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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 4179 条记录，以下是671-680 订阅

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AI based Real-Time Traffic Signal Control System using machine learning 4

AI based Real-Time Traffic Signal Control System using Machi...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Heltin Genitha, C. Abishek Danny, S. Hepsi Ajibah, A.S. Aravint, S. Angeline Valentina Sweety, A. St. Joseph's College of Engineering Department of Information Technology Chennai India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai Chennai India

ISBN: (纸本)9798350300093

this study presents a novel system that utilizes computer vision and machine learning approaches to address the problem of traffic congestion in urban areas. the proposed system leverages the advanced object detection algorithm, You Only Look Once (YOLO), to detect and track vehicles in live camera footage from traffic junctions. the system then calculates the traffic density in real-time by analyzing the number and speed of vehicles passing through the intersection. the proposed system utilizes an intelligent algorithm that optimizes traffic flow by switching traffic lights based on the calculated traffic density. this approach reduces congestion and minimizes delays, resulting in faster transit times and reduced fuel consumption and air pollution. To assess the performance of the proposed system, experiments are carried on real-world traffic data. the results demonstrate that the system can accurately detect and track vehicles with high precision and recall rates. the real-time traffic density calculations produced by the system were found to be highly reliable, and the traffic light switching algorithm led to a significant reduction in traffic congestion and improved traffic flow. the proposed system has several advantages over traditional traffic management systems, including lower implementation and maintenance costs, improved accuracy and efficiency, and the ability to adapt to changing traffic conditions in real-time. © 2023 IEEE.

关键词： Object detection

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Fourth international conference on Signal Processing and computer Science, SPCS 2023

Fourth International Conference on Signal Processing and Com...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

ISBN: (纸本)9781510672505

the proceedings contain 152 papers. the topics discussed include: research on process route optimization method based on improved hybrid genetic algorithm;simulation of road;multi-node short-term power load forecasting model based on neural network ensemble;compressive sensing based one-bit sparse array direction-of-arrival estimation;directional modulation schemes in dual-function radar-communication system;elevator door motion recognition utilizing an imitation C3D network;radar radiation identification based on online transfer learning;inductance calculation and analysis of planar spiral coils in the wireless energy transmission system of implantable cardiac pacemaker;identification of Peking duck breed based on hyperspectral imaging with machine learning;and visible and near infrared image fusion based on texture information.

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An Online Shoppers Purchasing Intention Model Based on Ensemble learning 4

An Online Shoppers Purchasing Intention Model Based on Ensem...

引用

4th international Informatics and Software Engineering conference, IISEC 2023

作者： Karakaya, Aykut Karakaya, Irem Temizceri, Talya Bülent Ecevit University Dept. of Computer Technologies Zonguldak Turkey Bartin University Dept. of Management and Organization Bartin Turkey Istanbul Bilgi University Dept. of Logistics Management Istanbul Turkey

ISBN: (纸本)9798350318036

the sales trends and the visitors' purchase intention of e-commerce are crucial issues for companies. therefore, companies want to know whether products are purchased based on customer visits. this study proposes an ensemble learning-based model to analyze customers' purchase intentions for e-commerce companies. It is possible to create a more accurate model using several machine learning methods within a system, which is the ensemble learning approach. the prediction performance of the proposed model is evaluated by different metrics, such as accuracy, precision, recall, AUC, and F-score. this paper aims to improve the purchasing rates of online shopping sites by determining the rate of visitors who intend to purchase but leave the site without taking action by using information such as the click flow of visitors. © 2023 IEEE.

关键词： learning systems

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application of Impact Energy Analysis in the Abnormal Noise Fault Diagnosis of Centrifuge 4

Application of Impact Energy Analysis in the Abnormal Noise ...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Zhou, Jian Luo, Jiayou Chao, Qun Wang, Yuanhang Sun, Lijun Wu, Helong China Elect Prod Reliabil & Environm Testing Res Guangzhou 510610 Peoples R China Cent South Univ Dundee Int Inst Changsha 410083 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shenzhen Technol Univ Shenzhen 518118 Peoples R China Guangdong Prov Key Lab Elect Informat Prod Reliab Guangzhou 510610 Peoples R China

ISBN: (纸本)9798350375084;9798350375077

A Plate-type centrifuge has obvious abnormal sound during operation. To solve this problem, the intelligent monitoring terminal is developed, the vibration signal of centrifuge is collected, and the vibration signal is analyzed and diagnosed by impact energy analysis. Firstly, the vibration measuring point is arranged on the centrifuge, and the intelligent monitoring terminal is developed based on Linux. the working condition data and vibration signal of the centrifuge are obtained respectively. then, in order to improve signal-to-noise ratio effectively, the vibration waveform envelope method is used to detect and demodulate the acquired vibration signal. the filtered signal is processed by Fast Fourier Transform (FFT), and then the characteristic frequency caused by low frequency impulse is extracted. Finally, the impact energy sum of each characteristic frequency point is obtained, and the calculated result is used as a new characteristic quantity to evaluate the failure state of the centrifuge. the monitoring results show that the uneven initial feeding after each centrifuge cleaning will cause abnormal sound phenomenon, impact energy and can effectively identify the failure state of the centrifuge.

关键词： component Centrifuge Abnormal sound Fault diagnosis Impact energy analysis

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application of artificial intelligence online monitoring technology in fault diagnosis of cigarette packaging equipment 4

Application of artificial intelligence online monitoring tec...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Wang, Haijiao Jilin Tobacco Industry Co. Ltd. Yanji Cigarette Factory Jilin Yanji133000 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the production process of modern tobacco machinery is highly precise and complex, and when unpredictable failures occur in the production line, they can lead to economic losses for tobacco companies. Artificial Intelligence online monitoring technology can provide real-time fault alarms for tobacco production lines in production, and at the same time, without affecting the overall operation of the production line, it can improve the overall operational efficiency of the tobacco enterprises, which in turn can generate economic benefits. the fundamental process of visual inspection of cigarette package appearance quality is to classify the inspection image to distinguish qualified and unqualified packages, how to accurately judge the "qualified" and "unqualified" packages is the key to improve the accuracy and reduce the false reject rate. How to make accurate judgment on "qualified" and "unqualified" cigarette packs is the key to improve accuracy and reduce false reject rate. By analyzing the collected images of strip cigarettes, there are certain feature differences between qualified and unqualified images, in order to effectively determine the abnormal strip cigarettes, machine learning-based detection methods can be used to analyze the image feature information, extract the feature parameters, establish the cigarette packaging classification model, judge the defective strip cigarette products and reject them. the main content of the research in this chapter is to extract the image data features by combining wavelet transform and grayscale covariance matrix, and then use support vector machine and BP neural network algorithms respectively to sample and learn the training sample set of cigarette, and then classify and predict the test sample set to verify the classification performance and compare the classification effect of the two classifiers. © 2025 SPIE.

关键词： Wavelet transforms

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A Forest Smoke Recognition System Based on Deep learning 4

A Forest Smoke Recognition System Based on Deep Learning

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4th international conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Gong, Jinghao Yan, Xiaofei Wang, Yanqiu College of Information Science and Engineering Zaozhuang University Zaozhuang277160 China

ISBN: (纸本)9781510683242

Forests, as vital ecological resources of the Earth, frequently witness devastating wildfires that pose enormous threats to the environment, ecosystems, and human life. To mitigate the losses caused by forest fires, it is imperative to enhance fire prediction and control measures, with video surveillance technology playing a pivotal role in fire watch. Focusing on the extraction of prominent features from wildfire videos and the application of deep learning techniques, the system employs unmanned aerial vehicles (UAVs) to acquire navigational video data in forest areas, integrating multiple payloads for real-time forest fire detection. the project enhances methods for identifying smoke from forest fires in videos, ensuring a stable and reliable forest fire monitoring system alongside real-time data acquisition. © 2024 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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the New Answer to Drug Discovery: Quantum machine learning in Preclinical Drug Development 4

The New Answer to Drug Discovery: Quantum Machine Learning i...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Wong, Yew Kee Zhou, Yifan Liang, Yan Shing Qiu, Haichuan Wu, Yu Xi He, Bin Basis International School Guangzhou Guangzhou China

ISBN: (纸本)9798350324303

the Research & Development (R&D) phase of drug development Drug discovery and development (D&D) is a complex and costly endeavor, typically requiring six to nine years [1] and four hundred to fourteen hundred million USD [2] for the research and development (R&D) phase alone. To transform this process, we propose a novel concept that combines Quantum-based machine learning network (QML) and Quantum Computing Simulation (QS) to expedite the R&D phase to three to six months and reduce the cost to merely fifty to eighty thousand USD. Our approach takes as inputs the target protein/gene structure and the primary assay [3]. For Hit Generation [3], the QML network generates potential hits [4] based on the molecular structure of the target protein, while the QS filters molecules from the primary assay according to their reaction and binding efficacy with the target protein. then, for Lead Optimization [3], the resulting molecules from QML and QS are compared, and the ones that are common to both processes are subjected to dozens of molecular variations, while others are only modified slightly. Finally, all optimized molecules undergo multiple rounds of QS filtering with high standards for reaction efficacy and safety, producing a few dozen pre-clinical-trial-ready drugs. Our concept of QML and QS integration can also be applied to other fields, such as agricultural research, genetic editing, and aerospace engineering. Keywords: Quantum computer, Hit to Lead, Lead Optimization Simulation, machine learning. © 2023 IEEE.

关键词： Molecules

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Analysis of Deep learning based Optimization Techniques for Oral Cancer Detection 4

Analysis of Deep Learning based Optimization Techniques for ...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Subha, K.J. Bennet, Anto M. Pranay, Gaddam Bharadwaj, Ketham Reddy, Polu Vikram Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350300093

the main aim is to examine the application of deep learning technique in detecting oral cancer at an early stage. the focus is on evaluating the performance of various optimization techniques to deep learning technique through analyzing images of the mouth and throat for oral cancer detection. the goal is to determine the most effective method for identifying oral cancer and gain insights into potential improvements. the importance of early detection of oral cancer cannot be overstated, as it plays a crucial role in improving patient outcomes through earlier treatment and higher survival rates. the findings have the potential to contribute to the development of more accurate and efficient methods for oral cancer diagnosis and make a positive impact in the field. © 2023 IEEE.

关键词： Convolutional neural networks

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Plant Species Identification Based on Plant Leaf using machine learning Techniques 4

Plant Species Identification Based on Plant Leaf using Machi...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Arya, Ashima Agarwal, Arpit Kumar, Arun Singh, Manu Shekhar KIET Department of CSIT Ghaziabad India

ISBN: (纸本)9798350341126

In the absence of plants, we would not be able to live on this planet. A growing number of plant species are available, and they number in the hundreds. Foresters, farmers, environmentalists, and educators need knowledge of species to make informed decisions. As a result, species identification belongs to an interdisciplinary field of study. It may be challenging and tedious for nonexperts who are not familiar with typical botanical terms. In the fields of computer vision and machine learning, advances in this area can make this task easier. Although some efforts have been made, all plant species cannot be identified with one system. In this study, a similar approach was taken. In order to identify a plant, four steps have to be completed: acquisition of images, pre-processing, feature extraction, and classification. For this study, 1,125 images of 15 different species of leaves were used from the Swedish leaf dataset. Pre-processing is performed using Gaussian filtering mechanisms, and then texture and colour features are extracted. © 2023 IEEE.

关键词： Feature extraction

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Improving Quantum Circuit Synthesis with machine learning 4

Improving Quantum Circuit Synthesis with Machine Learning

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

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin University of California Department of Electrical Engineering and Computer Science Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9798350343236

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that mini-mize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. this paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7 x in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023 IEEE.

关键词： machine learning

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