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An Empirical Evaluation of Machine learning Models for Stroke Risk Assessment with Explainable AI

An Empirical Evaluation of Machine Learning Models for Strok...

引用

2025 international conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Yashwanth Reddy, J.V.N. Sai Saketh Ram, G. Varshini, Vuyyuri Srinivas, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Amaravati India Amrita Vishwa Vidyapeetham Dept. Computer Science & Engineering Amaravati India

ISBN: (纸本)9798331527518

Stroke has significant socioeconomic effects, particularly in low- and middle-income nations, and is a major cause of mortality and disability worldwide. This study applies machine learning models to predict stroke, leveraging clinical and demographic data to highlight those who may be at risk. We applied a range of models, including Random Forest, XGBoost, Gradient Boosting, and Neural Networks, after comprehensive data preprocessing. Among these, Random Forest achieved the highest accuracy (98.87%) and ROC AUC (1.0), making it highly effective for stroke risk prediction. We improved transparency in healthcare applications by emphasizing the relevance of individual risk factors in forecasts using LIME for model interpretability.. These findings support the use of ensemble models and explainable AI for reliable, interpretable stroke prediction, paving the way for preventive healthcare. © 2025 IEEE.

关键词： Risk assessment

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A Novel Approach To Brain Tumor Detection using AI ML, DL, with Synthetic Data Augmentation 5

A Novel Approach To Brain Tumor Detection using AI ML, DL, w...

引用

5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Appavu, Narenthirakumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350356236

Effective treatment and better patient outcomes depend on early brain tumor identification. Even if they are trustworthy, traditional diagnostic techniques frequently need manual analysis, which can be laborious and arbitrary. In order to improve brain tumor diagnosis from MRI scans, this work introduces a unique method that combines Support Vector Machine (SVM) clustering, Statistical Gray Level Co-occurrence Matrix (LBP) features, ResNet50, and synthetic data augmentation. SVM clustering overcomes the drawbacks of linear clustering techniques by accurately segmenting tumor areas. Texture-based characteristics like homogeneity and contrast are extracted using LBP, offering vital information for categorization. By utilizing its deep learning architecture, the ResNet50 model attains great accuracy after being adjusted based on the retrieved characteristics. By mitigating data scarcity, synthetic data augmentation ensures the resilience of the model. Improved accuracy, sensitivity, and specificity are demonstrated by experimental findings on publicly available datasets, indicating the efficacy of this strategy in providing scalable, dependable solutions for automated brain tumor diagnosis. © 2025 IEEE.

关键词： Deep learning

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Empowering Fake News Detection Through Innovative Hybrid Deep learning-Based Approach

Empowering Fake News Detection Through Innovative Hybrid Dee...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Suresh, A. Mallika, R.M. Gireesh, S. Singh, G. Hari Kiran Jeevanandham, E.S. Hemalatha, A. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Puttur India Puttur India

ISBN: (纸本)9798331538538

The essence of getting information to the populace in the shortest time possible through social media and other interactive technologies has made it easier for fake news to thrive hence creating a menace to any society as well as its decision-making systems. This research work presents a sound AI-based approach to classify fake news using DL algorithms embedded within a CNN-BiLSTM model. Using the ISOT dataset, the comparison of the outcomes achieved by the proposed model with the original models shows that the proposed model has the highest accuracy score of 94.07%, a precision of 93.50%, a recall of 94.60%, and an F1-score of 94.00%. The proposed hybrid model architecture of CNN for spatial feature extraction and BiLSTM for capturing contextual dependencies that are essential for text analysis and have relatively high accuracy compared to conventional ML algorithms, including CNN, RNN, LSTM, and other DL methods. The proposed approach is verified by a set of comprehensive measures, including ROC curve analysis and various training-validation metrics that reveal the effectiveness of the presented approach in overcoming the challenges of fake news identification. © 2025 IEEE.

关键词： Deep learning

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Enhancing Web Security: A Phishing Detection System Integrated with Password Managers 6

Enhancing Web Security: A Phishing Detection System Integrat...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Subbulakshmi, T. Meghana, G. Sunkara, S. Ravi Sailendra Shanmukha Sai, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331522667

Phishing attacks have become a widespread and intricate threat, targeting individuals and organizations through deceptive URLs to steal sensitive information such as login credentials and financial data. These attacks exploit human vulnerabilities and frequently employ advanced techniques like URL obfuscation and social engineering to evade traditional security measures. Despite ongoing advancements in cybersecurity, phishing remains a persistent issue due to its adaptability and the continual evolution of attack strategies. This paper introduces a comprehensive phishing detection system that implements both rule-based analysis with machine learning models to address these challenges effectively. The system utilizes YARA rules to detect known phishing patterns and integrates real-time threat intelligence feeds for up-to-date phishing data. Additionally, it employs machine learning algorithms to enhance detection accuracy and adapt to emerging threats. The system is further strengthened by its integration with a password manager, providing real-time URL validation and alerts to users during their online activities. This multi-faceted approach aims to improve the accuracy of phishing detection and offer robust protection against the increasingly advanced methods employed by attackers. © 2025 IEEE.

关键词： Phishing

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DeepRetina: A Deep learning Approach for Diabetic Retinopathy Detection and Stage Classification

DeepRetina: A Deep Learning Approach for Diabetic Retinopath...

引用

2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Nithishvikraman, H. Sudharsan, G. Anand Joseph Daniel, D.I. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolution neural network (CNN) to consistently diagnose diabetic retinopathy and grade patients into five groups or stages. An automatically generated diagnostic may be provided by a data-enhanced CNN architecture network that can identify the complex components involved in the class task, as well as exudates, hemorrhages, and micro-aneurysms in the retina, without requiring human input. This work trained CNN using data that was made accessible to the public. When compared to other algorithms on the same dataset, it demonstrated an astounding performance. It was used to the global dataset and obtained a great accuracy of 97% on the validation image. © 2024 IEEE.

关键词： Complex networks

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LSTM Based Off-Loading for Edge Intelligence

LSTM Based Off-Loading for Edge Intelligence

引用

2024 international conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Niveditha, P.S. John, Saju P. Thejus Engineering College Apj Abdul Kalam Technological University Kerala Thrissur India Jyothi Engineering College Computer Science Engineering Kerala Thrissur India

ISBN: (纸本)9798350387230

'Edge Intelligence' or 'Edge AI' is a topic created by combining Edge computing (EC) and Artificial Intelligence. By utilizing the edge resources which are extensively available, edge intelligence allows AI applications to run partially off the cloud. The benefits and necessity of running artificial intelligence programs on the edge have led to a recent surge in interest in edge AI. This work presents a Mobile EC (MEC) in which different wireless devices select for off-loading their computation process into the Edge Server (ES). In this work, the Deep learning (DL) Long Short-Term Memory (LSTM) based offloading is presented for MEC and it makes off-loading decision. The experimental analysis proved that the suggested model can make near optimum off-loading decision at less computation time. © 2024 IEEE.

关键词： Mobile edge computing

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Pre-conference Workshop: Let's Play- Improving our teaching in the Medium of Board Games 54

Pre-Conference Workshop: Let's Play- Improving our Teaching ...

引用

54th IEEE Frontiers in Education conference, FIE 2024

作者： Jamieson, Peter Davis, Karen C. Rapos, Eric J. Miami University Department of Ece OH United States Miami University Department of Cse OH United States Ontario Tech University Faculty of Science ON Canada

ISBN: (纸本)9798350351507

This workshop paper focuses on exposing faculty to how the medium of board games provides an exceptional space for faculty development for our teaching and student learning. The 'Let's Play' intervention [1] takes the form of a workshop where participants have opportunities to experience role reversal through being a learner again. Participants become active learners by playing board games that help them remember the experience of being a learner again. By choosing different types and styles of games, we can provide a space for the participants to discuss broader teaching practices, such as the importance of technical vocabulary, scaffolding ideas as we teach them, and the benefits of student-centered learning approaches. Another critical aspect of this intervention is that we hope to use role reversal to remind teachers how hard it is to learn in the hope that teachers will have more empathy for their learners. In this paper, we describe our workshop structure and pertaining literature and ideas on why board games are part of this medium. NOTE that many past participants are looking for how to use games in the classroom. This workshop does not address that aspect of the medium even though we have experience in this space [2]. © 2024 IEEE.

关键词： Students

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Advances in Multimodal Fusion of EHR and Medical Imaging Data Using deep learning techniques for advanced treatment of brain cancer 24

Advances in Multimodal Fusion of EHR and Medical Imaging Dat...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Siva Raja, P.M. Vidhya, S. Sumithra, R.P. Anjana, S. Rejini, K. Ramanan, K. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Nagercoil Tamil Nadu Nagercoil India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798400718304

In this research, the performance of different deep learning models, including CNN + RNN, VGG16 + RNN, VGG19 + RNN, ResNet50 + RNN, and InceptionV3 + RNN in predicting brain tumour grades by employing Electronic Health Record (EHR) and imaging data are evaluated. Confusion matrices show the models' abilities to predict, showing the results of the prediction capability of each model over tumour grades. From the result and analysis, InceptionV3+RNN is performing better than all other ML models. With the multimodal fusion techniques, an informed decision can be made about the grades of the cancer cells, and the clinicians can give the proper treatment to the patients. The proposed approach can be implemented in real-time to predict the cancer grades with the highest level of accuracy for better treatment. © 2024 Copyright held by the owner/author(s).

关键词： Electronic health record

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Medical Prescription Generator using Natural Language Processing 3

Medical Prescription Generator using Natural Language Proces...

引用

3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Paithankar, Siddhesh Patil, Snehal Shivgan, Ashlesha Mahabudhe, Mandar Dharmadhikari, Pramod MIT Academy of Engineering School of Computer Engineering Pune India MIT Academy of Engineering School of Electrical Engineering Pune India

ISBN: (纸本)9798331523893

With the increasing population in India and the limited availability of medical professionals, time efficiency in diagnosis and prescription generation has become crucial. This paper proposes an automated system leveraging Natural Language Processing (NLP) and Natural Language Generation (NLG) to assist doctors in generating patient history and prescription suggestions. The system utilizes previously typed words to predict the next word, drawing on a medical notes dataset tailored to the doctor's specialization. By giving keywords or short descriptions about a patient's condition, the system processes the input using NLP for contextual under-standing and employs NLG to generate detailed histories and prescriptions in a standardized format. This dual approach ensures that the output aligns with medical conventions while maintaining accuracy and efficiency. The system is trained on extensive medical datasets, optimizing its predictions over time through machine learning techniques. Our solution not only enhances accuracy but also reduces the time doctors spend on manual documentation, improving overall efficiency and patient care. © 2025 IEEE.

关键词： Diagnosis

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MRI Brain Tumor Segmentation and Classification using different deep learning models

MRI Brain Tumor Segmentation and Classification using differ...

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2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Modi, Dhruvin R. Senthil Kumar, A.M. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

Brain tumors have significant challenges in diagnostic precision due to their complex morphological subtleties evident in MRI (Magnetic Resonance Imaging) scans. Clinical Decision Support Systems (CDSS) have become indispensable in this regard, with MRI brain tumor segmentation and classification at their forefront. This comprehensive survey deals with the profound impact of deep learning techniques on MRI brain tumor analysis, particularly within the context of CDSS. Emphasizing the models validated against the various Dataset, the exploration ranges from the intricate convolutional processes of CNNs (Convolutional Neural Networks) to the advanced self-attention mechanisms offered by recent Transformer-based methodologies. The significance of 3D datasets is underscored, given their ability to offer detailed and holistic representations of brain tumors. Metrics such as DSC (Dice Similarity Coefficient), JI (Jaccard Index), and sensitivity rates serve as pivotal benchmarks to evaluate the efficiency of these deep learning models. Moreover, this survey investigates the performance trade-offs inherent within these methodologies, pointing towards potential avenues for further enhancement and adaptability. By synthesizing this expansive body of knowledge, the goal is to harmonize cutting- edge technological strides with tangible clinical utility, thereby refining diagnostic accuracy and forging a brighter traj ectory for patient care in the realm of brain tumor studies. © 2024 IEEE.

关键词： Magnetic resonance imaging

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