This paper presents a classification model that predicts delays in Indian lower courts based on case information available at filing. The model is built on a dataset of 4.2 million court cases filed in 2010 and their outcomes over a 10-year period. The data set is drawn from 7000+ lower courts in India. The authors employed AutoML to develop a multi-class classification model over all periods of pendency and then used binary decision forest classifiers to improve predictive accuracy for the classification of delays. The best model achieved an accuracy of 81.4%, and the precision, recall, and F1 were found to be 0.81. The study demonstrates the feasibility of AI models for predicting delays in Indian courts, based on relevant data points such as jurisdiction, court, judge, subject, and the parties involved. The paper also discusses the results in light of relevant literature and suggests areas for improvement and future research. The authors have made the dataset and Python code files used for the analysis available for further research in the crucial and contemporary field of Indian judicial reform.

Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity prediction models. However, ordinal categories represent a simplification of an underlying continuous severity spectrum. Using continuous scores instead of ordinal categories is more sensitive to detecting small changes in disease severity over time. Here, we present a generalized framework that accurately predicts continuously valued variables using only discrete ordinal labels during model development. We found that for three clinical prediction tasks, models that take the ordinal relationship of the training labels into account outperformed conventional multi-class classification models. Particularly the continuous scores generated by ordinal classification and regression models showed a significantly higher correlation with expert rankings of disease severity and lower mean squared errors compared to the multi-class classification models. Furthermore, the use of MC dropout significantly improved the ability of all evaluated deep learning approaches to predict continuously valued scores that truthfully reflect the underlying continuous target variable. We showed that accurate continuously valued predictions can be generated even if the model development only involves discrete ordinal labels. The novel framework has been validated on three different clinical prediction tasks and has proven to bridge the gap between discrete ordinal labels and the underlying continuously valued variables.

Word Embeddings is the most fundamental concept in Deep Natural Language Processing. And word2vec is one of the earliest algorithms used to train word embeddings. In this post, I want to go deeper into the first paper on word2vec -- Efficient Estimation of Word Representations in Vector Space (2013), which as of now has 24k citations, and this number is still growing. I am attaching my Github project with word2vec training. We will go through it in this post.

Word Embeddings is the most fundamental concept in Deep Natural Language Processing. And word2vec is one of the earliest algorithms used to train word embeddings. In this post, I want to go deeper into the first paper on word2vec -- Efficient Estimation of Word Representations in Vector Space (2013), which as of now has 24k citations, and this number is still growing. I am attaching my Github project with word2vec training. We will go through it in this post.

One of my first assignments as a new electrical engineering graduate was to diagnose and troubleshoot an out-of-service induction motor. The first step was to exam the symptoms of the fault and determine the root cause of failure. In order to do this, I required a specific diagnostic tool and a detailed testing procedure. Unfortunately, the diagnostic tool was unavailable in the short-term and the testing procedure required a minimum of 3 weeks to execute. I continue to think about this scenario and I classify it as an opportunity to improve the current standards for motor diagnostics and repair.

Machine Learning has become very famous currently which assist in identifying the patterns from the raw data. Technological advancement has led to substantial improvement in Machine Learning which, thus helping to improve prediction. Current Machine Learning models are based on Classical Theory, which can be replaced by Quantum Theory to improve the effectiveness of the model. In the previous work, we developed binary classifier inspired by Quantum Detection Theory. In this extended abstract, our main goal is to develop multi-class classifier. We generally use the terminology multinomial classification or multi-class classification when we have a classification problem for classifying observations or instances into one of three or more classes.