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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading 5

The Impact of Transformer Models and Regression Algorithms o...

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nampoothiri, Abhijith. N. Thushara, M.G. Pradeesh, N. Anand Krishna, K.R. Santhosh, Abhai Abhijith Kumar, S. Kumar, Suryan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India

ISBN: (纸本)9798350356236

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models - including BERT, Sentence-BERT, T5, Genism, and XLNet - are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations. © 2025 IEEE.

关键词： Teaching

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An Analysis of YOLOvX Deep Learning Models for Colon Cancer Detection

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Procedia computer Science 2025年 260卷 81-90页

作者： Raseena T.P. Aditya Kumar Soni Jitendra Kumar T. Akilan Ashutosh Kumar Singh Department of Computer Applications National Institute of Technology Tiruchirappalli 620015 Tamilnadu India Kotak Mahindra Bank Thane West Mumbai 400604 Maharastra India Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal 462003 M.P. India Department of Software Engineering Lakehead University Thunder Bay Canada Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal India

Around the world, Colorectal cancer is still a prevalent form of cancer, and effective treatment and its impact depends on early diagnosis. Recently, polyp detection methods employ a convolutional neural networks to identify precancerous or malignant polyps in colonoscopy images accurately with high speed and precision. This study evaluates four deep learning models namely YOLOv3n, YOLOv5s, YOLOv7, and YOLOv7x, to determine their effectiveness in detecting colorectal cancer polyp frames efficiently. Among these, YOLOv7x model exhibited outstanding performance on the Hyper Kavasir dataset compared to other models, achieving an F1 score of 88.0%, a recall of 86.4%, a precision of 89.5%, and a mAP of 92.0% at a confidence threshold of 0.353. The achieved results highlight the unique capability of models in detecting CRC polyps for diagnosis. Incorporating such detection models can help physicians significantly in clinical practice to improve their ability to identify malignant polyps, ultimately leading to better treatment for patients. Gastroenterologists can benefit considerably from these models as diagnostic tools because of their speed and accuracy.

关键词： Colorectal cancer Colonoscopy Deep learning YOLO

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A New Hybrid Model for Brain Tumor Recognition in MRI Images Based on Hand-Crafted Features and Deep Learning

A New Hybrid Model for Brain Tumor Recognition in MRI Images...

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Digital Signal Processing and its applications (DSPA), International Conference on

作者： Yasser Nizamli Weaam Fadel Anton Filatov Yulia Shichkina Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation Department of Computer Science Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Brain tumors are among the most life-threatening cancers that can affect people of any gender or age. MRI scans manually evaluated by an expert are the initial diagnostic procedure that precedes an appropriate treatment plan. However, the human factor involves the possibility of error, and the process itself can be time-consuming and resourceconsuming, ultimately putting patients’ lives at risk. Therefore, efforts are being made to automate the diagnostic process through computer-aided systems, of which artificial intelligence has become the basis. This paper presents a novel system for detecting brain abnormalities in MRI images that ensures high precision without compromising the simplicity of its operational structure. In the proposed model, numerical features are extracted from grayscale MRI images using the rectangular histogram of oriented gradients method. The resulting features are fed after dimensionality reduction to a specialized sequential deep network composed of 11 learnable layers. The classification accuracy of the model reached 99.51%, outperforming many analogues, making it a promising option for supporting the diagnostic decision-making process.

关键词： Deep learning Histograms Image recognition Magnetic resonance imaging Decision making Brain tumors Feature extraction Brain modeling Numerical models Testing

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Autonomous Computing Emerging Networks Mobile devices Smart network Trusted Data Management

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Retrieval-Augmented Generation for Multiple-Choice Questions and Answers Generation

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Procedia computer Science 2025年 259卷 504-511页

作者： Pradeesh N Remya T MG Thushara K Arun Krishna Pranav V Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Generating a large volume of diverse questions for educational purposes can be a challenging and time-consuming task for educators. Traditional methods like manually writing questions or using templates often fall short when it comes to variety and relevance. In order to cope with these di ffi culties, we have examined a di ff erent approach adopting Retrieval-Augmented Generation ( RAG). RAG is an enhancement technique that allows retrieving independent documents and combines it with artificial intelligence that is able to qualitatively generate contextually adequate questions. In this research, RAG was employed through the Ample LMS platform in which PDF documents were the source of generating MCQ questions. The results of the research indicate that with this method, the process of formulating the questions is relatively faster and the questions generated are of better and diverse in nature. With RAG, the burden of constructing questions is lessened for teachers and the students are provided with better interactive learning opportunities. This approach presents a better alternative that is flexible and suitable for various educational requirements, demonstrating the e ff ectiveness of AI in the improvement of the learning and teaching processes.

关键词： RAG Education Question Generation LLM

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Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation

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Procedia computer Science 2025年 260卷 906-913页

作者： Tushar Prasanna Swaminathan Christopher Silver Thangarajah Akilan Jitendra Kumar Department of Computer and Electrical Engineering Lakehead University Thunder Bay ON P7B5E1 Department of Software Engineering Lakehead University Thunder Bay ON P7B5E1 Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal 462003 M.P. India

The proliferation of complex deep learning (DL) models has revolutionized various applications, including computer vision-based solutions, prompting their integration into real-time systems. However, the resource-intensive nature of these models poses challenges for deployment on low-computational power and low-memory devices, like embedded and edge devices. This work empirically investigates the optimization of such complex DL models to analyze their functionality on an embedded device, particularly on the NVIDIA Jetson Nano. It evaluates the effectiveness of the optimized models in terms of their inference speed for image Classification and video action detection. The experimental results reveal that, on average, optimized models exhibit a 16.11% speed improvement over their non-optimized counterparts. This not only emphasizes the critical need to consider hardware constraints and environmental sustainability in model development and deployment but also underscores the pivotal role of model optimization in enabling the widespread deployment of AI-assisted technologies on resource-constrained computational systems. It also serves as proof that prioritizing hardware-specific model optimization leads to efficient and scalable solutions that substantially decrease energy consumption and carbon footprint.

关键词： Deep learning edge devices optimization NVIDIA Jetson Nano TensorRT

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Cybersecurity Threat Detection using Machine Learning on Network Traffic

Cybersecurity Threat Detection using Machine Learning on Net...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Rithi N. Nithya A. Akila R. Saravanakumar M. Vargheese S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Web phishing is a prevalent online attack in which victims are tricked into revealing their personal information by visiting fraudulent websites. Phishers often use URL shortening tools to disguise malicious links, making it easier to steal sensitive data such as usernames, passwords, and banking details. These phishing websites are designed to mimic legitimate ones, making detection more challenging. As phishing techniques continue to evolve rapidly alongside technological advancements, the need for effective anti-phishing systems has become increasingly crucial. Machine learning has emerged as a powerful tool for recognizing and mitigating phishing scams. This article provides a comprehensive overview of machine learning techniques for phishing detection and their effectiveness in combating online threats.

关键词： Uniform resource locators Accuracy Reviews Phishing Scalability Machine learning Telecommunication traffic Passwords Threat assessment Real-time systems

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading

The Impact of Transformer Models and Regression Algorithms o...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Abhijith. N. Nampoothiri M. G. Thushara Pradeesh. N Anand Krishna K. R Abhai Santhosh Abhijith Kumar S Suryan Kumar Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models—including BERT, Sentence-BERT, T5, Genism, and XLNet—are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations.

关键词： Support vector machines Measurement Accuracy Education Forestry Transforms Transformers Numerical models Reliability Random forests

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Machine Learning on Retail Stores for Customer Churn

Machine Learning on Retail Stores for Customer Churn

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Advanced Computing Technologies (ICoACT), International Conference on

作者： P. Brito Clinton P. Sathiya Priya S. Jagadeesan S. Nagarajan K. Mohanapriya S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The pace at which customers discontinue engaging in commercial activity with a corporation is known as customer churn, and it frequently indicates discontent or competition. Machine learning analyzes customer data to identify patterns, predict churn, and personalize retention strategies. It enables businesses to target at-risk customers and enhance engagement through tailored interventions and offers. A public dataset gathered from Kaggle is used in this research work. Exploratory data analysis is carried over to understand the patterns and trends in the churn rate. The bivariate analysis is held to find the correlation of each feature against the churn rate. With these observations, the importance of marketing strategies is exhibited. Additionally, four distinct models—Logistic Regression, Random Forest, Decision Tree, and the XGB Classifier-are used to train and assess the dataset. From the evaluation of all four models with the help of the ROC score, the XGB is found to outperform and provide better results obtaining 98% accuracy and a 1.00 ROC score.

关键词： computers Data analysis Correlation Computational modeling Market research Classification algorithms Churn Regression tree analysis Random forests Optimization

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