arXiv subjects

Shubham Joshi

Publications and source records attributed to Shubham Joshi.

4 recordsLinked to original sources

arXiv2025

An Empirical Study of Bitwise Operators Intuitiveness through Performance Metrics

Objectives: This study aims to investigate the readability and understandability of bitwise operators in programming, with the main hypothesis that there will be a difference in the performance metrics (response time and error rate) between participants exposed to various bitwise operators related questions and those who are not. Participants: Participants in this human research study include people without programming background, novice programmers, and university students with varying programming experience (from freshmen to PhD level). There were 23 participants in this study. Study Methods: This study uses a within-subjects experimental design to assess how people with diverse programming backgrounds understand and use bitwise operators. Participants complete tasks in a JavaScript program, and their task completion times and task accuracy are recorded for analysis. Findings: The results indicate that operators can be one of the factors predicting response time, showing a small but significant effect (R-squared = 0.032, F(1, 494) = 16.5, p < .001). Additionally, operators such as OR, NOT, and Left Shift showed statistical significance in task completion times compared to other operators. Conclusions: While the complexity of bitwise operators did not generally result in longer task completion times, certain operators were found to be less intuitive, suggesting the need for further investigation and potential redesign for improved understandability.

cs.SE↗

arXiv2023

Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models

Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and behavior. Early intervention plays a pivotal role in enhancing cognitive abilities and reducing autistic symptoms in children with ASD. Numerous clinical studies have highlighted distinctive facial characteristics that distinguish ASD children from typically developing (TD) children. In this study, we propose a practical solution for ASD screening using facial images using YOLOv26s model. By employing YOLOv26s, a deep learning technique, we achieved exceptional results. Our model achieved a remarkable 92.86% accuracy in classification and an F1-score of 0.9291. Our findings provide support for the clini- cal observations regarding facial feature discrepancies between children with ASD. The high F1-score obtained demonstrates the potential of deep learning models in screening children with ASD. We conclude that the newest version of YOLOv26s which is usually used for object detection can be used for classification problem of Austistic and Non-autistic images.

cs.CV↗

arXiv2023

Ngram-LSTM Open Rate Prediction Model (NLORP) and Error_accuracy@C metric: Simple effective, and easy to implement approach to predict open rates for marketing email

Our generation has seen an exponential increase in digital tools adoption. One of the unique areas where digital tools have made an exponential foray is in the sphere of digital marketing, where goods and services have been extensively promoted through the use of digital advertisements. Following this growth, multiple companies have leveraged multiple apps and channels to display their brand identities to a significantly larger user base. This has resulted in products, worth billions of dollars to be sold online. Emails and push notifications have become critical channels to publish advertisement content, to proactively engage with their contacts. Several marketing tools provide a user interface for marketers to design Email and Push messages for digital marketing campaigns. Marketers are also given a predicted open rate for the entered subject line. For enabling marketers generate targeted subject lines, multiple machine learning techniques have been used in the recent past. In particular, deep learning techniques that have established good effectiveness and efficiency. However, these techniques require a sizable amount of labelled training data in order to get good results. The creation of such datasets, particularly those with subject lines that have a specific theme, is a challenging and time-consuming task. In this paper, we propose a novel Ngram and LSTM-based modeling approach (NLORPM) to predict open rates of entered subject lines that is easier to implement, has low prediction latency, and performs extremely well for sparse data. To assess the performance of this model, we also devise a new metric called 'Error_accuracy@C' which is simple to grasp and fully comprehensible to marketers.

cs.IR↗

arXiv2019

Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.

cs.CV↗