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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4173 条记录，以下是281-290 订阅

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Predicting Customer Satisfaction in Brazil E-commerce: A Comparative Study of machine learning Techniques 4

Predicting Customer Satisfaction in Brazil E-commerce: A Com...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pandey, Trilok Nath Vasudev, Anirudh Sagayanathan, Dennis Anjan, Govind Arshad, Danish Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350306118

Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online industries in the world which till date is not completely understood from a customer's standpoint. Brazil being a developing country is the 12th largest market for e-commerce in the world and accounts for 30 % of e-commerce in Latin America and remains largely unexplored. In this study, we used machine learning (ML) techniques such as LSTM, XGBClassifier, Logistic Regression, Neural Network, and Random Forest to predict customer satisfaction using a retail dataset from Brazil. We contrasted the efficacy of these models using a variety of evaluation criteria, such as precision, recall, and Fl-score. the LSTM model had the highest accuracy with a score of 0.89, followed by the XGBClassifier with 0.83, Naive Bayes with 0.73, Logistic Regression with 0.74, Support Vector Classifier with 0.74, and Random Forest with 0.78. the superior performance of the LSTM model over other models suggests that it has superior predictive abilities. © 2023 IEEE.

关键词： Customer satisfaction

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4th international conference on Mathematics and its applications in Science and Engineering, ICMASE 2023

4th International Conference on Mathematics and its Applicat...

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4th international conference on Mathematics and its applications in Science and Engineering, ICMASE 2023

ISBN: (纸本)9783031492174

the proceedings contain 25 papers. the special focus in this conference is on Mathematics and its applications in Science and Engineering. the topics include: Some Identities for Balancing and Lucas-Balancing Numbers in Bidimensional Version;sequences of Uncountable Iterated Function Systems: the Convergence of the Sequences of Fractals and Fractal Measures Associated;method of Hydrodynamic Images and Quantum Calculus in Fock-Bargmann Representation of Quantum States;on Leonardo Numbers and Fibonacci Fundamental System;a Quadratic Estimation Approach from Fading Measurements Subject to Deception Attacks;SOLO Taxonomy in the Evaluation of Engineering Students: A Case Study in Mathematics;is Collaborative learning a Voluntary Process?;elliptic Biquaternionic Sequence with Vietoris’ Numbers as Its Components;Teaching Mathematics in STEM Education;application of Discrete Wavelet Transform and Tree-Based Ensemble machine learning for Modeling of Particulate Matter Concentrations;fixed Point theorems in Orthogonal F-Metric Spaces;new Trends on Malware Propagation: From IoT Environments to Drone Swarms;forms of Assessment in view of Development of Mathematical Competencies;deep-Control of Memory via Stochastic Optimal Control and Deep learning;exponentiated Weibull Mixture Cure Model to Handle Right-Censored Data Set;on Strong Fuzzy Partial Metric Spaces;an application of Linear Diophantine Fuzzy Sets to the Edge Detection Techniques;delamination Resistance of Laminated Glass Plates Having Ethyl Vinyl Acetate, Polyvinyl Butyral and Sentryglas Plus Interlayers;algebraic and Quantum Mechanical Approach to Spinors;new G-Closed Sets with Related to an Ideal;service-learning Activity in a Statistics Course;fermatean Fuzzy Type a three-Way Correlation Coefficients;on Some Gaussian Oresme Numbers.

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Integrating Predictive Models and Remote Sensing for Sustainable Agriculture, Energy, and Environmental Management 4

Integrating Predictive Models and Remote Sensing for Sustain...

引用

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

作者： Kumar, Raj Ather, Danish Sagar, Pinki Yusupov, Ibrohimbek Duggal, Sonia Jain, Vishal Department of Computer Applications Faridabad India Amity University Department of It and Engineering Tashkent Uzbekistan G.L. Bajaj Institute of Technology and Management Greater Noida India Tashkent University of Information Technologies Named after Muhammad ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

this paper focuses on a critical analysis of the application of integrated works of predictive models and remote sensing in agriculture and renewable energy, and environmental conservation. this method involves the use of deep learning approaches, UAV data collection and hyperspectral imagery which gives the researchers an understanding on the real world effectiveness of the systems and technologies analyzed;on predictive models and resource utilization efficiency. © 2024 IEEE.

关键词： Agriculture Deep learning Energy Management Environmental Man-Agement Predictive Models Remote Sensing UAV

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A Review on the Role of Deep learning for Brain Tumor Detection and Classification 4

A Review on the Role of Deep Learning for Brain Tumor Detect...

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

作者： Gupta, Priyanka Sandhu, Ramandeep School of Computer Science & Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350387490

through the application of advanced deep-learning techniques, brain tumor detection and classification have advanced significantly in the last ten years in medical imaging. the purpose of this work is to study how deep learning models, namely convolutional neural network models, can improve the accuracy and speed of brain tumor diagnosis. Deep learning has been applied to analyze brain MRI data, and the results have been promising in terms of categorizing different types of brain tumors-a crucial component of comprehensive treatment plans. Large datasets of brain scans are used to train deep neural network models, which enable them to identify subtle patterns and traits that may be invisible to human observers. When compared to traditional methods, these models have the potential to produce faster diagnostic results, which would shorten the time patients must wait for a diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Bone Fracture Detection in Radiology: A machine learning and Explainable AI Approach 4

Enhancing Bone Fracture Detection in Radiology: A Machine Le...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Deka, Krishnav Rabha, Madhurjya Saikia, Deepratim Jena, Junali Jasmine Kumar Gourisaria, Mahendra Behera, Dayal Kumar School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar India

ISBN: (纸本)9798331515720

Bone fractures, caused by impacts, trauma or conditions such as osteoporosis, require timely and precise detection for effective medical care. Traditional approaches rely on radiologists' interpretations of X-rays, which are labor-intensive and prone to errors. machine learning (ML) presents a promising solution for enhancing fracture identification. However, the opacity of ML models presents challenges in their integration into healthcare, necessitating transparency and interpretability. this work examines the use of ML in fracture detection and incorporates Explainable AI (XAI) techniques to clarify decision-making processes and bolster model reliability. Here, Local Directional Pattern (LDP) and Local Binary Pattern (LBP) have been used for extracting the features and it is found that the Random Forest Classifier achieved the highest accuracy of 95 %. By assessing both the accuracy and interpretability of ML models in identifying fractures, this study aims to promote wider acceptance and application of AI-driven diagnostic tools in medical settings. © 2024 IEEE.

关键词： Radiology

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A Brief Review of the Performance of Energy-Based Models in Embedded Devices 4

A Brief Review of the Performance of Energy-Based Models in ...

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4th international conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Yazar, Selçuk Sadou, Isma-Ilou Kırklareli University Kırklareli Turkey Digitalisation Department HISMA Association Hamburg Germany

ISBN: (纸本)9798350369694

Today, embedded devices are widely used in both personal and industrial applications such as monitoring and control, visual data processing and many more. ARM-based processors are often preferred in embedded devices and especially in single-board platforms. the increased processing power and capabilities of these processors are highly effective in increasing the efficiency of these processes. Energy-based models are one of the many machine learning methods that can be used in these devices and are becoming increasingly popular. In this paper, the performance of the Restricted Boltzmann machine, an energy-based modeling approach, on different ARM-based processors is analyzed and its potential applications are discussed. © 2023 IEEE.

关键词： learning systems

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Advances in Quantum Medical Image Analysis Using machine learning: Current Status and Future Directions 4

Advances in Quantum Medical Image Analysis Using Machine Lea...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Matondo-Mvula, Nadine Elleithy, Khaled University of Bridgeport Department of Computer Science and Engineering Bridgeport United States

ISBN: (纸本)9798350343236

Quantum machine learning (QML) is an interdisciplinary field combining Quantum Computing (QC) and machine learning (ML). It has gained increased attention due to advances in near-term hardware implementations of quantum devices. the use of QML has proven to result in a significant improvement in performance and computational speed. Consequently, QML has become an effective technique for data processing and classification. Researchers have recently proposed various QML solutions in the medical image analysis field to gain an advantage of quantum supremacy. the main objective of this work is to present a holistic review of current leading-edge published works in the quantum medical image analysis field with a focus on supervised learning using artificial neural networks. A comparative study is used to pinpoint the potential of existing techniques, the most promising techniques, and the future of research in this area. © 2023 IEEE.

关键词： machine learning

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Classification of Grapevine Leaf Images with Deep learning Ensemble Models 4

Classification of Grapevine Leaf Images with Deep Learning E...

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Lv, Qingcong Shandong Technology and Business University Department of Computer Science and Technology Yantai China

ISBN: (纸本)9798350326444

Grape is a fruit with high nutritional value, and its uses are also very extensive, which can be used for brewing and eating. there are many varieties of grapes, and how to identify grape varieties based on the morphology of their leaves is of great significance for grape reproduction and variety evolution. this article takes the leaves of different grape varieties at maturity as the research object. First, the leaf images are collected and preprocessed, and then five pre-trained deep learning models(VGG19, VIT, Inception ResnetV2, DenseNet201, and ResneXt) are trained and fine-tuning using transfer learning. Finally, two voting ensemble machine learning models combine the predictions from the five models. the highest accuracy of 98.1 % is achieved through the ensemble classifier in which decision is made based on soft voting. © 2023 IEEE.

关键词： Cell proliferation

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2024 4th international conference on Information Communication and Software Engineering, ICICSE 2024

2024 4th International Conference on Information Communicati...

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4th international conference on Information Communication and Software Engineering, ICICSE 2024

ISBN: (纸本)9798350351699

the proceedings contain 29 papers. the topics discussed include: research on security protection mechanism of android app;research on end-to-end forwarding path selection method for CFN;indoor localization algorithm based on SRD and adaptive Kalman filter;behind the TCP-newCWV implementation to improve bursty application performance;enhancing MAFIS: a study on the usability and user experience of the mobile automated fingerprint identification system;a feature-reconstruction based multi-task learning model for sleep staging;multi-secret threshold sharing scheme based on Chinese remainder theorem;extended principlism approach to software engineering ethics;enhancing SAR ATR model generalization through Cycle-GAN augmented multi-angle training;and the adoption of machine learning algorithm for rust classification in steel structure.

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A machine learning Approach for Diagnosis of Hepatitis 4

A Machine Learning Approach for Diagnosis of Hepatitis

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sachdeva, Ravi Kumar Jain, Madhur Bathla, Priyanka Ahuja, Rakesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350306118

Unbeknownst to themselves, hundreds of millions of individuals worldwide are dealing with a chronic infection. Without exaggeration, chronic hepatitis is one of the most significant medical and social issues in any nation in the world. the authors created an accurate machine-learning (ML) model to increase forecast success rates. the medical information of 155 patients is contained in the publicly accessible hepatitis dataset, which can be found on the UCI machine learning repository. the authors have applied ML classifiers such as Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Gaussian Naive Bayes (GNB) to forecast the disease. RF had the highest accuracy, at 100%, of all the ML classification approaches that were used. © 2023 IEEE.

关键词： Logistic regression

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