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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Heart Disease Prediction Using machine learning Algorithms: Performance Analysis 3

Heart Disease Prediction Using Machine Learning Algorithms: ...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Saha, Subrata Rahman, Md. Motinur Suki, Tahmid Tamrin Alam, Md. Mahbub Alam, Md. Shah Haque, Mohammod Abu Sayid Bangladesh Atomic Energy Commission Institute of Electronics Dhaka Bangladesh Bangladesh University of Professionals Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Early detection of this disease can decrease the mortality rate significantly and it can also reduce the costs of medical treatment. Researchers are trying to develop automated systems using data mining techniques that can help in this regard. Although they would be extremely difficult to build, machine learning (ML) based methods for heart disease prediction might be very useful in clinical settings. Moreover, the majority of nations lack doctors specialized in cardiovascular treatments, and a high percentage of cases are misdiagnosed. These issues could be resolved by creating an accurate and effective early-stage heart disease prediction system using ML. In this paper, we propose an ML technique to identify cvd patients depending on different features. We collected a heart disease dataset from the Center for Disease Control and Prevention (CDC) then applied different pre-processing steps, and transformed several attributes to make them readable for the ML algorithms. 5 supervised ML algorisms were implemented and the Random Forest algorithm provided the best outcomes in all the performance metrics that we studied, with maximum accuracy of 91% and, ROC-AUC (Area under the ROC Curve) of 0.91. It also surpasses the performance of other algorithms with a maximum True Positive Rate (TPR) of 0.922 and a minimum False Negative Rate (FNR) of.078. © 2024 IEEE.

关键词： Forecasting

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A Comparative Study of Clustering Algorithms for Profiling Researchers in Universities Through Google Scholar 3

A Comparative Study of Clustering Algorithms for Profiling R...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Al-Kerboly, Doreyed Muhammed Ahmed Al-Kerboly, Dhamyaa Zohdi Fatah Univ Anbar Coll Educ Pure Sci Anbar Iraq Minist Educ Al Anbar Distinguished Sch Anbar Iraq

ISBN: (纸本)9798350372977;9798350372984

This study compares the clustering algorithms used by four outstanding Iraqi institutions in Anbar, including Anbar University, Fallujah University, Al-Marf University and Al-Huda University. Meta-studies for clustering, studies by themselves ensure clustering, clustering Reinforcement learning, definition including clustering, and deep clustering include methods to be considered The analysis is based on three basic metrics of cluster performance: the Silhouette score, the Calinski-Harabasz index, and the Davies-Bouldin index. The collected evaluations show unique performance characteristics for each algorithm, which provides helpful information for algorithm selection in an educational setting. Meta-getting to know for clustering is an effective method. It works well on all parameters with a silhouette rating of 0.4655, a Calinski-Harabasz index of 189.8185, and a Davies-Bouldin index of 0.8202, just like 0.7287. The locating, which is supported by way of silhouette rankings and equal Calinski-Harabasz and Davies-Bouldin indexes, suggests that the efficacy of all 3 of those methods compares nicely inside the context of experimental measurements from the s. Deep clustering, alternatively, plays in another way, with an extensively decreased silhouette score of 0.0880 and extensively higher Calinski-Harabasz (83.4026) and Davies-Bouldin (3.2493) indices. This shows that its clustering performance may be more severe when compared to the other strategies studied. The most successful method is meta-gaining knowledge of for clustering, which plays nicely on all parameters with a silhouette rating of 0.4655, a Calinski-Harabasz index of 189.8185, and a Davis- Bouldin index of 0.8202, just like 0.7287 Finding this, similarly determined by silhouette scores and almost same Calinski-Harabasz, and Davis- Bouldin indices, shows that the effectiveness of these three technology is comparable in the context of experimental measures of the. On the other hand, deep clustering has un

关键词： data Minim Web mining Clustering Silhouette score the Calinski-Harabasz index the Davies-Bouldin index

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Research on the application of data mining technology in software engineering 3

Research on the application of data mining technology in sof...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yitao, Lin School of Software Heilongjiang University Heilongjiang Province Harbin450000 China

The role of data mining in software engineering is obvious, but there is a lack of mining depth. In the past, engineering layout and function distribution problems in software development, and the framework construction was unreasonable. Therefore, this paper proposes a software system based on data mining technology. First, the software engineering project is divided, and the software engineering project design is carried out according to the interactive requirements to realize the preprocessing of software engineering. Then, according to the design criteria, the design collection of software engineering is formed, and the information system is analyzed in depth. MATLAB simulation shows that the design interactivity and accuracy of data mining are superior to traditional design methods under the condition that software engineering requirements are consistent. © 2023 Elsevier B.V.. All rights reserved.

关键词： data mining

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An Advance Implementation of machine learning Techniques for the Prediction of Cervical Cancer 3

An Advance Implementation of Machine Learning Techniques for...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Roy, Vandana Roy, Lipika Ahluwalia, Ritu Khambra, Geetanjli Ramesh, M. Rajasekhar, K. Gyan Ganga Institute of Technology and Sciences Dept. of Ec M.P. Jabalpur India Gyan Ganga Institute of Technology and Sciences Ug Course M.P. Jabalpur India The Bhopal School of Social Sciences Department of Computer Applications M.P Bhopal India St. Martin's Engineering College Dept. of It Telangana Bharat Secunderabad India B v C Engineering College Dept. of Ece AP Bharat Odalarevu India

ISBN: (纸本)9798350343274

Cancer has now become a widespread issue, and it might be difficult to diagnose it. The analysis and diagnosis of disease in a patient through a monitoring system has advanced significantly, but there are still some obstacles to overcome. Technology-assisted disease discovery is urgently required since it could enable clinicians to make an accurate diagnosis more quickly. The process makes use of event-driven datasets produced by the Internet of Things (IoT). Rarely are the cloud and IoT used to process the real-time dataset for illness discovery. Cancer detection is a serious difficulty since the data generated by sensors constitutes huge data and cannot be quickly processed without the use of data mining and machine learning. A precise and intelligent method is needed for cancer prognosis because it has been discovered that cancer cannot be accurately predicted from offline datasets. The international Federation of Obstetrics and Gynecology (IFOG) realworld data set has been used to predict cervical cancer using machine learning. Five metrics, including true-positive-rate, false-positiverate, f-measure, MCC, and accuracy, have been used to examine the results of the training of six machine learning models using the latest UCI repository. The outcomes demonstrate that the tree-based decision stump machine learning classifier and the meta-based iterative classifier optimizer achieved the maximum prediction accuracy of 77.97%, which is the greatest among the other ML classifiers. The proposed model demonstrated noteworthy performance in terms of prediction accuracy. © 2023 IEEE.

关键词： Internet of things

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Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection 26

Adv-Cut Paste: Semantic adversarial class specific data augm...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - Theory and Applications (IMTA)

作者： Arun, Kumar S. Pal, Abhijit Mopuri, Konda Reddy Gorthi, Rama Krishna Indian Inst Technol Tirupati Tirupati Andhra Pradesh India Indian Inst Technol Guwahati Gauhati India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.

关键词： Training Deep learning Semantics Object detection data models Adversarial machine learning pattern recognition

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Fine-grained image recognition based on multi-branch and multi-scale learning 3

Fine-grained image recognition based on multi-branch and mul...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Li, Peng-Song Lin, Tong Zhou, Bing-Qian Ji, Zhi-Yi Northeastern Electric Power University 169 Changchun Road Jilin Jilin China

ISBN: (纸本)9781510667563

To address the problems of accurate region localization and effective data enhancement, this paper proposes a bilinear convolutional neural network model based on multiple branches and multiple scales. The method uses three branching networks to supervise the image recognition process, which enables the network model to learn fine-grained image features of different scales and different parts. The experimental results show that the bilinear convolutional neural network model based on multi-branch and multi-scale proposed in this paper has practical application value. © 2023 SPIE.

关键词： Image recognition

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Facial Emotional recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model 3

Facial Emotional Recognition Using Faster Regional Convoluti...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yamsani, Nagendar Jabar, Muhammed Basim Adnan, Myasar Mundher Hussein, A.H.A. Chakraborty, Subhra School of Computer Science and Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317022

Facial Emotion recognition (FER) poses a significant challenge when training deep learning (DL) models like Artificial Neural Network (ANN) and machine learning (ML). The primary issue in FER is the detection of the face, the creation of bounding boxes, and the classification of emotional expressions. This paper proposes the use of Faster Regional Convolutional Neural Network (RCNN) for facial recognition and employs a median filter to filter the image dataset. The model leverages the Facial Emotional recognition (FER 2013) dataset for training and predicting accurate classifications. The Median Filter is used to eliminate noise in the image, such as Salt and Pepper noise, and data augmentation is utilized to generate synthetic data for addressing minor imbalances in classes. Subsequently, the VGG 16 architecture is implemented for precise feature extraction to reduce complexity in the classification process. This paper, achieved accuracy, precision, recall, specificity, and f1-score values of approximately 78.22%, 75.40%, 80.20%, 85.90%, and 71.40%, respectively, when compared to other classification methods like Convolutional Neural Network, ResNet 50, Divide-And-Conquer-Based learning, and Resnet 18. © 2023 IEEE.

关键词： Artificial Neural Network Convolutional Neural Network Deep learning Facial Emotion recognition Median Filter

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Ensemble learning and Time-Domain Feature mining based Appliance Power Consumption pattern recognition 22

Ensemble Learning and Time-Domain Feature Mining based Appli...

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22nd international learning and Technology conference, L and T 2025

作者： Qaisar, Saeed Mian Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

As technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. The classification is carried out using the robust ensemble learning (EL) classifiers. The performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73. © 2025 IEEE.

关键词： Smart power grids

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Phishing Detection in Browser-in-the-Middle: A Novel Empirical Approach Incorporating machine learning Algorithms 3rd

Phishing Detection in Browser-in-the-Middle: A Novel Empiric...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Shahriyar, Md Farhan Seyam, Zarif Sadeque Ullah, Ahsan Sakib, Md Nazmus World Univ Bangladesh Dept Comp Sci & Engn Ave 6 Lake Dr Uttara Sect 17H Dhaka Bangladesh

ISBN: (纸本)9783031640667;9783031640674

BitM (Browser-in-the-middle) is a new generation phishing that has a high probabilistic rate to infringe present-day two or multifactor authentication logins which are said to be protected. In response to counter this security threat an internally curated dataset is generated to facilitate the detection of BitM within the context of phishing. As the existing approaches emphasize the mitigation of BitM phishing instead of its detection, this study introduces a novel empirical approach that utilizes data packets incorporating machine learning algorithms for the detection of BitM phishing attacks. The limited availability of training datasets due to inadequate BitM testing facilities restricted the expansion of our dataset. This study is inspired by the High severity score of the BitM attack according to Common Attack pattern Enumeration and Classification (CAPEC). The accuracies of five classifiers being used namely SVM, MLP, Naive Bayes, Random Forest, and Decision Tree, are examined, with Random Forest having the highest performance of 99.1% accuracy.

关键词： BitM Browser in the middle machine learning Phishing Attack Network Security Packet Capture Multifactor Authentication Random Forest

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Decentralized CNN Training for Table Tennis Serve Classification Using Federated learning 3

Decentralized CNN Training for Table Tennis Serve Classifica...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mehta, Shiva Khurana, Saniya Chitkara University Institute of Engineering and Technology Chitkara University Center for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350369083

This paper provides an original FL CNN framework specially designed for categorizing serve shots in table tennis. The approach deals with data security protection and stability challenges in the model. Standard pattern recognition methods typically require the collection of data to be centralized, which is quite problematic in terms of privacy and limits the number of available datasets. The FL method is applied as a collaborative approach to training models with distributed datasets where privacy is maintained while the set datasets are augmented to improve the resulting model. Comparison was also made of the FL CNN architecture with that of a conventional centralized structure. The FL model reached a classification accuracy of 92.5%, Precision of 93.2%, and Recall of 91. 8%, and the F1-score was equal to 92. 5%. On the other hand, the accuracy rate of the centralized model I achieved was 89. 70% for centrepiece agreement and a precision rate of 90. 5%, a recall rate of ***, the percentage increases by 5%, and the median and metamorphotic increases by 19%. Specifically, it achieves an accuracy of 92%, Precision of 94%, Recall of 93% and F1-score of 89.3%. The study shows that the FL technique effectively controls many serve shots, and data privacy is well protected. The investigation of training loss revealed that the FL model had a lessened training loss compared to the centralized model over 20 epochs of training. The training losses in the FL model and the centralized model were 0 at the end, 22 and 18, respectively. © 2024 IEEE.

关键词： Federated learning

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