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Advancing Cardiovascular Disease Prediction Using LLMs 2

Advancing Cardiovascular Disease Prediction Using LLMs

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Zanab, Tazveen Bhavani, K. Shunmugapriya, B. Shenbagaraman, A. Kanthimathi, M. National Engineering College Department of Computer science and engineering Kovilpatti628503 India

ISBN: (纸本)9798331505745

The heart is perhaps one of the most vital organs supporting human life, but it is more at risk in our contemporary world with its rapid pace of life. The lifestyles in modem times and exert an immense burden on cardiovascular health. All these factors have led to a growing rate of cardiovascular diseases (CVDs) across age groups and turned heart disease into a predominant cause of death globally and an emergent public health issue. One of the most urgent challenges in the fight against heart disease is the challenge of obtaining timely diagnosis. Most people only get to know about critical cardiovascular diseases after they suffer from life-threatening incidents like heart attacks or associated complications. The delay is usually a result of the insidious and deceptive presentation of initial symptoms, which are often misunderstood as insignificant health issues. For example, breathlessness can be due to stress or exertion, whereas blackouts can be confused with dehydration or fatigue. But such innocuous-sounding symptoms can be a harbinger of severe cardiovascular illness. Early detection and treatment are the key to preventing the advancement of heart disease and lowering the risk of sudden, disastrous cardiac events. Without timely and correct diagnosis, life-saving treatment windows are lost, and severe consequences become more likely. Raising awareness and enhancing diagnostic techniques are thus the critical first steps in confronting the global cardiovascular health emergency. © 2025 IEEE.

关键词： Cardiology

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Embodied Generative AI Art for Enhanced Human-Robot Interaction Through a Human-Centric LLM-Guided Robotic Arm Drawing System 20

Embodied Generative AI Art for Enhanced Human-Robot Interact...

引用

20th Annual ACM/IEEE international conference on Human-Robot Interaction, HRI 2025

作者： Xie, Shengyuan Sandoval, Eduardo Benitez Shaik, Khaja Ahmed Cruz, Francisco School of Art and Design University of New South Wales Sydney Australia School of Mechanical and Manufacturing Engineering University of New South Wales Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Escuela de Ingeniería Universidad Central de Chile Santiago Chile

ISBN: (纸本)9798350378931

Generative AI is transforming the way humans interact with robots by integrating language-driven comprehension with embodied execution. While recent research leveraging large language models (LLMs) to enhance communication between humans and machines has shown significant progress, the exploration of integrating LLMs with robots to assist users in creating real-world artistic works remains challenging. This research introduces a novel GENAI-driven intelligent robotic arm drawing system. We fine-tuned GPT-3.5 Turbo to better capture meaningful information from conversations with users and output precise drawing commands, which are directly fed into the fine-tuned Stable Diffusion model to generate desired images. A UFactory xArm equipped with an end-effector holding a drawing pen is deployed and controlled by a speed control algorithm to perform accurate drawing movements based on AI-generated images. Our proposed framework facilitates enhanced HRI experiences with a focus on drawing tasks, effectively embodying the capabilities of generative AI in the field of artistic creation and enabling users to engage deeply in the process of using AI for art creation in real-world environments. © 2025 IEEE.

关键词： Robotic arms

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Optimized Hybrid Deep Learning Approach for Detecting DoS Attacks Using Feature Selection in Wireless Sensor Networks

Optimized Hybrid Deep Learning Approach for Detecting DoS At...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sasson Taffwin Moses, S. Abraham David, L. Emil Selvan, G.S.R. Ramkumar, M.P. Thiagarajar College of Engineering Department of Computer Science and Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331512965

The rapid growth of smart cities, healthcare monitoring, and environmental sensing relies heavily on the real-time data processing capabilities of Wireless Sensor Networks (WSNs). However, these networks face significant security challenges, particularly from malicious activities like Denial of Service(DoS) attacks, which can disrupt critical operations. A hybrid intrusion detection model that combines Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) to improve the detection of such threats has been proposed. The preprocessing phase includes the Synthetic Minority Over-sampling Technique (SMOTE) which mitigates class imbalances ensuring robust performance across diverse attack scenarios. The model incorporates advanced feature selection techniques including SelectKBest and Grey Wolf Optimization (GWO) to identify the most critical features for enhanced detection accuracy. The CNN component effectively captures local traffic patterns, which are then refined by the DNN for accurate classification. This hybrid model's effectiveness in detecting diverse DoS attack scenarios is evaluated using WSN-DS dataset, ensuring robust security for WSNs. By achieving an impressive 98% detection accuracy, this hybrid model proves to be effective in intrusion detection. Its applications can significantly strengthen the security and reliability of WSNs, ensuring uninterrupted operations in key sectors such as smart cities, healthcare, and environmental monitoring. © 2025 IEEE.

关键词： Network intrusion

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th international conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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Air Pollution Forecasting using Machine Learning with Temporal Fusion Transformer and Graph Neural Networks 3

Air Pollution Forecasting using Machine Learning with Tempor...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rath, Subhashree Maneesha, P. Dept.of.Computer Science and Engineering New Horizon College Of Engineering Autonomous College Affiliated to VTU Bengaluru560103 India

ISBN: (纸本)9798331527549

Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management and policy formulation. In this study, we employ sophisticated machine learning frameworks, such as Temporal Fusion Transformer (TFT) and Graph Neural Networks (GNNs), to predict air quality levels. TFT is designed to capture temporal dependencies, making it ideal for time-series forecasting, while GNNs model spatial relationships, enabling the analysis of pollutant dispersion across monitoring stations .A synthetic dataset comprising 50 rows was generated, simulating pollutant concentrations (PM2.5, PM10, CO, NO2, SO2) and meteorological variables (temperature, humidity, wind speed). The models were assessed using criteria like R2, RMSE, and MAE. TFT achieved an R2 of 0.97 and an RMSE of 4.2 µg/m3, significantly outperforming traditional models like Random Forest and XGBoost. GNN provided spatial insights, identifying pollutant hotspots and their dispersion patterns. This research highlights the superior accuracy and scalability of TFT and GNN for air quality prediction. The results underline their potential for real-time monitoring, smart city integration, and proactive environmental management, offering a robust solution for addressing urban air pollution challenges. © 2025 IEEE.

关键词： Graph neural networks

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A Real Time approach for detecting and counting Vehicles using OpenCV and NumPy

A Real Time approach for detecting and counting Vehicles usi...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Suneetha, Irala Prathima, Ch. Sirisala, Srinivasulu Mahalakshmi, S.S.D.K. Balaji Nanda Kumar Reddy, K. Sambana, Bosubabu Annamacharya Institute of Technology and Sciences Ece Department Tirupati India Mohan Babu University Dept. of Ds Tirupati India Cvr College of Engineering Department of Cse Telagana India Lendi Institute of Engineering and Technology Department of Cse Vizianagaram India Annamacharya Institute of Technology and Sciences Eee Department Tirupati India Mohan Babu University School of Computing Department of Data Science Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331508685

Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accurately counting them as they pass through a designated area. OpenCV, a popular computer vision library, is utilized for its extensive functionality and ease of implementation. First, the video stream is captured and processed to extract individual frames. Subsequently, each frame undergoes preprocessing techniques such as resizing, grayscale conversion, and noise reduction to enhance vehicle detection accuracy. Vehicle detection is achieved through the implementation of a deep learning- based object detection algorithm, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). These algorithms are trained on large datasets to recognize vehicles with high precision. Once vehicles are detected, a tracking algorithm is employed to maintain continuity between frames and accurately count the number of vehicles passing through the specified region of interest. This tracking algorithm ensures that vehicles are not double-counted and provides a reliable count for traffic analysis purposes. © 2025 IEEE.

关键词： Noise abatement

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Foaming study of polyurethane insulation for cold storage based on aspen plus 10

Foaming study of polyurethane insulation for cold storage ba...

引用

2024 10th international Forum on Manufacturing Technology and engineering Materials, ICMTEM 2024

作者： Zhang, Xiangsong Zhang, Yonglin School of Automation Jiangsu University of Science and Technology Jiangsu Zhenjiang China

Based on a polyurethane high-pressure foaming device, the process of synthesizing polyurethane from isocyanate (-NCO) and polyether polyol (-OH) under the catalytic effect of various additives was simulated. Using the Aspen Plus software platform, a flow model of the polyurethane high-pressure foaming process was established. The effects of isocyanate index, reaction temperature, catalyst mixing ratio, and other factors on the polyurethane process were investigated. The sensitivity analysis of the data was carried out by Energy Analyzer, and the optimized process operating parameters were obtained: isocyanate index R=1.15, reactor temperature of 60°C, and catalysts DMCHA and T-12 contents of 2 wt% and 0.05 wt%. Under these conditions, the processing time was reduced by 9.75% and the product conversion was improved by 12.19%. This provides a very important theoretical basis and practical significance for the process production of polyurethane materials. © Published under licence by IOP Publishing Ltd.

关键词： Sensitivity analysis

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IoT Based Monitoring System for Health During Menstruation

IoT Based Monitoring System for Health During Menstruation

引用

Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Chinmaya Khadse Shital Hajare Nishad Sheikh Roshni Dahule Priya Kate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawani Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD.

关键词： Data privacy Ethics software algorithms Prediction algorithms software Internet of Things Biomedical monitoring Wearable devices Smart phones Monitoring

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Toxic Comments Classification using Machine Learning and Word Embedding Techniques 2

Toxic Comments Classification using Machine Learning and Wor...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Prabha, Sneh Yadav, Aditya Yadav, Aman Kumar, Akash Tyagi, Manu Department of Computer Science and Engineering Inderprastha Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Toxic comments are comments which are disrespectful, unreasonable and infuriating that make the reading uncomfortable. Sometimes these comments are inappropriate and not good for the public. These comments make daily content consumers uncomfortable and sometime even cause emotional as well as emotional harm. There are some people whose purpose for coming online is to create trouble for others and make their work and daily life difficult by making inappropriate comments. This kind of behavior can be described as anti-social and harmful to society. Many steps have already been taken to catch these troublemakers and punish them accordingly by blocking users who are found guilty. This cannot be done personally by checking one by one, to achieve this purpose we need to make a model. We have applied three Machine Learning Models logistic regression, Naive bayes, SVM and three Word Embedding Techniques, Word2Vec, FastText and Glove which can classify comments, and the content creator take appropriate action against users who makes these comments by reporting and even by deleting their account if needed. Out of all six techniques SVM has outperformed for classification of toxic and non-toxic comments and normal text classification in general. SVM gives the best results with 93.61% accuracy and F1 score of 93%. Proposed technique has improvised accuracy score as compared to existing techniques. © 2025 IEEE.

关键词： Embeddings

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Scalable Explainable AI with a Cloud-Native Approach for Cybersecurity Threat Detection

Scalable Explainable AI with a Cloud-Native Approach for Cyb...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Thiyagarajan Mani Chettier Venkata Ashok Kumar Boyina Sunil Jorepalli Charanjit Singh Neha Gupta Independent Researcher South Windsor CT USA Independent Researcher Cumming GA USA Independent Researcher San Francisco USA Department of Computer Science & Engineering Chandigarh University Punjab India School of Computer Science and Engineering Geeta University Naultha Panipat

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The growing complexity and number of cyber threats call for sophisticated detection approaches that provide both high performance and interpretability. The proposed hybrid AI approach (AE-RF-CNN-LSTM) is a promising hybrid up-to-date improving SEO framework for cybersecurity. All these models play a steady role in performance improvement. Autoencoders are trained to sense patterns from normal data to assist in anomaly detection, random forests help with the robustness of the model with overfitting reduction, and CNN-LSTMs assist greatly in recognizing the complex temporal dependencies in network traffic data. Experimental results show that this approach achieves substantially improved accuracy nearest neighbor anomaly detection and surpasses every single model in terms of accuracy. This hybrid framework, thus, helps to detect known and unknown cyber threats, leading to a huge decrease in false positive and false negative rates. Furthermore, integrating explainable AI (XAI) methods, including SHAP (SHapley Additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations), to enhance decision interpretability. The methods enable security analysts to comprehend the most relevant features of making predictions and, therefore, trust the model and correct further cybersecurity actions. This not only allows for the improvement of defense mechanisms against cyberattacks but also builds confidence in AI-driven security solutions by providing interpretability and assurance of performance.

关键词： Accuracy Explainable AI Autoencoders Telecommunication traffic Threat assessment software Robustness Real-time systems software reliability Anomaly detection

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