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Digital Essay Assessment using Machine Learning Algorithms 9

Digital Essay Assessment using Machine Learning Algorithms

引用

9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Johnsi, R. Kumar, G. Bharadwaja School of Computer Science And Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365092

Essay are considered as the most prominent way in assessing the student performance. Manual grading of the essay are considered to be a hectic tasks for both students and instructors due to various reasons such as time consuming, prone to subjectivity, inconsistencies and lack of reliability may be influenced by biases, such as prior knowledge of the student or their back ground. The solution to all these problems has been rectified by using Automated Essay Scoring (AES) system. This technical paper proposes a novel AES system that utilizes various Machine Learning (ML) algorithms such as Linear Regression, Lasso Regression, Ridge Regression, Gradient Boosting Regression, Bayesian Regression, Elastic Net Regression, Random Forest Regression, Decision Tree Regression, Gaussian Process Regression, Support Vector Regression, Polynomial Regression, XG Boost regression and XGBoost with Polynomial Features and discovered that XGBoost with Polynomial features achieved the best results with an average Quadratic Weighted Kappa (QWK) value of 0.68. © 2024 IEEE.

关键词： Text processing

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Metal Surface Defect Detection Using Deep Learning Techniques

Metal Surface Defect Detection Using Deep Learning Technique...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Abhinav, S. Elakiya, E. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Metal defect detection plays a crucial role in ensuring the quality and safety of industrial manufacturing processes. Traditional methods of defect detection often rely on manual inspection, which can be time-consuming and prone to human error. In recent years, the application of deep learning techniques, particularly Convolutional Neural Networks (CNNs), has shown promising results in automating defect detection tasks. This research explores the effectiveness of using CNN architectures, specifically InceptionNet, ResNet34, and VGG16, for automated metal defect detection. The study involves training and evaluating these models on a dataset of metal surface images containing various types of defects. The performance metrics such as accuracy, precision, recall, and F1-score are analysed to assess the robustness and efficacy of each model in defect classification. Additionally, the research investigates the impact of transfer learning by fine-tuning pre-trained CNN models on the defect detection task. The experimental results demonstrate the superior performance of deep CNNs compared to traditional methods, highlighting the potential of these techniques for real-world applications in industrial quality control and inspection. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling of Surface Robotic Search Radar Detection in Complex Environments 6

Modeling of Surface Robotic Search Radar Detection in Comple...

引用

6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Huang, Yuxin Zhao, Tongzhou Li, Senhao Du, Rongtao School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

This paper investigates the ability of the search radar carried by the surface robot to detect and analyze the target and the change of the radar's performance in the complex environment, such as electromagnetic interference and ground and sea clutter background, to provide theoretical support for further improving the detection performance of the surface robot. According to the ground-sea clutter and multiple physical properties in the electromagnetic environment, the surface robotic search radar exploration model, electromagnetic interference clutter power model and ground-sea clutter radiation clutter power model are established. Using C++ to model the interference power of surface robotic search radar in the electromagnetic interference environment and the effect on the radar scattering coefficient under different terrain and sea conditions, to derive the relationship between the detection probability of surface robotic search radar and the signal-to-noise ratio under different environments. © 2024 IEEE.

关键词： Electromagnetic pulse

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MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated science 5

MAPS: Energy-Reliability Tradeoff Management in Autonomous V...

引用

5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Aliazam, Mahdieh Javadi, Ali Hosseini Monazzah, Amir Mahdi Akbari Azirani, Ahmad Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798331529284

As autonomous vehicles become more prevalent, highly accurate and efficient systems are increasingly critical to improve safety, performance, and energy consumption. Efficient management of energy-reliability tradeoffs in these systems demands the ability to predict various conditions during vehicle operations. With the promising improvement of Large Language Models (LLMs) and the emergence of well-known models like ChatGPT, unique opportunities for autonomous vehicle-related predictions have been provided in recent years. This paper proposed MAPS using LLMs as map reader co-drivers to predict the vital parameters to set during the autonomous vehicle operation to balance the energy-reliability tradeoff. The MAPS method demonstrates a 20% improvement in navigation accuracy compared to the best baseline method. MAPS also shows 11% energy savings in computational units and up to 54% in both mechanical and computational units. © 2024 IEEE.

关键词： Accuracy Autonomous vehicle Energy consumption Large Language Models (LLMs)

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Detection Method of Infrared Overhead Traverse Image Based on Improved YOLOv8 3

Detection Method of Infrared Overhead Traverse Image Based o...

引用

3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Ma, Hetao Zeng, Xiangjin Cai, Junyu Zheng, Qiming School of Computer Science and Engineering Wuhan Institue of Technology Wuhan China

ISBN: (纸本)9798331506582

Aiming at the problems of low inspection efficiency, missed detections, and false detections caused by the complex background and large aspect ratio variations of overhead conductors. In this paper, an overhead traverse detection model ES-YOLO with multi-scale features was proposed. Firstly, an ECABlock is designed as the feature extraction module, and a C2fECA enhanced backbone network is constructed to improve the feature extraction capability for overhead conductors. Simultaneously, the SE attention mechanism is embedded to enhance the expressive power of overhead conductor features. Experimental results on a self-made overhead conductor dataset show that compared with YOLOv8, ES-YOLO achieves an average detection precision of 85.6%, which is an improvement of 4.5 percentage points, while the model parameters and computational cost are reduced by 12.1% and 4.2%, respectively, satisfying the requirements of overhead conductor detection. © 2024 IEEE.

关键词： Object detection

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Enhancing Cyberbullying Detection Through Keyword Filtering: A Comparative Study of ML and DL Approaches

Enhancing Cyberbullying Detection Through Keyword Filtering:...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kaarthika, R. Hemamalini, R. Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

Cyberbullying represents the use of online platforms like social media, gaming platforms, and similar platforms to harass a person, hurt a person, or threaten a person. In this study, keyword filtering is introduced to enhance cyberbullying detection. Various machine learning models, including Logistic Regression, Random Forest, and Support Vector Machine, as well as a deep learning model Convolutional Neural Network, are implemented on original and keyword-labelled datasets. The performance of these models is compared using various evaluation metrics. Our findings demonstrate that the performance of models on the keyword-labeled dataset is significantly higher, with Logistic Regression achieving 98.24%, Random Forest at 99.77%, SVM at 99.67%, and CNN at 99.95% accuracy. This study highlights the effectiveness of keyword filtering features in enhancing the detection of cyberbullying, providing a robust approach to creating safer online environments. © 2024 IEEE.

关键词： Support vector regression

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AoI-Aware Optimization in RIS-Assisted mmWave Network 10

AoI-Aware Optimization in RIS-Assisted mmWave Network

引用

10th International Conference on Web Research, ICWR 2024

作者： Asadi, Somayeh Sheikhi, Marzieh Hakami, Vesal Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

In the era of next-generation wireless networks, real-time applications demand freshness in information delivery, notably in IoT, cyber-physical systems, and industrial IoT. We target the optimization of average Age of Information (AoI) in a multi-user RIS-assisted mmWave network, where the sensitivity of mmWave bands to propagation challenges is mitigated through reconfigurable intelligent surfaces (RIS). Notably, prior studies have overlooked AoI optimization in RIS-enhanced mmWave networks. Our contribution lies in devising a novel Markov Decision Process (MDP) framework and a model-free Deep Reinforcement Learning (DRL) algorithm tailored to high-dimensional action spaces. Our approach orchestrates user transmission timing, power allocation, base station beamforming, and RIS reflective coefficient adjustments. Through extensive simulations, we demonstrate the superiority of our method over existing schemes, showcasing enhanced network performance. This work represents an advancement in the optimization of AoI within the context of RIS-assisted mmWave communications, shedding light on efficient control policy learning for improved network operation. © 2024 IEEE.

关键词： Reinforcement learning

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Heart Attack Prediction Using Ensemble Learning

Heart Attack Prediction Using Ensemble Learning

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mali, Saurabh Veeramani, Karthika School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Heart attacks are a leading cause of death worldwide, making it a necessity to develop accurate prediction systems for timely intervention and prevention. This paper addresses the challenge of heart attack prediction through ensemble learning methodologies. Previous research has primarily focused on individual machine learning algorithms, often overlooking the potential of ensemble techniques to improve predictive accuracy. In this study, we employed a dataset comprising various clinical features and medical indicators. After preprocessing steps such as encoding categorical variables, scaling, and balancing data using SMOTE, we explored multiple machine learning models, including logistic regression, SVM, KNN, decision trees, random forests, gradient boosting, and artificial neural networks. Additionally, feature selection techniques and hyperparameter optimization were utilized to enhance model performance. Finally, we employed ensemble learning, specifically Stacking, to combine the strengths of individual models, resulting in a higher predictive accuracy of 93%. The results demonstrate the high efficiency of ensemble learning in heart attack prediction, showcasing a significant improvement over individual models. © 2024 IEEE.

关键词： Heart

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News Session Recommendation Based on Long and Short-Term User Interest Representation and Multi-View Learning

News Session Recommendation Based on Long and Short-Term Use...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： He, Jiaxing Li, Liang Mao, Xingbin School of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665410205

In the current context of proliferation of news information, personalised news recommendation is important to help users quickly access content of interest. Existing approaches tend to ignore the diversity of users' reading behaviours and the variability of news topics within a session. They tend to assume that all the news read or clicked within a session are attributed to one main interest, while in reality, each news session may cover a variety of news with different interests corresponding to different topics. The paper introduces a news session recommendation approach that utilizes both long-and short-term representations of user interests, incorporating multi-view learning techniques, and the method treats the clicking behaviours of anonymous users as a session: initially, a multi-scale convolutional neural network is employed to capture the features of user-clicked news articles from mul-tiple viewpoints;Subsequently, a GRU multi-channel interest decoupler is crafted to identify potential interests within each clicked news item, enabling the modeling of diverse interests to capture the user's long-term preferences. Concurrently, recent user-clicked news items are considered as short-term interests. These long and short-term interests are then amalgamated to create the ultimate user interest representation;Finally, the user's preference value for the candidate news is calculated by click-summing and the final recommendation result is generated. By comparing with the benchmark method on the real data set, the findings demonstrate strong performance on objective evaluation metrics such as Area Under the ROC Curve (AUC) and Normalised Discounted Cumulative Gain (NDCG), affirming the efficacy of the proposed method for news recommendation. © 2024 IEEE.

关键词： Contrastive Learning

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Mean Field Analysis of Serverless Edge Computing for Smart Vehicle Applications 4

Mean Field Analysis of Serverless Edge Computing for Smart V...

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4th International Conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Yao, Yuan He, Rong Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331534622

Smart cities and vehicles generate numerous time sensitive tasks, making edge computing a key solution for reducing latency. However, using serverless models at the edge faces challenges like dynamic city traffic and resource constraints. This study analyzes serverless edge computing for smart vehicles through modeling and experiments. we develop a mean field model captures steady-state system behavior under stochastic job arrivals, enabling efficient analysis of metrics like mean sojourn time and service degradation probability. Through experiments and simulations, we demonstrate the feasibility of serverless edge servers for low-latency offloading, providing insights for optimizing these systems in urban environments. © 2024 IEEE.

关键词： Edge Computing Mean field Model Performance Analysis Serverless

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