Furthermore, MMSFT yields better or equivalent summaries compared to existing MM summarization models trained separately for each language. Human and statistical evaluation reveal MMSFT’s significant improvement over existing models, with a p-value ≤ 0.05 in paired t-tests.

Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents an algorithm to handle negation. In sentence similarity, word pair similarity plays a significant role. Hence, this paper also discusses the similarity between word pairs. Existing semantic similarity measures do not handle antonyms accurately. Hence, this paper proposes an algorithm to handle antonyms. This paper also presents an antonym dataset with 111-word pairs and corresponding expert ratings. The existing semantic similarity measures are tested on the dataset. The results of the correlation proved that the expert ratings are in order with the correlation obtained from the semantic similarity measures. The sentence similarity is handled by proposing two algorithms. The first algorithm deals with the typical sentences, and the second algorithm deals with contradiction in the sentences. SICK dataset, which has sentences with negation, is considered for handling the sentence similarity. The algorithm helped in improving the results of sentence similarity.

The integration of electric vehicle charging stations (EVCSs) with standalone microgrids (MGs) offers a sustainable alternative to grid‐dependent power supply. However, managing stochastic EV charging demand while considering intermittent renewable power‐flow dynamics and limited battery storage system (BSS) capacity in isolated MG configurations poses significant challenges. To address these issues, an adaptive learning‐based hybrid prediction energy management scheme (AL‐PEMS) is proposed. Unlike traditional regression‐based predictive energy management schemes, AL‐PEMS employs a novel hybrid prediction framework that integrates a Polynomial Regression Predictive Model with a hierarchical adaptive window‐based learning algorithm to accurately predict stochastic EV charging demand and dynamically regulate dispatchable power flow. The adaptive learning‐assisted predictive energy manager hierarchically refines successive prediction windows according to the severity of BSS‐aware prediction errors, thereby reducing excessive BSS loading. The effectiveness of the proposed scheme is validated using real‐world EV charging data from 32,920 charging events at Oslo charging stations, while its real‐time feasibility is demonstrated through controller hardware‐in‐the‐loop testing. A comparative performance analysis of the proposed AL‐PEMS against rule‐based adaptive power management strategies, regression‐based model predictive control energy management schemes, and an adaptive model predictive control scheme demonstrates significant improvements, including 19.82%, 8.43%, and 7.87% enhancement in EVCS reliability, 17.60%, 20.20%, and 18.13% improvement in sustainability, 35.23%, 28.36%, and 28.36% reduction in BSS loading, and 14.16%, 9.20%, and 8.89% improvement in DC MG voltage regulation under stochastic EV charging conditions.