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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"

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Pap Smear Medical Image Classification Using Deep learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

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6th international conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. this study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. the performance of the models was analyzed and potential areas for improvements are suggested. the findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. the methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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optimizing Furniture Assembly: A CNN-based Mobile application for Guided Assembly and Verification 9

optimizing Furniture Assembly: A CNN-based Mobile Applicatio...

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9th international conference on Optimization and applications, ICOA 2023

作者： Elhassan, Gasm Elbary Yasser, Ibrahim Faizal, Mohamed Omer Zia, Huma Abu Dhabi University Department of Electrical Computer and Biomedical Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350312546

Building products based o written instructions can be challenging for individuals, often leading to difficulties during the assembly process. this project presents a mobile application that guides users through the assembly process and verifies assembly correctness using image comparison techniques. the application features manufacturer and consumer modes, allowing manufacturers to add assembly steps and users to access provided assembly instructions for a given product. Cross-platform compatibility is achieved with React-Native and Expo Go, while image matching is implemented by incorporating computer vision and machine learning techniques, specifically utilizing a Siamese Network architecture. Amazon Web Services (AWS) supports content delivery and storage. An IKEA table and kids chair assembly serves as a demonstrative example. By offering step-by-step guidance and feedback based on similarity indexes, the application optimizes the efficiency and accuracy of furniture assembly. the project showcases the system's practicality, particularly in enhancing the assembly process for various furniture items, exemplified by the IKEA products. © 2023 IEEE.

关键词： Assembly

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A survey of object detection based on deep learning 4

A survey of object detection based on deep learning

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

作者： Jiang, Wei Zhang, Zichao Xiong, Qian Yang, Bo Information Science Academy of China Electronics Technology Group Corporation Beijing China School of Information Science and Technology Beijing Forestry University Beijing China

ISBN: (纸本)9781510686106

Identifying and pinpointing objects within images or videos is a core objective in the field of computer vision, known as object detection. this study delves into the most recent advancements in object detection, with an emphasis on four primary approaches: Two-Stage Detectors, One-Stage Detectors, Anchor-Free Detectors, and Transformer-based Detectors. Each approach possesses distinct strengths and weaknesses, and the selection is dictated by the specific needs of the application. As research progresses, the techniques for object detection are advancing in precision and efficiency, while also expanding their ability to manage a wide array of object categories and scenarios. these advancements are pivotal in numerous domains, such as autonomous vehicles, security monitoring, and medical diagnostics. © 2024 SPIE.

关键词： Deep neural networks

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Security Evaluation Method of Distance Education Network Nodes Based on machine learning 4th

Security Evaluation Method of Distance Education Network Nod...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Fang, Jiajuan Department of Software Engineering Zhengzhou Technical College Zhengzhou450121 China

ISBN: (纸本)9783031200953

In the distance education network, the node security problem is often not guaranteed. therefore, a machine learning based distance education network node security evaluation method is designed to ensure the node security problem through the node security evaluation, and extract the attack characteristic attributes of the distance education network from the traffic and log information. According to this characteristic attribute, a network wide attack model of distance education network nodes is constructed based on hierarchical expansion. this paper uses AHP method to build a distance education network node security evaluation model based on machine learning, and tests the performance of this method. the test results show that this method has high accuracy and low error rate of network node security evaluation, which proves the evaluation performance of this method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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FRAMEWORK FOR ARTIFICIAL INTELLIGENCE BASED MONITORING OF ENERGY AND WASTE MANAGEMENT USING machine learning APPROACH 4

FRAMEWORK FOR ARTIFICIAL INTELLIGENCE BASED MONITORING OF EN...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Khalaf, Osamah Ibrahim Pawar, A.B. William, P. Ogudo, Kingsley A. Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Department of Computer Engineering Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India Depending of Electrical and Electronics Engineering FEBE University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9781839539954

this study examines how machine learning and artificial intelligence (AI) may be used to solve urgent worldwide issues with waste and energy management. It presents a thorough framework for employing AI technology to track and optimize trash production and energy use in residential communities. the framework reduces energy consumption and increases waste management efficiency by using sensors, data processing, predictive modelling, and real-time monitoring to enable data-driven decision-making. It emphasizes how AI has the power to transform conventional methods and usher in a more sustainable future. the study offers a cutting-edge framework for waste and energy management monitoring that is based on machine learning and provides real-time insights and forecasts to help with decision-making. Constant data analysis maximizes the use of resources, resulting in reduced costs, increased operational effectiveness, and advantages for sustainability. Although there are still obstacles to overcome, such as data security and upfront costs, this paradigm is an essential first step towards effective and accountable resource management. © the Institution of Engineering & Technology 2023.

关键词： Artificial Intelligence Energy Management Environmental Preservation machine learning Resource Allocation Sustainability Waste Management

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Turbulence Modeling through Deep learning: An In-Depth Study of Wasserstein GANs 4

Turbulence Modeling Through Deep Learning: An In-Depth Study...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Alghamdi, Wajdi Mayakannan, S. Sivasankar, G.A. Singh, Jagendra Ravi Naik, B. Venkata Krishna Reddy, Ch. King Abdulaziz University Faculty of Computing and Information Technology Department of Information Technology Jeddah21589 Saudi Arabia Vidyaa Vikas College of Engineering and Technology Tiruchengode Mechanical Engineering Tamilnadu Namakkal India Kit - Kalaignar Karunanidhi Institute of Technology Department of Aeronautical Engineering Coimbatore641402 India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Great Noida India St. Martin's Engineering College Department of Mechanical Engineering Telangana Secunderabad India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350300888

this research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein Gans (WGANs) to produce localized disturbances. Network features including the learning rate and loss function are examined as they pertain to the performance of the WGANs during training on turbulent data gleaned from high-resolution Direct Numerical Simulations (DNS). DNS input data and the generated turbulent structures are proven to agree qualitatively well. the projected turbulent fields are evaluated quantitatively and statistically. © 2023 IEEE.

关键词： Turbulence

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Gaussian Process for the machine learning-based Smart Fire Detection System 4

Gaussian Process for the Machine Learning-based Smart Fire D...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Li, Guangyu Yang, Boyu Chang, Shaocong Yi, Hongbin Zhang, Yingbo Wang, Yao Baishan Power Supply Company State Gird Jilin Electric Power Co. Ltd Baishan City134300 China

ISBN: (纸本)9798350342710

Artificial intelligence, machine learning, imaging devices, and computer vision are a few examples of contemporary technology that potentially replace traditional methods fire-detection systems with vision-based ones, thereby ensuring society's fire safety. Using the computer-aided system for quick and accurate fire detection could prevent a large-scale fire. Fire detection models perform poorly because they do not take into account the analysis of the fire particles (also known as pattern recognition) or the numerous moving objects in the background. To resolve this issue and concentrate a fire improvement in light of worldly edges from a film, this study gives a powerful even reenactment model of dynamic fire and a half-and-half fire division. In the model's preprocessing step, the candidate fire and non-fire Areas are segmented with the Hue-Saturation-Value (HSV) colour space and the Gaussian's Mixture Model (GMM) to isolate them from the multi-moving item on the complicated background. a unique approach that divides sensory data employing a Gabor filter library with desired orientations and estimates the process of fire. the energy of the wavelengths is also added as an additional aspect to the attribute vector when a temporally-differed image is employed. When all of these traits are added up, a support vector machine (SVM) is produced. to ensure accurate fire detection and better distinguish our data. the suggested fire detection model is validated using multi-conditional video clips. the results of the experiments indicate that the suggested model beats cutting-edge algorithms. © 2023 IEEE.

关键词： Support vector machines

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A Comparative Study on application of Artificial Intelligence for Quality Assurance in Manufacturing 4

A Comparative Study on Application of Artificial Intelligenc...

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4th international conference on Inventive Research in Computing applications, ICIRCA 2022

作者： Arora, Aashish Gupta, Rajeev Haryana Ambala India Department of Computer Science and Engineering Haryana Ambala India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Increasing demands in industrial processes necessitate early fault detection, efficient production, and quality assurance. In the manufacturing process, a s more sensors, smart devices, and cyber-physical networks are interconnected, more data is created. this data might be used to enhance manufacturing procedures and product quality. machine learning algorithms may improve quality assurance (QA). But integration of artificial intelligence (AI) and machine learning (ML) into manufacturing system seldom have the skills to obtain this data. this paper describes an automated visual inspection system that ensures optical quality. An automated quality assurance platform for additive manufacturing based on machine learning is described in this paper. this paper also presented a comparative feature analysis of existing AI based quality assurance in different application areas of manufacturing. After complete analysis of these techniques, the paper also explored the current challenges and future research directions. © 2022 IEEE.

关键词： Quality assurance

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Empirical Analysis of machine learning in Enhancing the E-Business through Structural Equation Modeling 4th

Empirical Analysis of Machine Learning in Enhancing the E-Bu...

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4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： William, P. Rageeb, Md. Boina, Md. Usman Roja Lakshmi, T. R. Vijaya Sharma, Ashish Marriwala, Nikhil Kumar Department of Information Technology Sanjivani College of Engineering Kopargaon India Department of Pharmacognosy Smt. Sharadchandrika Suresh Patil College of Pharmacy Maharashtra Chopda India Morrisville NC United States Mahatma Gandhi Institute of Technology Hyderabad Gandipet500075 India Department of Computer Engineering and Applications GLA University Mathura India Electronics and Communication Engineering Department University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India

ISBN: (纸本)9789819706990

the research’s main goal is to identify the crucial elements of putting machine learning techniques into practice in order to improve electronic commerce (E-business) inside the company. the data gathered will be analyzed to achieve this. In order to improve e-business and achieve sustainable development, researchers have concentrated their efforts on taking into account significant machine learning determinants that affect demand forecasting, the application of ML in purchase behavior, improving customer engagement, and support in overall cross-selling of the products. the major objective of this essay is to provide an in-depth and critical understanding of how machine learning technology may be used to enhance the performance of online businesses. As a result of the dynamic nature of the current environment in which businesses operate, enhance the value of their brands. Because of this, having an understanding of the machine learning approaches that the owners of these websites have used is more significant in this research. this is because management needs to be able to support their E-business and create the most money feasible for the owners of these websites. Because they use this kind of website approach, these commercial websites are better able to withstand the intense competition that exists in their field. In this brief message, we present these cutting-edge websites that make use of deep learning-driven machine learning and artificial intelligence. these websites are dynamic and cutting-edge at the same time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Websites

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4th Engineering Physics international conference 2023, EPIC 2023

4th Engineering Physics International Conference 2023, EPIC ...

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4th Engineering Physics international conference 2023, EPIC 2023

the proceedings contain 38 papers. the topics discussed include: design, simulation, and optimization of portable atmospheric water generator using vapor compression refrigeration system;electrically activated zeolite for reducing seawater salinity;reduction of rainwater turbidity using zeolite;activated carbon nanocomposite for supercapacitor electrodes;evaluation of optical characteristic of animal fat using Au nanoparticle in bamboo-derived nanocellulose paper substrate: a comparative study;design and development of IoT-based temperature and humidity monitoring system for thermal comfort evaluation in classrooms;hydrogen production using aluminum-water reaction and electrolysis with ultrasonic wave agitation;on the mechanism underlying the distinct activities of rutile-type metal oxides toward methane activation;and investigation of best QSPR-based machine learning model to predict corrosion inhibition performance of pyridine-quinoline compounds.

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