相关论文: Predicting delays in Indian lower courts using Aut…
The Indian judicial system faces a critical challenge with approximately 52 million pending cases, causing significant delays that impact socio-economic stability. This study proposes a cloud-based software framework to classify and…
Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To…
In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce…
We ascertain and compare the performances of AutoML tools on large, highly imbalanced healthcare datasets. We generated a large dataset using historical administrative claims including demographic information and flags for disease codes in…
Prosecutors across Mexico face growing backlogs due to high caseloads and limited institutional capacity. This paper presents a machine learning (ML) system co-developed with the Zacatecas State Prosecutor's Office to support internal case…
Recent advances and applications of language technology and artificial intelligence have enabled much success across multiple domains like law, medical and mental health. AI-based Language Models, like Judgement Prediction, have recently…
The Big Data analytics are a logical analysis of very large scale datasets. The data analysis enhances an organization and improve the decision making process. In this article, we present Airline Delay Analysis and Prediction to analyze…
The decision making involved behind the mode choice is critical for transportation planning. While statistical learning techniques like discrete choice models have been used traditionally, machine learning (ML) models have gained traction…
The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system.…
The persistent accumulation of unresolved legal cases, especially within the Indian judiciary, significantly hampers the timely delivery of justice. Manual methods of prioritizing petitions are often prone to inefficiencies and subjective…
Legal predictive models are of enormous interest and value to legal community. The stakeholders, specially, the judges and attorneys can take the best advantages of these models to predict the case outcomes to further augment their future…
Automated Machine Learning (AutoML) automatically builds machine learning (ML) models on data. The de facto standard for evaluating new AutoML frameworks for tabular data is the AutoML Benchmark (AMLB). AMLB proposed to evaluate AutoML…
Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the…
Legal judgment prediction is essential for enhancing judicial efficiency. In this work, we identify that existing large language models (LLMs) underperform in this domain due to challenges in understanding case complexities and…
The advent of machine learning techniques has made it possible to obtain predictive systems that have overturned traditional legal practices. However, rather than leading to systems seeking to replace humans, the search for the determinants…
The field of AutoML has made remarkable progress in post-hoc model selection, with libraries capable of automatically identifying the most performing models for a given dataset. Nevertheless, these methods often rely on exhaustive…
Bail decisions are among the most frequently adjudicated matters in Indian courts, yet they remain plagued by subjectivity, delays, and inconsistencies. With over 75% of India's prison population comprising undertrial prisoners, many from…
Accurate prediction of food delivery times significantly impacts customer satisfaction, operational efficiency, and profitability in food delivery services. However, existing studies primarily utilize static historical data and often…
The use of credit cards has recently increased, creating an essential need for credit card assessment methods to minimize potential risks. This study investigates the utilization of machine learning (ML) models for credit card default…
Machine learning (ML) has advanced quickly, particularly throughout the area of health care. The diagnosis of neurodevelopment problems using ML is a very important area of healthcare. Autism spectrum disorder (ASD) is one of the…