Volume 3, Issue 8 - 3 Articles
This issue highlights new advancements in technology that are changing education, industry, and design. The first paper talks about an automated grading system that helps teachers by giving students feedback based on their learning level. The second paper introduces a new way to classify gemstones using a CNN-based algorithm, making the process more accurate. The third paper focuses on formal verification in silicon design, showing how advanced tools can make verification faster and more reliable. These innovations show how technology can improve efficiency and accuracy in different fields.
Front Cover
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # i–i, 2024
Editorial Board
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # ii–ii, 2024
Editorial
by Paul Andrew
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # iii–iv, 2024
Table of Contents
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # v–v, 2024
Dynamic and Partial Grading of SQL Queries
by Benard Wanjiru, Patrick van Bommel and Djoerd Hiemstra
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # 1-14, 2024; DOI: 10.55708/js0308001
Abstract: Automated grading systems can help save a lot of time when evaluating students’ assignments. In this paper we present our ongoing work for a model for generating correctness levels. We utilize this model to demonstrate how we can grade students SQL queries employing partial grading in order to allocate points to parts of the queries… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Artificial Intelligence – Computer Science (AIC))
MCNN+: Gemstone Image Classification Algorithm with Deep Multi-feature Fusion CNNs
by Haoyuan Huang and Rongcheng Cui
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # 15-20, 2024; DOI: 10.55708/js0308002
Abstract: Accurate gemstone classification is critical to the gemstone and jewelry industry, and the good performance of convolutional neural networks in image processing has received wide attention in recent years. In order to better extract image content information and improve image classification accuracy, a CNNs gemstone image classification algorithm based on deep multi-feature fusion is proposed.… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Artificial Intelligence – Computer Science (AIC))
A Case Study on Formal Sequential Equivalence Checking based Hierarchical Flow Setup towards Faster Convergence of Complex SOC Designs
by Anantharaj Thalaimalai Vanaraj and Reshi Razdan
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # 21-27, 2024; DOI: 10.55708/js0308003
Abstract: Functional Verification Sign-Off is the crux of the design verification problem faced by latest Silicon Designs on the Simulation/Stimulus Driven and the Formal Verification Platforms. Formal Verification Convergence is a custom specific criterion depending on the success, failure, exhaustiveness and reachability of the verification goals generated and validated by the Formal Tool. One of the… Read More
(This article belongs to the Section Hardware and Architecture – Computer Science (HAC))