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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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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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Spam recognition Model Based on TextCNN 3

Spam Recognition Model Based on TextCNN

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Yixuan, Gao Yuanyuan, Zhang Ye, Cheng Fuzhou University Maynooth International Engineering College Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement of modern Internet technology, email is widely used in people's daily lives and has become one of the common communication tools. However, at the same time, email-based spam has also been widely spread, flooding people's lives and not only wasting public resources but also infringing on people's legitimate rights and interests. Based on this, we propose a Spam recognition model based on TextCNN by combining Convolutional Neural Network and Natural Language Processing, where natural language processing techniques are used to pre-process the text in the spam corpus to obtain text features, which are then imported into the constructed Convolutional Neural Network analysis model. A CNN spam recognition model is then designed and analyzed hierarchically using the TensorFlow framework. After extracting the features from the email dataset, the model was fed into the convolutional neural network again for several iterations of training. The results indicated that the model achieved an accuracy of 97.7% in spam recognition, which is an exceptionally significant improvement in accuracy and stability compared to the SVM model used previously, demonstrating good filtering performance. © 2022 IEEE.

关键词： Convolutional neural networks

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recognition of Leukemia Sub-types Using Transfer learning and Extraction of Distinguishable Features Using an Effective machine learning Approach 3

Recognition of Leukemia Sub-types Using Transfer Learning an...

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3rd international conference on Electrical, Computer and Communication Engineering, ECCE 2023

作者： Sajon, Tahsen Islam Chowdhury, Maria Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Mahedy Hasan, S.M. Sayeed, Abu Minhazur Rahman, A.F.M. Department of Cse Ruet Rajshahi Bangladesh

ISBN: (纸本)9798350345360

Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately;as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques. © 2023 IEEE.

关键词： Feature extraction

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Staying Ahead of the Curve: Early Prediction of Academic Probation among First-Year CS Students 3

Staying Ahead of the Curve: Early Prediction of Academic Pro...

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3rd international conference on Applied Artificial Intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Neda, Barbara Martinez Wang, Max Singh, Amanjeet Gago-Masague, Sergio Wong-Ma, Jennifer Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9798350328929

Application processes in higher education, particularly in fields like Computer Science (CS), have become more competitive in recent years. Additionally, at the University of California, Irvine (UCI), 57% of students who were admitted and enrolled in the CS program between Fall 2011 and Fall 2015 failed to maintain satisfactory academic standing and entered academic probation. Of these probation students, 41% entered probation during their first year. The objective of this study is to explore the feasibility of using machine learning tools to identify the academic probation status of first-year CS students at an early stage, with the ultimate aim of providing timely support to those who are at the greatest risk of entering academic probation. We trained various classifiers on a dataset containing demographic and academic performance variables for CS students at UCI, and performed feature analysis to identify the most influential variables. Since it is our priority to identify students who enter academic probation, our focus was on maximizing recall. The best-performing classifiers were Linear Support Vector machines (recall of 0.838) and Logistic Regression (recall of 0.812). Our feature analysis revealed that ethnicity, student region, and having taken the Advanced Placement CS exam had a greater impact on the classification task than GPA and summer course enrollment. Future work could focus on expanding the dataset to include academic data available after enrollment to improve our current results. Our findings could also be applied to assess their impact in strategies for student retention in CS. Overall, this study demonstrates the potential for machine learning tools to assist in identifying at-risk students in CS, and highlights the importance of considering a range of demographic and academic variables in this process.

关键词： academic performance prediction computer science education educational data mining academic probation academic probation detection machine learning first-year probation

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