arXiv subjects

Shashi Bhushan Jha

Publications and source records attributed to Shashi Bhushan Jha.

6 recordsLinked to original sources

arXiv2026

Claims, Designs, and Guarantees: A Critical Narrative Review of Statistical Inference Reform and Practice, 2016--2026

Statistics helps scientists decide what their data show, but its most familiar tool, the $p$-value, is widely misunderstood. Between 2016 and 2026, professional bodies issued new guidance, and researchers developed or popularised alternatives and complements: compatibility intervals, equivalence tests, Bayes factors, and e-values that remain valid when data are checked repeatedly. Reporting rules for trials and reviews were also rewritten. Yet practice has changed little: in the biomedical literature, about 96\% of papers that report $p$-values still include at least one below 0.05. This review argues that much of the confusion comes from treating these numbers as interchangeable. Each inferential procedure carries an intended interpretation and, where applicable, a formal guarantee, such as a 5\% false-alarm rate, that holds only if the study was run and analysed in a particular way. We call this the Claim--Design--Guarantee framework: a result licenses a claim only when the claim, the design (assignment, sampling, monitoring, selection, and analysis), and the guarantee of the method are aligned. We trace the main developments of the decade, explain each tool in plain terms with formal details in boxes, and show with reproducible examples when the choice of tool changes the conclusion. For example, in a simulated two-group study with 100 interim looks, repeatedly applying an unadjusted $p<0.05$ rule produced false positives in 37.6\% of studies, whereas the particular anytime-valid e-value procedure we used produced 2.2\%, below its theoretical 5\% upper bound. We end with a decision tree, a method-selection table, and a step-by-step workflow linked to current reporting guidelines.

stat.ME↗

arXiv2026

KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection

Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims. Most prior work focuses on text-image fusion or applies external knowledge only as a post-processing step, limiting their ability to detect deeper semantic inconsistencies. In this paper, we introduce KITE (Knowledge-Integrated Text-Image Encoder), a tri-modal fake news detection framework that jointly models textual, visual, and factual knowledge representations. KITE leverages Roberta and CLIP for linguistic and visual encoding, while a Graph Attention Network (GAT) processes structured facts retrieved from Wikidata. KITE uses cross-modal attention within a multimodal transformer to integrate text, visual, and knowledge features, helping it understand how each modality relates to one another. Modality-specific confidence scores are generated alongside the final prediction, offering interpretability by indicating which input type most influenced the decision. Evaluations on benchmark datasets demonstrate that KITE significantly outperforms unimodal and bimodal baselines, particularly in scenarios involving image-text mismatches or contradictions with external knowledge.

cs.LG↗

arXiv2024

Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States

The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.

cs.LG↗

arXiv2020

Machine Learning Approaches to Real Estate Market Prediction Problem: A Case Study

Home sale prices are formed given the transaction actors economic interests, which include government, real estate dealers, and the general public who buy or sell properties. Generating an accurate property price prediction model is a major challenge for the real estate market. This work develops a property price classification model using a ten year actual dataset, from January 2010 to November 2019. The real estate dataset is publicly available and was retrieved from Volusia County Property Appraiser of Florida website. In addition, socio-economic factors such as Gross Domestic Product, Consumer Price Index, Producer Price Index, House Price Index, and Effective Federal Funds Rate are collected and used in the prediction model. To solve this case study problem, several powerful machine learning algorithms, namely, Logistic Regression, Random Forest, Voting Classifier, and XGBoost, are employed. They are integrated with target encoding to develop an accurate property sale price prediction model with the aim to predict whether the closing sale price is greater than or less than the listing sale price. To assess the performance of the models, the accuracy, precision, recall, classification F1 score, and error rate of the models are determined. Among the four studied machine learning algorithms, XGBoost delivers superior results and robustness of the model compared to other models. The developed model can facilitate real estate investors, mortgage lenders and financial institutions to make better informed decisions.

cs.LG↗

arXiv2020

Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study

Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.

cs.LG↗

arXiv2020

Incorporating Image Gradients as Secondary Input Associated with Input Image to Improve the Performance of the CNN Model

CNN is very popular neural network architecture in modern days. It is primarily most used tool for vision related task to extract the important features from the given image. Moreover, CNN works as a filter to extract the important features using convolutional operation in distinct layers. In existing CNN architectures, to train the network on given input, only single form of given input is fed to the network. In this paper, new architecture has been proposed where given input is passed in more than one form to the network simultaneously by sharing the layers with both forms of input. We incorporate image gradient as second form of the input associated with the original input image and allowing both inputs to flow in the network using same number of parameters to improve the performance of the model for better generalization. The results of the proposed CNN architecture, applying on diverse set of datasets such as MNIST, CIFAR10 and CIFAR100 show superior result compared to the benchmark CNN architecture considering inputs in single form.

cs.CV↗