arXiv subjects

Md Tariqul Islam

Publications and source records attributed to Md Tariqul Islam.

3 recordsLinked to original sources

arXiv2026

MoQSplat: Adaptive Progressive Streaming of 3D Gaussian Splatting via MoQ

3D Gaussian Splatting (3DGS) enables photorealistic novel view synthesis, but transmitting gigabyte-scale scene data remains challenging for immersive applications. Traditional HTTP Adaptive Streaming over TCP introduces Head-of-Line (HOL) blocking and coarse segmenting ill-suited to fine-grained 3DGS delivery. We propose MoQSplat, which maps 3DGS content onto the Media over QUIC (MoQ) transport hierarchy. MoQSplat partitions scenes into spatial Tracks, clusters splats into semantically coherent Groups, and constructs progressive-quality Subgroups mapped to independent QUIC streams to eliminate connection-level HOL blocking. Using a stateless, subscriber-driven adaptation loop, clients dynamically request spatial regions and quality tiers based on six degrees of freedom (6-DoF) frustum visibility, distance, and foveal alignment. We evaluate the core components on a prototype implementation, showing that opacity-based pruning outperforms scale-based pruning for progressive delivery. The source code is available at https://github.com/emanuele-artioli/MoQSplat.

cs.MM↗

arXiv2021

IBM Employee Attrition Analysis

In this paper, we analyzed the dataset IBM Employee Attrition to find the main reasons why employees choose to resign. Firstly, we utilized the correlation matrix to see some features that were not significantly correlated with other attributes and removed them from our dataset. Secondly, we selected important features by exploiting Random Forest, finding monthlyincome, age, and the number of companies worked significantly impacted employee attrition. Next, we also classified people into two clusters by using K-means Clustering. Finally, We performed binary logistic regression quantitative analysis: the attrition of people who traveled frequently was 2.4 times higher than that of people who rarely traveled. And we also found that employees who work in Human Resource have a higher tendency to leave.

cs.CY↗

arXiv2020

Principal Component Analysis and Factor Analysis for Feature Selection in Credit Rating

The credit rating is an evaluation of a company's credit risk that values the ability to pay back the debt and predict the likelihood of the debtor defaulting. There are various features influencing credit rating. Therefore, it is essential to select substantive features to explore the main reason for credit rating change. To address this issue, this paper exploited Principal Component Analysis and Factor Analysis as feature selection algorithms to select important features, summarized the similar features together, and obtained a minimum set of features for four sectors, Financial Sector, Energy Sector, Health Care Sector, Consumer Discretionary Sector. This paper used two data sets, Financial Ratio and Balance Sheet, with two mappings, Detailed Mapping, and Coarse Mapping, converting the target variable(credit rating) into categorical variable. To test the accuracy of credit rating prediction, Random Forest Classifier was used to test and train feature sets. The results showed that the accuracy of Financial Ratio feature sets was higher than that of Balance Sheet feature sets. In addition, Factor Analysis can reduce the number of features significantly to obtain almost the same accuracy that can decrease dramatically the time spent on analyzing data; we also summarized seven dominant factors and ten dominant factors affecting credit rating change in Financial Ratio and Balance Sheet by utilizing Factor Analysis, respectively, which can explain the reason of credit rating change better.

q-fin.ST↗