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检索条件"任意字段=International Conference on Machine Learning and Computing"

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Smart Detection for Heart Health 24

Smart Detection for Heart Health

引用

10th international conference on computing and Artificial Intelligence, ICCAI 2024

作者： Mekni, Mehdi Souza, Devyn Smith, Matthew Shaw, Joshua Intravaia, Jake Bennett, Nicolas Connecticut Institute of Technology University of New Haven West HavenCT United States University of New Haven West HavenCT United States

ISBN: (纸本)9798400717055

Heart disease's widespread occurrence presents a major public health challenge, worsened by high rates of misdiagnosis. This paper introduces HeartBuddy, an innovative, open-source mobile software aiming to address this issue. Using machine learning on ECG signals, HeartBuddy provides predictive insights into potential heart problems. Its user-friendly interface and visual aids enhance understanding, serving as a decision-support tool, not a replacement for medical expertise. HeartBuddy complements healthcare professionals' knowledge, showing promise in improving diagnostic accuracy in heart disease. © 2024 ACM.

关键词： Adversarial machine learning

来源：评论

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machine learning-based Biomedical Antenna for Brain Tumor Detection

Machine Learning-based Biomedical Antenna for Brain Tumor De...

引用

2023 international conference on Next-Generation computing, IoT and machine learning, NCIM 2023

作者： Hasan, Nafiul Aktar, Mousumi Rana, Md. Masud Moni, Mohammad Ali Rajshahi University of Engineering and Technology Department of Electrical and Electronic Engineering Rajshahi Bangladesh School of Health and Rehabilitation Sciences The University of Queensland Artificial Intelligence & Data Science St Lucia Australia

ISBN: (纸本)9798350316001

Automated identification and categorization of brain anomalies like tumors is crucial to understanding and monitoring patient outcomes. In this study, we applied machine learning models to improve the effectiveness of a step-constant tapered slot antenna (STSA) for diagnosing gliomas in the brain. We have performed simulation with the CST Microwave Studio 2015 to a dataset consisting of astrocytoma (tumor radius 5 mm, skin-tumor distance 24 mm) and matching healthy controls. Phantom, an artificial human head, has four antennas placed at right angles to it. Later on, several machine learning models including the Support Vector machine (SVM), K Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Artificial Neural Network (ANN) were applied to detect and diagnose brain cancers. There was a rigorous investigation of the scattering (S), admittance (Y), and impedance (Z) parameters and we observed the best result (99.60% accuracy) from the application of ANN and the Z parameter. The proposed technology could be applied for brain tumor detection accurately and clinical applications for a more efficient healthcare system based on biomedical antennas. © 2023 IEEE.

关键词： Support vector machines

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Exploring the Potential of Residential Energy Sharing Communities: A Simulation Study on Load Profile Suitability and Profitability

Exploring the Potential of Residential Energy Sharing Commun...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Panwar, Dheerendra Dodda, Suresh Mahadeva, Rajesh San Jose United States Eudoxia Research Center United States Indian Institute of Technology Department of Polymer and Process Engineering Roorkee India

ISBN: (纸本)9798350378092

The potential of residential energy sharing communities - especially those with photovoltaic and battery storage systems - to facilitate the transition to decentralised, sustainable renewable energy is explored in this study. The study simulates the profitability of 1,000 communities and looks at the relationship between household load profiles and whether or not they are suitable for energy sharing using actual load profile data from USA. To concisely define the high-dimensional load profiles, a comprehensive collection of electricity parameters was built from both a thorough analysis of the literature and new creations. The inability of machine learning (ML) models to predict community revenues effectively, even with efforts in hyperparameteroptimisation and different training dataset configurations, led to recommendations for future research topics. Therefore, a thorough examination of the load profiles of both low- and high-performing communities, theories about the favourable traits of residential load profiles for energy sharing were developed. These theories highlighted the intricate relationship between household energy habits and community energy dynamics. © 2024 IEEE.

关键词： Battery Electricity Energy machine learning Photovoltaic

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machine learning for Mesothelioma: Early Detection and Treatment 3

Machine Learning for Mesothelioma: Early Detection and Treat...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Bhavika Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Mesothelioma is an extremely severe cancer that can easily transform into lung cancer. Mesothelioma diagnosis takes several months and treatment, including surgery, is expensive. Given the risk, early detection of Mesothelioma is essential for patient health, as it is connected to asbestos exposure. Various machine learning algorithms has been used in this research paper to compare with accurate results for mesothelioma detection. Support Vector machine (SVM), Random Forest (RF), Decision Tree (DT), k Nearest Neighbourhood (k-NN), and Linear Regression (LR) are some of the machine learning methods being used. For this research paper, dataset is available on UCI, called the University of California Irvine [1]. The test dataset contains 264 instances, 35 characteristics and 8 performance measures, which is used to evaluate the classifiers accuracy. The average accuracy of XGB, RF, DT, SGD, LR and Voting Classifier are 100% each. Combing all the classifiers, helps us to break through the Mesothelioma data and the creation of data driven insights to improve patient care. © 2023 IEEE.

关键词： Asbestos Exposure DT machine learning machine learning Algorithms-SVM Mesothelioma

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Multivariate Feature-based Diabetic Foot Ulcer Classification Using machine learning Algorithms 16

Multivariate Feature-based Diabetic Foot Ulcer Classificatio...

引用

16th international conference on Contemporary computing, IC3 2024

作者： Bansal, Nishu Vidyarthi, Ankit Department of CSE&IT Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

Diabetic foot ulcers (DFUs) are a serious consequence of diabetes mellitus, sometimes resulting in infection and amputation. Accurate and early classification of DFUs is critical for effective treatment and management. This study presents a multivariate feature-based approach to classify diabetic foot ulcers using machine learning based algorithms . The proposed methodology involves the extraction of comprehensive features from DFU images, encompassing texture, color, shape, and others. Several machine learning algorithms, including K-Nearest Neighbour (KNN), Support Vector machines (SVM) and Back Propagation Neural Network (BPNN) were evaluated for their effectiveness in classifying DFUs into categories such as Normal and Abnormal. The dataset used in this study comprised of annotated DFU images and corresponding clinical data from a diverse patient cohort. The experimentation results of the K-Nearest Neighbour, Support Vector machine, and Backpropagation Neural Network algorithms were 92.81%, 96.07%, and 99.04% respectively. Results indicated that BPNN outperformed other classification models, achieving an accuracy of 99.04%, which is significantly higher than other models. The integration of multivariate features significantly enhanced the model's ability to distinguish between different DFU stages compared to using single-modal features alone. This research highlights the capability of advanced machine learning techniques in improving the diagnostic process for DFUs, ultimately contributing to better patient outcomes and optimized treatment strategies. © 2024 Copyright held by the owner/author(s).

关键词： Backpropagation Neural Network K-Nearest neighbor multi-variate features Support Vector machine

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Enhancing Personalized Medicine Using Data Analytics and Artificial Intelligence 7

Enhancing Personalized Medicine Using Data Analytics and Art...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Tejpal, Karan Singh, Simar Preet University of Massachusetts Lowell United States Dav University Jalandhar India Bennett University Greater Noida India

ISBN: (纸本)9798350350067

Personalized medicine is a tailored method in healthcare that uses specific genetic, environmental, and lifestyle information to develop individualized treatment plans. Its aim is to improve treatment outcomes by staying away from one-size-fits-all model, which offers more precise and effective care. The goal of personalized medicine is to tailor the medical treatment to specific patient characteristics, which further improves the outcome and reduces the adverse effects. This paper discusses how data analytics, machine learning (ML), artificial intelligence (AI), and python can be integrated to provide enhanced personal medicine. The paper demonstrates how analytical methods are able to process complex datasets to predict patient response and optimize the treatment plans. Our study highlights notable improvements in predictive accuracy and clinical decision support, which reveals the potential of these technologies to transform health care delivery. © 2024 IEEE.

关键词： artificial intelligence data analytics machine learning medicine personalized medicine python programming

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Analysis of Density-Based Clustering Algorithm Variants on Real-World and Gaussian Motion Datasets 14

Analysis of Density-Based Clustering Algorithm Variants on R...

引用

14th international conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Arora, Jyoti Malhotra, Bhavay Agarwala, Pratham Chandwani, Chittaranjan Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Information Technology New Delhi India

ISBN: (纸本)9798350344837

Clustering is an unsupervised machine learning technique that segregates the data points into different clusters which have similar properties. Density-based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm which works on the principle that a data sample belongs to a cluster if it has a required number of neighbours within a specified radius. DBSCAN produces hard clustering boundaries and performs quite poorly in case of real-world datasets. In recent years, many fuzzy variants for the algorithm have been proposed by researchers which aim to provide soft clustering boundaries thereby allowing more data points to be a part of a cluster with varying membership values. The fuzzy variations tend to perform well on these datasets. In this paper, a comparison of results of classic and fuzzy DBSCAN on four real world datasets has been done along with analysis of the results of streaming fuzzy DBSCAN variation on streaming data. © 2024 IEEE.

关键词： machine learning

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Smart Precision Farming Using Internet of Things and machine learning 1

Smart Precision Farming Using Internet of Things and Machine...

引用

1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Yadav, Devendra Kumar Das, Subha Ranjan Sahoo, Satyam Barua, Ipsita Sahoo, Dibyam XIM University Computer Science And Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

machine learning (ML) and Internet of Things (IoT) together have a revolutionary impact on agriculture, such as precision farming, which uses cutting-edge technology to address the challenges of unsupervised and random farming practices. Soil testing is a major component of farming that provides knowledge regarding the properties of soil. By measuring the pH level and nutrient deficiencies, the outcome of this test provides knowledge about the fertility and health of the soil. This involves the sampling and testing of each nutrient with certain chemicals, which are further analyzed by experts. This paper presents an innovative framework that harnesses the fusion of IoT and ML to redefine traditional farming methods. In addition, a prototype was created with a combination of various sensing devices, and various analyses of algorithms were performed on the gathered dataset for recommendation. The findings highlight a potential crop suitable for a particular set of soil parameters. © 2024 IEEE.

关键词： Internet of things

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Big Mart Sales Prediction Using machine learning 10

Big Mart Sales Prediction Using Machine Learning

引用

10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Mondal, Sagar Debbarma, Anindita Prakash, B. Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai India Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350353068

In today's competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment. © 2024 IEEE.

关键词： Sales

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Leveraging machine learning to Detect Phishing Websites: Protecting Individuals and Businesses from Cyberattacks

Leveraging Machine Learning to Detect Phishing Websites: Pro...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kamalesh, S. Dinesh, V. Nagaarjun, T.V. Aswin Sankar, R. Karthikeyan, T.S. Velammal College of Engineering and Technology Department of Information Technology Madurai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Phishing attacks are one of the emergent threats in the cybersecurity domain as it involves malicious websites and emails focused on compromising the sensitive information of the victim. The research paper is focused on detecting the phishing URLs with the help of machine learning. The dataset contains more than 11,000 URLs;every URL is labeled as phishing or legitimate, with 89 features describing the URL, such as length, use of special characters, and domain details. The URLs are classified using ML models such as RF, SVC, and GBM. In this paper, a hybrid model is built by taking in an RF, an SVC, and a GBM, optimized using cross-validation and hyperparameter tuning. The efficiency of the models is grasped based on the evaluation metrics like precision, accuracy, recall, and F1-score. The hybrid model outperformed the individual algorithms with a detection accuracy of 95% on phishing URLs, thus indicating its ability to be used as an efficient phishing solution to enhance cybersecurity defenses. © 2024 IEEE.

关键词： Phishing

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