J. Sanghvi, is an engineering college in Vile Parle, Mumbai. The college was established by Shri Vile Parle Kelavani Mandal in 1994. J.
Legal argumentation requires sophisticated reasoning, fact analysis, and a subtle appreciation of statutory and case law. The project provides an AI-based framework that uses large language models (LLMs) to aid legal practitioners by creating context-specific counter-arguments, mimicking courtroom-style argumentation, and case outcome simulation. The model is incorporated with a refined GPT-3.5 Turbo model that was trained on expert-curated triplets of case summaries, legal arguments, and counter-arguments from actual court transcripts and legal discussions. To improve factual grounding and minimize hallucinations, the model is supplemented with retrieval-based legal context from statutory law corpora. A new assessment metric—designed with feedback from working attorneys—was proposed to measure the quality of arguments on grounds of persuasiveness, legal appropriateness, and completeness. Quantitative metrics such as cosine similarity and tailored scoring measures demonstrated that AI-generated counter-arguments attained an average similarity of 0.598 with human-written counterparts and were 6.91
The peer review process serves as a critical gatekeeper in scholarly communication; it provides constructive feedback, determines the credibility of research, and validates the scientific claims and overall quality of research papers. However, human reviews are often subjective and inconsistent. Due to the voluntary nature of the reviewing task, reviewers may not always devote time to thoroughly evaluating manuscripts. The peer review process remains vulnerable to bias and lackluster evaluations. Recent advancements in Large Language Models (LLMs) offer a promising testbed for their potential for automating or augmenting the peer review process that can complement or benchmark human reviewers. However, the potential of large language models (LLMs) remains unexplored regarding the extent to which these models can replicate human evaluation, particularly in terms of critical depth, reasoning accuracy, and alignment with human decision-making. To test this hypothesis, In this paper, we introduce Co-Reviewer, an agentic AI framework composed of four specialized agents that work together to generate, evaluate, critique, and refine peer reviews. Additionally, we conduct a multi-dimensional evaluation comparing LLM-generated reviews with human-written reviews, using evaluation metrics such as content informativeness, sentiment polarity and variability, score consistency, and alignment with final editorial decisions. Our research shows that while LLMs can create well-written and clear reviews, they have consistent problems like sounding too confident, favoring acceptance, and struggling to adjust to changes in manuscripts. Additionally, LLMs often confuse linguistic fluency with substantive critique, missing the nuanced and context-sensitive reasoning found in expert human assessments. To address these limitations, we propose several enhancements: domain-adaptive fine-tuning on peer review datasets, structured aspect-based critique generation, sentiment modulation for more calibrated feedback, and hybrid pipelines that combine LLM outputs with human oversight. Our work contributes to the growing body of research on AI-assisted scholarly evaluation and underscores both the potential and the limitations of using LLMs as Co-Reviewer in academic publishing workflows. The dataset and code that replicate our findings are publicly available at https://github.com/PrabhatkrBharti/Co-Reviewer.git .
Jupyter Notebooks have become the de-facto choice for data scientists and machine learning (ML) engineers for prototyping and experimenting with ML pipelines, offering a rich interactive interface for code, data, and visualization in one place. However, notebooks provide limited support for testing. As a result, during continuous development, many silent (non-crashing) regressions often go unnoticed, making notebooks unreliable and results hard to reproduce. We introduce NBTestGen, the first automated assertion generation approach for ML notebooks. NBTestGen generates regression-based assertions that check properties of data processing, model building, and model evaluation steps in a typical ML notebook. To support integration of such assertions in notebooks, we introduce the first regression testing framework (called NBTest) that can be used as a Jupyter plugin and allows developers to write cell-scoped assertions in notebooks. Such assertions are 1) cell-scoped: they are linked to specific notebook cells and execute only after those cells are executed, 2) non-intrusive: they do not block notebook execution (in a Jupyter session), so that development can continue when they fail, and 3) they integrate with pytest and CI pipelines, allowing developers to easily do regression testing of their notebooks. We evaluate NBTestGen on a corpus of 585 notebooks from the popular Kaggle platform. NBTestGen generates a total of 21542 assertions (36.82 on average per notebook). The generated assertions kill 72.21
Non-parametric tests are important in statistical analysis where data fails to meet assumptions like normality or homogeneity of variance. Nevertheless, choosing and implementing the right test is frequently statistical in nature, which is a limitation for most users. Therefore, we suggest an intelligent framework that automates the selection and running of non-parametric tests through machine learning. A well-curated dataset of statistical problems is fed into a Decision Tree Classifier, which is labeled as the best model with 93.4
With increasing global attention on sustainable energy solutions, hydrogen Fuel Cells (FCs) have gained prominence as a viable technology for addressing environmental concerns. Despite increasing research, a systematic bibliometric analysis focusing on the safety, reliability, and performance of these methods remains limited. This gap restricts the ability of regulators and risk–assessment communities to identify critical risks and research priorities. This study presents a comprehensive bibliometric analysis to map the global research landscape on hydrogen FC technologies from 2020 to 2024. Scopus-indexed data is used to get citation metrics and VOSviewer-based network visualizations. A detailed assessment of publication trends, author collaborations, and thematic developments is carried out. Results indicate a significant increase in research activity, with 18,722 publications generating 380,174 citations. China, India, and the United States lead in publication volume, while Switzerland and Sweden show the highest citation impact per paper. The Dominant research themes include FC performance, hydrogen production, and emerging areas, such as microbial FCs and hydrogen storage. The study provides valuable insights for researchers, institutions, and policymakers, supporting informed decisions in journal selection, funding allocation, and strategic research planning. The need for interdisciplinary collaboration and real-world data integration is emphasised with recommendations on future research focused on commercialization, policy alignment, and expanding application domains.
