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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13588 条记录，以下是701-710 订阅

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OHMBC: Optimized Hybrid deep learning Model for Classification of Breast Cancer 4

OHMBC: Optimized Hybrid deep learning Model for Classificati...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Saju, Binju Asha, V. Tressa, Neethu Kondikoppa, Pooja B. Pooja Pavan Kumar, V.J. Kalady India New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350343670

Men are much less likely than women to develop breast cancer. Breast lumps, bloody nipple discharge, and changes in the nipple's or breast's shape or texture are all indications of breast cancer. the study proposes a hybrid model to classify Diabetic retinopathy images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. the proposed model has obtained 96% accuracy, 98.1% sensitivity, 98.2% specificity, 98% precision, and 98.18% F1 Score. © 2024 IEEE.

关键词： Breast Cancer DCNN Gaussian Mixture IAO Resnet

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Adaptive boundary oversampling through feature weighting for class imbalance learning 4

Adaptive boundary oversampling through feature weighting for...

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4th international conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Leng, Qiangkui Li, Zhiyong School of Electronics and Information Engineering Liaoning Technical University Huludao125105 China

ISBN: (纸本)9781510686106

Classification is a crucial learning task in machine learning, which fundamentally involves predicting the category of test examples using a classifier generated from a training example set. However, many real-world applications have training sets with imbalanced class distributions, which often hinder the classification performance of learning algorithms. To address this, this paper proposes a feature-weighted adaptive boundary oversampling method. Initially, the method acquires neighbor information of sample points based on weighted Euclidean distance (WED), identifies minority class samples on the boundary according to the distribution of minority class neighbors, calculates the synthetic factor corresponding to boundary samples, and updates the number of samples to be generated for the sample based on its value. Finally, it randomly selects minority class samples from the neighbors to generate new samples according to the synthetic factor. the proposed method is compared with seven sampling methods on a decision tree classifier and 12 imbalanced datasets from KEEL. the results show that the proposed method achieves the best values in F1, G-mean, and AUC (Area under Curve) on most datasets, and has the best Friedman ranking, proving that compared with other sampling methods, it has better performance in handling classification problems in imbalanced data. By setting certain constraints and allocation strategies for the synthetic factor, it can provide ideas for similar research. © 2024 SPIE.

关键词： Contrastive learning

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OBE-Based Course Outcomes Prediction Using Machine learning algorithms 4

OBE-Based Course Outcomes Prediction Using Machine Learning ...

引用

4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Tjandra, Ellysa Ferdiana, Ridi Setiawan, Noor Akhmad University of Surabaya Department of Informatics Engineering Surabaya Indonesia Gadjah Mada University Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798331506490

Educational data mining has emerged as a powerful tool for exploring hidden patterns in student data, predicting academic success, and reducing the dropout rate, especially in higher education. the OBE approach has been widely used in almost all educational institutions to ensure that students have the competencies determined by the study program and to measure student achievement. this study proposes a prediction model using machine learning algorithms using Regression to predict student course outcomes in an integrated OBE attainment system utilizing midterm component scores. this study identifies the most potential machine learning techniques for predicting learning outcomes in an OBE-based framework. By predicting student course outcomes early, it is possible to identify at-risk students who fail to meet desired outcomes early so that lecturers can take preventive actions. the performance of the decision trees, random forests, support vector, and K-nearest neighbor algorithms was computed and compared to predict the students' course outcome results. this study uses a dataset of 2423 students enrolled in 30 courses in 6 study programs at a private university in Indonesia during the first semester of 2023-2024. the predictions were made using mid-term course components' scores defined in the course learning plan, such as assignments, class engagement, quizzes/exams, and case-based projects. this research finds support vector regression recommended for use in large classes, while decision trees and random forests are more suitable for smaller classes. © 2024 IEEE.

关键词： Prediction models

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Human activity recognition using residual deep network with extreme learning 4th

Human activity recognition using residual deep network with ...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Bohra, Garima Jha, C.K. Sharma, Neelam Department of Computer Science Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781032911571

In recent years, human activity recognition (HAR) has emerged as an important topic of research due to the multiple applications it has found in various fields, including fitness and strength surveillance, residential care, biometric verification, aged care, and many others. Sensors are a great alternative for activity recognition because of their widespread use. the bulk of wearable sensors, including accelerometer, gyroscope, camera, GPS, microphone, compass, and others, are built into the newest smart devices. Such sensors measure different properties of an object, are simple to use, and are inexpensive. Researchers using machine learning (ML) now have new potential to accurately recognize human behaviours thanks to the usage of sensors in the HAR sector. Deep learning (DL) is gaining popularity among HAR researchers because it outperforms standard machine learning (ML) approaches. In this paper, hybrid features of mechanisms and learning are used by employing deep learning and machine learning;deep learning uses RESNET-50 and machine learning uses XG boosting. In experiments, the proposed approach was validated by a 10-cross comparison with the state-of-the-art approach. In experiment also classified human activities and intruders. © 2025 the Author(s).

关键词： Active learning

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Language identification using machine learning on social media text 4th

Language identification using machine learning on social med...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Raj, Rishav Ankita Dath, S. Sai Sholanki, Shivani Yadav, Anshuman Uchoi, Enjula Department of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9781032911571

One of the main areas researchers are looking into nowadays is how to change words from one language to another, known as transliteration. this is especially useful for cross-lingual applications where people need to translate words. Sometimes, people write in a mix of languages on social network platforms like X(twitter), Facebook, WhatsApp, which is called "mixed code language." To understand this kind of writing better, it’s important to figure out which language each word belongs to. In a recent study, a special type of deep learning model called Bidirectional Long Short-Term Memory (BLSTM) was introduced to analyze social media conversations in India. this model looks at the words that come before to figure out where each word comes from. Interestingly, the study found that using word embedding (a way of representing words) worked better than using character embedding. © 2025 the Author(s).

关键词： Adversarial machine learning

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the Intersection of AI, ML, and Industry: A Review of Emerging Trends in Real-World applications 4

The Intersection of AI, ML, and Industry: A Review of Emergi...

引用

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Joshi, Ankush Kumar, Vikash Chauhan, Naman Kumar, Anuj Singh, Ritik Kumar Coer University Computer Science and Application Roorkee India

ISBN: (纸本)9798350364729

Machine learning (ML) and artificial intelligence (AI) are transforming a number of industries, spurring creativity, and improving productivity. this review article examines the new developments at the nexus of industry, artificial intelligence, and machine learning. It offers a thorough rundown of current developments and real-world uses. We demonstrate how artificial intelligence (AI) and machine learning (ML) are being incorporated into industrial processes to solve complex problems and enhance operational workflows by looking at important industries including healthcare, banking, manufacturing, retail, and transportation. We highlight the revolutionary power of these technologies, talk about the difficulties and moral issues, and suggest future paths for study and development through in-depth case studies and practical examples. © 2024 IEEE.

关键词： Smart manufacturing

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On Penalty-based Bilevel Gradient Descent Method 40

On Penalty-based Bilevel Gradient Descent Method

引用

40th international conference on Machine learning

作者： Shen, Han Chen, Tianyi Rensselaer Polytech Inst Dept ECSE Troy NY 12180 USA

Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization, meta-learning and reinforcement learning. However, bilevel problems are difficult to solve and recent progress on scalable bilevel algorithms mainly focuses on bilevel optimization problems where the lower-level objective is either strongly convex or unconstrained. In this work, we tackle the bilevel problem through the lens of the penalty method. We show that under certain conditions, the penalty reformulation recovers the solutions of the original bilevel problem. Further, we propose the penalty-based bilevel gradient descent algorithm and establish its finite-time convergence for the constrained bilevel problem under some lower-level error bound conditions weaker than strong convexity. the experimental results showcase the efficiency of the proposed algorithm. the code is available on Github (link).

关键词： Reinforcement learning

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Early Prediction Model for Anemia in Infants Using Clinical Data from Perú Applying Supervised Machine learning algorithms 10

Early Prediction Model for Anemia in Infants Using Clinical ...

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10th international conference on optimization and applications, ICOA 2024

作者： Rivera, Jonathan Cardenas, Diego Castillo-Sequera, Jose Luis Wong, Lenis Universidad Peruana de Ciencias Aplicadas Lima Peru Universidad de Alcalá Alcalá de Henares Spain

ISBN: (纸本)9798350387353

Anemia is a disease that is always present from early stages;children are the most prone to contracting it from birth if it's not diagnosed, which can lead to more severe illnesses. therefore, a predictive model is proposed for the detection of anemia in infants using their clinical data. the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed, along with Analysis Services for the Extract, Transform, Load (ETL) process and four Machine learning (ML) algorithms: Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). All four phases of CRISP-DM were utilized: Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation. the dataset used consists of 400,000 records related to the medical history of children from a healthcare institution in Peru, from which 27 variables were selected. the results showed that the DT algorithm performed better, achieving an AUC-ROC Curve greater than 93%, and the 'Scikit Learn' library for ML in Python facilitated obtaining the results. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Knowledge Graph Representation learning Method Based on Logical Rule Fusion 4

Research on Knowledge Graph Representation Learning Method B...

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4th IEEE international conference on Software Engineering and Artificial Intelligence (SEAI)

作者： Li, Lin Zhao, Haili Meng, Lingshuai Luo, Haitao Chen, Zixuan Li, Weikuang Lin, Shenwen Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China Changan Commun Technol Co LTD Beijing Peoples R China

ISBN: (纸本)9798350374353;9798350374346

this paper presents a knowledge graph representation learning framework based on Horn clause rules, designed to efficiently integrate logical information into knowledge graphs (KGs) in continuous vector spaces. Due to the challenge of rule uncertainty, it is difficult to devise a principled framework in continuous vector spaces where encoding the logical background knowledge of rules is usually not straightforward. therefore, we propose a solution that calculates the Horn rule constraint among relations, obtained through iterative optimization learning with labeled triplets, objective score functions, and relation modeling. this method enables us to achieve better regulation of rule-based effects, merely enforcing relation representations to satisfy constraints introduced by Horn rules. Finally, we analyze the proposed method on several FB15K datasets. the analysis results demonstrate that our scheme effectively improves the performance of link prediction evaluation on public datasets.

关键词： knowledge graph representation learning Horn rule embedding learning

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Predictive Modeling for Self-Compacting Concrete: Evaluating Machine learning Approaches in Real-World Construction Scenarios 4th

Predictive Modeling for Self-Compacting Concrete: Evaluating...

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4th FIB international conference on Concrete Sustainability

作者： Aldawish, Abdulaziz Kulasegaram, Sivakumar Cardiff Univ Sch Engn Cardiff Wales

ISBN: (纸本)9783031806742;9783031806728;9783031806711

this study provides a comprehensive review and analysis of the applications of various machine learning techniques in predicting properties of self-ompacting concrete (SCC). this study also integrated experimental data from existing literature to build and evaluate ML models. We critically assess methodologies, strengths and limitations of Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Tree Regression s(DTR), and other machine learning models, emphasizing their predictive accuracy in real-world scenarios. We found that ANN showed significant promise for handling complex data structures and adaptability, but was dependent on extensive and high-quality datasets. SVM exceled in generalisability and effectiveness, even with limited data, while DTR and its advanced forms, such as XGBoost, offered a balance of accuracy and efficiency. the objective of this study was to identify the most effective model based on predictive accuracy and efficiency in real-world construction scenarios. Furthermore, this study explored challenges such as data diversity, model generalizability and real-world applicability. Future research should focus on hybrid models, expanding datasets, and applying these models to diverse concrete mixtures and conditions, offering significant implications for efficient and sustainable SCC use in construction.

关键词： Self-Compacting Concrete (SCC) machine learning (ML) of Artificial Neural Networks (ANN) Support Vector Machines (SVM) Decision Tree Regression (DTR)

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