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Automated Tooth Segmentation in X-ray Images using Attention Integrated U-Net++ Model 3

Automated Tooth Segmentation in X-ray Images using Attention...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Yaswanth, Julakanti Sai Kumar, Kanderi Johith Singh, Rimjhim Padam Bengaluru Amrita Vishwa Amrita School of Computing Dept. of Computer Science and Engineering Vidyapeetham India

ISBN: (纸本)9798331523893

Oral functioning, the ability to perform various oral functions, pain-free and without being ill, is one of the indicators of overall well-being and life satisfaction. Even though these diseases are mostly preventable, a majority of individuals all over the world suffer from periodontal disease, dental caries, and oral malignancies to an estimated of 5 billion people worldwide. However, there are still use of traditional radiographic techniques like periapical and panoramic radiography that assist in diagnosis. A better diagnosis is enhanced through the use of modern techniques in imaging such as MRI, ultrasonography and CBCT imaging. Deep learning-based solutions enhance diagnostic capabilities through dental segmentation in images since they learn from unmodified data in contrast to traditional machine learning and deep learning techniques which produce inferior results when dealing with massive datasets. Therefore, in an effort to solve the problem of efficient dental segmentation, anencoder-decoder basedtooth segmentation model based on U-Net++ architecture modified using Efficient Channel Attention Network is proposed that aims at enhancing the accuracy of dental diagnosis. The paper also focus of the performance of different state-of-art CNN techniques namely, U-Net, FCNs, ENet, U-Net++, U-Net 3+, SwiftNet on the standard Tuffs Dental dataset for model selection and comparisons. The proposed model outperforms other segmentation models and achieves the best mean average precision of 96.4% and a dice coefficient of 90%. © 2025 IEEE.

关键词： X ray radiography

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms 15

Machine Learning Techniques for Identifying Child Abusive Te...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Uddin, Mohammad Kamal Aziz, Mohammad Tarek Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention. © 2024 IEEE.

关键词： Deep Learning Machine Learning Natural Language Processing Sentiment Analysis Supervised Model

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Multi Layered Defense Architecture for DDoS Protection in Private Cloud Environments 2

Multi Layered Defense Architecture for DDoS Protection in Pr...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Shashwat Sharma, Anubha Soni, Shani Prakash, Ayushi Ajay Kumar Garg Engineering College Dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350368109

Private cloud solutions have increasingly been adopted because of the demand from organizations for enhanced data security, control over regulatory compliance, and IT infrastructure has been rising. Although public cloud services offer built-in security solutions, private clouds must possess a custom built architectures to address critical challenges, including Distributed Denial of Service (DDoS) attacks, among *** are often deployed behind private cloud environments without the benefits of prebuilt SaaS based DDoS protection. In this exposition, we present an architecture for multilayer protection against DDoS attacks in private cloud environments, which includes the following key components: Global Load Balancer, Traffic Scrubbing Center, Signature based IDPS, and Command and Control Center. Further, we also present a custom Command and Control Center, which utilizes CNNs for runtime handling of network traffic flow, based on real-time monitoring and activity patterns, to mitigate the menace of DDoS. Experimental results show that the architecture is efficient in filtering volumetric and application layer DDoS attacks, ensuring a malicious packet drop rate greater than 95%, while maintaining latency below 150 ms. © 2024 IEEE.

关键词： Cloud security

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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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Land Use Land Cover Classification Using Machine Learning and Remote Sensing Data: A Case Study of Karnaprayag, Uttarakhand, India 1

Land Use Land Cover Classification Using Machine Learning an...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Bauder, Chandler J. Fathy, Aly E. University of Tennessee Knoxville Dept. of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-dept. metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases. © 2025 IEEE.

关键词： Electrocardiography

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Support Vector Regression Based Traffic Prediction Machine Learning Model∗ 8

Support Vector Regression Based Traffic Prediction Machine L...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Srivastava, Ansh Singh, Mrigaannkaa Nandi, Somesh Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Bengaluru India Rv College of Engineering Dept. of AI/ML Bengaluru India

ISBN: (纸本)9798331505462

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues. © 2024 IEEE.

关键词： Mean square error

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Predicting Customer Purchase Intent Using Logistic Regression: A Data-Driven Approach 2

Predicting Customer Purchase Intent Using Logistic Regressio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Rajarajachozhan, C. Karthika, G. Rajakrishnammal, A. Mano, M. Kumar, P. Nithish Jayahari, E. Saveetha Engineering College Dept. of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Engineering College Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development. © 2025 IEEE.

关键词： Logistic regression

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Understanding the Student Confidence in Viva Responses Through Audio Analysis 3

Understanding the Student Confidence in Viva Responses Throu...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Reddy, Kunuthuru Navya Keerthi, Medikonda Sree, R. Nithya Pati, Peeta Basa Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

Assessment of student responses during viva examinations presents challenges in terms of consistency, objectivity, and efficiency. Traditional methods rely on manual evaluation, which can be lengthy and prone to human bias. To address this, machine learning techniques are applied to automatically evaluate student confidence levels based on their oral responses. Student answers are transformed into numerical features using word embeddings such as TF-IDF, RoBERTa, BERT, GloVe, T5, GPT-2, FastText, and SBERT. A variety of machine learning classifiers, including MLP, AdaBoost, and Logistic Regression, were used to classify answers into confidence levels: weak, moderate, and strong. Among these, MLP with GloVe embedding models yielded the best results, achieving a test accuracy of 76%, an F1-score of 76%, and minimal overfitting with a training-test performance gap of 4%. These results demonstrate the strong potential of ML-based systems in improving automated grading and ensuring reliability and scalability in evaluating student responses. © 2025 IEEE.

关键词： Semi-supervised learning

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