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Natural Language Processing for Sentiment Analysis in ESL Writing: Understanding Emotional Tone in Learner Essays 5

Natural Language Processing for Sentiment Analysis in ESL Wr...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Kumar, M. Vinoth Ansari, Subuhi Kashif Gupta, Deepak Kavitha, P. Kumar, G.M. Prem Bala, B. Kiran Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Technology Department of English Avadi Chennai India Jazan University College of Engineering & Computer Science Jazan Saudi Arabia Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Mechanical Engineering Uttarakhand Dehradun India Department of English Erode India Ddgd Vaishnav College University of Madras Pg Department of English Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

In recent years, the significance of sentiment analysis in educational contexts has gained considerable attention, particularly in understanding the emotional expressions of English as a Second Language (ESL) learners. Accurately capturing these sentiments is essential for providing targeted support and feedback to enhance student writing skills. This study presents an advanced natural language processing (NLP) framework for sentiment analysis in ESL writing, specifically focusing on understanding emotional tones in learner essays. Utilizing a dataset sourced from Kaggle, which categorizes tweets based on emoticons, the methodology incorporates rigorous data preprocessing techniques, including text cleaning, tokenization, and error correction to enhance data quality. A comprehensive feature extraction process captures linguistic, syntactic, and sentiment-related features using dependency parsing and word embeddings, such as Word2Vec and BERT. The proposed model, implemented in Python, demonstrates a remarkable accuracy of 98.5%, significantly surpassing existing methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), which achieved accuracies of 95.12% and 96.97%, respectively. This enhanced performance highlights the model's ability to effectively capture nuanced emotional expressions in ESL writing. The validation of the model's predictions against human evaluations ensures the reliability and accuracy of the findings. This research not only contributes to the field of sentiment analysis but also informs educational practices, offering targeted interventions to support ESL learners in improving their emotional expression and writing skills. Ultimately, the proposed NLP framework serves as a robust tool for enhancing understanding and assessment of emotional tone in educational contexts. © 2025 IEEE.

关键词： Emotion Recognition

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Enhancing Real-Time Threat Detection with YOLOv8 and LSTM Integration for Intelligent Surveillance Systems 5

Enhancing Real-Time Threat Detection with YOLOv8 and LSTM In...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Behera, Prakash Chandra Rani, V. Kanpur Gupta, Deepak Hamid, Noor Kaylan Rao, Vuda Sreenivasa Dhaaraani, R. St. Claret College Autonomous Department of Science Bangalore India Hindustan Institute of Technology and Science Department of Eee Chennai India Department of Mechanical Engineering Graphic Era Hill University Uttarakhand Dehradun India Department of Mechanical Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Al Kitab University Department of Computer Education Kirkuk Iraq Koneru Lakshmaiah Education Foundation Department of Cse A.P. Vaddeswaram India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

In the face of increasing security threats, efficient surveillance systems are crucial for timely threat detection and response. This study presents a comprehensive approach that integrates the YOLOv8 object detection framework with Long Short-Term Memory (LSTM) networks to enhance real-time threat detection capabilities. The UCF-Crime dataset, which encompasses a variety of crime-related scenarios, was utilized to evaluate the proposed model's effectiveness. Traditional methods often struggle with accurately detecting objects and understanding complex behavioral patterns in dynamic environments. The existing methods primarily rely on conventional object detection techniques that lack the ability to capture temporal dependencies, leading to limitations in anomaly detection. In contrast, our proposed YOLOv8-LSTM model combines fast and accurate object detection with advanced sequential analysis, enabling the identification of unusual activities such as loitering or aggressive behavior over time. Performance metrics, including precision, recall, and accuracy, were employed to assess both object detection and anomaly recognition capabilities, demonstrating significant improvements in real-time threat detection. The results indicate that the integrated model not only enhances the reliability of surveillance systems but also opens pathways for future enhancements, such as incorporating multimodal data and optimizing the model for edge device deployment. This research highlights the potential of advanced deep learning techniques to improve security measures in public spaces, ultimately contributing to safer communities. The successful integration of YOLOv8 and LSTM marks a significant advancement in the development of intelligent surveillance systems, addressing modern security challenges effectively. © 2025 IEEE.

关键词： Deep learning

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Optimizing Soccer Match Prediction with Deep Convolutional Neural Networks and Grey Wolf Optimization 5

Optimizing Soccer Match Prediction with Deep Convolutional N...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Shukla, Tushar Dhar Rani, V.Kanpur Kumar, Indrajeet Hussein, Omar Mohsen Banu, Shaik Balkhis Bhavani, P. University Institute of Physical Education & Sports Chandigarh University Mohali India Hindustan Institute of Technology and Science Department of Eee Chennai India Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Fatima College of Health Sciences Department of Physiotherapy Al Ain United Arab Emirates K. Ramakrishnan college of Engineering Dept of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

Predicting soccer match outcomes has become an essential area of interest in sports analytics, yet many existing studies rely on traditional machine learning methods that may not adequately capture the complex dynamics inherent in the sport. This study aims to address these limitations by proposing a novel approach utilizing a Deep Convolutional Neural Network (DCNN) optimized through Grey Wolf Optimization (GWO) to significantly enhance prediction accuracy. The methodology leverages a comprehensive dataset from the European Soccer database, gathered from Kaggle, which includes detailed match statistics, player attributes, and betting odds, allowing for a richer analysis. The results of the study indicate that the proposed DCNN model outperforms several baseline models, achieving notable improvements in accuracy, precision, and F1-scores, thereby showcasing its ability to learn complex patterns and relationships within the data effectively. Despite these promising findings, the study acknowledges certain limitations, such as the potential for further enhancement through the integration of additional features like spatiotemporal tracking data and more detailed player performance metrics. The outcomes suggest that the combination of advanced deep learning techniques with effective optimization strategies can yield substantial improvements in predictive analytics in sports. In conclusion, this research contributes significantly to the field by demonstrating the potential of deep learning in the accurate prediction of soccer match outcomes. It encourages future exploration of diverse datasets and methodologies to further refine predictive capabilities, ultimately supporting more informed decision-making processes for teams and coaches within the competitive landscape of soccer. Overall, the study underscores the transformative potential of machine learning in sports analytics. © 2025 IEEE.

关键词： Convolutional neural networks

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AutoMiner: Reinforcement Learning-Based Mining Attack Simulator 24th

AutoMiner: Reinforcement Learning-Based Mining Attack Simula...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Wei Xue, Lide Han, Ziyang Chen, Bingren Zhang, Xishan Zhou, Xuehai University of Science and Technology of China Hefei China ShangHai Municipal Big Data Center Shanghai China Anban Tech Co. Shanghai China State Key Laboratory of Processor Technologies Institute of Computing Technology Chinese Academy of Sciences Beijing China Cambricon Technologies Beijing China

ISBN: (纸本)9789819615247

As blockchain technology rapidly advances, it faces significant security threats from attacks like selfish mining, which exploit consensus algorithm vulnerabilities, undermining system security. Traditional analysis methods, primarily reliant on Markov models, are often utilized to address these complex threats. However, these methods frequently fall short in accurately simulating the multifaceted nature of blockchain attacks, leading to gaps in security measures. To bridge this gap, we introduce AutoMiner, an innovative reinforcement learning-based framework that integrates Miner Monte Carlo Tree Search (MMCTS) with Long Short-Term Memory (LSTM) networks for simulating and detecting potential mining attacks within the Proof of Work (PoW) framework. Our experimental results demonstrate AutoMiner’s capability to outperform traditional selfish and honest mining strategies, achieving up to 20% higher profits under specific scenarios. This highlights AutoMiner’s potential in enhancing blockchain security by providing a more comprehensive and effective approach to analyzing and mitigating mining attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Reinforcement learning Security analysis

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Hierarchical Knowledge Distillation for Human Activity Recognition 1

Hierarchical Knowledge Distillation for Human Activity Recog...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Hu, Di Wu, Xingchen Xiong, Dezhu Department of Optoelectronic Science and Technology Ji Hua Laboratory Foshan China Jiushao Institute of AI Algorithm Ji Hua Laboratory Foshan China Intelligent robot research center Ji Hua Laboratory Foshan China

ISBN: (纸本)9781643685779

The widespread adoption of IoT and mobile devices with a variety of sensors has made it possible to develop applications that combine time-series data from multiple sensor inputs. However, current deep neural network architectures for multimodal fusion struggle with consecutive missing data and noise, common in real-world scenarios. We introduce a Hierarchical Knowledge Distillation Framework (HKDF) designed to address noise and data missingness challenges in Human Activity Recognition (HAR). Specifically, we introduce a cross-sample correlation distillation mechanism, leveraging contrastive learning to capture and transfer holistic semantics across samples, thereby enabling effective feature reconstruction. Additionally, a cross-category variation distillation strategy is proposed to learn inter- and intra- category dynamics based on category prototypes to produce robust joint multimodal representations. The superior performance achieved on both datasets demonstrates the remarkable advantages of HKDF. © 2025 The Authors.

关键词： Contrastive Learning

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Feature Norm-Aware and Hardness-Guided Complementary Entropy Balanced Loss for Long-Tailed Image Classification 25th

Feature Norm-Aware and Hardness-Guided Complementary Entrop...

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25th international conference on Parallel and Distributed computing, Applications and technologies, PDCAT 2024

作者： Zhang, Feng Wang, Jia-Xin Hua, Qiang Dong, Chun-Ru Zhu, Jie Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9789819642069

To handle model bias from long-tailed image datasets, most existing studies have proposed to strengthen the focus on tail classes. However, they usually overlook the intricacies of sample learning hardness and feature norms’ impact on accuracy, as well as the non-target class contribution. We designed a new loss using logit adjustment. It considers the distribution of the number of class samples, sample learning hardness, and feature norms. Moreover, it combines a complementary entropy regularization term designed for long-tailed data. Extensive experiments on long-tailed datasets confirm the proposed loss’s superiority in enhancing classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Development of Digital Telecommunication to Analyse and Store Complex Media data: A Way to data Engineering

Development of Digital Telecommunication to Analyse and Stor...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Yesu Ratnam Pachigolla Pflugerville Texas

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In the era of digital world data flows are saturated with development of universal multifunctional system to solve problems and to optimise the computing resources. The information system is highly loaded with modern data and large number of resources. The user request and heterogeneity of the incoming streams can be evaluated using different types of multimedia services and its requirement for computing resources and its performance with the entire data. The incoming flow of data heterogeneity is considered as the distinctive feature of request in the modern information system to support different types of multimedia services in single platform. Large volumes of data and data heterogeneity Creates numerous problems related to data storage security and the speed of digital system. To address these challenges artificial intelligence technology can be used for execution of digital telecommunication complex for processing and storing the dynamic flow of data that are in multi format. The prospects and trends can be identified to develop these models based on the perspective characteristics. The development of digital communication with multi object analytic system for storing and analysing complex data with data engineering. An fuzzy based model is used for data processing with enhanced accuracy of 98%.

关键词： Memory Multimedia computing Streaming media Media data engineering data processing Communications technology Artificial intelligence Streams Information systems

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A Real-Time Object Detection System with Audio Feedback for Visually Impaired Persons

A Real-Time Object Detection System with Audio Feedback for ...

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Yadav, Neha Kumawat, Saloni Mishra, Sushil Kumar Jaseja, Mahek Khare, Shanu Chandigarh University Department of Computer Science & Engineering Chandigarh India Chandigarh University Department of Computer Science & Engineering Unnao India

ISBN: (纸本)9798331527495

Object detection technologies offer attractive pos-sibilities to visually impaired people, enabling them to avoid full-on collision with obstacles and recognize objects. This paper represents a modern real-time object detection system with integrated audio feedback is presented in this study in an effort to improve accessibility and situational awareness. The YOLO (You Only Look Once) algorithm is used by the system to efficiently recognize objects in images, videos, and real-time object detection (WebCam), and the IDE we used is Jupyter Notebook. It is implemented with OpenCV. Our method combines visual and audio output using text-to-speech technology to announce the presence of detected items, while also showing bounding boxes and labels on them. The method exhibits great precision in distinguishing numerous objects in intricate settings, maintaining a 0.5 confidence threshold to guarantee dependable identifications. Important features include threaded audio announcements to eliminate interface lag, dynamic label positioning for clear visual feedback, and nonmaximum suppression to reduce overlapping detections. This multimodal method creates opportunities for applications in autonomous systems, augmented reality, and assistive technologies, in addition to improving the interpretability of detection data. Our study advances computer vision by providing an integrated system that combines real- time auditory feedback with visual detection. This technology could help visually impaired users and improve human-computer interaction in a range of applications. © 2025 IEEE.

关键词： Object detection

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP international Federation for Information Processing 2025.

关键词： Convex optimization

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Evaluating Deep Learning Models for Brain Tumor Detection Utilizing Transfer Learning and Fine-Tuning Approach 5

Evaluating Deep Learning Models for Brain Tumor Detection Ut...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Chauhan, Nitin Kumar Kumar, Dharmendra Gupta, Shiv Narain Kumar, Amit Kumar, Mukesh Chatterjee, Pinaki Sankar Indore Institute of Science and Technology Department of Ece Indore453331 India Thapar Institute of Engineering and Technology Department of Eied Punjab 147004 India Greater Noida Institute of Technology Department of Ece Greater Noida India CNRS@Create 138602 Singapore Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneshwar751024 India

ISBN: (纸本)9798331522100

Brain tumor identification is a significant task in medical imaging, necessitating precise and fast diagnostic methods to enhance patient outcomes. Despite advances in medical imaging technologies, prompt and precise diagnosis of brain tumors remains a face-off. Specifically, Deep Learning networks that make utilization of transfer learning and fine-tuning have demonstrated that they have the potential to be effective in resolving this issue. This investigation compares the performance of some prevalent progressive Deep Learning networks for brain tumor diagnostics, using Transfer Learning and Fine-Tuning approaches. Ten Deep Learning models namely Inception, Xception, ResNet-50, VGG-16, DenseNet-121, ResNet-152, DenseNet-169, VGG-19, EfficientNetB0, and MobileNetV2 are used for performance evaluation in the 4-class classification of brain tumor images. To improve detection capabilities, the models are pre-trained on a huge 'ImageNet' database and then fine-tuned using an annotated brain tumor image dataset. The assessment demonstrates that VGG-16 surpasses others with an accuracy of 99.24% for the 4-class classification of brain tumor data. © 2025 IEEE.

关键词： Deep learning

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