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

共 13584 条记录，以下是791-800 订阅

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Sentiment Analysis of Translated BBC Hindi News Articles Using Machine learning: A Comparative Study of Classification algorithms 4

Sentiment Analysis of Translated BBC Hindi News Articles Usi...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Menon, Vishnu Achutha Sateesh Kumar, T.K. thomas, Juby thomas, Lijo P. Institute for Educational and Developmental Studies Department of Education Noida India Kristu Jayanti College Department of Commerce Bengaluru India Kristu Jayanti College Department of Media Studies Bengaluru India Kristu Jayanti College Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529635

News consumption in India has shifted significantly with the rise of Web 2.0 technologies, leading to the prominence of platforms like BBC Hindi News, which blends international credibility with regional relevance. the dataset comprises over 5000 articles from BBC Hindi (Kaggle), with sentiment distributed as 97% neutral, 2.1% positive, and 0.7% negative. Text preprocessing steps include cleaning, tokenization, stopword removal, and lemmatization. Features were extracted using TF-IDF vectorization, and two machine learning models-Logistic Regression and Random Forest-were employed for classification. Performance metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used for evaluation. Logistic Regression achieved an accuracy of 97.38%, whereas Random Forest marginally outperformed with 97.99%. Random Forest also demonstrated better handling of minority classes, reflected in higher macro-average metrics compared to Logistic Regression. Despite the improvements, both models exhibited a strong bias toward the dominant neutral class, with difficulties in accurately classifying positive and negative sentiments. the study highlights the importance of addressing class imbalance through techniques like oversampling minority classes or adjusting class weights. Random Forest's performance suggests ensemble techniques are better suited for imbalanced datasets, though further optimization through hyperparameter tuning and feature refinement is recommended. © 2024 IEEE.

关键词： Contrastive learning

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Exploring Data-Efficient Physics-Informed Neural Networks for Fluid Simulation 4

Exploring Data-Efficient Physics-Informed Neural Networks fo...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Wang, Bohan New York University Tandon School of Engineering Department of Computer Science and Engineering BrooklynNY United States

ISBN: (纸本)9798350394375

Computational Fluid Dynamics (CFD) is crucial in engineering applications such as aerospace and bioengineering, but it often require substantial computational resources. learning-based simulation methods have the potential to accelerate the computation process but may require a large amount of data. this paper addresses the challenge of data-intensive demands in learning physics-based fluid simulation model by investigating the data efficiency of Physics-Informed Neural Networks (PINNs) for fluid dynamics. It quantifies the minimal data requirements for training PINNs, highlighting their potential to overcome the limitations of traditional CFD methods in resource-constrained environments. By establishing data efficiency benchmarks, our research supports the broader applicability of PINNs across various industries, including aerospace and environmental monitoring. © 2024 IEEE.

关键词： Machine learning

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A Review on the Identification of Face Mask Object from Multiple Environments Using Deep learning Techniques 4

A Review on the Identification of Face Mask Object from Mult...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： thakkar, Chandani Shah, Arpit Himabindu, K. Parul University Piet Computer Engineering Department Gujarat Vadodara India

ISBN: (纸本)9798331540364

Deep learning (DL) has revolutionized object detection, making it more accurate even in difficult situations. this review explores the capabilities of modern models and applications designed to detect masked or unmasked objects, especially in adverse conditions such as rain, blur, and changing illumination. We examine the effectiveness of existing deep learning techniques, including new design models such as convolutional neural networks (CNN) Transfer learning, YOLO and more conventional approaches like Support Vector Machines (SVM) and Principal Component Analysis (PCA). In order to increase model resilience, we also go over a combination of feature extraction strategies, picture enhancement models Even with great progress, problems including managing various types of noise, detecting real time problems, and enhancing dependability in practical uses still need to be solved. this analysis emphasizes the need for more investigation to improve the accuracy and dependability of detection tools in real-world deployments utilizing both conventional and deep learning techniques. © 2024 IEEE.

关键词： Support vector machines

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An Efficient Detection Model for Brain Stroke Based on Transfer learning 4

An Efficient Detection Model for Brain Stroke Based on Trans...

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4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Mohammed, Faroq H. M. Alawi, Abdulfattah Ba Bozkurt, Ferhat Geyikoglu, M. Dilruba Ataturk Univ Elect & Elect Engn Dept Fac Engn Erzurum Turkiye Ataturk Univ Comp Engn Dept Fac Engn Erzurum Turkiye Taiz Univ Software Engn Dept Fac Engn & IT Taizi Yemen

ISBN: (纸本)9798350354140;9798350354133

Brain stroke is one of the most common causes of death, ranking as the second leading cause worldwide. With the advancements of deep learning, the detection of brain strokes from CT images becomes possible. In this study, we propose a method for classifying brain stroke images and predicting the presence of a stroke using convolutional neural networks (CNNs), which are particularly effective in image classification tasks. these techniques involve training different CNN models (EfficientNetB0-B7, InceptionResNetV2, ResNet50, ResNet152, and VGG19) on a dataset of labeled brain strokes and normal brain images. through this process, the model learns to identify patterns and features that are indicative of a stroke. the results show that the different CNN models were able to classify stroke images with high accuracy and predict the presence of stroke with an overall accuracy of 99.95%, which is a significant improvement compared to similar studies. this demonstrates the potential of deep learning-based approaches using CNNs in assisting the diagnosis of strokes and aiding in early-stage treatment. Finally, we provide an analysis of model performance, highlighting each CNN model used, and providing insights for future research.

关键词： Brain stroke classification Deep learning CNN stroke detection Classification Detection

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Accelerating Gradient Slope Computation with K1 Fluu: A Novel Technique for Neural Network Training 4

Accelerating Gradient Slope Computation with K1 Fluu: A Nove...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Chaudhary, Naina Gupta, Astha Singh, Gurinder thakur, Ayush Patil, Pratap Paraji Sharma, Vinita Amity University Tashkent Uzbekistan Amity International Business School Amity University Uttar Pradesh Noida India Amity Institute of Information Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350387490

this report is all about the invention called the K1 Fluu, a new method in fast learning neural networks. K1 Fluu gets gradient slopes easier and yields hidden weights higher. It applies higher order derivatives, weight change, and a unique smoothing technique to improve the speed of the gradient slope. We demonstrate many difficult math operations that describe the essence of the functioning of K1 Fluu. I hope these equations make K1 Fluu understand how the partial derivatives to the parameters of the shapes of the loss function. We compared K1 Fluu on standard benchmarks and state-of-art neural networks. the testing confirms that K1 Fluu is more effective than other procedures. this makes the neural networks more precise, acquire knowledge within a faster time and also have a short training time. the performer K1 Fluu has the advantages in that it can change and smoothes things in order to make the learning process more stable and efficient. this report provides the field of neural network training with a powerful new tool. It enhances network performance and creates the need for other areas to be researched. © 2024 IEEE.

关键词： Dynamic Weighting Gradient Slope Acceleration Higher-Order Derivatives Neural Network Training optimization algorithms

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Imaging Modalities in Brain Cancer Detection and Diagnosis 4

Imaging Modalities in Brain Cancer Detection and Diagnosis

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4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Almuhaya, Basheer Saha, Bishal Bazel, Mahmood A. Mohammed, Rehab A. A. Mohammed, Ahmed A. A. Esmail, Ezzaldeen Lovely Profess Univ Sch CSE Phagwara 144411 Punjab India Univ Sci & Technol Fac Engn & Comp Taiz City Yemen Taiz Univ Software Engn Dept Taizi Yemen

ISBN: (纸本)9798350354140;9798350354133

Brain cancer is a highly heterogeneous and complex illness, making diagnosis and treatment extremely difficult. the purpose of this study is to clarify how different data types-such as imaging, genomic, proteomic, and clinical data-affect brain tumor characterizations and therapeutic decision-making. this research explores the application of customized medicine, the molecular categorization of brain tumors, and the monitoring of disease progression. Additionally, a comparative examination of various imaging modalities, including fMRI, MRI, CT, PET, and SPECT, is conducted. As a result, this study highlights the importance of many data kinds in tracking disease development, identifying brain tumor subtypes, and devising tailored treatments. these data types include imaging, genomic, proteomic, and clinical data. It also emphasizes the difficulties and possibilities associated with combining multimodal data, with the goal of stimulating more research on the identification and management of brain cancer. Ultimately, the analysis highlights the current obstacles and potential paths for combining and evaluating multimodal data to improve the prognosis for brain cancer.

关键词： Brain cancer Imaging modalities Machine learning Deep learning Diagnostic methods Prognosis Treatment planning Genomic data Proteomic data Data analysis

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Machine learning Techniques For Wastewater And Water Purification Enhancing Efficiency And Sustainability 4

Machine Learning Techniques For Wastewater And Water Purific...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Manikandan, M. Venkatesh, P. Murugan, K. Ramu, M. Kannaa, K. V. Dinesh Senthilnathan, C.R. Sri Sairam Engineering College Department of Management Studies Chennai India Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798331508845

With the increasing global demand for clean water and the growing concerns about water scarcity and pollution, there is a pressing need for innovative solutions in wastewater and water purification processes. Machine learning (ML) techniques have emerged as powerful tools for optimizing these processes, enhancing efficiency, and ensuring sustainability. this paper provides a comprehensive review of recent advancements in ML applications for wastewater and water purification, highlighting their potential to revolutionize the water treatment industry. We discuss various ML techniques, including supervised learning, unsupervised learning, and reinforcement learning, and their applications in different stages of water treatment, such as pretreatment, primary treatment, secondary treatment, and advanced treatment processes. Furthermore, we examine the challenges and opportunities associated with implementing ML in water purification systems, including data quality issues, model interpretability, scalability, and regulatory considerations. Finally, we identify future research directions and potential areas for innovation to further leverage ML for enhancing the efficiency and sustainability of wastewater and water purification processes. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of Late Blight in Potato Leaves Using Image Processing and Machine learning 3rd

Identification of Late Blight in Potato Leaves Using Image P...

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3rd international conference on optimization, learning algorithms and applications (OL2A)

作者： Leepkaln, Renan Lemes de Re, Angelita Maria Wiggers, Kelly Lais Midwestern Parana State Univ Guarapuava Parana Brazil Fed Technol Univ Parana Pato Branco Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

Potato is a widely consumed food worldwide, and its productivity has increased due to new varieties and the use of technologies related to irrigation, nutrition, and soil preparation, among others. However, diseases such as late blight disease can often affect the crop, impacting many farmers around the world. As a way to help production, technology in agriculture is increasing. Among the various computational techniques that can be applied, those based on digital image processing associated with machine learning algorithms stand out, producing excellent results. this work aimed to develop a methodology for recognizing late blight disease in potato leaves using digital image processing techniques and machine learning algorithms. It was possible to obtain promising results. the experiments were carried out in a set of images from a public database containing images of healthy and unhealthy leaves (with late blight). We compare the performance of machine learning algorithms using feature vectors obtained with SIFT algorithm and RGB descriptors. the best performance was using the Decision Tree algorithm and SIFT vectors, with 99.24% of accuracy.

关键词： Automatic disease recognition Digital Image Machine learning Computer Vision

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Customer Churn Prediction Using Machine learning: A Case Study of Libyan Internet Service Provider Company 4

Customer Churn Prediction Using Machine Learning: A Case Stu...

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4th IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2024

作者： Alteer, Shefaa A. Alariyibi, Alhaam University of Benghazi Faculty of Information Technology Benghazi Libya

ISBN: (纸本)9798350372632

Artificial intelligence (AI) is a field of science and technology that simulates human intelligence to develop intelligent machines. Machine learning (ML), on the other hand, is a subset of AI that enables computers to learn from data without being explicitly programmed. In the area of business, data science is the practical application of ML, AI, and statistics, which focuses on extracting valuable insights from data. Nowadays, data science relies heavily on ML techniques, which are used for a variety of applications. One of its applications is Customer Churn Prediction (CCP). Customer churn (or attrition) is the tendency of customers to stop using a company's products or services. It is important to predict churn where businesses can proactively target the customers, who are at high risk of leaving and create marketing campaigns to retain them. this study aims to build a predictive model to predict customer churn for Internet Service Providers (ISPs). Several experiments have been conducted on a dataset obtained from a Libyan ISP company to train the models. the dataset distribution was imbalanced, with only 5% of customers churned. To mitigate this, various oversampling techniques were used. In the proposed system, five diverse classifiers were used. Namely, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM) tree algorithm, and Extreme Gradient Boosting (XGBoost) algorithms due to their relevance in this predictive context. the experimental findings showed that the XGBoost tree model yielded the most favorable outcomes, achieving an AUC value of 94%. © 2024 IEEE.

关键词： Decision trees

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Comparative Analysis of Multi optimization Techniques under Load Variations in Optimal Reactive Power Dispatch 4

Comparative Analysis of Multi Optimization Techniques under ...

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4th IEEE international conference in Power Engineering applications, ICPEA 2024

作者： Salimin, Rahmatul Hidayah Musirin, Ismail Hamid, Zulkiffli Abdul Aminuddin, Norziana Zakaria, Fathiah Senthil Kumar, A.V. Universiti Teknologi MARA School of Electrical Engineering College of Engineering Shah Alam40450 Malaysia Hindusthan College of Arts and Science Department of Research and PG in Computer Applications Coimbatore India

ISBN: (纸本)9798350344578

Power System Compensation Schemes encompass a range of strategies, including the integration of distributed generation (DG), flexible AC transmission system (FACTs) devices, power scheduling, capacitor bank placement, and optimal reactive power dispatch (ORPD). ORPD, a current trend, also involves optimizing transformer tap changer settings (TTCS). However, the efficacy of TTCS optimization is questioned due to its potential impact on transmission line parameters. To avoid over-compensation and under-compensation, these schemes necessitate optimization. Evolutionary Programming (EP) and Artificial Immune System (AIS) are widely employed in power system optimization, yet their susceptibility to getting stuck at local optima limits their effectiveness. this hybrid, termed Integrated Accelerated Clonal Evolutionary Programming (ICAEP), exhibits promising results in optimizing ORPD under load variations. the paper introduces the implementation of the accelerated mutation technique in ICAEP, showcasing exceptional performance compared to standalone EP and AIS methods. ICAEP can be adapted for various optimization problems, marking a significant advancement in addressing challenges associated with power system compensation, particularly in optimizing ORPD under varying loads. © 2024 IEEE.

关键词： Electric load dispatching

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