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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

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Survey on DL - Driven Multi-Modal data in Cardiovascular Disease Prediction 2

Survey on DL - Driven Multi-Modal Data in Cardiovascular Dis...

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

作者： Gawande, Namrata Goyal, Dinesh Sankhla, Kriti Poornima University Department of Computer Science & Engineering Jaipur India Poornima Institute of Engineering & Technology Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798331505745

One of the primary reasons of illness and mortality across the world is cardiovascular disease (CVD), for the timely detection of which the need for accurate detection methods persists. The last years have seen the development of cutting aged multimodal machine learning methods to enhance diagnosis accuracy that consider multiple types of data. In such scenarios, Phonocardiogram (PCG) signals and Electrocardiogram (ECG) images form integrated data. This review presents the summary of recent research works published on heart abnormality detection models based on a multimodal dataset by combining PCG and ECG data. The PCG signals capture acoustic heart information, including murmurs and other mechanical abnormalities, while ECG images documentation of the heart's electrical functions, aiding in the identification of arrhythmias and other rhythm-related issues. We delve into the various deep learning architectures used in implementing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and process of attention in a complementary way to extract and combine features within these modalities. Building on the insights gained from existing literature in heart abnormality detection using multimodal datasets, we suggest a hybrid deep learning model that effectively integrates ECG pictures and PCG signals. This approach leverages the complementary nature of the two modalities to enhance detection accuracy and reliability. © 2025 ieee.

关键词： Electrocardiography

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Generative AI in Computer science Education: Insights from Topic Modeling and Text Network Analysis 2

Generative AI in Computer Science Education: Insights from T...

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2nd International conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alashwal, May Jeddah International College Department of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

The rapid evolution of generative artificial intelligence (AI) is reshaping educational landscapes., particularly in computer science education. This study investigates research trends in generative AI applications through text network and topic modeling analyses. A comprehensive literature review across ieee Xplore., Scopus., and Web of science identified 151 studies published between 2023 and 2024. Text network analysis revealed that 'AI.,' 'student.,' 'education.,' 'learning.,' and 'performance' were the most frequently occurring terms., highlighting key research themes. Ego-network analysis demonstrated strong interconnectivity between educational AI tools and student learning outcomes. Using Latent Dirichlet Allocation (LDA)., four major research topics emerged: educational chatbots (35.1 %)., AI literacy (25.8%)., exam performance (21.8%)., and technology integration (17.2%). Findings indicate a dominant focus on chatbots for student engagement., while ethical concerns and multimodal AI applications remain underexplored. The study underscores the necessity of addressing AI literacy gaps and enhancing interdisciplinary AI integration in educational settings. Despite its potential., generative AI adoption is hindered by privacy risks., uneven technological access., and the lack of standardized policies. Future research should focus on ethical AI frameworks., multimodal AI tools., and long-term learning outcomes. This study provides a data-driven foundation for understanding the evolving role of generative AI in education and its implications for educators., policymakers., and researchers. © 2025 ieee.

关键词： Students

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AI-Enhanced Imaging Techniques for Understanding Alzheimer's Progression 2

AI-Enhanced Imaging Techniques for Understanding Alzheimer's...

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

作者： Chaudhari, Aarti Saratkar, Saniya Thute, Trupti Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Alzheimer's disease (AD) is a fatal neuronal disorder which occurs mainly in the elderly and causes a gradual dementia with or without memory loss, functional disability and death. AD is the most prevalent form of dementia, and the condition is positively affiliated with amyloid-beta plaques, tau protein tangles, neuronal ischemic losses, and brain atrophy. Even after studying for a long, the cause(s) of AD has not been well established, though it is well understood that age, genetics (specifically APOE ?4 gene), and lifestyle are some of the leading causes. At the moment there is no cure and management involves the alleviation of symptoms and the prevention of disease progression. The key for better patient management is the early-stage triage as this would increase the chance of getting proper care, fulfilling criteria for clinical trials, and receive better therapeutic management. For a long time now, an early diagnosis has been emphasized due to the improved prognosis, but little has been done in that area instead a lot of efforts are directed at treatment. MRI, PET, and fMRI consisting of imaging approaches that enables the identification of brain changes and progression of the disease. Healthcare & medicine;Artificial Intelligence (AI) is changing the way Alzheimer's is diagnosed and treated. In fact, classifiers such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformer models can investigate imaging data and distinguish the features of early stage, progression, and survive rate, so as to assist with medical treatment planning. The general strength of AI to analyses and manage large amounts of information provides new chances for enhancing clinical practice and investigation in Alzheimer's disease. © 2025 ieee.

关键词： Neurodegenerative diseases

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Hybrid CNN-LSTM Framework for Accurate Seismic Event Prediction 2

Hybrid CNN-LSTM Framework for Accurate Seismic Event Predict...

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

作者： Vinod, Dasari Naga Kapileswar, N. Simon, Judy Padmavathi, B. Polasi, Phani Kumar Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India SRM Institute of Science and Technology Department of ECE Chennai India Chalapathi Institute Technology Department of ECE Guntur India

ISBN: (纸本)9798331505745

This study investigates the application of deep learning techniques, specifically Convolutional Neural Networks (CNN) as well as Long Short-Term Memory (LSTM) networks, for earthquake prediction using a binary dataset. Predicting Earthquakes is a difficult task because of various factors the complex and non-linear nature of seismic data, and traditional models often fall short in capturing the intricate patterns involved. In this research, a 1D CNN is utilized to automatically extract spatial features from time-series seismic data, effectively reducing the need for manual feature extraction. The CNN is designed to detect local patterns in the data that might indicate upcoming seismic events. Meanwhile, the LSTM element is integrated to capture the temporal addictions as well as long-term trends extant in the seismic signal, given its ability to hold information over prolonged periods. The hybrid CNN-LSTM model aims to predict the occurrence of earthquakes by classifying each instance as either an imminent earthquake (positive class) or no earthquake (negative class) within a specific time frame. The binary nature of the dataset simplifies the prediction task, allowing the model to focus on learning the critical features associated with earthquake precursors. Preliminary results indicate that the integrated CNN-LSTM model is capable of identifying patterns in the data this paper obtained an accuracy of 97% which shows the feasibility of the proposed model. © 2025 ieee.

关键词： Convolutional neural networks

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AI-Driven Cyber Security for Safeguarding Critical Infrastructure and Patient data 2

AI-Driven Cyber Security for Safeguarding Critical Infrastru...

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

作者： Ahmed, Nisher Hossain, Md Emran Hossain, Iffat Sania Hossain, Zakir Kabir, Md Farhad Begum, Nazma College of Technology & Engineering Westcliff University IrvineCA United States Martin V. Smith School of Business& Economics California State University CamarilloCA United States College of Engineering andComputer Science California State University NorthridgeCA United States Marshall School of Business University of Southern California Los AngelesCA United States College of MBA-BAn International American University Los AngelesCA United States

ISBN: (纸本)9798331505745

The growing digitization of crucial infrastructure, especially in areas like health care, has put these sectors at risk of complex cyberattacks. Managing a patient's protected health information (PHI) and operational systems of health care facilities is highly sensitive;breaches can result in data theft, service interruptions, and even endanger the safety of patients. This paper presents the use of AI cybersecurity solutions for the protection of vital infrastructure and patient data with advanced anomaly detection, intrusion prevention, and threat intelligence systems. AI algorithms can analyze large amounts of information in real time and identify new threats while adapting defenses automatically. The study covers central applications of AI: machine learning-enhanced intrusion detection systems, endpoint protection, and behavior-based mitigation of cyber threats. It also tackles issues of model accuracy - false positives, privacy, and model size. Further, the paper presents an integrated cyberspace security approach designed specifically for the healthcare systems with the goals of minimizing exploitable assets and maximizing sensitive data security. The core directions are aimed at building transparent AI models and employing blockchain technology for improved assurance of data integrity. © 2025 ieee.

关键词： Sensitive data

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Intelligent Dengue Prediction and Management: a Companied-Optimized Ensemble Approach With Advanced Feature Selection and Dynamic Recommendations 2

Intelligent Dengue Prediction and Management: a Companied-Op...

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

作者： Muthuselvi, P. Dhanasekaran, S. Vasudevan, V. Kalasalingam Academy of Research and Education Deemed to be university Department of Computer Science and Engineering Tamil Nadu Krishnankoil India Kalasalingam Academy of Research and Education Deemed to be university Department of Information Technology Taminadu Krishnankoil India

ISBN: (纸本)9798331505745

Dengue fever poses a significant health challenge, necessitating efficient resource allocation. Complex internal and external factors contribute to non-linear fluctuations in dengue fever occurrences, making resource planning challenging. While machine learning (ML), as well as deep learning (DL) algorithms, are pivotal in healthcare analysis, there's a need for enhanced predictive capabilities and intelligent recommendations. This research aims to execute an advanced early-warning system for dengue fever forecasting, emphasizing improved prediction accuracy and refined recommendation strategies. In data pre-processing phase, techniques like outlier removal as well as missing data imputation have been employed to elevate data quality. To optimize feature selection, we introduce an Advanced Neighbour Count-based Dragonfly Electric Fish Optimization (ANC-DEFO) algorithm. This method aims to determine most relevant features for accurate dengue fever prediction. Subsequently, selected features undergo an Optimized Ensemble Classifier (OEC), leveraging a combination of 'Convolutional Neural Network, Artificial Neural Network, and Support Vector Machine' based on their high-ranking performance. Beyond prediction, our model contributes to public health by offering tailored medical recommendations in areas where dengue fever is predicted. Recommendations span precautionary measures, immunity-boosting remedies, drug prescriptions, and strategies for minimizing disease spread. The research evaluates the model against various comparative algorithms, demonstrating its efficiency and superiority in early dengue fever diagnosis. © 2025 ieee.

关键词： Health insurance

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Early Detection of Alzheimer's Disease Using Machine Learning and Neuroimaging Techniques 2

Early Detection of Alzheimer's Disease Using Machine Learnin...

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

作者： Rama Krishna, K.V.S.S. Priyanka, G. Sofiya, Sk. Sri, M. Kavya Jyothirmai, Y. Vignan's Nirula Institute of Technology and Science for Women Department of IT Guntur India

ISBN: (纸本)9798331505745

Alzheimer's disease is one of the largest challenges faced in modern medicine, and this form of dementia has symptoms ranging from basic cognitive decline to specific neurodegenerative changes due to genetics. Early and correct diagnosis are needed for timely intervention and management of the disease. We propose a cutting-edge study, DeepAD-Net, which involves creating as well as training of deep learning kind algorithm to improve the detection rate in early stages for Alzheimer's disease via combination of all data from clinical and neuroimaging. To analyze MRI, PET and CT scans as well as clinical features like demographic information, cognitive scores and genetic markers, we proposed a model that combines both high end Convolutional Neural Networks (CNNs) with attention mechanisms. This work is a pioneer in this field, and thus more similar evaluation data between our DeepAD-Net model with HNB-PDPA Model or EIDR Model, which will be conducted as future works. This study highlights the promise of merging deep learning approaches with multimodal data to improve early Alzheimer's disease diagnosis. The competitive performance of DeepAD-Net illustrates the potential to improve prediction accuracy and robustness by leveraging state-of-the-art neural network architectures as well as attention mechanisms. Results could be particularly exciting for clinicians using DeepAD-Net as a diagnostic aid and ultimately improving patient management. Going forward, we will continue to refine our model for use with other data sources and in clinical settings related to early diagnosis, as well as management of Alzheimer's disease. © 2025 ieee.

关键词： Neurodegenerative diseases

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Prediction of Liver Cirrhosis Disease by using XGBOOST Majority Voting Technique in Machine Learning 2

Prediction of Liver Cirrhosis Disease by using XGBOOST Major...

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

作者： Saranya, Ms. K. Pandeeswari, N. KIT - KalaignarKarunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India PSNA College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798331505745

Cirrhosis is a disease where chronic liver injury causes the liver to become extensively scarred. The liver's capacity to operate is compromised when healthy liver cells are replaced by scar tissue. Chronic infections with the hepatitis B (HBV) and hepatitis $C$ (HCV) viruses are the primary causes of cirrhosis, which can develop from viral hepatitis to cirrhosis over a period of 10 to 20 years. Alcohol consumption is another significant risk factor for developing cirrhosis. Liver disease is becoming increasingly common in India, with one out of every five adults affected. Liver-related deaths in India contribute significantly to the global number of liver-related deaths, with 268,580 deaths annually, accounting for 18.3% of global liver-related mortality. Surface nodularity is an important imaging biomarker for detecting cirrhosis. It refers to irregularities in the surface texture of the liver that can be observed through imaging techniques such as ultrasound or CT scans. The XGBoost algorithm has been proposed as an effective tool for detecting liver tumours and classifying liver disease stages. Specifically, to increase its accuracy, XGBoost is used with the majority voting technique. The study compares XGBoost to a number of machine learning methods, such as Support Vector Machine, AdaBoost, K-nearest neighbour and Naive Bayes, XG Boost outperforms these algorithms, with the maximum accuracy of 99.4% in detecting liver tumours. Classifying the stages of liver disease can assist diagnosis and treatment recommendations. The effectiveness of early intervention can be increased by classifying the stages of liver disease using machine learning algorithms. Our work achieves state-of-the-art performance in liver disease categorization by combining the majority voting technique with an enhanced XGBoost algorithm. The model demonstrated great classification accuracy when tested and trained on a Kaggle dataset of liver diseases. © 2025 ieee.

关键词： Adaptive boosting

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Hybrid Routing and Energy Management for Performance Optimization in Mobile Ad-hoc Networks 2

Hybrid Routing and Energy Management for Performance Optimiz...

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

作者： Nirmala, B. Vijaya Francis Raj, J. Relin Krishnan, R. Santhana Lavanya, V. Pitchirani, A. Kumar, V. Vinoth PSNA College of Engineering and Technology Department of Information Technology Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331505745

Mobile Ad-hoc Networks (MANETs) encounter challenges related to dynamic topologies, energy constraints, and link instability. To enhance performance, a hybrid model integrates Ad-hoc On-demand Multipath Distance Vector (AOMDV), Power-Efficient Gathering in Sensor information System (PEGASIS), and Deep Q-Network (DQN). OMNeT++, a discrete event simulation framework, facilitates comprehensive data collection, capturing key network parameters such as node density, signal strength, packet transmission rate, residual energy, routing paths, link breakage frequency, and packet delivery status. Stored in scalar and vector files, the dataset enables energy-efficient communication modeling and adaptive routing optimization. data preprocessing involves cleaning, normalizing, and handling missing values using interpolation techniques while filtering noisy data through a sliding window averaging method. Feature extraction focuses on hop count, queue length, residual energy, and link expiration time, ensuring consistency and facilitating faster convergence. The hybrid model architecture integrates AOMDV for reliable multipath routing, PEGASIS for energy-efficient communication, and DQN for adaptive path optimization. Simulation results indicate a 15% improvement in throughput, a 10% rise in packet delivery ratio, a 20% reduction in end-to-end delay, and a 30% decrease in average energy consumption. Applications extend to military operations, disaster recovery, vehicular networks, IoT sensor networks, and smart city infrastructure, ensuring efficient and adaptive communication. © 2025 ieee.

关键词： Vehicular ad hoc networks

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support 2

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Kattankulathur Department of Computational Intelligence Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 ieee.

关键词： Neurodegenerative diseases

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